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Cross-Sectional Study | Definition, Uses & Examples

Cross-Sectional Study | Definition, Uses & Examples

Published on May 8, 2020 by Lauren Thomas . Revised on June 22, 2023.

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyze the relevant data.

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, other interesting articles, frequently asked questions about cross-sectional studies.

The opposite of a cross-sectional study is a longitudinal study . While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals that are connected by a common trait.

Both types are useful for answering different kinds of research questions . A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study.

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When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

Sometimes a cross-sectional study is the best choice for practical reasons – for instance, if you only have the time or money to collect cross-sectional data, or if the only data you can find to answer your research question was gathered at a single point in time.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

An analytical study tries to answer how or why a certain outcome might occur.
A descriptive study only summarizes said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organizations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

However, these datasets are often aggregated to a regional level, which may prevent the investigation of certain research questions. You will also be restricted to whichever variables the original researchers decided to study.

If you want to choose the variables in your study and analyze your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
Since cross-sectional studies only study a single moment in time, they cannot be used to analyze behavior over a period of time or establish long-term trends.
The timing of the cross-sectional snapshot may be unrepresentative of behavior of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

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Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a “cross-section”) in the population
Follows in participants over time	Provides of society at a given point

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

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Cross-Sectional Study: Definition, Designs & Examples

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A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.

This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time.

It can be used to assess the prevalence of outcomes and exposures, determine relationships among variables, and generate hypotheses about causal connections between factors to be explored in experimental designs.

Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population.

In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment.

Cross-sectional studies aim to describe a variable , not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time.

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

For example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity.

While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating.

Cross-sectional studies can be categorized based on the nature of the data collection and the type of data being sought.

Cross-Sectional Study	Purpose	Example
	To describe the characteristics of a population.	Examining the dietary habits of high school students.
	To investigate associations between variables.	Studying the correlation between smoking and lung disease in adults.
	To gather information on a population or a subset.	Conducting a survey on the use of public transportation in a city.
	To determine the proportion of a population with a specific characteristic, condition, or disease.	Assessing the prevalence of obesity in a country.
	To examine the effects of certain occupational or environmental exposures.	Studying the impact of air pollution on respiratory health in industrial workers.
	To generate hypotheses for future research.	Investigating relationships between various lifestyle factors and mental health conditions.

Analytical Studies

In analytical cross-sectional studies, researchers investigate an association between two parameters. They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population.

The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.

Descriptive Studies

Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population.
They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.
This is the most common type of cross-sectional study.
Evaluating the COVID-19 positivity rates among vaccinated and unvaccinated adolescents
Investigating the prevalence of dysfunctional breathing in patients treated for asthma in primary care (Wang & Cheng, 2020)
Analyzing whether individuals in a community have any history of mental illness and whether they have used therapy to help with their mental health
Comparing grades of elementary school students whose parents come from different income levels
Determining the association between gender and HIV status (Setia, 2016)
Investigating suicide rates among individuals who have at least one parent with chronic depression
Assessing the prevalence of HIV and risk behaviors in male sex workers (Shinde et al., 2009)
Examining sleep quality and its demographic and psychological correlates among university students in Ethiopia (Lemma et al., 2012)
Calculating what proportion of people served by a health clinic in a particular year have high cholesterol
Analyzing college students’ distress levels with regard to their year level (Leahy et al., 2010)

Simple and Inexpensive

These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.

Minimal room for error

Because all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.

Multiple variables and outcomes can be researched and compared at once

Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.

The data can be a starting point for future research

The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Limitations

Does not help determine cause and effect.

Cross-sectional studies can be influenced by an antecedent consequent bias which occurs when it cannot be determined whether exposure preceded disease. (Alexander et al.)

Report bias is probable

Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented.

The timing of the snapshot is not always representative

Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.

It cannot be used to analyze behavior over a period of time

Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods.

Cross-Sectional vs. Longitudinal

Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment.

However, cross-sectional studies differ from longitudinal studies in that cross-sectional studies look at a characteristic of a population at a specific point in time, while longitudinal studies involve studying a population over an extended period.

Longitudinal studies require more time and resources and can be less valid as participants might quit the study before the data has been fully collected.

Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects.

Cross-sectional studies can be done much quicker than longitudinal studies and are a good starting point to establish any associations between variables, while longitudinal studies are more timely but are necessary for studying cause and effect.

Alexander, L. K., Lopez, B., Ricchetti-Masterson, K., & Yeatts, K. B. (n.d.). Cross-sectional Studies. Eric Notebook. Retrieved from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf

Cherry, K. (2019, October 10). How Does the Cross-Sectional Research Method Work? Verywell Mind. Retrieved from https://www.verywellmind.com/what-is-a-cross-sectional-study-2794978

Cross-sectional vs. longitudinal studies. Institute for Work & Health. (2015, August). Retrieved from https://www.iwh.on.ca/what-researchers-mean-by/cross-sectional-vs-longitudinal-studies

Leahy, C. M., Peterson, R. F., Wilson, I. G., Newbury, J. W., Tonkin, A. L., & Turnbull, D. (2010). Distress levels and self-reported treatment rates for medicine, law, psychology and mechanical engineering tertiary students: cross-sectional study. The Australian and New Zealand journal of psychiatry, 44(7), 608–615.

Lemma, S., Gelaye, B., Berhane, Y. et al. Sleep quality and its psychological correlates among university students in Ethiopia: a cross-sectional study. BMC Psychiatry 12, 237 (2012).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.

Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61 (3), 261–264.

Shinde S, Setia MS, Row-Kavi A, Anand V, Jerajani H. Male sex workers: Are we ignoring a risk group in Mumbai, India? Indian J Dermatol Venereol Leprol. 2009;75:41–6.

Further Information

Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian journal of dermatology, 61(3), 261.
Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. Bmj, 348.

1. Are cross-sectional studies qualitative or quantitative?

Cross-sectional studies can be either qualitative or quantitative , depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

2. What’s the difference between cross-sectional and cohort studies?

A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas cohort studies follow individuals over extended periods.

Another difference between these two types of studies is the subject pool. In cross-sectional studies, researchers select a sample population and gather data to determine the prevalence of a problem.

Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.

3. What’s the difference between cross-sectional and case-control studies?

Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk.

In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies are used to determine what factors might be associated with the condition and help researchers form hypotheses about a population.

4. Does a cross-sectional study have a control group?

A cross-sectional study does not need to have a control group , as the population studied is not selected based on exposure.

In a cross-sectional study, data are collected from a sample of the target population at a specific point in time, and everyone in the sample is assessed in the same way. There isn’t a manipulation of variables or a control group as there would be in an experimental study design.

5. Is a cross-sectional study prospective or retrospective?

A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time.

Cross-sectional studies are not designed to follow individuals forward in time ( prospective ) or look back at historical data ( retrospective ), as they analyze data from a specific point in time.

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Knowledge Base
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Cross-Sectional Study | Definitions, Uses & Examples

Cross-Sectional Study | Definitions, Uses & Examples

Published on 5 May 2022 by Lauren Thomas .

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, frequently asked questions about cross-sectional studies.

Prevent plagiarism, run a free check.

When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

An analytical study tries to answer how or why a certain outcome might occur.
A descriptive study only summarises said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organisations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

If you want to choose the variables in your study and analyse your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
Since cross-sectional studies only study a single moment in time, they cannot be used to analyse behavior over a period of time or establish long-term trends.
The timing of the cross-sectional snapshot may be unrepresentative of behaviour of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a ‘cross-section’) in the population
Follows in participants over time	Provides of society at a given point

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Thomas, L. (2022, May 05). Cross-Sectional Study | Definitions, Uses & Examples. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/research-methods/cross-sectional-design/

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Methodology Series Module 3: Cross-sectional Studies

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How Do Cross-Sectional Studies Work?

Gathering Data From a Single Point in Time

Defining Characteristics

Advantages of Cross-Sectional Studies

Challenges of cross-sectional studies, cross-sectional vs. longitudinal studies.

A cross-sectional study looks at data at a single point in time. The participants in this type of study are selected based on particular variables. Cross-sectional studies are typically used in developmental psychology , but they are useful in many other areas as well, including social science and education.

Cross-sectional studies are observational and are known as descriptive research, not causal or relational—meaning you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables .

This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.

Example: Researchers studying developmental psychology might select groups of people who are different ages but investigate them at one point in time. By doing this, any differences among the age groups can be attributed to age differences rather than something that happened over time.

Defining Characteristics of Cross-Sectional Studies

Some of the key characteristics of a cross-sectional study include:

The study takes place at a single point in time
It does not involve manipulating variables
It allows researchers to look at numerous characteristics at once (age, income, gender, etc.)
It's often used to look at the prevailing characteristics in a given population
It can provide information about what is happening in a current population

Verywell / Jessica Olah

Think of a cross-sectional study as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.This type of research is frequently used to determine the prevailing characteristics in a population at a certain point in time. For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes.

A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example. While this type of study cannot demonstrate cause and effect, it can provide a quick look at correlations that may exist at a particular point.

For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further.

Cross-sectional studies are popular because they offer many benefits for researchers.

Inexpensive and Fast

Cross-sectional studies typically allow researchers to collect a great deal of information quickly. Data is often obtained inexpensively using self-report surveys . Researchers are then able to amass large amounts of information from a large pool of participants.

For example, a university might post a short online survey about library usage habits among biology majors, and the responses would be recorded in a database automatically for later analysis. This is a simple, inexpensive way to encourage participation and gather data across a wide swath of individuals who fit certain criteria.

Can Assess Multiple Variables

Researchers can collect data on a few different variables to see how they affect a certain condition. For example, differences in sex, age, educational status, and income might correlate with voting tendencies or give market researchers clues about purchasing habits.

Might Prompt Further Study

Although researchers can't use cross-sectional studies to determine causal relationships, these studies can provide useful springboards to further research. For example, when looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that can spur further experimental studies.

For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Conducting such a study can give researchers clues about the types of exercise that might be most beneficial to the elderly and inspire further experimental research on the subject.

No method of research is perfect. Cross-sectional studies also have potential drawbacks.

Difficulties in Determining Causal Effects

Researchers can't always be sure that the conditions a cross-sectional study measures are the result of a particular factor's influence. In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. This type of research simply doesn't allow for conclusions about causation.

For example, a study conducted some 20 years ago queried thousands of women about their consumption of diet soft drinks. The results of the study, published in the medical journal Stroke , associated diet soft drink intake with stroke risk that was greater than that of those who did not consume such beverages. In other words, those who drank lots of diet soda were more prone to strokes. However, correlation does not equal causation. The increased stroke risk might arise from any number of factors that tend to occur among those who drink diet beverages. For example, people who consume sugar-free drinks might be more likely to be overweight or diabetic than those who drink the regular versions. Therefore, they might be at greater risk of stroke—regardless of what they drink.

Cohort Differences

Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events.

Report Biases

Surveys and questionnaires about certain aspects of people's lives might not always result in accurate reporting. For example, respondents might not disclose certain behaviors or beliefs out of embarrassment, fear, or other limiting perception. Typically, no mechanism for verifying this information exists.

Cross-sectional research differs from longitudinal studies in several important ways. The key difference is that a cross-sectional study is designed to look at a variable at a particular point in time. A longitudinal study evaluates multiple measures over an extended period to detect trends and changes.

Evaluates variable at single point in time

Participants less likely to drop out

Uses new participant(s) with each study

Measures variable over time

Requires more resources

More expensive

Subject to selective attrition

Follows same participants over time

Longitudinal studies tend to require more resources; these are often more expensive than those used by cross-sectional studies. They are also more likely to be influenced by what is known as selective attrition , which means that some individuals are more likely to drop out of a study than others. Because a longitudinal study occurs over a span of time, researchers can lose track of subjects. Individuals might lose interest, move to another city, change their minds about participating, etc. This can influence the validity of the study.

One of the advantages of cross-sectional studies is that data is collected all at once, so participants are less likely to quit the study before data is fully collected.

A Word From Verywell

Cross-sectional studies can be useful research tools in many areas of health research. By learning about what is going on in a specific population, researchers can improve their understanding of relationships among certain variables and develop additional studies that explore these conditions in greater depth.

Levin KA. Study design III: Cross-sectional studies . Evid Based Dent . 2006;7(1):24-5. doi:10.1038/sj.ebd.6400375

Morin JF, Olsson C, Atikcan EO, eds. Research Methods in the Social Sciences: An A-Z of Key Concepts . Oxford University Press; 2021.

Abbasi J. Unpacking a recent study linking diet soda with stroke risks . JAMA . 2019;321(16):1554-1555. doi:10.1001/jama.2019.2123

Setia MS. Methodology series module 3: Cross-sectional studies . Indian J Dermatol . 2016;61(3):261-4. doi:10.4103/0019-5154.182410

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cross-Sectional Study in Research

Introduction

What is a cross-sectional study in research, what is the difference between cross-sectional and longitudinal research, cross-sectional study examples, types of cross-sectional studies, benefits of cross-sectional studies, challenges of cross-sectional studies.

Cross-sectional studies are a fundamental research method used across various fields to analyze data at a specific point in time. By comparing different subjects without considering the time variable, these studies can provide valuable insights into the prevalence and characteristics of phenomena within a population.

This article explores the concept of cross-sectional research, outlining its key features, applications, and how it differs from longitudinal studies. We will also examine examples of cross-sectional data, discuss the various types of cross-sectional studies, and highlight both the advantages and challenges associated with this research method. Understanding when and how to employ research methods for a cross-sectional study design is crucial for researchers aiming to draw accurate and meaningful conclusions from their data .

A cross-sectional study is a type of observational research design that analyzes data from a population, or a representative subset, at one specific point in time. Unlike longitudinal studies that observe the same subjects over a period of time to detect changes, cross-sectional studies focus on finding relationships and prevalences within a predefined snapshot. This method is particularly useful for understanding the current status of a phenomenon or to identify associations between variables without inferring causal relationships.

In practice, cross-sectional studies collect data across a wide range of subjects at a single moment, aiming to capture a comprehensive picture of a particular research question. Researchers might analyze various factors, including demographic information, behaviors, conditions, or outcomes, to discern patterns or correlations within the population studied.

Though these studies cannot determine cause and effect, they are invaluable for generating hypotheses or propositions, informing policy decisions, and guiding future research. Their descriptive nature and relative ease of execution make cross-sectional studies a common starting point in many research endeavors, providing a foundational understanding of the context and variables of interest.

The primary distinction between cross-sectional and longitudinal research lies in how and when the data is collected. Cross-sectional studies differ in that they capture data at a single point in time, offering a snapshot that helps to identify the prevalence and relationships between variables within a specific moment that further research might be able to explore. In contrast, a longitudinal study involves collecting data from the same subjects repeatedly over an extended period of time, enabling the observation of changes and developments in the variables of interest.

While cross-sectional studies are efficient for gathering data at one point in time and are less costly and time-consuming than longitudinal studies, they fall short in tracking changes over time or establishing cause-and-effect relationships. On the other hand, longitudinal studies excel in observing how variables evolve, providing insights into dynamics and causal pathways. However, longitudinal data collection requires more resources, time, and a rigorous design to manage participant attrition and ensure consistent data collection over the study period.

Another key difference is in the potential for cohort effects. A cross-sectional analysis might conflate age-related changes with generational effects because different age groups are compared at one particular point in time. Longitudinal research, by observing the same individuals over time, can differentiate between aging effects and cohort effects, offering a clearer view of how specific and multiple variables change throughout an individual's life or over time.

Cross-sectional studies are employed across various disciplines to investigate multiple phenomena at a specific point in time. These studies offer insights into the prevalence, distribution, and potential associations between variables within a defined population.

Below are three examples from different fields illustrating how cross-sectional research is applied to glean valuable findings.

Healthcare: Prevalence of a medical condition

In medical research, cross-sectional studies are frequently used to determine the prevalence of diseases or health outcomes in a population. For instance, a study might collect cross-sectional data from a diverse sample of individuals to assess the current prevalence of diabetes. By analyzing factors such as age, lifestyle, and comorbidities, researchers can identify patterns and risk factors associated with the disease, aiding in public health planning and intervention strategies.

Education: Analyzing student performance

Educational researchers often use a cross-sectional design to evaluate student performance across different grades or age groups at a single point in time. Such a study could compare test scores to analyze trends and disparities in educational achievement. By examining variables like socio-economic status, teaching methods, and school resources, educators and policymakers can identify areas needing improvement or intervention.

Economics: Employment trends analysis

In economics, a cross-sectional survey can provide snapshots of employment trends within a specific region or sector. An example might involve analyzing the employment rates, job types, and economic sectors in a country at a given time. This data can reveal insights into the economic health, workforce distribution, and potential areas for economic development or policy focus, informing stakeholders and guiding decision-making processes.

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Cross-sectional studies can be categorized into different types based on their objectives and methodologies . These variations allow researchers to adapt the cross-sectional approach to suit specific research questions and contexts.

By understanding the different types of cross-sectional studies, researchers can select the most appropriate design to obtain reliable and relevant data. Below are four common types of cross-sectional studies, each with its unique focus and application.

Descriptive cross-sectional studies

Descriptive cross-sectional studies aim to provide a detailed snapshot of a population or phenomenon at a particular point in time. These studies focus on 'what exists' or 'what is prevalent' without delving into relationships between variables or concepts.

For example, a descriptive research study might catalog various health behaviors within a specific demographic group to inform public health initiatives. The primary goal is to describe characteristics, frequencies, or distributions as they exist in the study population.

Analytical cross-sectional studies

Unlike descriptive studies that focus on prevalence and distribution, analytical cross-sectional studies aim to uncover potential associations between variables. These studies often compare different groups within the population to identify factors that may correlate with certain outcomes.

For instance, an analytical cross-sectional study might investigate the relationship between lifestyle choices and blood pressure levels across various age groups. While these studies can suggest associations, they do not establish cause and effect.

Exploratory cross-sectional studies

Exploratory cross-sectional studies are conducted to explore potential relationships or hypotheses when little is known about a subject. These studies are particularly useful in emerging fields or for new phenomena. By examining available data, they can generate hypotheses for further research without committing extensive resources to long-term studies.

An example might be exploring the usage patterns of a new technology within a population to identify trends and areas for in-depth study.

Explanatory cross-sectional studies

Explanatory cross-sectional studies go beyond identifying associations; they aim to explain why certain patterns or relationships are observed. These studies often incorporate theoretical frameworks or models to analyze the data within a broader context, providing deeper insights into the underlying mechanisms or factors.

For example, an explanatory cross-sectional study could investigate why certain educational strategies are associated with better student outcomes, integrating theories of learning and cognition.

Cross-sectional studies are a crucial tool in the repertoire of research methodologies , offering unique advantages that make them particularly suitable for various research contexts. These studies are instrumental in providing a snapshot of a specific point in time, which can be invaluable for understanding the status quo and informing future research directions. Below, we explore three significant benefits of employing cross-sectional studies in research endeavors.

Cost-effectiveness

One of the primary benefits of cross-sectional studies is their cost-effectiveness compared to longitudinal studies . Since they are conducted at a single point in time and do not require follow-ups, the financial resources, time, and logistical efforts needed are considerably lower. This efficiency makes cross-sectional studies an appealing option for researchers with limited budgets or those seeking preliminary data before committing to more extensive research.

Cross-sectional studies are inherently timely, providing quick snapshots that are especially valuable in fast-paced research areas where timely data is crucial. They allow researchers to collect and analyze data relatively quickly, offering insights that are current and relevant. This timeliness is particularly beneficial for informing immediate policy decisions or for studies in fields where trends may change rapidly, such as technology or public health.

Versatility

The versatility of cross-sectional studies is evident in their wide applicability across various fields and purposes. They can be designed to explore numerous variables and their interrelations within different populations and settings. This flexibility enables researchers to tailor studies to specific research questions, making cross-sectional studies a versatile tool for exploratory research, hypothesis generation , or situational analysis across disciplines.

Despite their utility in various fields of research, cross-sectional studies face distinct challenges that can affect the validity and applicability of their findings. Understanding these limitations is crucial for researchers to design robust studies and for readers to interpret results appropriately. Here are three key challenges commonly associated with cross-sectional studies.

Causality determination

One of the inherent limitations of cross-sectional studies is their inability to establish causality. Since data is collected at a single point in time, it is challenging to ascertain whether a relationship between two variables is causal or merely correlational. This limitation necessitates cautious interpretation of results, as establishing temporal precedence is essential for causal inference, which cross-sectional designs cannot provide.

Selection bias

Selection bias can occur in cross-sectional studies if the sample is not representative of the population from which it was drawn. This can happen due to non-random sampling methods or non-response, leading to skewed results that do not accurately reflect the broader population. Such bias can compromise the generalizability of the study's findings, making it critical to employ rigorous sampling methods and consider potential biases during analysis.

Cross-sectional confounding

Cross-sectional studies can also be susceptible to confounding, where an external variable influences both the independent and dependent variables , creating a spurious association. Without longitudinal data , it is difficult to control for or identify these confounding factors, which can lead to erroneous conclusions. Researchers must carefully consider potential confounders and employ statistical methods to adjust for these variables where possible.

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What is a cross-sectional study?

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6 February 2023

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Read on to learn about cross-sectional studies. We’ll explore examples, types, advantages, and limitations of cross-sectional studies, plus when you might use them.

Analyze cross-sectional studies

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A cross-sectional study is also known as a prevalence or transverse study. It’s a tool that allows researchers to collect data across a pre-defined subset or sample population at a single point in time. The information is typically about many individuals with multiple variables, such as gender and age. Although researchers get to analyze these variables, they do not manipulate them.

This study type is commonly used in clinical research, business-related studies, and population studies.

Once the researcher has selected the ideal study period and participant group, the study usually takes place as a survey or physical experiment.

Characteristics of cross-sectional studies

Primary characteristics of cross-sectional studies include the following:

Consistent variables : Researchers carry out a cross-sectional study over a specific period with the same set of variables (income, gender, age, etc.).

Observational nature : Researchers record findings about a specific population but do not alter variables—they just observe.

Well-defined extremes : The analysis includes defined start and stop points which allow all variables to stay the same.

Singular instances : Only one topic or instance can be analyzed with a cross-sectional study. This allows for more accurate data collection .

Examples of cross-sectional studies

Variables remain the same during a cross-sectional study. This makes it a useful research tool in various sectors and circumstances across multiple industries.

Here are some examples to give you better clarity:

Healthcare : Scientists might leverage cross-sectional research to assess how children aged 3–10 are prone to calcium deficiency.

Retail : Researchers use cross-sectional studies to identify similarities and differences in spending habits between men and women within a specific age group.

Education : These studies help reveal how students with a specific grade range perform when schools introduce a new curriculum.

Business: Researchers might leverage cross-sectional studies to understand how a geographic segment responds to offers and discounts.

Types of cross-sectional studies

We can categorize cross-sectional studies into two distinct types: descriptive and analytical research. However, the researcher may use one or both types to gather and analyze data.

Here is a description of the two to help you understand how they may apply to your work.

Descriptive research

A descriptive cross-sectional survey or study assesses how commonly or frequently the primary variable occurs within a select demographic. This enables you to identify any problem areas within the group.

Descriptive research makes trend identification easy, facilitating the development of products and services that fit a particular population.

Analytical research

An analytical cross-sectional study investigates the relationship between two related or unrelated parameters. Outside variables may affect the study while the investigation is ongoing, however.

Note that the original results and data are studied together simultaneously in an analytical cross-sectional study.

Cross-sectional versus longitudinal studies

Although longitudinal and cross-sectional studies are both observational, they are relatively different types of research design.

Below are the main differences between cross-sectional and longitudinal studies :

Sample group

A cross-sectional study will include several variables and sample groups, meaning it will collect data for all the different sample groups at once. However, in longitudinal studies, the same groups with similar variables can be observed repeatedly.

Cross-sectional studies are usually cheaper to conduct than longitudinal studies, so they are ideal if you have a limited budget.

Participants in longitudinal studies have to commit for an extended period, which significantly increases costs. Cross-sectional studies, on the other hand, are shorter and require less effort.

Data is collected only once in cross-sectional research. In contrast, longitudinal research takes considerable time because data is collected across numerous periods (potentially decades).

Researchers don’t necessarily seek causation in longitudinal research. This means the data will lack context regarding previous participant behavior.

Longitudinal research, on the other hand, clearly shows how data evolves. This means you can infer cause-and-effect relationships.

How to perform a cross-sectional study

You will need to follow these steps to conduct a cross-sectional study:

Formulate research questions and hypotheses . You will also need to identify your target population at this stage.

Design the research . You will need to leverage observation rather than experiments when collecting data. However, you can always use non-experimental techniques such as questionnaires or surveys. As a result, this type of research will let you collect both quantitative and qualitative data .

Conduct the research . You can collect your data or assemble it from another source. In most instances, governments make cross-sectional datasets available to the public (through censuses) that can help with your research. The World Bank and World Health Organization also provide cross-sectional datasets on their websites.

Analyze the data . Data analysis will depend on the type of data collection method you use.

Advantages and disadvantages of cross-sectional studies

Are you considering whether a cross-sectional study is an ideal approach for your next research? It’s an efficient and effective way to gather data. Check out some of the key advantages and disadvantages of cross-sectional studies.

Advantages of cross-sectional research

Quick to conduct

Multiple outcomes are researched at once

Relatively inexpensive

Used as a basis for further research

Researchers gather all variables at a single point in time

It’s possible to measure the prevalence of all factors

Ideal for descriptive analysis

Disadvantages of cross-sectional research

Preventing other variables from influencing the study is challenging

Researchers cannot infer cause-and-effect relationships

Requires large, heterogeneous samples, which increases the chances of sampling bias

The select population and period may not be representative

When to use a cross-sectional design

Cross-sectional studies are useful when:

You need answers to questions regarding the prevalence and incidence of a situation, belief, or condition.

Establishing the norm in a particular demographic at a specified time. For instance, what is the average age for completing studies in Dallas?

Justifying the need to conduct further research on a specific topic. With cross-sectional research, you can infer a correlation without determining a direct cause. This makes it easier to justify conducting other investigations.

The bottom line

A cross-sectional study is essential when researching the prevailing characteristics in a given population at a single point in time. Cross-sectional studies are often used to analyze demography, financial reports, and election polls. You could also use them in medical research or when building a marketing strategy, for instance.

Are cross-sectional studies quantitative or qualitative?

Cross-sectional research can be both qualitative and quantitative.

Do cross-sectional studies have control groups?

Cross-sectional studies don’t need a control group as the selected population is not based on exposure.

What are the limitations of cross-sectional studies?

Limitations of cross-sectional studies include the inability to make causal inferences, study rare illnesses, and access incidence. Researchers select a subject sample from a large and heterogeneous population.

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Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations

Affiliations.

1 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. Electronic address: [email protected].
2 Department of Respiratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
PMID: 32658654
DOI: 10.1016/j.chest.2020.03.012

Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study. This article reviews the essential characteristics, describes strengths and weaknesses, discusses methodological issues, and gives our recommendations on design and statistical analysis for cross-sectional studies in pulmonary and critical care medicine. A list of considerations for reviewers is also provided.

Keywords: bias; confounding; cross-sectional studies; prevalence; sampling.

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Evidence Based Practice: Study Designs & Evidence Levels

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Introduction

This section reviews some research definitions and provides commonly used evidence tables.

Levels of Evidence Johns Hopkins Nursing Evidence Based Practice


Experimental study, randomized controlled trial (RCT) Systematic review of RCTs, with or without meta-analysis	: Consistent, generalizable results; sufficient sample size for the study design; adequate control; definitive conclusions; consistent recommendations based on comprehensive literature review that includes thorough reference to scientific evidence
Quasi-experimental study Systematic review of a combination of RCTs and quasi experimental, or quasi-experimental studies only, with or without meta-analysis	: Reasonably consistent results; sufficient sample size for the study design; some control, fairly definitive conclusions; reasonably consistent recommendations based on fairly comprehensive literature review that includes some reference to scientific evidence
Non-experimental study Systematic review of a combination of RCTs, quasi-experimental and non-experimental studies, or non-experimental studies only, with or without meta-analysis Qualitative study or systematic review with or without a meta-synthesis	: Little evidence with inconsistent results; insufficient sample size for the study design; conclusions cannot be drawn

Opinion of respected authorities and/or nationally recognized
expert committees/consensus panels based on scientific evidence

Includes:
Clinical practice guidelines
Consensus panels

: Material officially sponsored by a professional, public, private organization, or government agency; documentation of a systematic literature
search strategy; consistent results with sufficient numbers of well-designed studies;
criteria-based evaluation of overall scientific strength and quality of included studies
and definitive conclusions; national expertise is clearly evident; developed or
revised within the last 5 years

: Material officially sponsored by a professional, public, private
organization, or government agency; reasonably thorough and appropriate
systematic literature search strategy; reasonably consistent results, sufficient
numbers of well-designed studies; evaluation of strengths and limitations of
included studies with fairly definitive conclusions; national expertise is clearly
evident; developed or revised within the last 5 years

: Material not sponsored by an official organization or agency; undefined, poorly defined, or limited literature search strategy; no evaluation of strengths and limitations of included studies, insufficient evidence with inconsistent results, conclusions cannot be drawn; not revised within the last 5 years

Based on experiential and non-research evidence

Includes:
Literature reviews
Quality improvement, program or financial evaluation
Case reports
Opinion of nationally recognized experts(s) based on experiential evidence

: Clear aims and objectives; consistent results across multiple settings; formal quality improvement, financial or program evaluation methods used; definitive conclusions; consistent recommendations with thorough reference to scientific evidence

: Clear aims and objectives; consistent results in a single setting;
formal quality improvement or financial or program evaluation methods used;
reasonably consistent recommendations with some reference to scientific evidence

: Unclear or missing aims and objectives; inconsistent
results; poorly defined quality improvement, financial or program evaluation
methods; recommendations cannot be made

:
: Expertise is clearly evident; draws definitive conclusions; provides
scientific rationale; thought leader(s) in the field

: Expertise appears to be credible; draws fairly definitive conclusions;
provides logical argument for opinions

: Expertise is not discernable or is dubious; conclusions
cannot be drawn

Dang, D., & Dearholt, S. (2017). Johns Hopkins nursing evidence-based practice: model and guidelines. 3rd ed. Indianapolis, IN: Sigma Theta Tau International. www.hopkinsmedicine.org/evidence-based-practice/ijhn_2017_ebp.html

Identifying the Study Design

The type of study can generally be figured out by looking at three issues:

Q1. What was the aim of the study?

To simply describe a population (PO questions) = descriptive
To quantify the relationship between factors (PICO questions) = analytic.

Q2. If analytic, was the intervention randomly allocated?

Yes? = RCT
No? = Observational study

For an observational study, the main type will then depend on the timing of the measurement of outcome, so our third question is:

Q3. When were the outcomes determined?

Some time after the exposure or intervention? = Cohort study ('prospective study')
At the same time as the exposure or intervention? = Cross sectional study or survey
Before the exposure was determined? = Case-control study ('retrospective study' based on recall of the exposure)

Centre for Evidence-Based Medicine (CEBM)

Definitions of Study Types

Case report / Case series: A report on a series of patients with an outcome of interest. No control group is involved.

Case control study: A study which involves identifying patients who have the outcome of interest (cases) and patients without the same outcome (controls), and looking back to see if they had the exposure of interest.

Cohort study: Involves identification of two groups (cohorts) of patients, one which received the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest.

Randomized controlled clinical trial: Participants are randomly allocated into an experimental group or a control group and followed over time for the variables/outcomes of interest.

Systematic review: A summary of the medical literature that uses explicit methods to perform a comprehensive literature search and critical appraisal of individual studies and that uses appropriate statistical techniques to combine these valid studies.

Meta-analysis: A systematic review that uses quantitative methods to synthesize and summarize the results.

Meta-synthesis: A systematic approach to the analysis of data across qualitative studies. -- EJ Erwin, MJ Brotherson, JA Summers. Understanding Qualitative Meta-synthesis. Issues and Opportunities in Early Childhood Intervention Research, 33(3) 186-200 .

Cross sectional study: The observation of a defined population at a single point in time or time interval. Exposure and outcome are determined simultaneously.

Prospective, blind comparison to a gold standard: Studies that show the efficacy of a diagnostic test are also called prospective, blind comparison to a gold standard study. This is a controlled trial that looks at patients with varying degrees of an illness and administers both diagnostic tests — the test under investigation and the “gold standard” test — to all of the patients in the study group. The sensitivity and specificity of the new test are compared to that of the gold standard to determine potential usefulness.

Qualitative research: answers a wide variety of questions related to human responses to actual or potential health problems.The purpose of qualitative research is to describe, explore and explain the health-related phenomena being studied.

Retrospective cohort: follows the same direction of inquiry as a cohort study. Subjects begin with the presence or absence of an exposure or risk factor and are followed until the outcome of interest is observed. However, this study design uses information that has been collected in the past and kept in files or databases. Patients are identified for exposure or non-exposures and the data is followed forward to an effect or outcome of interest.

(Adapted from CEBM's Glossary and Duke Libraries' Intro to Evidence-Based Practice )

American Association of Critical Care Nursing-- Levels of Evidence

Level A Meta-analysis of multiple controlled studies or meta-synthesis of qualitative studies with results that consistently support a specific action, intervention or treatment

Level B Well designed controlled studies, both randomized and nonrandomized, with results that consistently support a specific action, intervention, or treatment

Level C Qualitative studies, descriptive or correlational studies, integrative reviews, systematic reviews, or randomized controlled trials with inconsistent results

Level D Peer-reviewed professional organizational standards, with clinical studies to support recommendations

Level E Theory-based evidence from expert opinion or multiple case reports

Level M Manufacturers’ recommendations only

Armola RR, Bourgault AM, Halm MA, Board RM, Bucher L, Harrington L, Heafey CA, Lee R, Shellner PK, Medina J. (2009) AACN levels of evidence: what's new ? J.Crit Care Nurse. Aug;29(4):70-3.

Flow Chart of Study Designs

Figure: Flow chart of different types of studies (Q1, 2, and 3 refer to the three questions below in "Identifying the Study Design" box.) Centre for Evidence-Based Medicine (CEBM)

What is a "Confidence Interval (CI)"?

A confidence interval (CI) can be used to show within which interval the population's mean score will probably fall. Most researchers use a CI of 95%. By using a CI of 95%, researchers accept there is a 5% chance they have made the wrong decision in treatment. Therefore, if 0 falls within the agreed CI, it can be concluded that there is no significant difference between the two treatments. When 0 lies outside the CI, researchers will conclude that there is a statistically significant difference.

Halfens, R. G., & Meijers, J. M. (2013). Back to basics: an introduction to statistics. Journal Of Wound Care , 22 (5), 248-251.

What is a "p-value?"

Categorical (nominal) tests This category of tests can be used when the dependent, or outcome, variable is categorical (nominal), such as the difference between two wound treatments and the healing of the wound (healed versus nonhealed). One of the most used tests in this category is the chisquared test (χ2). The chisquared statistic is calculated by comparing the differences between the observed and the expected frequencies. The expected frequencies are the frequencies that would be found if there was no relationship between the two variables.

Based on the calculated χ2 statistic, a probability (p value) is given, which indicates the probability that the two means are not different from each other. Researchers are often satisfied if the probability is 5% or less, which means that the researchers would conclude that for p < 0.05, there is a significant difference. A p value ≥ 0.05 suggests that there is no significant difference between the means.

Halfens, R. G., & Meijers, J. M. (2013). Back to basics: an introduction to statistics. Journal Of Wound Care, 22(5), 248-251.

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What (Exactly) Is A Cross-Sectional Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’ve just started out on your dissertation, thesis or research project and it’s your first time carrying out formal research, you’ve probably encountered the terms “cross-sectional study” and “cross-sectional research” and are wondering what exactly they mean. In this post, we’ll explain exactly :

What a cross-sectional study is (and what the alternative approach is)
What the main advantages of a cross-sectional study are
What the main disadvantages of a cross-sectional study are
Whether you should use a cross-sectional or longitudinal study for your research

What is a cross-sectional study or cross-sectional research?

What (exactly) is a cross-sectional study?

A cross-sectional study (also referred to as cross-sectional research) is simply a study in which data are collected at one point in time . In other words, data are collected on a snapshot basis, as opposed to collecting data at multiple points in time (for example, once a week, once a month, etc) and assessing how it changes over time.

Example: Cross-Sectional vs Longitudinal

Here’s an example of what this looks like in practice:

Cross-sectional study: a study which assesses a group of people’s attitudes and feelings towards a newly elected president, directly after the election happened.

Longitudinal study: a study which assesses how people’s attitudes towards the president changed over a period of 3 years after the president is elected, assessing sentiment every 6 months.

As you can probably see, while both these studies are analysing the same topic (people’s sentiment towards the president), they each have a different focus. The cross-sectional study is interested in what people are feeling and thinking “ right now ”, whereas the longitudinal study is interested in not just what people are feeling and thinking, but how those thoughts and feelings change over time .

What are the advantages of a cross-sectional study?

There are many advantages to taking a cross-sectional approach, which makes it the more popular option for dissertations and theses. Some main advantages are:

Speed – given the nature of a cross-sectional study, you can complete your research relatively quickly, as information only needs to be gathered once.
Cost – because information only needs to be collected once, the cost is lower than a longitudinal approach.
Control – because the data are only collected at one point in time, you have a lot more control over the measurement process (i.e. you don’t need to worry about measurement instruments changing over a period of years).
Flexibility – using a cross-sectional approach, you can measure multiple factors at once. Your study can be descriptive (assessing the prevalence of something), analytical (assessing the relationship between two or more things) or both.

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What are the disadvantages of a cross-sectional study?

While the cross-sectional approach to research has many advantages, it (naturally) has its limitations and disadvantages too. Some of the main disadvantages are:

Static – cross-sectional studies cannot establish any sequence of events, as they only assess data with a snapshot view.
Causality – because cross-sectional studies look at data at a single point in time (no sequence of events), it’s sometimes difficult to understand which way causality flows – for example, does A cause B, or does B cause A? Without knowing whether A or B came first, it’s not always easy to tell which causes which.
Sensitivity to timing – the exact time at which data are collected can have a large impact on the results, and therefore the findings of the study may not be representative.

One of the disadvantages of the cross-sectional approach is that it provides a static view, meaning that it's very sensitive to timing.

Should I use a cross-sectional study or longitudinal study design?

It depends… Your decision to use a cross-sectional or longitudinal approach needs to be informed by your overall research aims, objectives and research questions . As with most research design choices, the research aims will heavily influence your approach.

For example, if your research objective is to get a snapshot view of something, then a cross-sectional approach should work well for you. However, if your research aim is to understand how something has changed over time, a longitudinal approach might be more appropriate.

If you’re trying to make this decision for a dissertation or thesis, you also need to consider the practical limitations such as time and access to data. Chances are, you won’t have the luxury of conducting your research over a period of a few years, so you might be “forced” into a cross-sectional approach due to time restrictions.

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Critical Appraisal Resources for Evidence-Based Nursing Practice

Levels of Evidence
Systematic Reviews
Randomized Controlled Trials
Quasi-Experimental Studies
Case-Control Studies
Cohort Studies
Analytical Cross-Sectional Studies

What is an Analytical Cross-Sectional Study?

Pro tips: analytical cross-sectional study checklist, articles on cross-sectional study design and methodology.

Qualitative Research

E-Books for Terminology and Definitions

An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206). The purpose is to measure the association between an exposure and a disease, condition or outcome within a defined population. Cross-sectional studies often utilize surveys or questionnaires to gather data from participants (Schmidt & Brown, 2019, pp. 206-207).

Schmidt N. A. & Brown J. M. (2019). Evidence-based practice for nurses: Appraisal and application of research (4th ed.). Jones & Bartlett Learning.

Each JBI Checklist provides tips and guidance on what to look for to answer each question. These tips begin on page 4.

Below are some additional Frequently Asked Questions about the Analytical Cross-Sectional Studies Checklist that have been asked students in previous semesters.

Frequently Asked Question	Response
	A confounder or confounding factor/confounding variable is often referred to as a third variable that could potentially impact the study's results. Read a definition and description . Confounding factors/variables or confounders may be listed in the study's limitations section or within the study's main results section.
	Check for or regression analysis in the study's data analysis/statistical analysis section. Read a definition and description .

For more help: Each JBI Checklist provides detailed guidance on what to look for to answer each question on the checklist. These explanatory notes begin on page four of each Checklist. Please review these carefully as you conduct critical appraisal using JBI tools.

Kesmodel U. S. (2018). Cross-sectional studies - what are they good for? Acta Obstetricia et Gynecologica Scandinavica , 97 (4), 388–393. https://doi.org/10.1111/aogs.13331

Pandis N. (2014). Cross-sectional studies . American Journal of Orthodontics and Dentofacial Orthopedics , 146 (1), 127–129. https://doi.org/10.1016/j.ajodo.2014.05.005

Savitz, D. A., & Wellenius, G. A. (2023). Can cross-sectional studies contribute to causal inference? It depends . American Journal of Epidemiology , 192 (4), 514–516. https://doi.org/10.1093/aje/kwac037

Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations . Chest , 158 (1S), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012

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Analyzing longitudinal qualitative data: the application of trajectory and recurrent cross-sectional approaches

Daniel Grossoehme ORCID: orcid.org/0000-0002-5654-0693 1 , 2 , 3 &
Ellen Lipstein 1 , 3 , 4

BMC Research Notes volume 9 , Article number: 136 ( 2016 ) Cite this article

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Longitudinal qualitative research methods can add depth and understanding to health care research, especially on topics such as chronic conditions, adherence and changing health policies. In this manuscript we describe when and how to undertake two different applied approaches to analyzing longitudinal qualitative data: a recurrent cross-sectional approach and a trajectory approach.

A recurrent cross-sectional approach is most appropriate when the primary interest is comparing two time points, such as before and after a policy change, or when a cohort cannot be maintained, such as a study in which some participants are expected to die. In contrast, a trajectory approach is most appropriate when the purpose of the research is to understand individuals’ experiences over time or to understand longitudinal healthcare processes.

Conclusions

Qualitative research is an essential part of applied health care research [ 1 , 2 ]. The in-depth approaches used in qualitative research allow for a better understanding of the lived experience of disease, including the ways in which individuals interacted with the health care system and why they made specific health care choices. This research then helps generate hypotheses for future study and ultimately leads to improvements in health and health systems.

Within health care, most qualitative studies are cross-sectional. They employ a variety of data collection methods, such as interviews or focus groups, and the analyses often focus on understanding experiences in a specific time and place, or on participants’ recollections of prior experiences. However, typically, individuals’ experiences with health and the health care system occur over time. Therefore, a prospective understanding of the longitudinal experience may provide insight and direction that differs from that of cross-sectional data. For example, researchers have used longitudinal qualitative methods to examine decision making [ 3 ] and information needs [ 4 ] in cancer care, medication adherence, [ 5 ] and the health care experiences children with serious health conditions [ 6 , 7 ].

Existing studies in the healthcare literature often have sparse methods descriptions, making it hard for others to replicate a study or emulate the methods in a new study. Additionally, many citations for analytic methods come from books and studies where the applicability to health care research may not obvious. For these reasons, we sought to describe and clarify applied qualitative research methods that are accessible to researchers focused on health service research, medicine and health, or health care. In this manuscript we provide detailed explanation of two different approaches to longitudinal qualitative data analysis: a recurrent cross - sectional approach, for analyzing group-level data, and a trajectory approach, focused on individuals’ or small groups’ of individuals (e.g., families) experiences over time. By providing straightforward explanations of these methods, we hope to assist other researchers interested in understanding changes in health and healthcare experiences over time, through pursuit of longitudinal qualitative research.

Choosing an analytic approach

For the purposes of this article, we assumed that there are two primary approaches to analyzing longitudinal qualitative data: recurrent cross-sectional and trajectory (see Table 1 ). Although likely to be less common, there may be some specific research questions that are amenable to the researcher combining the two approaches. As in any research analysis, the key to determining the right approach is considering the focus of the research question.

Recurrent cross - sectional analysis explores themes and changes over time at the level of the entire study sample, although there may also be variation of interest in the samples at different time points. If the researcher’s primary interest is comparing two time points then cross-sectional analysis is likely preferred. For example, research seeking to understand reactions to a new health guideline might want to include a cross-sectional analysis from before and after implementation of the guideline. Additionally, questions of how group-level beliefs change over time, for example how “healthy eating” is defined, might be addressed appropriately in a recurrent cross-sectional study. Finally, there are situations in which maintaining a cohort is not feasible, either because of a long time span or because of the subject of the study. The latter is exemplified in a study by Ragsdale and colleagues of children under-going bone marrow transplant [ 8 ]. The authors knew that participants might die prior to follow-up and thus a cross-sectional design was more feasible, in order to not exclude participants who were interviewed early in the study and subsequently died.

Trajectory analysis focuses on changes over time for an individual or small group of individuals. When the purpose of the research is to understand individuals’ experiences over time or to understand longitudinal healthcare processes, we recommend using a trajectory approach. When the research interest is an experience or process, and the reactions to it, it becomes important to conduct analyses in a manner that emphasizes individual trajectories. For example, one of the authors (EL) has conducted research focused on decision making in chronic conditions [ 9 ]. By following the individual trajectories, we found that the factors influencing individuals’ decisions vary over time. Had we instead analyzed the data as recurrent cross-sections, such change would not have been visible because the factors considered by the population as a whole did not change; rather individuals changed the factor on which they focused. Likewise, if a study were designed to understand how individuals’ cope with test results, such as from cancer screening tests, cross-sectional analysis could lead to erroneous conclusions by focusing on how individuals are doing at a set time after testing, rather than on their experience over time. While some shifts in preferences or attitudes could be ascertained through individuals’ comments about the past, such recall is subject to significant bias [ 10 ]. Moreover, individuals are often unaware of how their perspective has changed over time based on experiences and information [ 11 , 12 ].

Setting up the analysis: data coding

Regardless of the chosen analytic approach, cross-sectional or trajectory, prior to beginning data collection the research team should consider both their research question, as discussed above, and the theoretical approach they plan to use for analyzing the data (e.g., grounded theory. This a priori decision making will ensure that data is collected, coded and structured in a manner consistent with the research plan. This coding step is amenable to an array of theoretical (e.g., grounded theory or phenomenological) and practical (e.g., using software or coding by hand) approaches.

For either analytic approach, utilizing a framework [ 13 ] or a list of analytic questions [ 14 ] may assist in structuring the data. For example, Saldaña’s often-cited reference for longitudinal qualitative research outlines 16 questions to help structure the analytic process. These questions can be divided into framing questions, descriptive questions and interpretive questions, all of which can be applied to either method of analyzing longitudinal data. Framing questions situate the context of the data and the health care process in which the data have arisen. Typical framing questions include describing how data collected at each time point relates to data from the other time points (e.g., defining changes in context, or when changes occur). Descriptive questions are intended to guide the interpretive phase of data analysis. The answers to these questions describe behavior in a particular environment [ 15 ]. Interpretive questions lead to descriptions of the behavior of interest within its context of relationships. These may include how changes in the behavior relate to one another; mediators and barriers to the behavior; or the data’s consistency with current practices.

Conducting a recurrent cross-sectional analysis

The recurrent cross-sectional approach has been the more commonly used approach to longitudinal qualitative research in healthcare. The analytic process is very similar to studies that focus on a single point in time, so details of this approach will only briefly be discussed. For readers interested in more in-depth guidance on cross-sectional approaches many good resources have been published including qualitative research and evaluation methods by Patton [ 16 ], qualitative data analysis by Miles and Huberman [ 17 ], and constructing grounded theory by Charmaz [ 18 ]. The recurrent cross-sectional approach can be thought of as a series of smaller studies study given that at each time point the data from all participants are analyzed as a unit. After this analysis is completed, a second analysis focuses on differences and similarities between time points [ 14 ]. A potential advantage of this approach is that analysis of early time points can be completed before data is even collected for subsequent time points. For example, one of the authors (DG) conducted a study of how parents of children used spirituality to cope in the first 12 months after their child’s cystic fibrosis diagnosis. We found four major themes related to, “We can handle this” [ 19 ]. After an additional 12 months elapsed, parents were re-interviewed; the longitudinal analysis showed parents now understood themselves to have been “chosen” to parent a child with CF [ 20 ]. Both the initial study and the follow-up study were independent grounded theory analyses of parental interview data [ 18 ]. In each case, transcripts were coded line by line by a team to isolate participants’ descriptions in their own words. These fragments were grouped into categories based on apparent similarity. These categories were further combined and a central theme that explained the emergent major categories was identified. The follow-up study also included a second analysis using Saldaña’s framing, descriptive, and interpretive questions. For example, the central themes of the two studies (“We can handle this” and “We were chosen as a family”) were placed next to each other and the framing question of differences between the time points explored. While one might have anticipated increasing confidence in parents’ coping and caring for a child with a life-shortening disease, what this method made clear was that their confidence was expressed in terms of religious vocation. Applying Saldaña’s questions to both sets of data allowed for the creation of a richer, single narrative that focused on the process of coping spanning 2 years.

Conducting a trajectory analysis

To our knowledge, few studies have utilized a trajectory approach despite the importance of individuals’ longitudinal experiences in the healthcare system. Moreover, the limited methods descriptions in many such studies may make it difficult for other researchers, especially those new to qualitative research, to reproduce the methods in their own work. For these reasons, we have developed a more structured approach to trajectory analysis that could be utilized by those new to the field. Specifically, we recommend using time-ordered, sequential matrices. Time-ordered displays have been previously described as a method to help preserve “chronological flow” and permit understanding of what led to what [ 17 ]. Trajectory analysis expands upon this base through the use of sequential matrices.

Once coded the data is organized into matrices, with one matrix per unit of analysis. The unit of analysis could be the individual, the family, or some other grouping of people. A combination could also be considered. For instance, one may be interested in how similar or different the experience is for individuals within a family. In that case, each matrix should contain the largest unit of analysis (example family) and codes from individuals within that group which can be identified via labeling. The use of color coding or font variations is recommended, rather than text labels, in order to facilitate a visual overview of the data. However, some software programs used for data analysis may not permit such visual labeling. This first set of matrices is organized with themes, based on your theoretical approach, along the Y-axis and time along the X-axis (see Table 2 ). To illustrate the basic principle of the matrix, themes which occurred at all three points are presented in Table 1 , although it may be helpful for some research questions to include topics which occur at only some time points.

Once data collection and coding are completed for each unit of analysis, longitudinal analysis can begin. In this step the focus is on how the data, in the thematic groupings, changed or did not change over time. To organize the findings, another matrix is needed. The Y-axis is again organized by themes. This time the X-axis is organized according to the primary units of analysis, in other words one column per unit of analysis (see Table 3 ). If the first set of matrices included labels for individuals within a unit of analysis, that labeling should continue in this matrix. The data codes entered in this matrix focus on the element of time. Codes may be used that indicate concepts that change over time or remain stable. For instance if a theme in the first set of matrices was family stress, the codes in the second matrix would focus on increases or decreases in stress over time. In this step it is particularly important to pay attention to data absences in the first set of matrices [ 14 ]. This may not indicate a deficit in coding but rather signal variation over time. As an example, if a participant discusses concerns about side effects of treatment at time one, but not at time two, it may indicate, depending upon how the data was collected, that the concern about side effects has dissipated over time.

As in most qualitative approaches, as coding for the second matrix progresses, new conceptual groupings may be needed as the original groupings likely focused on cross-sectional concepts and new, time-related concepts may emerge during coding. Data analysis is then conducted from this second matrix in which the codes are focused on time, with reference back to the first set of matrices when specific examples are needed.

Longitudinal qualitative research has the potential to be a powerful approach to understanding the complexities of health care: from relationships between providers and patients, to the experience of chronic disease, to the impact of health policy. This research will be strengthened by careful consideration of the research question at hand, followed by application of the appropriate analytic approach. A recurrent cross-sectional approach is best utilized for questions that focus on comparing discrete time points or where logistical challenges prevent retention of a research cohort. However, when the focus is on how experiences or processes unfold over time, a trajectory approach should be considered. A lack of methodological clarity in published studies has been a barrier to undertaking such research and potentially limited its impact. By presenting the rationale for using longitudinal qualitative methods, their description, accompanying examples and citations we hope to stimulate use of these methods to further enhance health care research.

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EL conceived of the study, carried out some of the analyses and drafted a portion of the manuscript. DG carried out some of the analyses and drafted a portion of the manuscript. Both authors read and approved the final manuscript.

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Thank you to Drs. Courtney Brown and Lori Crosby for their helpful comments on an earlier version of this manuscript.

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This chapter addresses cross-sectional research designs’ peculiarities, characteristics, and major fallacies. The significant advantage of cross-sectional research lies in cross-case analysis. A cross-sectional study aims at describing generalized relationships between distinct elements and conditions. The specific case and its particularities are not the focus, but all instances and cases. So cross-sectional studies try to establish general models that link a combination of elements with other elements under certain conditions. The results represent tested (or rejected) theories about these relationships. Also, researchers find relevant information on how to write a cross-sectional research design paper and learn about typical methodologies used for this research design. The chapter closes by referring to overlapping and adjacent research designs.

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Published: 11 September 2024

Factors affecting infant feeding choices with a focus on barriers to exclusive breastfeeding in Western Jamaica: a qualitative study

Claudia Datnow-Martinez 1 ,
Brittany Ransom 1 ,
Soumya J. Niranjan 2 ,
Chanice Howard 1 ,
Maung Aung 3 &
Pauline E. Jolly 1

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Despite the many benefits of exclusive breastfeeding to infants and mothers, only 33% of Jamaican infants are exclusively breastfed up to the recommend six months. This study was conducted to identify factors affecting mothers’ feeding choices focusing on barriers to exclusive breastfeeding of infants six weeks to less than six months old.

A qualitative study consisting of four focus group discussion sessions was conducted among 22 mothers attending postnatal clinics in western Jamaica from May to August 2016. The transcripts were coded by three independent coders and content analysis conducted to generate themes.

Four themes were identified namely, perceived advantages of breastfeeding centered mainly on the benefits of breastfeeding for the infant and mother, perceived barriers of breastfeeding highlighting physical pain and fatigue, supplementing culturally acceptable complementary foods and herbal remedies, and cultural norms including perception of how breastfeeding affects a woman’s body, societal sources of breastfeeding information, satiation of infants, and family and other support. Mothers overwhelmingly agreed that breastfeeding was inexpensive, allowed them to bond with their infants and was good for the overall health and intellectual development of the infants. They identified painful nipples, engorged breasts, lack of sleep, physical exhaustion and pressure to return to work as barriers to breastfeeding. Mothers named a number of complementary foods, such as pumpkin, carrots, potato, banana, and chocho (Chayote), that were culturally accepted for feeding infants in Jamaica and discussed herbs that were considered to aid in infants’ nutrition and overall health. Other cultural factors that were noted to influence exclusive breastfeeding were mothers feeling that breastfeeding would help their bodies, especially their bellies, go back to their pre-maternity figure, sources of breastfeeding information in the society including the internet, belief that breast milk alone does not satisfy babies, and family and other support.

Mothers in this study identified unique challenges to exclusive breastfeeding that if addressed, would help to increase exclusive breastfeeding so that the World Health Organization’s exclusive breastfeeding recommendations can be achieved.

Factors associated with exclusive breastfeeding have been examined by multiple studies conducted in Latin American and Caribbean (LAC). A study conducted in Peru reported that women who were married, self-identified as native, received breastfeeding training, resided in the highlands or jungle, or had their second or older child had a higher likelihood of breastfeeding [ 1 ]. Other studies conducted in several LAC countries showed that sociodemographic factors such as the mother’s employment outside of the home, high income families, female wage and salaried workers, living in urban area, and high educational and economic level of mother were negatively associated with duration of exclusive breastfeeding [ 2 , 3 , 4 , 5 , 6 ]. Delivery via caesarian section was also found to be associated with cessation of exclusive breastfeeding [ 3 ].

In LAC countries, the exclusive breastfeeding rate for children under six months is 43%. compared to the global average of 48% [ 7 ]. A time series study of the 1990–2017 Demographic and Health Survey data from six LAC countries (Bolivia, Colombia, Dominican Republic, Guatemala, Peru, and Haiti) reported that the exclusive breastfeeding rate increased from 38.1% in the 1990s to 46.6% in the 2010s, with higher prevalence of exclusive breastfeeding in rural than in urban areas [ 5 ]. However, these rates of increase are insufficient to achieve the WHO exclusive breastfeeding target rate of at least 50% by 2025 (70% by 2030) for the first 6 months [ 8 ].

The rate of exclusive breastfeeding in Jamaica is low. Only 33% of Jamaican mothers were exclusively breastfeeding their babies 0–5 months old in 2022 [ 9 ]. The percent was higher for rural mothers (40%) compared to urban mothers (25%). This represents a 9.2% increase over the rate of 23.8% in 2011 [ 10 ]. Approximately 42% of children under the age of six months are being predominantly breastfed – these are children who are either exclusively breastfed or who, in addition to breastmilk, also receive plain water and other non-milk liquids [ 9 ].

A significant percentage of Jamaican mothers who use formula in place of exclusive breastfeeding, especially in rural areas, are from low-income families. In 2017 it was estimated that low-income Jamaican families spent around 14–65% of their annual incomes on infant formula depending on whether they breastfeed for the first three months or not at all for six months [ 11 ].

With the low levels of exclusive breastfeeding rates and small change over time, the JMOHW ramped up its breastfeeding campaign in 2022 emphasizing recommendations of the WHO and the United Nations Children’s Fund (UNICEF) such as “initiating breastfeeding within one hour of birth, breastfeeding exclusively for the first six months, thereafter, providing nutritionally adequate and safe complementary foods, and continuing breastfeeding for up to two years of age or beyond” [ 12 ]. Additionally, the JMOHW is implementing a five-target policy and strategic plan that includes a draft policy for the international code of marketing breast milk substitutes to ensure the proper use of these substitutes when necessary, establishing “Baby Friendly Hospitals” and training clinic staff to ensure successful breastfeeding, training healthcare workers in monitoring child growth and development, training healthcare workers and caregivers in communication, developing posters on proper breastfeeding and childcare nutrition, and monitoring and evaluating children in child health clinics referring those with nutritional deficiencies for specialist nutrition care [ 12 ]. Although these interventions should promote optimal nutritional status, health, survival, and development of children, if specific barriers to breastfeeding experienced by mothers and their families are not assessed and addressed, exclusive breastfeeding targets will not be achieved. This qualitative study was conducted to hear firsthand from mothers of infants six-weeks to less than six-months old the specific barriers to breastfeeding that they experienced with a focus on barriers to exclusive breastfeeding, so that appropriate recommendations can be made to address these barriers and increase exclusive breastfeeding rates to move closer to achieving the WHO/UNICEF breastfeeding recommendations.

Study design and participant recruitment

A cross-sectional qualitative study was conducted from May to August 2016. Mothers of infants six-weeks to less than six-months old were recruited from postnatal clinics in the four parishes of Western Jamaica under the Western Regional Health Authority (WRHA). The women were told about the study by the clinic staff when they attended the clinic for their appointments and asked if they would be willing to participate in a focus group session. Those indicating willingness to participate were introduced to the research staff who asked them for possible days and times that they could attend a focus group session and for their phone numbers so that they could be contacted to schedule their return. Based on availability, focus group sessions were arranged for specific times at the clinics with six to eight women scheduled to attend a session.

At the focus group meeting, the women were asked to read the informed consent form and encouraged to ask questions. After questions were answered, women who agreed to participate were asked to sign the consent form. Each participant was assigned a number so that no names or identification information were included on the tape recordings. Four focus group sessions were conducted, three of the four with six women each and the other with four women. The focus group sessions were led by two trained research assistants, were recorded, and the recordings transcribed verbatim. During the sessions, the women were asked about breastfeeding practices, beliefs, attitudes, and barriers as outlined in the Focus Group Guide (Additional File 1 ). The Focus Group Guide was developed based on published literature and content expert review [ 11 , 13 , 14 , 15 , 16 ].

Inclusion and exclusion criteria

Eligible participants were women ≥ 18 years of age attending postnatal clinics at health facilities under the WRHA and who were or were not exclusively breastfeeding their infants six-weeks to less than six-months old. Women who did not meet these criteria were excluded from the study.

Data analysis

The focus group transcripts were thoroughly reviewed by members of the study team (PJ, CH, MA). Three independent coders (CD, BR, SN) coded all transcripts using QSR International’s NVivo 11.4.3 utilizing line-by-line coding of all responses to the focus group questions, followed by focused coding for directed codes [ 17 ]. Codes were compared at regularly scheduled meetings. There were no instances when consensus was not reached. SN also served as the peer-debriefer as she was methodologically and analytically adept, but not embedded in the research topic as much as the PI (PJ) and helped in elucidating the research endeavor and thereby contributing to the resonance of the research.

A content analysis approach including a constant comparative method was used to generate themes from the transcribed data [ 17 ]. The team (CD, BR, SN) discussed the coding process and contributed to the iterative data analysis. Inductive thematic saturation was reached, and trustworthiness was achieved through data triangulation (CD, BR, SN) [ 18 , 19 ]. Discussions regarding methods on convening focus groups and interpretation of data within the context of Jamaican culture was carried out (See Additional File 1 ).

Participant characteristics

Twenty-two mothers participated in the study. The majority (45%) were single or were in common-law relationships (41%; Table 1 ). Most of the women (59%) reported having high school education and 36% had education above high school. Half of the women earned ≤ JMD 25,000/month (minimum wage in Jamaica in 2016 was JMD 6,200 or USD 50 per week) and 45% earned above JMD 25,000. Slightly over two-thirds of the women had 1–2 children (68%) and the remainder had 3 or more children. Approximately 95% of mothers reported that they were breastfeeding, of whom 52% reported breastfeeding exclusively.

Four themes were identified from this study; namely, (1) perceived advantages of breastfeeding centered mainly on the benefits of breastfeeding for the infant and mother, (2) perceived barriers of breastfeeding highlighted the physical pain and fatigue, (3) supplementing culturally acceptable complementary foods and herbal remedies was prevalent, and (4) cultural norms including perception of how pregnancy affects a woman’s body, societal sources of breastfeeding information, satiation of infants and family and other support, dictate various aspects of breastfeeding.

Theme 1: Perceived advantages of breastfeeding centered mainly on the benefits of breastfeeding for the infant and mother

The most common advantage of breastfeeding given was that it allowed bonding between mothers and their infants.

“I think that breastfeeding allows the mother to bond with the baby and baby with the mother.” - Participant , Focus Group One . “You get a better connection with the child emotionally.” – Participant , Focus Group Three .

Health and development of infant

Another perceived advantage of breastfeeding that was immensely popular among the mothers was that it was good for the overall health of the infant. Mothers believed that infants receive nutrients from breast milk and that breastfed infants have better health outcomes and are more intelligent.

“[Provides] protection from diseases and the baby grows healthier- [grows to be] a healthy baby because as you know the breast milk has in ‘antibiotics and rich nutrients.” -Participant , Focus Group Two . “Also, as they say it protects the child from allergy; like when you breastfeed, allergic reactions.” - Participant , Focus Group Two . “They are more active and more intelligent.” - Participant , Focus Group Three .

Decreased cost

The third perceived advantage of breastfeeding by mothers was decreased cost from not having to buy formula. Mothers discussed how they did not have to obtain formula because they made the decision to breastfeed and that this was much more economical for them and their families.

“As you said it is one way of bonding with your child and it is cost effective- doesn’t cost anything. You might have some sleepless nights, but it won’t cost you anything.” - Participant , Focus Group Two . “Yes, everything is in breast milk, so you don’t have to worry about anything. Plus, it cost you less to feed the baby and the baby won’t get sick every minute.” - Participant , Focus Group Two . “The good thing about breastfeeding is that it is economical.” - Participant , Focus Group Three .

Theme 2: Perceived barriers to breastfeeding highlighted the physical pain and fatigue and need to return to work

Our study participants expressed that breast pain and lack of rest and sleep experienced from breastfeeding were barriers to breastfeeding. They explained that pain of their nipples from infants pulling on them or attempting to bite them and from engorged breasts made it harder for them to sustain breastfeeding.

“It [breast] hurts because it just feels like a rock. It’s just stiff and won’t move, the slightest thing that touches it makes it feels like it’s on fire.” - Participant , Focus Group Four . “Oh wow… so it’s painful [because] they’re biting whether they have teeth, or they don’t have teeth.” – Participant , Focus Group Four .

Physical exhaustion

The mothers also expressed that breastfeeding was physically exhausting and stressful for them since their infants require frequent feeding and do not give them enough time to rest and sleep between feedings. Furthermore, mothers reported getting only short amounts of rest since their infants would cry or demand to be fed. This would only further perpetuate the stress and anxiety many mothers reported feeling. One participant stated that she would have sleepless nights due to the stresses of breastfeeding and being hypertensive. Participants acknowledged that they would attempt to sleep while their infants were asleep but reported often getting inadequate rest.

“Because I am not sleeping it would be said that it is stress. Yes, we are told to sleep while the baby is sleeping but sometimes you don’t want to do that as yet.” – Participant , Focus Group Two . “I don’t sleep at nights, so it is best if I get a little rest…” – Participant , Focus Group Two . One participant stated that she needed to pretend to be asleep for her infant to fall asleep. “Her eyes open too, so sometimes I have to pretend to sleep beside her; I’ll close my eyes like I’m sleeping.” – Participant , Focus Group Three .

Pressure to return to work

The mothers stated that there was an inordinate amount of pressure to return to work which meant they had to switch the infants from breastfeeding to formula feeding. They expressed that it takes time for breastfed infants to adjust to taking formula when breastfeeding mothers return to work. One participant pointed out that some mothers express breast milk when they have to work (implying that the breast milk is used to feed the baby while they are at work), while some mothers may feed the baby breast milk or formula when they return home from work.

“There might just be one possibly like in her case where she has to go to work, and she is going to have to leave the baby. When you introduce the baby to breastfeeding, they will take more to the breastfeeding than the bottle-feeding; [since] you have started them on the breast [milk], it will take some time to break them into taking formula. You know that can be more of an inconvenience than before.” - Participant , Focus Group One . “I have to go back to work so I have to leave my baby with the babysitter. I don’t want to have any problems, so I have to buy the formula.” - Participant , Focus Group Three . “ So, if the parents [or] mother is working, and you know you have to… You have some parents nowadays who give [their babies] formula while some express [the milk from] the breast when they have to go to work. You know some may [even] give them formula or breast milk when they come home from work.” - Participant , Focus Group One .

Theme 3: Supplementing culturally acceptable complementary foods and herbal remedies was prevalent

Although there was a strong consensus among the mothers that breast milk is best, some mothers had introduced the baby to complementary foods considered culturally acceptable in Jamaica. They named foods such as, pumpkin, carrots, potato, banana and “chocho” (a greenish vegetable with a mild taste known as Chayote), gave examples of traditional mixtures that maternal figures had used in past generations and attested to the use of herbs being introduced to their infants to aid in their nutrition and overall health.

“You can even too mash Irish [potato], green bananas and pumpkin… Yeah, with butter and you can put a little gravy on it as well… Sardine too… Sardine? Yeah, that would go well with the banana… and chocho… Sardine and egg” - Participants , Focus Group Two . “I give him tea, bush tea” Participant , Focus Group Four “What do you mean its name is ‘gripe bush’? Do you mean [the] rosemary [plant] with the yellow flowers? There is one that has ‘gold’ [in its name]…Yes Mari-gold.” - Participant , Focus Group Four .

Theme 4: Cultural norms including perception of how pregnancy affects a woman’s body, societal sources of breastfeeding information, satiation of infants and family and other support dictate various aspects of breastfeeding

Our participants’ decision to exclusively breastfeed was heavily influenced by the explicit or implicit opinions of others in the society and in their social network regarding the pros and cons of breastfeeding and the optimal duration of breastfeeding. Perception of how breastfeeding affects a woman’s body, sources of breastfeeding information in the society, ability of breast milk to sufficiently satisfy the babies, and supporters/support groups were discussed.

Women’s perceptions of how breastfeeding affect their bodies

The women felt that breastfeeding would positively impact their bodies in that it would help their pregnant bodies go back to their pre-pregnancy figure. They seemed to imply that the weight gain, mainly in the abdominal region, from pregnancy is a negative aspect of pregnancy.

“Because it [breastfeeding] brings your belly back [down]” – Participant , Focus Group 1 . “Because it is healthier for the baby at the moment and also the mom benefits. As my friends said here you don’t have to go to the gym because the baby (referring to breastfeeding) helps to bring back the body and to keep [your] figure/shape. So, it has its benefits” – Participant , Focus Group Two .

Sources of breastfeeding information

Participants discussed the lack of awareness regarding breastfeeding among pregnant women and the fact that teaching about breastfeeding is restricted to local public health clinics managed by the JMOHW. They explained that most pregnant women would not know how to breastfeed until they arrived at the local public health clinic and are taught by a nurse. Regarding the sources of breastfeeding information in the society, the women stated that they learned from the public health clinics, support groups, their maternal figures, television, and the internet. Something compelling about these participants’ statements on how they obtain breastfeeding information is that they often mentioned the use of the internet, particularly Google and YouTube.

“Most of the mothers that go to the private clinic or private doctor, the private doctor refers them to the [clinic] hospital. Sometimes they even find out that it is the same procedure at the clinic and the private doctor, so it is best to come to the public clinic. Sometimes the nurses at the private clinic not educating them like how they do at the public clinic.” Participant , Focus Group Two . “Usually, it starts with the nurses you come in contact with first. Then you go to the hospital to deliver, you will have… you will see as you said in the hospital and health centers the little posters. So basically, it is in your face almost everywhere you turn. You will get opinions from grandparents and mothers who have experience. Though not all advises are good but you will get them.” -Participant Focus Group Two . “ Usually, the same persons as we have said before and now I realize that the media is coming in a lot of advertisement on the TV about breastfeeding and so.” -Participant Focus Group Two . “ Anything that I’m not sure about [since] I have the internet at my fingertips, I’ll just check on Google and it will tell all that you need to know.” -Participant , Focus Group Two .

Satiation of infants

A common and pervasive belief in the Jamaican society is that breast milk alone does not satisfy babies (does not fill their stomachs). Consequently, many mothers introduce their babies to complementary foods and formula early even while breastfeeding to ensure that the babies are satisfied.

“One night I got up and I was feeding him, he drank and drank. So, I put him down [to sleep] but even though his [diapers] were dry and I hushed him, he wouldn’t fall asleep. To me this is because his belly is not full; so that is why I had to buy the formula.” Participant , Focus Group Three . “ Breast milk cannot full their bellies.” - Participant , Focus Group Four . “ If when I give him [breast milk], he’s still crying and crying, I’ll then realize that the milk is not filling his belly so then I’ll have to start giving him ‘ tin feed ’ [formula].” - Participant , Focus Group Four .

Family and other support

Support from family members or other support groups was crucial to breastfeeding. The women often cited obtaining support from a maternal figure in their lives. Some spoke about how their mothers was there for the first few weeks of the baby’s life, but others expressed the need for support.

“One of the things that came out in your question section was the fact about a supportive group. I think that if we had a supportive group… where people come together and discuss the benefits of breastfeeding apart from them coming to the health center or so. So, I think if we could raise the need for that, anybody, some charitable organization or some hotel or social group could develop something like that more people would be able to get the information. Just take a person [for instance] who has what we call postpartum depression, they are going through something, and they can’t bother to breastfeed in the night, or they are ready to give up or whatever. A next person can say you know I had the feeling too but did this or I did that. So, the supportive group will make it easier and easier; you will see that we are more comfortable to breastfeed.” – Participant , Focus Group Two . “ Yes that [support group] would be good because you can learn from each other.” – Participant , Focus Group Three . “Well, they [support group] will tell you about how to breastfeed, bond with the baby, the proper way [for the baby] to latch on and they tell you what to expect as a mother.” - Participant , Focus Group One .

This study shows that the mothers knew of the many benefits of breastfeeding on the overall health, emotional, and intellectual development of their infants, in helping their bodies to recover from the weight gain from pregnancy, in promoting their emotional health, and in bonding with their newborns. Previous breastfeeding studies conducted in Jamaica support our finding that majority of mothers had satisfactory knowledge of the benefits of breastfeeding and that 95–98% of mothers initiate breastfeeding their newborns [ 15 , 16 ]. Therefore, the fact that only 33% of mothers practiced exclusive breastfeeding to six months is not due to lack of knowledge but to conditions beyond their control during the early postpartum period and beyond [ 15 , 16 ]. A recent paper by Baker et al. discusses in depth societal, political and economic systems that undervalue women and inadequately protect the rights of mothers and children resulting in inadequate support and promotion of breastfeeding [ 20 ]. These authors strongly recommend reforms to overcome the many structural barriers. Many of the barriers to breastfeeding reported by our study participants such as lack of family and child support, physical exhaustion of mothers, pressure to return to work, inadequate breastfeeding education including lack of discussion on potential breastfeeding problems such as sore nipples and painful breasts that occur when mothers return home, are associated with societal, political, and economic structures in the society.

The UNICEF Baby Friendly Hospital Initiative (BFHI) was launched by the WHO in 1991 and adopted by the JMOHW in 1993. However, in a survey conducted by the WHO during August 2016-January 2017 (the time that this study was conducted), Jamaican health officials reported that only 2.3% of births in the country occurred in designated Baby Friendly Hospitals and Maternities [ 21 ]. In 2023, the JMOHW officials reported that ten institutions that provide maternity services have been certified as Baby Friendly (eight of which were being prepared for reassessment) and that four new hospitals were being targeted for assessment [ 22 , 23 ]. Thus, the staff providing maternity services are trained using the JMOHW BFHI manual to inform all pregnant women about the benefits and management of breastfeeding, ensure skin to skin contact between mother and baby immediately after birth, and initiate breastfeeding within half hour of birth. The staff are also trained to show mothers how to breastfeed, how to maintain lactation even if they should be separated from their infants, and on other matters pertaining to exclusive breastfeeding and feeding infants on demand. Maternity clinic staff are also expected to discuss issues such as sore nipples, painful breasts, and breast care with each woman during antenatal visits and inform the women of how to get help so that they can be prepared to deal with these issues when they arise at home. Posters on breastfeeding and proper child nutrition are also displayed in maternity clinics. The JMOHW has also launched a breastfeeding video that discusses the benefits of breastfeeding and demonstrates how to feed and safely express breast milk [ 12 ]. This should be helpful since some of the women indicated that they are already using social media to educate themselves.

Although pregnant women receive training in the maternity clinics, the specific problems that they reported such as soreness of their nipples and breasts, insufficient sleep, fatigue, and stress occur at home after they are discharged from the hospitals with their babies. These are barriers that are more likely to be overcome if the WHO recommendations regarding providing pregnant women, new mothers, and caregivers, with supportive care including community support, support groups, and community-based health promotion and education activities including demonstrative activities are instituted [ 24 ]. Pregnant women need appropriate prenatal preparation but also need significant postnatal support to help them breastfeed successfully. The BFHI and the JMOHW also encourages maternity service providers to foster establishing breastfeeding support groups and referring mothers to these groups upon their discharge from the maternity facility. Support Groups for mothers exist in some parishes in Jamaica and WhatsApp groups have also been established in areas with internet connections, however, we cannot tell how widespread or prevalent support groups are in the western region without additional specific studies. There may also be a gap in education and support for women who do not attend clinics throughout the antenatal period or those who attend some private maternity facilities that do not provide all the intended information in an effective and demonstrable way. The women in our study were favorable of having support groups to discuss matters related to breastfeeding as well as personal matters such as postpartum depression.

The JMOHW has also encouraged the entire family to support the care of infants and children. Since mothers complained that the frequent required feeding of their infants did not allow them to get sufficient sleep and rest and left them fatigued, a more involved and guaranteed supportive post-delivery childcare plan that includes fathers, grandparents and other willing and available family members would be beneficial and may foster increase in exclusive breastfeeding. A systematic review of research conducted in a variety of low- and high-income countries on the effect of grandmothers on breastfeeding found that in some studies, grandmothers who had previously breastfed their infants or who were positively inclined towards breastfeeding had a significant positive impact on exclusive breastfeeding of their grandchildren [ 25 ]. Aspects of these studies that foster breastfeeding can be investigated among Jamaican grandmothers and other maternal figures.

A revolutionary change regarding breastfeeding over the years is that many mothers have been using electric breast pumps to express breast milk so that infants can be fed with mother’s milk by the father, other family member, or caregiver, when the mother is at rest, at appointments, or otherwise separated from the infant such as when they return to work [ 26 , 27 ]. Pumping milk allows for collection of larger volumes that can be stored frozen and used over time. This also provides opportunity for fathers to bond with the infants. A 2019 survey reported that 95% of breastfeeding mothers pump breast milk [ 28 ]. The mothers would continue to express milk when they return to work and are separated from the baby providing they have the appropriate facilities [ 29 , 30 , 31 ].

Mothers in our study reported that the need to return to work early interrupts their ability to exclusively breastfeed and bond with their infants. The Maternity Leave Act of Jamaica allows women 12 weeks (60 working days) of maternity leave of which eight weeks are paid, if the women have been working for the employer for a minimum of one year (52 weeks) [ 32 ]. Women may apply for no-pay leave or vacation leave to extend the period spent with their infants. However, many women may be dependent on the income and so need to return to work after eight weeks. The Breastfeeding Act “Right to Nursing Breaks or Daily Reduction of Hours of Work” states that “An employer shall provide a reasonable break time for an employee to express breast milk for her nursing child for one year after the child’s birth each time such employee has need to express the milk” [ 33 ]. However, pumping and storing facilities are not widely available at businesses, especially those that employ minimum wage earners, so businesses need to be encouraged to facilitate pumping and refrigeration of breast milk by mothers. Further, in Jamaica, the cost of breast pumps varies from JMD4,000 to JMD200,000 depending on the brand and if the pump is manual or electric and has any ‘high-tech’ functions [ 34 ]. Since electric pumps are more efficient, acquisition of a pump by a mother on minimum wage is a major investment. Mothers also need to plan to safely transport the milk to avoid contamination and transmission of infection to the infants.

In deciding to resume work without pumping and storing breast milk, mothers face the difficulty of having to purchase formula for the infant. If the infant is breastfed for the first three months and partially breastfed for months 4–6, the cost is estimated to be JMD10,532 [ 11 ]. For households earning minimum wage (JMD 6,200 per week in 2016), at least 14% of the monthly income would be spent to feed the infant alone. Thus, there is a relatively high level of spending on infant feeding when breast milk is not utilized. Mothers also discussed difficulty in getting infants to adjust to formula when they need to return to work after having started the infants on breast milk. This is a crucial finding of this study and one for which a solution should be given serious consideration by health officials.

The mothers in this study named a variety of complementary foods and teas that they feed their infants. One mother expressed that a complementary food such as porridge helped babies to be “big, thick and healthy.” Thus, many infants may not be receiving the optimal nutrition to give them the healthiest start and promote good health and development later in life. An issue that some mothers brought up to justify feeding infants complementary food is that they felt that breast milk alone could not fill the babies’ stomachs. Some mothers felt that their babies cried because they were still hungry after breastfeeding. Harrison et al. found that the mothers’ belief that exclusive breastfeeding satisfied the infants was significantly associated with exclusive breastfeeding [ 16 ]. The belief that breast milk does not fully satisfy infants is pervasive in the Jamaican society [ 14 ] and needs to be addressed in the training given to maternity staff so that they can educate mothers and the larger society about the appropriate time for adding complementary foods to infants and the time that different types of complementary foods can be added. Baker et al. point out that common early infant adaptative/adjustment behaviors such as crying and irregular sleep durations are often misconceived by mothers and caretakers as signs of feeding problems resulting in addition of commercial milk formula to the infant’s diet. Additional educational efforts are needed for health workers, families, and the public to eradicate these misconceptions and to uphold breastfeeding [ 20 ].

Body image was a cultural factor that was discussed in relation to exclusive breastfeeding. Some mothers were positive about breastfeeding because it helped women return to their pre-pregnancy figure faster after giving birth. Although we could find no published paper on body image as it relates to body size in Jamaica, we found a study conducted in St. Kitts that reported that participants were somewhat more likely to value heavier than thinner women [ 35 ]. In Jamaica, a fulsome body is favored similar to more traditional “non-western” societies including African cultures where there is acceptance of larger body size [ 36 ]. The women in our study seemed to be more concerned about losing the weight in their abdominal region more than overall body weight, but further studies need to be conducted to verify this.

Limitations

There are certain limitations that should be considered in interpreting the results of this study. First, the results may not be generalizable to the total population of postnatal mothers in Jamaica since it involved a convenience sample of women from western Jamaica. Although the sample size is small, we did achieve data saturation and the findings are comparable to national data reported in the MICS and in other studies conducted in northeastern and southeastern Jamaica. Since the data were self-reported, they are subject to social desirability bias and recall bias of participants. Additionally, since these data were collected in 2016, several changes started pre-COVID and ramped up post-COVID may lead to increased pace in exclusive breastfeeding rates in Jamaica. Despite these limitations, this study highlights difficult challenges to exclusive breastfeeding that if addressed would help to make it possible for mothers to exclusively breastfeed and for the early and long-term benefits of breastfeeding to be achieved.

The results of this study show that the mothers in western Jamaica are aware of the significant benefits of exclusive breastfeeding and overwhelmingly initiate breastfeeding but experience specific barriers to exclusive breastfeeding at home after delivery and discharge from health facilities. These barriers include the need for help with breast care and lactation, family and community support to help relieve the burden of lack of sleep and fatigue of mothers, and need to return to work which results in switching infants from breast to formula feeding. These findings suggest that a means of expressing and storing breast milk at home so that infants can still be fed with the mother’s milk while mothers are resting, pre-planning the time when mothers will return to work and whether infants will be fed breast milk exclusively, and conducting interventions to improve family and other support would facilitate exclusive breastfeeding. Hopefully, approaches can be developed to address the specific barriers to breastfeeding identified in this study to hasten achievement of the WHO/UNICEF recommendation of exclusively breastfeeding infants to 6 months of age.

Data availability

The dataset used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Jamaican Ministry of Health and Wellness

Latin America and the Caribbean

Multiple Indicator Cluster Survey

World Health Organization

Western Regional Health Authority

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Acknowledgements

We thank the nurses in the clinics who facilitated the study and the mothers who participated. We thank Ms. Loleeta Crooks for preparing the focus group transcripts and Ms. Sarah Franklin for editing and preparing the manuscript for submission.

This study was funded by the Minority Health Research Training (MHRT) grant no. T37-MD001448 from the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA, and the Western Regional Health Authority, Ministry of Health, Montego Bay, Jamaica.

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Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd, RPHB 217, Birmingham, AL, 35294-0022, USA

Claudia Datnow-Martinez, Brittany Ransom, Chanice Howard & Pauline E. Jolly

Health Services Administration, School of Health Professions, University of Alabama, Birmingham, USA

Soumya J. Niranjan

Medical Epidemiology and Research Unit, Western Regional Health Authority, Montego Bay, Jamaica

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Contributions

P.J. and M.A. worked on the conceptualization and design, protocol and questionnaire development, review, supervision of data collection, and editing of the study and manuscript; C.H. worked on the data collection and data entry; S.N. provided supervision of data analysis and data interpretation for this study, C.D.M. and B.R. contributed to the data analysis, interpretation of results and writing original draft; P.J. and MA contributed to the data interpretation and revision of the manuscript.

Corresponding author

Correspondence to Pauline E. Jolly .

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The protocol was reviewed and approved in May 2016 by the Institutional Review Board of the University of Alabama at Birmingham, United States of America, and the Western Regional Health Authority, Jamaica (IRB-160401009).

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Datnow-Martinez, C., Ransom, B., Niranjan, S.J. et al. Factors affecting infant feeding choices with a focus on barriers to exclusive breastfeeding in Western Jamaica: a qualitative study. Int Breastfeed J 19 , 63 (2024). https://doi.org/10.1186/s13006-024-00671-8

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BMC Res Notes

Analyzing longitudinal qualitative data: the application of trajectory and recurrent cross-sectional approaches

Daniel grossoehme.

Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH USA

Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, MLC2021, 3333 Burnet Avenue, Cincinnati, OH 45229 USA

James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229 USA

Ellen Lipstein

Division of Adolescent and Transition Medicine, Center for Innovation in Chronic Disease Care, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229 USA

Conclusions

Choosing an analytic approach

Table 1

Comparison of recurrent cross-sectional and trajectory analysis

Considerations	Recurrent cross-sectional analysis	Trajectory analysis
Research focus	Describe the differences between time points	Describe how process or experience changes over time
Sample considerations	The cohort at each time point may be the same or different May be preferred if sample is highly transient or has high mortality over study duration	Must maintain same cohort
Theoretical approach	Determined by the research question Any analytic approach may be used consistently throughout the study	Determined by the research question Any analytic approach may be used consistently throughout the study
Level of data analysis	Whole sample (or subsamples)	Individual people or individual groups (e.g., families)
Timing of analysis	May analyze as each time point is completed	Must wait until data is collected at all time points

Setting up the analysis: data coding

Conducting a recurrent cross-sectional analysis

Conducting a trajectory analysis

Table 2

Sample family matrix

Themes	Time 1	Time 2	Time 3
Theme A (example: family stress)	Lots of stress about health	Feeling stressed about treatment decision
Theme B (example: concerns about side effects)		No concerns about side effects	No concerns about side effects
Theme C	Idea from mother		Idea from mother
Theme D	Idea from mother		Idea from mother

Plain font indicates mother; italics indicates father

Table 3

Sample longitudinal analysis matrix

Themes	Family 1	Family 2	Family 3
Theme A (example: change in family stress over time)	Change from stress about health to stress about treatment	Idea from mother	Idea from mother
Theme B (example: change in concerns about side effects over time)	Never developed any concerns	Idea from mother	Idea from mother
Theme C	Idea from mother	Idea from mother	Idea from mother
Theme D	Idea from mother	Idea from mother	Idea from mother

Authors’ contributions

Acknowledgements

Thank you to Drs. Courtney Brown and Lori Crosby for their helpful comments on an earlier version of this manuscript.

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Daniel Grossoehme, Phone: 513 636 0848, Email: [email protected] .

Ellen Lipstein, Phone: 513 803 1626, Email: [email protected] .

Open access
Published: 09 September 2024

The association between dietary antioxidant quality score and intensity and frequency of migraine headaches among women: a cross-sectional study

Sara Hajishizari 1 ,
Atieh Mirzababaei 1 ,
Faezeh Abaj 4 ,
Niki Bahrampour 2 ,
Sajjad Moradi 5 ,
Cain C.T. Clark 3 &
Khadijeh Mirzaei 1

BMC Women's Health volume 24 , Article number: 497 ( 2024 ) Cite this article

65 Accesses

Metrics details

Migraine is an episodic disorder and a frequent form of headache. An impaired balance between free radical production and an impaired antioxidant defense system leading to oxidative damage may play a major role in migraine etiology. We sought to investigate whether dietary antioxidant quality score (DAQS) is associated with migraine intensity and frequency among women suffering from migraine.

This cross-sectional study was conducted on 265 women. The data related to anthropometric measures and dietary intake were collected. DAQS score was calculated based on FFQ (food frequency questionnaire) vs. the reference daily intake (RDI) quantity. To measure migraine intensity, the migraine disability assessment questionnaire (MIDAS) and visual analog scale (VAS) were used. The frequency of headaches was defined as the days the participants had headaches in the last month and a 30-day headache diary was used.

The results of the study demonstrated that VAS, MIDAS, and frequency of headaches were reduced significantly from the low DAQS (poor quality of antioxidants) to high DAQS (high quality of antioxidants) after adjusting covariates. Also, multinomial regression showed there was an inverse association between higher DAQS and the frequency of headaches. In the adjusted model, subjects with the higher DAQS were 69% less likely to have moderate migraine disability, compared with those with the lower DAQS. Linear regression showed, there was an inverse association between vitamin C intake and the grades of pain severity.َAlso in a crude model, a negative association was found between vitamin E and the frequency of headaches.

In conclusion, Participants with higher DAQS had lower migraine intensity and headache frequency. In addition, the consumption of vitamin C may potentially associate with decreasing the severity of headaches. Dietary antioxidants should be monitored closely in individuals suffering from migraine.

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Introduction

Migraine is a complex neurovascular inflammatory brain disorder that affects over 1 billion individuals across the world [ 1 ]. Migraine has been recognized as the greatest cause of disability in persons under the age of 50, affecting between 12 and 16% of the population, with women having a higher incidence than males (3:1), and it has a proclivity towards family occurrence [ 2 , 3 , 4 , 5 , 6 ]. It is a frequent form of headache and debilitating disease that is divided into two categories based on frequency. Chronic migraine (15 and more days per month) for at least 3 months and episodic migraine (less than 15 days per month) [ 7 , 8 ]. Although considerable research has been done to understand the etiology of migraine headaches, the exact underlying mechanism is still unknown. Various causes have been postulated thus far, including neurogenic inflammation and trigeminovascular circuit activation [ 9 ]. However, there is mounting evidence that suggests the hypothalamus may be the trigger of migraine attacks [ 10 ]. As migraine attacks frequently follow a daily, monthly, or even seasonal rhythm, it is possible that the hypothalamic regions, which regulate the biological clock, have a role in the disease’s onset [ 11 , 12 ]. Moreover, Brain imaging studies showed that during the very early phases of spontaneous migraine attacks, there is increased blood flow in the hypothalamic area [ 13 ].

Migraineurs are at a higher risk of cardiovascular disease and death [ 14 ]. As a result, figuring out the best way to treat and manage this condition is critical. Nonsteroidal anti-inflammatory drugs (NSAIDs) are by far the most used class of drugs for the acute treatment of headaches in general, and migraine in particular [ 15 , 16 , 17 ]. However, considering the potential for serious side effects from these drugs [ 18 ], identifying disease-modifying risk factors to avoid headaches is critical. Nutrition may have a role, according to research.

It was proposed that an impaired balance between free radical production and an impaired antioxidant defense system leading to oxidative damage may play a major role in pathological conditions including cancer, diabetes, hepatic disorders, cardiovascular disease (CVD), and neurodegenerative illnesses [ 19 ]. For decades, the concept of oxidative stress in migraine sufferers has been debated. The so-called nutraceuticals have received a lot of interest in recent years as compounds that may potentially be utilized to alleviate migraines [ 20 ]. Curcumin and coenzyme Q10, two antioxidants, were reported to reduce migraine frequency in previous studies [ 21 , 22 ]. Vitamin E was found to reduce menstrual migraines [ 23 ]. Finally, previous investigations found that an antioxidant mixture of pine bark extract, vitamin C, and vitamin E reduced migraine symptoms [ 24 , 25 ]. Antioxidants in food decrease oxidative stress by reducing the oxidative chain reaction’s start, dissemination, and completion. Scavenging free radicals, molecular oxygen quenching, and acting as reductants in oxidative processes are some of the various methods of action of antioxidants from food [ 26 ]. Furthermore, as previously stated, oxidative stress is thought to have a role in migraine etiology. Antioxidant supplementation can help to reduce the effects of oxidative stress [ 27 ].

Individual nutrients were the most often employed strategy for analyzing the possible role of antioxidant dietary consumption in health outcomes. This method, which focuses on the effects of a few specific antioxidants on health outcomes, leaves out a lot of data regarding the complicated or cumulative linkages and interactions that exist among antioxidant elements in foods [ 28 ]. The content and quantity of specific antioxidant components in the diet have been the most frequently used approach in establishing the possible influence of antioxidant dietary intake on health outcomes. The dietary antioxidant quality score (DAQS), which adds up the amounts of various dietary antioxidants and provides a score based on the computed quantity vs. the reference daily intake (RDI) quantity, has been proposed as a sensitive and accurate technique [ 28 ]. There is no available evidence regarding the association between DAQS and migraine severity, as far as we know. Thus, the purpose of this study was to assess the association of DAQS with migraine severity among Iranian females.

Study population

We designed a cross-sectional study and finally enrolled 265 women who lived in Tehran, Iran, and had attended neurology clinics at two hospitals (Sina and Khatam Alanbia) and a professional headache clinic for migraine diagnosis from March to September 2016 (Fig. 1 ). The participants were selected based on the following inclusion criteria: women with the age range of 18–50 years, and BMI in the range of 18. 5–30 kg/m 2 , first visit in the headache clinic (had never been diagnosed with migraine, previously), and confirmation of migraine by a neurologist using the International Classification of Headache Disorders 3 criteria (ICHD3) [ 8 ]. We considered exclusion criteria included: having cardiovascular disease, liver, kidney, thyroid, cancer, diabetes, heart failure, and acute or chronic infections based on patient statements and medical history, consumption of drugs and supplements, pregnant, lactating, and postmenopausal women, drug and alcohol use, reluctance to continue reading were excluded. To control over- or under-reporting of food intake, subjects with daily energy intakes lower than 500 kcal or higher than 3500 kcal were excluded from the analysis. All procedures were followed in accordance with the ethical standards of the Tehran University of Medical Sciences (ethic number: 95-01-103-31348), which approved all aspects of the study. All participants signed a written informed consent prior to the start of the study.

A flowchart showing participants through the study

Migraine diagnosis

According to the headache classification committee of the International Headache Society (IHS) [ 29 ], an expert neurologist diagnosed migraine in the subjects. Two forms of migraine, with and without aura, are included in the criteria for diagnosing episodic migraine. According to the IHS, the following criteria can be used to make a diagnosis of migraine without aura: a headache with five or more bouts lasting 4–72 h; the headache should contain two or more of the following characteristics: unilateral, pulsating, moderate or severe pain intensity, worsened by or causing avoidance of regular activities, as well as one or more of the following signs and symptoms: nausea, vomiting, photophobia, and phono-phobia. Dizziness and vertigo, slurred speech, ataxia, tinnitus, visual disruption, and physical imbalance are all warning indications of migraine with aura [ 30 ].

MIDAS and VAS questionnaires

The Migraine Disability Assessment questionnaire was used to measure migraine severity (MIDAS) [ 31 ]. This questionnaire has previously been translated and validated by Iranian people [ 32 ]. With five questions during the previous three months, this questionnaire assesses the severity of migraine headaches and their influence on patient performance. The patients were divided into four groups based on their total score on these five questions: Midas Grade I, Little or no disability (0–5); MIDAS Grade II, Mild disability [ 6 , 7 , 8 , 9 , 10 ]; MIDAS Grade III, Moderate disability [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]; and MIDAS Grade IV, Severe disability (21+).

In addition, pain intensity was measured using the VAS questionnaire. A VAS is usually a standard 100-mm visual analog scale (VAS) labeled No pain’ on the left side and Pain as intense as you can imagine’ on the right side. The participant marks on the line the point that they feel represents their perception of their current state. The VAS score is calculated by measuring in centimeters from the left-hand end of the line to the point that the patient marks. The following cut-off points present the severity of pain: mild pain [ 1 , 2 , 3 ], moderate pain [ 4 , 5 , 6 , 7 ], and severe pain [ 8 , 9 , 10 , 33 ]. All Participants were asked to precisely complete a 30-day headache diary to collect information on the time of migraine attack onset, headache frequency and severity scores (based on VAS, from 0 to 10) precisely after each migraine attack no matter what time of day. The directions for completing the 30-day headache diary were provided by a qualified neurologist, even though individuals could contact researchers to resolve any issues while filling out their 30-day headache diary. At recruitment, the subjects were told to complete their headache diaries during the month ahead.

Anthropometric measurements

Body weight was determined using a standard body weight scale (Seca 707; Seca GmbH & Co. KG., Hamburg, Germany). The participant’s height was measured, unshod, using a stadiometer (Seca GmbH & Co. KG.). To measure the waist-hip ratio, waist circumference (WC) in centimeters was divided by hip circumference in centimeters. We used a non-stretch tape measure to measure WC between the midpoint of the bottom ribs and the iliac crest hip bone following a normal exhale. Hip circumference was measured using a tape measure, while the participants were standing, at the point yielding the maximum circumference over the buttocks. Anthropometric measurements were applied with the minimum cloth and without shoes. The BMI was determined by dividing weight in kilos by height in meters squared.

Dietary assessment

A person’s usual dietary intake over the past year was assessed by face-to-face interview using a semi-quantitative 147-item food frequency questionnaire (FFQ). It was administered by trained dieticians. Based on this questionnaire, the subjects were asked to report the frequency of their food consumption for each food item on a daily, weekly, monthly, or yearly basis. The reliability and validity of this questionnaire in Iran had already been confirmed [ 34 ]. Standard unit sizes and items reported on the household measures were converted to grams using the household measures Guide [ 35 ]. The energy content of the food items in the feed frequency questionnaire was determined using data from the USDA Food Ingredients Table in the Nutritionist 4 nutrition software database modified for Iranian foods (version 7.0; N-Squared Computing, Salem, OR, USA).

Measurement of DAQS

DAQS was obtained from some vitamins and minerals that have antioxidant functions including selenium, zinc, vitamin A, vitamin C, and vitamin E [ 36 ]. To create a DAQS, we compared the daily intake of nutrients to that of the RDI [32]. Each of the 5 antioxidant intakes was assessed and then we allocated a value of 0 or 1, separately, for all components. According to Tur et al. [ 36 ] method when the intake was lower than 2/3 of the RDI, it was assigned a value of 0. Similarly, when the intake was higher than 2/3 of the RDI, it was assigned a value of 1. Thus, the total DAQS ranged from 0 (very poor quality) to 5 (high quality) [ 36 ] The percentage of the RDI as well as the proportion of individuals with intakes below the RDI, 2/3 of the RDI, and 1/3 of the RDI were calculated. The proportion of individuals with intakes below 2/3 of the RDI was the criterion used to estimate the risk of inadequate intake [ 37 ].

Demographic characteristics

A demographic questionnaire was collected by researchers, containing questions about age, marital status, education, occupation, history of chronic disease, family history of migraine, drug consumption, and special diets. To assess the physical activity of the participants, the short form of the International Physical Activity Questionnaire (IPAQ) designed by the World Health Organization was used [ 38 ]. The validity and reliability of this tool have already been evaluated and accepted in Iranian adult women. The physical activity score is represented as metabolic equivalent (MET)/h/week.

Statistical analysis

Data are presented as mean ± SD or frequency (%) for quantitative and qualitative, respectively. To evaluate the relationship between DAQS and the severity of migraine headaches, participants were categorized into 2 groups according to DAQS. To compare quantitative and qualitative variables across DAQS, the independent sample t-tests, and chi-square were used. The association between DAQS and migraine severity (MIDAS and VAS), and headache frequency (categorized into two groups: ≤15 days/month and > 15 days/month) were determined using multiple linear regression and multinomial regression. In the crude model MIDAS, VAS, and headache frequency, were entered into the model as response variables, and DAQS were entered as independent variables. In the adjusted model, the effects of age, weight, hip circumference (HC), job, education, physical activity, and energy intake were controlled. Data were analyzed using SPSS software version 24 (IBM Corp. IBM SPSS Statistics for Windows, Armonk, NY). P values ≤ 0.05 were considered statistically significant.

Study population characteristics

265 subjects participated in the present study with mean age, height, weight, and BMI of 34.32 ± 7.86 years, 161.87 ± 5.15 cm, 69.41 ± 13.0 kg, and 26.50 ± 4.90 kg/m 2 , respectively as shown in Table 1 . The MIDAS percentages of without, mild, moderate, and severe disability (based on the questionnaire) were 13.2, 24.9, 17.4, and 44.5%, respectively. Also, based on the VAS questionnaire, 16.2%, 42.9% and 41% of the study population had mild, moderate, and severe headaches, respectively. Besides, the frequency of the headache was less than 15 days/month in 62% and more than 15 days/month in 38% of the population.

Association between population characteristics and DAQS

All participants were dichotomized based on DAQS. We assessed the differences in demographic variables between the low and -high-intake DAQS groups. Based on the results, higher DAQS was associated with lower HC ( p = 0.03). The results of the comparison indicated that mean VAS and MIDAS were reduced significantly from the low DAQS to high DAQS, after adjusting for confounders ( p < 0.05). Also, the frequency of headaches was reduced significantly from the low DAQS to high DAQS, moreover, the results remained significant after adjusting for confounders ( p < 0.05). No differences were found in mean age, height, weight, BMI, WC, WHR, physical activity, education, job, and marital status ( p > 0.05) between the low and high intake DAQS groups, even after adjusting for confounders, as shown in Table 2 .

Association between dietary intakes and DAQS

The dietary intakes of the participants based on DAQS are shown in Table 3 . The results of the comparison showed that the mean energy, protein, carbohydrate, total fat, cholesterol, vitamin B1, B2, B3, B6, D, E, A, folate, zinc, selenium, and magnesium were significantly higher in subjects with higher adherence to DAQS ( p < 0.001). After adjusting for confounders including age, physical activity, and energy intake the mean carbohydrate, total fat, vitamin B2, D, E, A, and zinc remained significant ( p ≤ 0.05).

Association between MIDAS, VAS, and headache frequency with DAQS

The association between DAQS and migraine severity is shown in Tables 4 and 5 . According to the analysis, the grades of pain severity were lower in subjects with higher adherence to DAQS. In the high-intake DAQS group, there was a lower percentage of participants with grade Ӏ and IV migraine disability though, there was no statistically significant difference across the two groups ( p > 0.05). However, the Frequency of headaches was significantly lower in participants with higher adherence to DAQS ( p < 0.05). Also, there was an inverse association between higher DAQS and the frequency of headaches (OR = 0.53, 95%CI = 0.31–0.88, p = 0.01). After controlling for confounding variables including age, physical activity, weight, energy intake, and job status of participants, the results remained significant (OR = 0.51, 95%CI = 0.25,1.04, p = 0.05). Individuals with higher DAQS were 49% less likely to have more than 15 days per month headaches (> 15 /month) compared with those with lower DAQS. Also, there was a relationship between moderate migraine disability and DAQS. In the adjusted model, subjects with higher DAQS were 69% less likely to have moderate migraine disability, compared with those with lower DAQS (OR = 0.31, 95%CI = 0.09–1.07, p = 0.05). Though, no relationship was observed between DAQS groups and mild and severe disability even after adjusting for potential confounders. In other words, there was no statistically significant correlation between higher DAQS and reduced disability in patients with mild disability (OR = 0.41, 95%CI = 0.13–1.27, p = 0.12), or severe disability (OR = 0.85, 95%CI = 0.29–2.49, p = 0.76). The analysis did not find any association between moderate pain, severe pain, and DAQS (OR = 0.52, 95%CI = 0.25–1.10, p = 0.08 and OR = 0.59, 95%CI = 0.28–1.24, p = 0.16, respectively). After adjusting for the effect of age, physical activity, weight, energy intake, and job status of participants as confounding variables the results remain insignificant.

Association between components of DAQS and MIDAS, VAS, and headache frequency

The association between migraine severity and antioxidant nutrients was examined using multiple regression analysis models adjusted by age, marital status, education, job, physical activity, and energy intake, and is presented in Table 6 . The analysis showed that there was an inverse association between vitamin C intake and the migraine pain intensity (β= -0.18, 95%CI= -29.00, -5.72, P = 0.004). After adjusting for confounding variables, the results remained significant ( p = 0.006).َAlso in a crude model, a negative association was found between vitamin E and the frequency of headache (β= -0.11, 95%CI= -4.41,0.15, p = 0.05). Moreover, DAQS had a significant negative association with headache frequency (β= -0.13, 95%CI= -4.82, -0.23, p = 0.03). After adjustment for confounding factors, the association remained significant ( p = 0.05).

This is the first research to investigate the relationship between DAQS and migraine headaches among women based on our knowledge and literature search. A significant inverse association was found between DAQS and headache frequency after adjusting for confounders. Individuals with higher DAQS scores were 49% less likely to have more than 15 days per month headaches (> 15 days/month) compared with those with lower DAQS. Furthermore, the mean score of VAS, MIDAS, and headache frequency were reduced significantly from the low DAQS to the high adherence of DAQS.

One of the processes involved in migraine etiopathogenesis is thought to be oxidative stress, which is regarded as changes in the balance between ROS production and degradation. It has been known for years that oxidative stress plays a role in the pathogenesis of migraines [ 39 , 40 ]. By providing antioxidants, the impact of oxidative stress may be modulated [ 27 ]. Additionally, the medications now being used to prevent migraines do have some antioxidative activity [ 27 ].

In the present study, we have demonstrated a negative association between vitamin C intake and migraine pain intensity. Furthermore, vitamin E was also inversely correlated with headache frequency. In line with this study, Ferroni et al. study emphasized the critical role of antioxidant agents as a dietary intervention due to reducing the brain oxidative redox system [ 27 ]. Additionally, Chayasirisobhon et al. in an uncontrolled open-label study found that receiving 60 mg of vitamin C, and 30 International Units (IU) of vitamin E can improve both headache frequency and headache severity in patients suffering from migraine [ 41 ].

To date, no randomized controlled trial (RCT) has been conducted to examine the effectiveness of vitamin C as a preventative therapy for migraine. However, the findings of different studies in which vitamin C was administered following wrist or ankle injury, as a daily dose from 200 to 1500 μg, led researchers to hypothesize that consumption of vitamin C, which is a ROS scavenger and an antioxidant, may also modulate the effects of neuroinflammation and ROS activity during migraine [ 42 , 43 ] Fig. 2 . Apart from supplements, plant-based foods such as fruits, vegetables, flowers from edible plants, and spices are excellent dietary sources of natural antioxidants [ 44 , 45 ]. Polyphenols, Carotenoids, and vitamins C and E are the most prevalent plant antioxidants [ 46 , 47 , 48 ].

Schematic representation depicting the possible roles of vitamin C in migraine pathophysiology. ROS: Reactive oxygen species

In our study, we found that vitamin E was also inversely associated with headache frequency. In accordance with our study, Ziaei et al. demonstrated that there was a decline in the pain severity and improvement in the functional disability scales among female migraineurs who utilized vitamin E for five days during their menstruation periods [ 49 ].

Vitamin E, an anti- prostaglandins substance with relatively few side effects, is useful for reducing migraine symptoms and headache pain [ 49 , 50 ]. Additionally, it lessened the requirement for rescue drugs and functional disability [ 49 , 50 ]. Vitamin E may be effective by inhibiting the release of arachidonic acid and producing prostaglandins. The enzymes phospholipase A2 and cyclooxygenase will be blocked by vitamin E, especially in menstrual migraine headaches [ 51 ] Fig. 3 .

Schematic representation depicting the possible roles of vitamin E in relation to PGs as prophylaxis of menstrual migraine. CNS: central nervous system and PG: prostaglandins. Red circled times symbol: inhibition of the pathway

In addition, a previous study reported that following a nutrient pattern full of calcium, vitamin A, vitamin K, vitamin C, vitamin B6, vitamin B2, and magnesium may reduce the severity of headaches [ 52 ]. In addition, one review study which was aimed to investigate the role of nutrients in the pathogenesis and treatment of migraine headaches did not find any improving effects of nutrients except magnesium, carnitine, riboflavin, niacin, CoQ10, vitamin D, vitamin B12, and alpha lipoic acid [ 53 ]. In contrary with this study, one study found that selenium administration has a protective effect on mice brains by the antioxidant phenomena [ 54 ]. So, the results are in the ways of the previous study.

In the present study, we showed that DAQS had a significant negative association with headache frequency. Individuals with higher DAQS were 49% less likely to have more than 15 days per month headaches (> 15 days/month) compared with those with lower DAQS.

Antioxidants supplied with food prevent oxidative stress by inhibiting initiation, propagation, and the oxidative chain reaction itself. Other mechanisms that antioxidants from food act, include scavenging free radicals, quenching molecular oxygen, and functioning as reductants in oxidative processes [ 26 ].

It may be due to decreasing tissue damage and microvascular dysfunction after following high DAQS intakes [ 51 ]. Antioxidants may reduce reactive oxygen species (ROS) due to preventing produce of neuropeptides such as substance P (SP) and calcitonin gene-related peptide (CGRP) [ 51 ]. We should consider that the mean (SD) of intakes of vitamin A, C, zinc, and selenium were equal to or higher than the recommended daily allowances (RDA) in the both lower and higher median of DAQS [ 55 ]. A double-blind randomized placebo-controlled clinical trial found that zinc supplementation which has a role in neuronal signaling, can reduce the frequency of migraine attacks but not the duration and severity of cold-type migraine headaches [ 56 ]. In this study, some of the insignificant results may attribute to the type of headaches. Overall, a cross-sectional study found that reducing the total intake of food may have a better influence regardless of the type of food [ 57 ]. This can justify our results.

One of the limitations of this study is the failure to consider different types of headaches. Second, the cross-sectional design of the study can only examine the relationship, not casual effects. Third, we did not consider foods like caffeine that trigger migraine. Fourth, we did not assess the menstrual time of participants which may have effects on the severity of migraine. Finally, a larger sample size is needed to increase the accuracy of the results. DAQS and pain intensity data are based on questionnaires and interviews with patients, which are subjective and based on patients’ memory and their interpretation of pain. This was the first study to investigate the relationship between DAQS and headaches in migraineurs. The population was free of any chronic diseases, and this can reduce the effects of confounders.

Our study found that higher consumption of DAQS nutrients may reduce headache frequency among women. In addition, the consumption of vitamin C may potentially associate with decreasing the severity of headaches. Also, a higher DAQS score was related to lower moderate migraine disability. Although this study did not present a significant relationship between all DAQS subcategories and migraine headaches severity, it should be considered that having a balanced diet full of vegetables and antioxidants alongside maintaining a normal weight is proven to reduce headaches. It is evident that more prospective studies are needed to confirm the veracity of our results.

Data availability

The authors confirm that the data supporting the findings of this study are available within the manuscript and in the included tables.

Abbreviations

Body Mass Index

Confidence Interval

Dietary Antioxidant Quality Score

Food Frequency Questionnaire

Hip Circumference

International Physical Activity Questionnaires

International Unit

Metabolic Equivalents

Migraine Disability Assessment

Nonsteroidal anti-inflammatory drugs

Recommended Daily Allowances

Reference Dietary Intake

Visual analog scale, WC, waist circumference

Waist to hip ratio

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Acknowledgements

Authors would like to thank the participants for their kind cooperation.

The research is financially supported by the Tehran University of Medical Sciences. (Grants ID: 95-01-103-31348)

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KM designed the study. FA and SM contributed to the statistical analyses and interpretation of the data; SH wrote the first draft and revised the manuscript. NB wrote the first draft of discussion section. KM, AM, CC and SH critically checked the manuscript; and agree to be fully accountable for ensuring the integrity and accuracy of the work. All authors read and approved the final manuscript.

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Hajishizari, S., Mirzababaei, A., Abaj, F. et al. The association between dietary antioxidant quality score and intensity and frequency of migraine headaches among women: a cross-sectional study. BMC Women's Health 24 , 497 (2024). https://doi.org/10.1186/s12905-024-03260-3

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Factors associated with age at first screening for cervical cancer among adult Cape Verdean women: a cross-sectional study

Joshua Okyere 1 , 2 ,
Castro Ayebeng 1 &
Kwamena Sekyi Dickson 1

BMC Public Health volume 24 , Article number: 2444 ( 2024 ) Cite this article

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Cervical cancer ranks third in terms of cancer incidence and mortality in Cape Verde. Understanding the factors associated with the age of cervical cancer screening (CCS) is essential because it helps identify populations at risk of delayed screening, enabling targeted interventions to ensure timely detection and treatment, ultimately reducing the burden of cervical cancer. We examined the factors associated with age at first screening for cervical cancer among adult Cape Verdean women.

Data from the 2020 WHO STEPs survey were used. We analyzed data from 1,082 women aged 30–69 years who had ever screened for cervical cancer. Bivariable and multivariable logistic regression models were computed in STATA version 18.

Overall, 30.6% of women in the study had their first CCS before or at age 30. Except for visits to the health facility within the last 12 months, all variables significantly predicted women’s first age for CCS in the crude model. In the adjusted model, women with tertiary education showed greater odds [AORs = 9.85; 95% CI: 4.12–23.54] compared to those with no formal education. Compared to those who were never married, previously married women had significantly lower odds of screening at an early age [AOR = 0.63; 95% CI: 0.39–0.99]. Women without hypertension had higher odds [AOR = 1.66; 95% CI: 1.18–2.34] of early screening compared to those with hypertension. Also, women who were currently working had significantly higher odds of early screening than those unemployed [AOR = 1.49; 95% CI: 1.09–2.04].

In conclusion, implementing targeted educational campaigns, addressing socio-economic barriers, and integrating cervical cancer screening into routine healthcare services can increase the early screening uptake among Cape Verdean women. There is a need to integrate CCS in the routine healthcare services of women living with hypertension. Also, the positive association between formal education and age at first screening, it is imperative for the Cape Verdean public health departments to implement comprehensive education programs within schools to promote awareness about CCS.

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Worldwide, there is acknowledgement of the significance of early identification and detection of cervical cancer [ 1 ]. This recognition is exemplified in the World Health Organization’s new recommendation for cervical cancer screening [ 2 ], the WHO Director’s call for the elimination of cervical cancer [ 3 ], and also articulated in the 90-70-90 strategy [ 1 ]. Cervical cancer screening (CCS) offers an opportunity for healthcare providers to identify the disease at its early stages, thereby improving treatment outcomes, survivorship, and quality of life of the woman. Despite the recognized importance of CCS, its uptake remains low coupled with high incidence and mortality rates, particularly in low-and-middle-income countries (LMICs) and sub-Saharan Africa (SSA).

Report from the Global Cancer Observatory [ 4 ] indicates that globally, there were 662,301 new cases and 348,874 cervical cancer related deaths; thus, making the disease the 8th most reported cancer in the world, and the 9th leading cancer-related mortality. The report further shows that cervical cancer was the fourth leading cancer among women in 2022, with Asia and Africa contributing to 60% and 19% of all new cases of the disease, respectively [ 4 ]. In the context of Cape Verde, cervical cancer ranks third in terms of cancer incidence and mortality in the country [ 4 ]. Thus, making cervical cancer an important public health concern for Cape Verde.

In respect to CCS, one study involving 55 LMICs [ 5 ] revealed that 43.6% of women had ever screened for cervical cancer in their lifetime, with those in SSA having the lowest screening uptake (16.9%). However, in 2019, the WHO reported that the lifetime prevalence of CCS uptake in Cape Verde was 53% while screening uptake within the last five years was 42% [ 6 ]. While CCS uptake in Cape Verdean women is higher than in other SSA countries [ 5 , 6 ], the prevalence falls short of the WHO’s recommendation of ensuring that by 2030, 70% of women would be screened by age 35 [ 1 ]. It must be noted that CCS in Cape Verde is not free but paid out-of-pocket. Also, like many African countries, Cape Verde relies on low sensitivity modalities of screening (e.g., visual inspection with acetic acid) due to the unavailability and high cost of operating high sensitivity screening tests (e.g., human papillomavirus [HPV] tests and cytology-based screening) [ 7 ]. Vilares et al. [ 7 ] further assert that most CCS in Cape Verde are based on opportunistic screenings as there is no national screening program.

The WHO recommends CCS initiation at age 30 for the general population and age 25 for those living with HIV [ 2 ]. However, in the context of Cape Verde, it is unclear what the situation is. There is currently no published empirical research that has investigated age at first screening for cervical cancer. Meanwhile, understanding the factors associated with the age of cervical cancer screening is essential because it helps identify populations at risk of delayed screening, enabling targeted interventions to ensure timely detection and treatment, ultimately reducing the burden of cervical cancer. This paucity of evidence on age at first screening for cervical cancer presents a significant knowledge gap that has implications for policy targeting and cost-effectiveness of CCS interventions. As such, we asked the following questions: (a) At what age do Cape Verdean women undergo their first screening for cervical cancer? and (b) What factors predict the age at first screening for cervical cancer? To find answers to these questions, we examined the factors associated with age at first screening for cervical cancer among adult Cape Verdean women.

Data source and design

Data was sourced from the 2020 WHO STEPS survey of non-communicable disease (NCD) risk factors conducted in Cape Verde. This survey was conducted between February and March 2020 [ 8 ]. It encompassed three steps. Step 1 involved gathering socio-demographic and behavioral information. In Step 2, physical measurements such as height, weight, and blood pressure were collected. Step 3 involved collecting blood and urine samples for biochemical measurements, including assessing blood glucose levels, cholesterol levels, and salt intake. This survey was conducted on a population-based sample of adults aged 18–69 [ 8 ]. A multiple-stage probability sampling design was employed to ensure the representativeness of the data for that age group in Cape Verde. A total of 4,563 adults participated in Steps 1 and 2, while a subsample of 2,436 adults participated in Step 3 [ 8 ]. The overall response rate was 64%.

Outcome variable

The outcome variable was age at first screening for cervical cancer. This was derived from the question, “At what age did your first test for cervical cancer”. We recoded the raw ages as ‘1 = Before or at age 30’ and ‘0 = After age 30’. This categorization was informed by the WHO’s recommendation for CCS to be initiated at age 30 for the general population [ 2 ].

Explanatory variables

While our literature review did not show evidence of any existent studies that have examined the age at first screening for cervical cancer, we relied on a plethora of studies [ 9 , 10 , 11 , 12 , 13 ] that have investigated the determinants of CCS uptake to select the explanatory variables. These variables included educational level, place of residence, marital status, alcohol consumption, hypertension status, visit to health facility in the last 12 months, and employment status.

Statistical analysis

The dataset had a total of 4,563 observations. However, for the purposes of this study, there was a need to exclude all those who had never undergone CCS. This brought the data to 1,950 observations. The 1,950 included observations of women who had or had not undergone CCS. However, our inferential analysis was based on only those who had undergone CCS ( n = 1,082). We then applied the sample weight to address any issues of over or under-estimation of the age at first screening and the explanatory variables. Descriptive analysis was conducted to know the distribution of age at first screening for cervical cancer. The results were presented in frequencies and percentages. Also, Pearson’s chi-square test was computed to check for statistical differences in the distribution of age at first screening for cervical cancer. A bivariable logistic regression was then conducted to examine the association between each variable and age at first screening. We also conducted a multivariable logistic regression to adjust for the effects of all the variables. The results from the multivariable logistic regression model were presented in adjusted odds ratio (AOR) with their corresponding 95% confidence interval (CI). All analyses were conducted in STATA version 18 (StataCorp, College Station, TX, USA) and R.v.4.3.2.

Ethical approval

We did not seek ethical approval as this has already been done for all the STEPS survey of NCD risk factors. Rather, we formally requested the data from the WHO NCD Microdata Repository: https://extranet.who.int/ncdsmicrodata/index.php/home .

Distribution of overall CCS uptake, location, timing and main reason for last test

More than half of respondents (53%) reported having undergone CCS. Among those who had undergone screening, 32.1% had their screening within the past year, while 25% had it over five years ago. The primary reason for the last CCS was that it was part of a routine exam (53%), followed by recommendations from healthcare providers (33.7%). Regarding the location of the last CCS, most screenings took place in a doctor’s office (44.3%), with hospitals and community clinics also being common venues (Fig. 1 ).

Distribution of the age at first screening for cervical cancer

Overall, only 30.6% of women in the study had their first CCS before or at age 30 (Table 1 ). Early age at screening was higher among individuals with tertiary education (48.1%), urban residents (33.3%), never married women (35.7%), those who consumed alcohol (34.5%), and those living without hypertension (34.9%). Additionally, women who visited the health facility in the last 12 months (32.0) and those employed (36.6) reported higher early age at screening. These differences in distribution were statistically significant with p -values less than 0.05 (see Table 1 ).

Factors associated with age at first screening for cervical cancer

Table 2 presents the factors associated with women’s age at first CCS uptake. Except for visits to the health facility within the last 12 months, all variables significantly predicted women’s first age for CCS in the crude model. In the adjusted model, women with tertiary education showed greater odds [AORs = 9.85; 95% CI: 4.12–23.54] of getting screened early compared to those without formal education. Compared to those who were never married, previously married women had significantly lower odds of screening at an early age [AOR = 0.63; 95% CI: 0.39–0.99]. Women without hypertension had higher odds [AOR = 1.66; 95% CI: 1.18–2.34] of early screening compared to those with hypertension. Also, women who were currently working had significantly higher odds of early screening than those unemployed [AOR = 1.49; 95% CI: 1.09–2.04].

The age at which women undergo their first screening for cervical cancer is a critical factor in early detection and prevention efforts. In this study, we examined the factors influencing age at first screening among adult Cape Verdean women, utilizing data from the 2020 WHO STEPS survey. We found that 53% of adult women in Cape Verde had ever undergone screening for cervical cancer. This aligns with the WHO’s report on Cape Verde that also found a screening uptake rate of 53% [ 6 ]. Consistent with previous literature [ 14 , 15 ], we found that recommendations from healthcare providers and screening being part of routine health examinations were the main reasons why Cape Verdean women got screened for cervical cancer. This highlights the critical role of healthcare professionals as champions in encouraging to avail themselves for CCS. In line with Agbeko et al. [ 16 ], our study showed that women sought for CCS after they have experience pain or other cervical cancer symptoms. The implication of women seeking cervical cancer screening only after experiencing symptoms is that many cases may be detected at more advanced stages when treatment options are limited and less effective. This delay in screening can lead to higher morbidity and mortality rates, as early-stage cervical cancer is often asymptomatic and more treatable.

Our findings also revealed that only 30.6% of Cape Verdean women who had undergone CCS initiated screening before or at age 30. This implies that 69.4% of Cape Verdean women initiate screening after age 30 – a result that suggests non-compliance to the WHO’s recommended age for initiating CCS [ 2 ]. Such delayed initiation of screening increases the risk of detecting cervical abnormalities at more advanced stages [ 17 , 18 ], potentially compromising treatment outcomes and exacerbating the burden of cervical cancer morbidity and mortality in Cape Verde.

Regarding the associated factors, the study shows that higher educational attainment is positively associated with age at first screening for cervical cancer. This means that women with higher education are more likely to initiate screening earlier than those with no formal education. This finding aligns with previous research conducted by Zeleke et al. [ 18 ], and is also corroborated by studies from SSA [ 9 ], Cameroon [ 19 ], and Pacific Island territories [ 20 ]. Plausible explanations for this association may include increased health literacy among educated women, enabling them to recognize the importance of early screening and take proactive steps towards initiating screening at the recommended age. Furthermore, higher education levels may be associated with greater access to healthcare information and resources, as well as increased autonomy in healthcare decision-making [ 9 ], all of which contribute to earlier engagement with CCS services.

Women who were employed were 1.49 times more likely to initiate CCS before or at age 30 compared to their counterparts who were unemployed. Thus, underscoring the significance of economic status as a significant predictor of age at first screening for cervical cancer. The observed association is inconsistent with a study conducted in Harare, Zimbabwe [ 21 ] which found no significant association between employment status and CCS. Nonetheless, our result is synonymous with Ba et al.’s study [ 22 ] which reported that women who are employed have a 13% higher likelihood of undergoing CCS. We argue from the perspective that being employed may confer greater financial stability, reducing economic barriers to accessing screening services and enabling women to initiate CCS earlier than their counterparts who are unemployed. In some instances, being employed increases women’s access to healthcare resources and benefits among employed women, including employer-sponsored health insurance or workplace wellness programs that facilitate access to preventive healthcare services such as CCS.

Our study also revealed that women who did not have hypertension were more likely to initiate CCS earlier compared to those who were hypertensive. This aligns with a study [ 22 ] that identified hypertension as a barrier to CCS uptake. Ordinarily, it would be expected that the reverse association would be the case as women living with hypertension would have frequent visits to the healthcare facility and may be more exposed to health messages including that of CCS. However, this was not the case in our study. According to Constantinou et al. [ 23 ], women living with chronic diseases tend to have poorer CCS practice due to the presence of competing health priorities and concerns. This may explain the observed association between hypertension status and timing of first CCS.

Implications for policy

The alarmingly high rate of Cape Verdean women initiating screening after age 30 (69.4%) highlights the urgent need for policy measures to promote adherence to the World Health Organization’s recommended age for initiating CCS. Efforts should focus on increasing awareness and education about the importance of early screening. Additionally, addressing socio-economic factors such as educational attainment and employment status is crucial. Policies aimed at improving access to education and employment opportunities may indirectly facilitate early engagement with CCS services by empowering women with the resources and knowledge needed to prioritize their health.

Strengths and limitations

This study was based on a population-based sample – thus, allowing us to extrapolate the findings to the larger population of women who screen for cervical cancer in Cape Verde. Also, appropriate statistical analyses were conducted which adds to the validity of the results. However, we cannot infer causality since the data is based on a cross-sectional design. There is the possibility of recall bias with respect to the age at first screening for cervical cancer since it was self-reported rather than from a health facility register. As such, there is the possibility of over or under-estimation. We are also unable to tell whether the individual screened before knowing their hypertension status. The relatively low overall response rate in the data is another limitation of the study.

Based on the findings, we conclude that the majority of Cape Verdean women initiate CCS after age 30. Implementing targeted educational campaigns, addressing socio-economic barriers, and integrating cervical cancer screening into routine healthcare services can increase the early screening uptake among Cape Verdean women. There is a need to integrate CCS into the routine healthcare services of women living with hypertension. Also, the positive association between formal education and age at first screening, it is imperative for the Cape Verdean public health departments to implement comprehensive education programs within schools to promote awareness about CCS.

Data availability

The datasets generated and/or analysed during the current study are available in the WHO NCD Microdata Repository: https://extranet.who.int/ncdsmicrodata/index.php/home.

Abbreviations

Adjusted Odds Ratio

Cervical Cancer Screening

Low-and-middle-income Countries

Sub-Saharan Africa

World Health Organization

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Acknowledgements

We acknowledge the WHO for granting us free access to the dataset used in this study.

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Department of Population and Health, University of Cape Coast, Cape Coast, Ghana

Joshua Okyere, Castro Ayebeng & Kwamena Sekyi Dickson

Department of Nursing, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Joshua Okyere

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JO conceptualized the study. JO and CA designed the analyses. JO curated the data and performed the formal analyses. JO and CA drafted the initial manuscript. KSD reviewed the initial manuscript for its accuracy. All authors reviewed the final manuscript and approved its submission. JO had the final responsibility of submitting the manuscript.

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Correspondence to Joshua Okyere .

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We did not need to seek ethical clearance because the WHO STEPS data we used is publicly available. We obtained the datasets from the WHO NCD Microdata Repository: https://extranet.who.int/ncdsmicrodata/index.php/home . We followed all the ethical guidelines that pertain to using secondary datasets in research publications.

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Okyere, J., Ayebeng, C. & Dickson, K.S. Factors associated with age at first screening for cervical cancer among adult Cape Verdean women: a cross-sectional study. BMC Public Health 24 , 2444 (2024). https://doi.org/10.1186/s12889-024-19917-5

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Received : 16 April 2024

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DOI : https://doi.org/10.1186/s12889-024-19917-5

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Section snippets A brief overview of cross-sectional studies. A cross-sectional study, also known as a prevalence or transverse study, uses a snapshot of participants' beliefs, behaviors, or other variables of interest of a study population (e.g., a group of individuals or organizations) at a specified point in time (Grimes and Schulz, 2002, Hua and David, 2008) to examine research questions ...
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The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the ...
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This finding aligns with previous research conducted by Zeleke et al. , and is also ... Ethiopia, 2019: cross-sectional study. Cancer Manage Res. 2021 Jan;22:579-85. Okyere J, Duodu PA, Aduse-Poku L, Agbadi P, Nutor JJ. ... Wanyenze E. Motivations and barriers to cervical cancer screening among HIV infected women in HIV care: a qualitative ...

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