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Epidemiology of Tuberculosis and Progress Toward Meeting Global Targets — Worldwide, 2019

Rena fukunaga , phd, philippe glaziou , md, jennifer b harris , phd, anand date , md, katherine floyd , phd, tereza kasaeva , phd.

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Corresponding author: Rena Fukunaga, [email protected] .

Corresponding author.

Collection date 2021 Mar 26.

All material in the MMWR Series is in the public domain and may be used and reprinted without permission; citation as to source, however, is appreciated.

Although tuberculosis (TB) is curable and preventable, in 2019, TB remained the leading cause of death from a single infectious agent worldwide and the leading cause of death among persons living with HIV infection ( 1 ). The World Health Organization’s (WHO’s) End TB Strategy set ambitious targets for 2020, including a 20% reduction in TB incidence and a 35% reduction in the number of TB deaths compared with 2015, as well as zero TB-affected households facing catastrophic costs (defined as costs exceeding 20% of annual household income) ( 2 ). In addition, during the 2018 United Nations High-Level Meeting on TB (UNHLM-TB), all member states committed to setting 2018–2022 targets that included provision of TB treatment to 40 million persons and TB preventive treatment (TPT) to 30 million persons, including 6 million persons living with HIV infection and 24 million household contacts of patients with confirmed TB (4 million aged <5 years and 20 million aged ≥5 years) ( 3 , 4 ). Annual data reported to WHO by 215 countries and territories, supplemented by surveys assessing TB prevalence and patient costs in some countries, were used to estimate TB incidence, the number of persons accessing TB curative and preventive treatment, and the percentage of TB-affected households facing catastrophic costs ( 1 ). Globally, TB illness developed in an estimated 10 million persons in 2019, representing a decline in incidence of 2.3% from 2018 and 9% since 2015. An estimated 1.4 million TB-related deaths occurred, a decline of 7% from 2018 and 14% since 2015. Although progress has been made, the world is not on track to achieve the 2020 End TB Strategy incidence and mortality targets ( 1 ). Efforts to expand access to TB curative and preventive treatment need to be substantially amplified for UNHLM-TB 2022 targets to be met.

TB data are reported annually to WHO by 215 countries and territories and are reviewed and validated in collaboration with reporting entities ( 1 ). Four methods are used to estimate TB incidence: 1) TB prevalence surveys; 2) notifications from country surveillance systems, adjusted by a standard factor to account for underreporting, overdiagnosis, and underdiagnosis; 3) TB inventory studies that measure the level of underreporting of persons with diagnosed TB combined with capture-recapture modeling; and 4) national notification data supplemented by expert opinion regarding case detection gaps. For HIV-negative persons, estimates of TB mortality were based on all-cause mortality data from civil registration and vital statistics, mortality surveys, or the product of TB incidence and the case-fatality rate (CFR) (i.e., the proportion of persons with TB who die from the disease) ( 1 ). Among persons living with HIV infection, TB mortality estimates were calculated as the product of incidence and the CFR. The number of persons receiving TB curative and preventive treatment is reported by individual countries directly to WHO. National TB patient cost surveys were used to assess the proportion of TB-affected households facing catastrophic costs.

Globally, TB illness developed in an estimated 10 million persons in 2019 (130 per 100,000 population), 815,000 (8.2%) of whom were HIV-infected ( Table ). Overall, TB incidence declined 2.3% from 2018 and 9% from 2015. An estimated 1.4 million persons died from TB in 2019, including 208,000 persons who were living with HIV infection. The total number of TB deaths declined by 7% from 2018 to 2019 and by 14% since 2015 ( 1 ).

TABLE. Estimated number of incident tuberculosis (TB) cases, TB incidence rate, number of TB-associated deaths among all persons and among HIV-positive persons, and number of TB patients with rifampicin-resistant TB (RR-TB), by World Health Organization region — worldwide, 2019.

Source : Adapted from World Health Organization. Global tuberculosis report 2020. Geneva, Switzerland: World Health Organization; 2020. https://www.who.int/teams/global-tuberculosis-programme/tb-reports

Abbreviation: CFR = case-fatality rate.

* Number of cases per 100,000 population.

† Includes multidrug-resistant TB.

During 2019, multidrug-resistant (MDR) TB illness (TB that is resistant to at least isoniazid and rifampicin, the two most potent anti-TB drugs) ( 5 ) or rifampicin-resistant TB illness (RR-TB) developed in an estimated 465,000 persons. These patients accounted for 4.7% of all persons with TB, 3.3% of persons with a new TB diagnosis, and 18% of persons previously treated for TB.

Most persons who became ill with TB in 2019 lived in the WHO regions of South-East Asia (44%), Africa (25%), and the Western Pacific (18%), with smaller percentages in the Eastern Mediterranean (8.2%), the Americas (2.9%), and Europe (2.5%) ( Table ). Eight countries accounted for two thirds of the total global TB cases: India (26%), Indonesia (8.5%), China (8.4%), the Philippines (6.0%), Pakistan (5.7%), Nigeria (4.4%), Bangladesh (3.6%), and South Africa (3.6%). The WHO European and African regions have experienced the largest declines in incidence (19% and 16%, respectively) and mortality (31% and 19%, respectively) since 2015.

If persons who received a TB diagnosis that was reported to national authorities are assumed to be treated for TB ( 1 ), then in 2019, a total of 7.1 million persons were treated for TB, a slight increase from 7.0 million in 2018. With an estimated 10 million incident cases, this leaves a gap of 2.9 million persons with incident TB who either did not receive a diagnosis or did receive a diagnosis but were not reported to national authorities. Among the estimated 815,000 HIV-infected persons with cases of incident TB, 456,426 (56%) persons were reported as having received a diagnosis and been treated. Among the estimated 465,000 persons with incident MDR or RR-TB, only 177,099 (38%) were enrolled in MDR or RR-TB treatment.

A total of 4.1 million persons received TPT in 2019 ( Figure ), an 86% increase from 2.2 million in 2018 and a 300% increase from 1.0 million in 2015. Most persons who received TPT were persons living with HIV infection (3.5 million in 2019 and 1.8 million in 2018). Among the estimated 1.3 million children aged <5 years who were household contacts of TB patients, 433,156 (33%) received TPT in 2019, compared with 349,796 (27%) in 2018 (an 18% increase in the number of children treated). Among older household contacts, the number of persons who received TPT was 105,240 persons in 2019 and 73,811 in 2018 (a 43% increase). The total number of older household contacts is unknown.

Percentage of persons living with HIV infection and on antiretroviral treatment who received tuberculosis preventive treatment — worldwide, 2019

Source: Adapted from World Health Organization. Global tuberculosis report 2020. Geneva, Switzerland: World Health Organization; 2020. https://www.who.int/teams/global-tuberculosis-programme/tb-reports

Among 17 countries that have completed national TB patient cost surveys since 2015, an average of 49% of TB-affected households faced catastrophic costs (country-level estimates = 19%–83%). This figure increased to 80% in households affected by drug-resistant TB (country-level estimates = 67%–100%) ( 1 ).

Globally, although TB incidence and mortality have been steadily decreasing, these declines are likely not occurring quickly enough for WHO End TB Strategy targets to be reached, with only a 9% decrease in incidence (2020 target = 20% decrease) and a 14% decrease in the number of deaths (2020 target = 35% decrease) from 2015 to 2019. The WHO European region is on track to reach both the incidence and mortality targets, and the African region has made progress toward meeting the targets. However, with one half of TB-affected households facing catastrophic costs, the world is far from reaching the WHO target of zero TB-affected households facing catastrophic costs.

Although challenges remain, assessment of the UNHLM-TB targets after the second year of the 2018–2022 timeline is more encouraging. During 2018 and 2019, a total of 14.1 million persons (35% of the UNHLM target) received TB treatment globally. For the target of 40 million patients treated to be achieved, an additional 26 million persons need to be treated during 2020–2022, which would represent substantial progress toward closing the gap between the number of persons who become ill with TB and the number who receive a diagnosis and are treated each year.

Although substantial progress has been made in TPT implementation, only 6.3 million persons, less than one fourth (23%) of the UNHLM-TB target, received TPT in 2018 and 2019. For the target of 30 million persons receiving TPT during 2018–2022 to be achieved, approximately 24 million additional persons must be reached with TPT during 2020–2022. Most persons who have received TPT to date are living with HIV infection, and the world is on track to reach the UNHLM target for this group. Despite strong growth in TPT provision to these persons, providing TPT to household contacts of TB patients, especially persons aged ≥5 years, continues to face substantial challenges.

Acceleration of TB service provision in 2020 was not possible in most countries because of the COVID-19 pandemic. Stay-at-home orders, movement restrictions, and the prioritization of COVID-19 mitigation activities have affected TB services through restricted service provision, diverted human resources, and disrupted supply chains ( 6 ). This has likely led to reductions in timely diagnosis and treatment of new tuberculosis cases ( 7 ). India, Indonesia, the Philippines, and South Africa reported monthly decreases in TB case notifications to approximately 50% of the January 2020 total during the first 6 months of 2020, with reductions of smaller magnitudes (25%–30%) reported by other high-incidence countries ( 1 ). The COVID-19 pandemic is continuing in 2021 and will have a long-term impact on national TB programs as well as global TB incidence and prevalence ( 7 ).

The findings in this report are subject to at least three limitations. First, underlying data quality, particularly for surveillance, might affect the accuracy of country estimates. Second, the differing methodologies used to generate country-level estimates might affect the comparability of estimates between regions and countries. Finally, a limited number of countries completed a national survey of costs faced by TB patients and their households, which might affect the generalizability of this indicator.

Programmatic efforts will need to be substantially enhanced for UNHLM targets for TB curative and preventive treatment to be reached by 2022, and more broadly, for future WHO End TB strategy targets to be met. For global TB targets to be achieved, innovations and adaptations in TB diagnosis, care, and treatment are needed to accelerate global TB progress and to meet the additional challenges presented by the COVID-19 pandemic ( 8 ), which threatens not only to slow future progress but also to reverse the gains made in recent years. However, the pandemic also provides new and unique opportunities to implement and evaluate innovations such as dual TB and COVID-19 screening of patients with respiratory symptoms, as well as multi-month dispensing of TPT and TB treatment combined with the use of digital health technologies to monitor patients in the context of fewer face-to-face encounters. Services for TB are an essential component of resilient health systems and can be strengthened by promoting synergies in the responses to both TB and COVID-19.

What is already known about this topic?

The 2018 United Nations High Level Meeting on Tuberculosis (TB) and the World Health Organization’s End TB Strategy set ambitious goals for reducing TB incidence, deaths, and patient costs and increasing the provision of TB curative and preventive treatment.

What is added by this report?

With an estimated 10 million incident TB cases and 1.4 million TB deaths in 2019, the world is not on track to achieve global targets. Further, the COVID-19 pandemic has hampered TB-related service delivery in many countries.

What are the implications for public health practice?

Innovations and adaptations in TB diagnosis, care, and treatment are needed to accelerate global TB progress and overcome the COVID-19 pandemic–associated challenges to TB diagnosis and treatment.

Acknowledgments

Ministries of Health and National Tuberculosis (TB) Programs of all countries; World Health Organization (WHO) Global TB program, Geneva; WHO regional and country offices; U.S. Agency for International Development, Bureau for Global Health, Office of HIV/AIDS and Division of TB; Adam Macneil, Susan Maloney, CDC.

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

• 1. World Health Organization. Global tuberculosis report 2020. Geneva, Switzerland: World Health Organization; 2020. https://www.who.int/teams/global-tuberculosis-programme/tb-reports
• 2. World Health Organization. The end TB strategy. Geneva, Switzerland: World Health Organization; 2015. https://www.who.int/tb/strategy/end-tb/en/
• 3. United Nations. Draft resolution submitted by the President of the General Assembly: scope, modalities, format and organization of the high-level meeting on the fight against tuberculosis. New York, NY: United Nations; 2018. https://undocs.org/en/A/72/L.40
• 4. Stop TB Partnership. UNHLM on TB: key targets and commitments. Geneva, Switzerland: STOP TB Partnership; 2020. http://www.stoptb.org/global/advocacy/unhlm_targets.asp [ Google Scholar ]
• 5. CDC. Drug-resistant TB. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/tb/topic/drtb
• 6. World Health Organization. Pulse survey on continuity of essential health services during the COVID-19 pandemic. Geneva, Switzerland: World Health Organization; 2020. https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2020.1
• 7. Hogan AB, Jewell BL, Sherrard-Smith E, et al. Potential impact of the COVID-19 pandemic on HIV, tuberculosis, and malaria in low-income and middle-income countries: a modelling study. Lancet Glob Health 2020;8:e1132–41. 10.1016/S2214-109X(20)30288-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
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Global, regional and national trends in tuberculosis incidence and main risk factors: a study using data from 2000 to 2021

• Wentao Bai   ORCID: orcid.org/0009-0009-2060-1165 1 &
• Edward Kwabena Ameyaw   ORCID: orcid.org/0000-0002-6617-237X 1 , 2  

BMC Public Health volume  24 , Article number:  12 ( 2024 ) Cite this article

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Despite the significant progress over the years, Tuberculosis remains a major public health concern and a danger to global health. This study aimed to analyze the spatial and temporal characteristics of the incidence of tuberculosis and its risk factors and to predict future trends in the incidence of Tuberculosis.

This study used secondary data on tuberculosis incidence and tuberculosis risk factor data from 209 countries and regions worldwide between 2000 and 2021 for analysis. Specifically, this study analyses the spatial autocorrelation of Tuberculosis incidence from 2000 to 2021 by calculating Moran’s I and identified risk factors for Tuberculosis incidence by multiple stepwise linear regression analysis. We also used the Autoregressive Integrated Moving Average model to predict the trend of Tuberculosis incidence to 2030. This study used ArcGIS Pro, Geoda and R studio 4.2.2 for analysis.

The study found the global incidence of Tuberculosis and its spatial autocorrelation trends from 2000 to 2021 showed a general downward trend, but its spatial autocorrelation trends remained significant (Moran’s I = 0.465, P < 0.001). The risk factors for Tuberculosis incidence are also geographically specific. Low literacy rate was identified as the most pervasive and profound risk factor for Tuberculosis.

Conclusions

This study shows the global spatial and temporal status of Tuberculosis incidence and risk factors. Although the incidence of Tuberculosis and Moran’s Index of Tuberculosis are both declining, there are still differences in Tuberculosis risk factors across countries and regions. Even though literacy rate is the leading risk factor affecting the largest number of countries and regions, there are still many countries and regions where gender (male) is the leading risk factor. In addition, at the current rate of decline in Tuberculosis incidence, the World Health Organization’s goal of ending the Tuberculosis pandemic by 2030 will be difficult to achieve. Targeted preventive interventions, such as health education and regular screening of Tuberculosis-prone populations are needed if we are to achieve the goal. The results of this study will help policymakers to identify high-risk groups based on differences in TB risk factors in different areas, rationalize the allocation of healthcare resources, and provide timely health education, so as to formulate more effective Tuberculosis prevention and control policies.

Peer Review reports

Tuberculosis (TB), caused by the mycobacterium Tuberculosis complex, is a longstanding global health challenge. Its origins can be traced back 9,000 years through the detection of TB in ancient human remains [ 1 ]. TB primarily spreads through respiratory droplets released during activities such as coughing, sneezing, and talking, allowing the inhalation of Mycobacterium Tuberculosis particles by others. Additionally, the infection can occur through the mouth, intestines, and skin [ 2 ]. With approximately 25% of the global population infected with Mycobacterium Tuberculosis, new infections occur in about 1% of the population annually [ 3 ]. To combat the TB epidemic, several global strategies have been implemented. In 2018, the United Nations held a high-level meeting on TB, prioritising discussions on the pandemic and eradication strategies to the level of heads of state and government [ 4 ]. All UN member nations have pledged to strengthen efforts and eliminate TB by 2030 [ 5 , 6 ]. Countries like China, India, and the United States have developed national programs and policies to prevent and control TB [ 4 , 7 , 8 ].

Individuals with active TB can transmit the disease to approximately 10–15 people each year through close contact [ 9 ]. Despite a net reduction of around 10% in TB incidence between 2015 and 2021, it remains a significant public health challenge globally. Worryingly, there has been a 3.9% increase in the incidence of TB between 2020 and 2021, reversing the downward trend observed for the majority of the past two decades [ 10 ]. Additionally, the emergence of drug-resistant TB, exacerbated by the misuse of antibiotics, further complicates the fight against the TB epidemic. Furthermore, the disruption of health services due to the COVID-19 pandemic has contributed to an increase in TB-related deaths worldwide between 2019 and 2021 [ 10 , 11 ]. Although TB may frequently be remedied with the correct treatment plan, a significant reduction in its burden is still a distant goal for many countries [ 12 ].

The spatial distribution of TB incidence exhibits significant regional variations. Southeast Asia and Africa account for nearly 70% of all global TB cases, with the majority of high-incidence countries situated in these regions [ 13 , 14 ]. Moreover, the decline in the global burden of TB has varied considerably across countries and regions. For instance, the annual percentage decline in TB incidence among HIV-negative individuals between 2006 and 2016 ranged from 6.2% in Kazakhstan to 1.2% in the Philippines [ 15 ].

The incidence of TB is influenced by various risk factors. Diabetes is a significant risk factor for active TB, with diabetic patients having a three-fold higher risk of acquiring TB compared to those without diabetes [ 16 ]. Undernourishment is also a crucial risk factor, associated with increased TB incidence, severity, poorer treatment outcomes, and higher mortality rates [ 17 , 18 , 19 ]. Workplace exposure to PM2.5 has been linked to smear-positive TB, as it may increase the risk of Mycobacterium TB transmission [ 20 ]. Additionally, social and economic factors, such as low socio-economic status and limited literacy, contribute to the risk of TB [ 21 , 22 ]. Age is another important risk factor, with TB prevalence rates increasing significantly beyond the age of 65 [ 23 , 24 ]. Besides, the incidence of TB is substantially higher in males compared to females [ 25 , 26 , 27 ]. Tuberculosis is also a social disease with medical aspects, it is closely related to the social factors of a country or region [ 28 , 29 ].

Understanding the spatial distribution characteristics of TB incidence and the associated risk factors is essential for effective prevention and control strategies [ 30 ]. Spatial analysis can optimize resource allocation and aid in early diagnosis, transmission reduction. Consequently, this study aims to spatially investigate the global, regional and national trends in Tuberculosis incidence and the key underlying risk factors over time, thus from 2000 to 2021. This will contribute to the implementation of evidence-based and targeted tuberculosis prevention and control measures by policymakers in different countries and regions, thus assisting in achieving the global goal of ending the TB epidemic.

Data source

This study used secondary data for analysis. The quantitative data were obtained from World Bank Open Data. The data have been collected and compiled by the World Bank from its original sources. In order to make the study as comprehensive and complete as possible, we chose to analyze data from all years and all countries and regions included in the database, encompassing incidence of TB and TB risk factor data from 209 major countries and regions worldwide spanning the years 2000 to 2021 [ 10 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. The incidence of TB and different tuberculosis risk factor data have different original sources and range of data values, as indicated in Table  1 . The geographical data used in this study was obtained from Natural Earth, we used geographic location information and boundary demarcation information for countries and regions from their large-scale world map data for spatial analysis.

Data standardization

In this study, we compared the magnitude of the regression coefficients to directly determine the priority of risk factors, but the range of values of each independent variable involved in this study varies widely, in order to avoid the regression coefficients will be affected by the scale of the values of each independent variable, it is necessary to do the data standardization. Since this study is based on real-world data, we wanted to preserve as much of the relationship between the original data values as possible, and in addition, since all of the data are known and no new values will be added, we chose to use the max-min normalization method for data standardization. This method facilitates the comparison of indicators of different units or magnitudes. The formula is shown below, \({x}_{caled}\) represents the normalized value, x represents the initial value.

Missing data management

For missing data within countries and regions, we use the nearest neighbor interpolation for data that are unevenly distributed and have no obvious linear relationship, and we use linear interpolation for data that are uniformly distributed and have smoother changes. For interpolation of missing data between countries and regions, we use the inverse distance interpolation weighted (IDW) method. This method interpolates on the assumption that each location has a local impact that declines with distance. It gives more weight to the nearest point to the projected position, and the weight diminishes with distance [ 38 ]. It can model the spatial variation of variable data and this interpolation method is widely used in spatial data interpolation in fields such as public health and epidemiology.

Data analysis

We used ArcGIS Pro and GeoDa for spatial statistical analysis and utilized RStudio 4.2.2 for regression analysis. We first cleaned and collated the data, then, spatial correlation analysis was conducted using Moran’s I index and regression analysis was used to identify the risk factors for TB by comparing regression coefficients, and finally, ARIMA models were used to predict future trends.

Moran’s I was used for the spatial analysis and it is a way of measuring spatial autocorrelation. Specifically, the global and local Moran’s I were utilized to investigate the spatial distribution characteristics of TB incidence rates. Simply said, it is a method of quantifying how tightly values are grouped together in a 2-D space [ 39 ]. Moran’s I is equivalent to the Pearson correlation coefficient in 2-D space [ 40 ]. Local Moran’s I provides for a more precise categorization of geographical clusters into four types. The value for Moran’s I can range from − 1 to 1. Positive values of Moran’s I means that neighboring areas tend to have similar values for the variable being analyzed. Negative values of Moran’s I means that surrounding areas tend to have dissimilar values for the variable being analyzed. A value of zero for Moran’s I means that the values of the variable are randomly distributed across space. The Moran’s I is often used in spatial epidemiological studies, where spatial autocorrelation between the number of diseases may reflect the true correlation between cases due to infection [ 41 ].

In this study, multiple stepwise linear regression was employed to examine the main risk factors of TB in various countries and regions. Multiple stepwise linear regression is used to minimize the AIC value as a criterion for the inclusion and exclusion of variables, avoiding over-complexity and over-fitting of the model and multicollinearity, thus improving the interpretability of the model and creating a model that simulates the real-world situation as closely as possible. We used the stepAIC function from RStudio’s MASS package for the regression analysis, and we determined the main risk factors of TB in different regions by comparing the absolute values of the regression coefficients.

ARIMA model was used to forecast future trends. ARIMA (p, d, q) is a popular time series analysis model that combines three components: autoregression (AR), differencing (I), and moving average (MA). The ‘auto.arima’ function in R is a popular tool for selecting the appropriate ARIMA model for a given time series dataset. It uses an algorithm that searches through different combinations of p, d, and q to find the best fit model based on evaluation criteria such as unit root tests, minimax AICc (Akaike Information Criterion corrected), and maximum likelihood estimation (MLE). The function can automatically determine the optimal values for p, d, and q based on these evaluation criteria [ 42 ].

Descriptive results

As evidenced in Fig.  1 , the incidence of TB in 2021 was high in Africa, especially in Central and East Africa. In Southeast Asia and South Asia, the incidence of TB was also relatively high. The countries and regions with lower incidence of TB were mainly concentrated in North America and Europe.

Global distribution of TB incidence in 2021

The regional grouping in Fig.  2 is based on the region grouping used in the United Nations 2019 Sustainable Development Goals Indicators Report [ 43 ]. From 2000 to 2021, except for Oceania, the incidence of TB exhibited a downward trend in all other areas. The greatest substantial drop was observed in sub-Saharan North Africa, while the decline in Latin America and the Caribbean was the least pronounced, and there was even a slight rebound from 2016 to 2021. TB incidence in sub-Saharan Africa and Central and South Asia has been at a high level, well above the world average, and the East and Southeast Asia TB incidence rates are closer to the world average and have similar trends (Correlation coefficient = 0.96, P < 0.05). TB incidence in Oceania, North Africa and West Asia, Europe and North America, and Latin America and the Caribbean is at a lower level compared to the world average.

Global and region category trends in incidence of TB (2000–2021)

Income levels are categorized according to the “World Bank country classifications by income level: 2022–2023”, with Gross National Income (GNI) per capita less than 1085 USD as low income, GNI per capita greater than 1086 USD and less than 13,205 USD as middle income, and GNI per capita greater than 13,205 USD as high income. GNI per capita greater than 1086USD and less than 13205USD is classified as middle income and GNI per capita greater than 13205USD is classified as high income [ 44 ]. We can find that from 2000 to 2021, the incidence of TB in middle- and low-income countries and regions was significantly higher than that in high-income countries and regions, and also higher than the world’s average. However, the incidence of TB in middle- and low-income countries and regions showed a significant downward trend, while the incidence in high-income countries and regions did not show a significant downward trend, as shown in Fig.  3 .

Global and income category trends in incidence of TB (2000–2021)

Spatial analysis results

Smaller values of K-NN (K-Nearest Neighbors) will make the model more sensitive and larger values of K-NN may lead to underfitting. The stability of the spatial autocorrelation results can be assessed by comparing Moran’s I at different K-NN values. If two different K-NN values give similar results, then the result can be considered stable. Therefore, we chose a smaller value (K-NN = 4) and a larger value (KNN = 7) as the spatial weights to compute Moran’s I separately to analyze the characteristics of the spatial distribution of TB incidence in 2021. When the spatial weight is set to the K-NN (K-Nearest Neighbors) method with 7 neighbors, the Global Moran’s I is 0.463, representing the existence of spatial autocorrelation. After 999 Monte Carlo simulations, the P-value is 0.000 and the Z-value is 14.028, indicating at least 99.9% confidence level. When the spatial weight is set to the K-NN method with 4 neighbors, the Global Moran’s I index is 0.432. After 999 Monte Carlo simulations, the P-value is 0.000 and the Z-value is 9.846, indicating a 99.9% confidence level, as shown in Fig.  4 .

Global Moran’s I of TB incidence in 2021

The above results further demonstrate the significant spatial autocorrelation of TB incidence in 2021. In addition, from 2000 to 2021, there is a general downward trend in the Moran’s I of global TB incidence, as shown in Fig.  5 .

Global Moran’s I of TB incidence from 2000 to 2021

Using 2021 TB incidence data to analyse the local Moran’s I, when the spatial weight is set to the K-NN method with 7 neighbors, local Moran’s I analysis revealed significant spatial clustering of TB incidence in most areas (P ≤ 0.05) as shown in Fig.  6 .

Significance map of local Moran’s I of TB incidence in 2021

Most areas in North America and Europe were categorized as low-low clusters. Low-low clusters zone indicates that the incidence of TB is low both in the region and in the areas adjacent to it. Most areas in Central Africa, South Africa and East Africa, Southeast Asia, and South Asia were classified as high-high clusters, high-high clusters area indicates a high incidence of TB in both the area and neighbouring areas. Most areas in East Asia and Oceania were identified as low-high clusters, low-high clusters indicate a low incidence of TB in the area and a high incidence in its immediate area. Haiti is a high-low cluster area, with the high-low cluster indicating a high incidence of TB in the area and a low incidence in the area adjacent to it. It is also known that most countries and regions fall into the low-low cluster region, followed by those in high-high cluster region, with only one country falling into the high-low cluster region (Haiti), as shown in Fig.  7 .

Cluster map of local Moran’s I of TB incidence in 2021

Regression analysis results

Figure  8 shows the leading risk factors for TB incidence in each country and region by comparing the regression coefficients from multiple linear regression analysis. The leading risk factor affecting the largest number of countries and regions is literacy rate, followed by the proportion of the male population, while PM 2.5 air pollution and prevalence of undernourishment were the leading risk factors for TB affecting the least number of countries and regions. In South Africa and Central Africa, the leading risk factors for TB incidence were mainly the population ages 65 and above and the population of males. In Europe (including Russia) and North America, the leading risk factor for TB incidence was mainly the literacy rate. In other areas, the leading risk factors vary considerably among neighbouring countries and regions. An additional table shows regression coefficient in more detail [see Additional file 1].

Analysis of the leading risk factors for TB

Figure  9 shows the second leading risk factor for TB incidence in each country and region by comparing the regression coefficients from multiple linear regression analysis. The second leading risk factor for TB incidence affecting the largest number of countries and territories was the prevalence of diabetes, followed by the population ages over 65 and above. The population of males and GDP per capita were the second leading risk factors that affect the incidence of TB in the fewest countries and regions. Overall, even in neighbouring countries and regions, there are relatively large differences in the second leading risk factor for TB incidence.

Analysis of the second leading risk factors for TB

Figure  10 shows the third leading risk factor for TB incidence in each country and region by comparing the regression coefficients from multiple linear regression analysis. The third leading risk factor for TB incidence affecting the largest number of countries and territories was PM2.5 air pollution, followed by the prevalence of undernourishment and poverty headcount ratio. The population of males was the third leading risk factor that affected the incidence of TB in the fewest countries and regions.

Analysis of the third leading risk factors for TB

Of the three leading risk factors for each country and region globally, the most frequent risk factor is literacy rate, followed by diabetes prevalence and population ages 65 and above, and the least frequent risk factor is the prevalence of undernourishment, as shown in Fig.  11 .

Analysis of the main risk factors for TB

ARIMA model prediction results

According to the projections, the incidence of TB will decrease to varying degrees in 2030 compared to 2015 in all regions except Oceania, Latin America and the Caribbean, north Africa and west Asia showed the largest decline of 50%. However, the WHO Stop TB Strategy sets targets for 2030 of 80% decrease in TB incidence compared to 2015, but projections from the ARIMA model suggest that this target will be difficult to achieve in any region, as shown in Fig.  12 .

Projections of global and region category in TB incidence to 2030

According to the projections, the incidence of TB in 2030 will decrease to varying degrees compared to 2015 in all regions by income except middle-income countries and regions, high income countries and regions showed the largest decline of 71%. However, groups of countries and regions at any income level are still unlikely to meet the targets set by the WHO End TB Strategy of reducing TB incidence by 80% in 2030, as shown in Fig.  13 .

Projections of Gl obal and income category in TB incidence to 2030

The prime focus of this study was to investigate the global trend of TB incidence and underlying risk factors between 2000 and 2021. This study found that there was a trend of clustering in the spatial distribution of tuberculosis incidence, but that the trend of clustering was decreasing from year to year. There are differences in TB incidence across countries and regions with different income levels, this finding is in line with previous research, which has revealed that a vast majority of individuals with TB are concentrated in low- and middle-income countries (LMICs), underscoring the strong association between TB and poverty, as well as other socioeconomic risk factors [ 45 ]. This may also be linked to the truth that people in LMICs do not have enough money to choose better health care services. The most important finding of this study is that the risk factors for tuberculosis vary across countries and regions, with literacy rate being the risk factor with the relatively widest and deepest impact. Based on the results of the projections, with the present trends, the World Health Organization’s goal of ending the tuberculosis pandemic by 2030 is unlikely to be achieved, which is worthy of our attention.

Studies have shown that there is a higher incidence of TB not being diagnosed in a timely manner in LMICs, as a significant number of TB patients in LMICs seek primary treatment from private medical institutions, drug suppliers and lower-level public health institutions that do not have access to TB diagnostic services, which contributes to the this has led to the further spread of the epidemic [ 46 , 47 ]. At the same time, health care professionals in LMICs are at an increased risk of TB infection, which can contribute to the epidemic’s spread [ 48 , 49 ]. However, although the incidence of TB in LMICs is at a higher level compared to high-income countries, it is also generally declining, suggesting that previous TB control strategies in LMICs have been effective.

This study found a high geographic spatial autocorrelation in the spatial distribution of TB incidence even though the incidence was declining, suggesting that TB patients may be more clustered in specific areas. In previous studies, Africa (particularly sub-Saharan Africa) and Southeast Asia have generally been considered to be the regions with the most severe TB epidemics [ 13 ]. In this study, we can see that in addition to the high incidence areas in Africa and Southeast Asia, most of South Asia and Mongolia in East Asia are also areas of high TB incidence, which is a slight departure from the previous view. These countries and regions not only have a high TB burden of their own, but also tend to spread to surrounding countries or regions and should be given priority attention. On the other hand, significant geographical inequalities in TB incidence have been observed, with the majority of countries with high concentrations being low- and middle-income countries, and economic polarization contributing to clustering of TB incidence. Studies have shown that one-third of people diagnosed with TB in the Republic of South Africa do not start treatment or are not informed of their disease, while the rising proportion of extensively drug-resistant TB may also be contributing to the spread of the TB epidemic in South Africa [ 50 , 51 ]. In addition, the number of cases of TB in South Asia is staggering yet has been under-appreciated. With a large, chaotic and unregulated private health sector, South Asia is also vulnerable to natural disasters and political disruption. When disaster inevitably strikes, emergency measures can greatly reduce the opportunistic spread of diseases such as TB [ 52 ].

In contrast, 19 countries in low-high cluster areas, which are surrounded by high prevalence areas but have managed to maintain low prevalence rates themselves, have TB control strategies that deserve further study and replication. In previous studies, Sudan has often been considered a country with a high TB burden [ 53 ]. However, this study shows that Sudan is in a low-high cluster area with a lower incidence of TB than the surrounding countries, suggesting that its TB control strategies are achieving some success. Of course, it is also argued that this is due to a lack of data management and higher levels of surveillance due to the conflict that has erupted in Sudan in recent years [ 54 ]. Rwanda, a low-high cluster country in Africa, on the other hand, has an effective TB surveillance system with precise, comprehensive, and both inside and outside consistent data that provides an excellent summary of the country, and TB control strategies developed through this system have been effective in decreasing the incidence of TB in Rwanda [ 55 ].

It is worth noting that although the incidence of TB in Oceania is still below the world average, and Fiji and Vanuatu are among the low-high cluster areas, the growing trend in the overall incidence of TB in Oceania is not negligible and requires attention [ 56 ]. The low-low cluster areas are mostly high-income countries and territories in Europe and the USA, which are most likely to be the first to achieve the 2030 complete elimination plan but should also be aware of the TB risks associated with migration from high-incidence countries [ 57 ].

Countries and regions with identical leading risk factors, comparable cultural affinities, and geographic proximity may opt for analogous TB control strategies. It is noteworthy that several neighboring countries in Central Africa have a population ages 65 and above as the leading risk factor for TB. This phenomenon can be attributed to the fact that although the degree of population aging in Africa is relatively low, this is due to a comparatively young age structure, high rates of fertility and death in the African population. However, elderly individuals in the African region generally experience a higher burden of chronic diseases and infectious diseases due to healthcare conditions, nutritional status, and other factors. This view is supported by relevant research, which indicates that immune function decreases with age, and the disease burden of elderly individuals in Africa is increasing [ 58 ].

In high-income and low TB incidence areas such as Europe and North America, low literacy rates have become a leading risk factor for TB incidence [ 59 ]. Low literacy rates may lead to low health literacy, which is detrimental to public access to health education. Europe and the United States have received a large influx of immigrants, whose health literacy is relatively poor, and who have also brought new burdens of TB [ 60 , 61 ]. Therefore, it is necessary to implement TB interventions targeted at immigrants [ 62 , 63 , 64 , 65 , 66 ].

From the perspective of the second leading risk factor, there is low similarity among neighboring countries and regions. However, countries that share the same leading risk factor and second leading risk factors can be regarded as homogeneous countries and adopt similar TB prevention and control measures. Considering the third leading risk factor, our study found that many countries and regions have multiple risk factors, especially those with a high incidence of TB, such as Africa and Southeast Asia. This suggests that the incidence of TB in these areas is a complex issue, influenced not only by a single factor but by multiple factors.

In these areas with a high incidence of TB, comprehensive interventions need to be developed, including improving people’s health literacy, improving the living environment and strengthening health services. In particular, interventions targeting these risk factors are necessary [ 67 ]. For example, for areas with a high proportion of men, we need to strengthen health promotion and education for men and encourage them to undergo health screening and preventive measures [ 68 ]. There is the need for regular screening and treatment in the community and appropriate medical services and support. In addition, we need to further strengthen disease surveillance and data collection in order to better understand and control the spread of TB in different areas and groups.

This study demonstrates that low literacy rates are one of the most common risk factors for the occurrence of TB. Literacy rates are closely related to education level. There is a significant link between education and health, and low levels of education may exacerbate health problems [ 69 , 70 ]. Therefore, enhancing education plays an important role in improving public health and preventing TB transmission. Governments should increase investment in education and health to raise the standard of public health and decrease the occurrence and spread of TB [ 71 ]. The study also found that diabetes prevalence is the second most common risk factor for the occurrence of TB. Previous research has shown that diabetes increases susceptibility to TB [ 72 ].

In addition, the study also points out that the 65 years and older age group is the third most common risk factor for the occurrence of TB. This is because the immune system of older people declines and their body’s resistance weakens, making them more susceptible to various diseases. Furthermore, aged persons are more likely to have other chronic conditions, which raises the chance of TB infection [ 73 , 74 ]. Therefore, the elderly should also pay attention to TB prevention, exercise regularly, maintain a healthy diet, and improve their body’s immunity. These risk factors have a widespread impact and should receive more attention from relevant authorities.

Limitations of the study

This study has some limitations that should be acknowledged. Firstly, the data used in the study are not exhaustive, and several risk factors were not included in the analysis. Moreover, some of the data used in this study have a high number of missing values, which could potentially bias the results. In addition, although the data used in this study are from official sources and we believe that they reflect the real situation, some countries may also have some errors in their official data due to their more backward level of development, which may have an impact on the results. Additionally, it is important to note that TB incidence rates often exhibit spatial variations within countries and regions, with notable differences between rural and urban areas. To obtain more comprehensive and accurate results, it is essential to incorporate more detailed data that capture these spatially aggregated trends.

Furthermore, it should be recognized that TB in many countries and regions is influenced by a multitude of risk factors, and these factors can interact with each other, potentially leading to complexities and inaccuracies in the results. Future studies should aim to address this issue by utilizing more comprehensive data sets and further reducing the effect of multicollinearity.

This study indicates that the literacy rate exhibits the most substantial influence on TB compared to other risk factors, impacting a significant number of countries and regions. Additionally, the literacy rate consistently emerges as one of the leading risk factors across all countries and regions. Notably, there exists substantial heterogeneity in the leading risk factors among different countries and regions. The findings of this study significantly contribute to our understanding of the global burden of TB and the spatial distribution patterns, breaking them down at national and regional levels. The model developed in this study holds the potential to assist policymakers in devising tailored interventions that are locally appropriate, thereby facilitating a more effective reduction in TB incidence and associated risk factors. Ultimately, such interventions can contribute to achieving the 2030 goal of ending the TB epidemic.

Data availability

The datasets supporting the conclusions of this article are available in the World Bank Open Data, https://data.worldbank.org/indicator .

Abbreviations

• Tuberculosis

Gross National Income

Inverse Distance Weighted

Moran’s Index

K-Nearest Neighbors

Akaike information criterion

Autoregressive Integrated Moving Average

Maximum Likelihood Estimation

Low- and Middle-Income Country

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• Published: 01 May 2023

Transforming tuberculosis diagnosis

• Madhukar Pai   ORCID: orcid.org/0000-0003-3667-4536 1 ,
• Puneet K. Dewan 2 &
• Soumya Swaminathan 3  

Nature Microbiology volume  8 ,  pages 756–759 ( 2023 ) Cite this article

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• Molecular biology
• Tuberculosis

Diagnosis is the weakest aspect of tuberculosis (TB) care and control. We describe seven critical transitions that can close the massive TB diagnostic gap and enable TB programmes worldwide to recover from the pandemic setbacks.

Introduction

Tuberculosis (TB) was the leading infectious killer of humankind, until SARS-CoV-2 emerged. In 2021, TB killed an estimated 1.6 million people, with most deaths occurring in low- and middle-income countries (LMICs) 1 . If detected early, screened for drug resistance, and fully treated with appropriate short-course regimens, TB can be cured. But globally, diagnosis is the weakest link in the TB cascade or continuum of care, with only one in two people with drug-sensitive TB completing all the steps of the care cascade 2 .

Before the COVID-19 pandemic, an estimated 10 million people fell ill with TB in 2019. Of this, 7.1 million people were diagnosed and notified, leaving a diagnostic gap of 2.9 million. The pandemic had a devastating effect on TB services, with the diagnostic gap swelling to 4.2 million 3 . In 2021, of the estimated 10.6 million people who developed TB, only 6.4 million people were diagnosed and notified to national TB programmes worldwide 1 . Also, for the first time in more than a decade, both the estimated incidence of TB and mortality owing to TB increased 1 . A reduction in TB case detection, increased transmission and worsening poverty are likely explanations 1 .

Although shorter drug regimens are now available to treat all forms of TB 4 , none of these regimens are likely to realize their potential public health impact until TB diagnosis is improved. Simply put, if we cannot find TB, we cannot treat TB. And if we cannot treat TB, we cannot end TB.

In this Comment, we identify seven crucial transitions (Fig. 1 ) that we argue are needed to transform how we diagnose TB. We believe that the time is opportune to action the opportunities created by the COVID-19 pandemic 5 and to call on the global TB community to make these transitions with urgency.

NAATs, nucleic acid amplification tests; HIC, high-income country.

Molecular testing must replace smear microscopy

For decades, TB programmes have relied on sputum smear microscopy as the frontline test. Microscopy has many limitations, including low sensitivity and an inability to detect drug resistance. Microscopy underperforms in people co-infected with TB and human immunodeficiency virus (HIV), in children, and in people with extrapulmonary disease. Microscopy requires complex systems of quality assurance to maintain performance. Further, the success of microscopy is operator dependent, and relies on high-quality specimens.

In contrast to sputum smear microscopy, molecular testing is more accurate, can reduce delays in diagnosis and can detect drug resistance. Rapid, decentralized molecular testing combined with implementation support to address barriers to delivery results in about 50% more patients receiving a diagnosis, with only a modest increase in per-test costs 6 .

Although the World Health Organization (WHO) recommends molecular diagnostics as the preferred frontline testing option, only 38% of all notified cases in 2021 were tested with a WHO-recommended rapid molecular diagnostic at initial diagnosis 1 , 7 . Furthermore, only 63% of all notified TB cases were bacteriologically confirmed by any method 1 , 7 . Of this group of people with bacteriologically confirmed TB, only 70% were tested for rifampicin resistance 1 .

It is crucial to phase out sputum smear microscopy and replace it with WHO-approved molecular diagnostics as the initial diagnostic. This would not only increase the sensitivity of TB diagnosis, but also widen access to drug-resistance testing, and reduce the risk of amplifying drug-resistant TB strains. With efficacious, safe, six-month, all-oral regimens now available for rifampicin-resistant TB 4 , no one should suffer or die from undiagnosed drug-resistant TB.

How can countries make the switch from microscopy to molecular diagnostics? During the COVID-19 pandemic, countries expanded molecular testing capacity to unprecedented levels 5 . This infrastructure should now be repurposed to diagnose TB and other infectious diseases.

In 2023, WHO released a WHO Standard: Universal Access to TB Diagnostics roadmap, which recommended that “in all facilities in all districts, the TB diagnostic algorithm requires the use of a WHO-recommended diagnostic as the initial diagnostic test for all patients with presumed TB, including people living with HIV, children and individuals with extrapulmonary TB” 7 . To assist countries in meeting this standard, the WHO roadmap offers enablers, solutions and benchmarks that can be used to assess progress.

Decentralized, point-of-care testing must complement centralized, lab-based testing

Research has shown that people with TB navigate long care-seeking pathways, with multiple visits to health providers before a diagnosis is made. Mystery client (standardized patient) studies in several countries have reported that primary care providers are reluctant to order microbiological tests during initial consultations 8 .

In the absence of simple, point-of-care (POC) testing, primary care providers prefer to empirically manage people with broad-spectrum antibiotics and other non-specific therapies that are more easily available and that help offer immediate relief of symptoms. Decentralized POC tests would enable diagnosis and therefore treatment decisions to be made in the first patient consultation.

Currently, centralized testing results in long turnaround times, and losses to follow-up during the cascade of care. However, the advantages that centralized testing brings in terms of existing infrastructure, cost and high-throughput, means that these tests are likely to be needed to expand drug-resistance testing and systematic screening efforts. Centralized testing may also be useful in urban areas with large test volumes, where turnaround time can be minimized. Given the complementary value of both POC and centralized testing, countries will need to rely on a mix of testing solutions to expand the reach of testing services and access for patients.

How can countries introduce decentralized POC tests? The COVID-19 pandemic resulted in substantial innovations in miniaturized, simplified, low maintenance molecular platforms that can be decentralized and used in primary health clinics or homes, for example, single-use, disposable, molecular self-tests for SARS-CoV-2 and influenza 5 . Efforts are underway to evaluate such POC technologies for TB, especially in combination with non-sputum samples that may be more convenient for patients and providers, as described later.

Multi-disease testing must replace single-disease testing

Even as countries strengthen pandemic preparedness, they must focus on achieving the Sustainable Development Goal of universal health coverage (UHC) by 2030. Universal health coverage requires countries to invest in a package of essential services, including diagnostics, at every level of the healthcare system 9 . This will require a rethink of the usual strategy of separate tests for separate diseases, and siloed testing programmes.

Single-disease testing has limitations because people present with symptoms and syndromes, not diseases. Ruling out one disease does not enable treatment of a patient with a different disease. In addition, multi-morbidity is common. Multi-disease testing offers a solution to this problem.

As molecular testing can detect multiple infectious diseases, implementation of molecular testing could enable a diverse range of tests to be carried out in tandem, for example, TB, HIV viral load, SARS-CoV-2, sexually transmitted infections, respiratory syncytial virus, influenza and human papilloma virus, to name a few. Indeed, reports have highlighted the value of integrating TB and HIV testing 10 , and TB and SARS-CoV-2 testing 11 .

In order to support countries in making a transition to multi-disease testing, WHO has developed an essential diagnostics list, and has encouraged countries to develop their own national essential diagnostics lists, which could inform UHC benefits packages. India and Nigeria have already developed national essential diagnostics lists, and multi-disease testing offers healthcare providers and policymakers in these countries a way to deliver the package of essential tests.

Some countries are investing in diagnostic network optimization as a way of consolidating, integrating and optimizing laboratory services across disease areas, and across the health system 12 . Organizations such as WHO, Unitaid and Global Fund have all promoted multi-disease testing as a way of expanding access to testing and optimizing resources.

Simple samples must complement or replace sputum samples

The crucial importance of simple, easy-to-collect samples to expanded testing and case finding was brought home by the COVID-19 pandemic. When nasopharyngeal swabs, which required skilled healthcare workers to collect, were expanded to include anterior nasal swabs, saliva and self-collection, COVID-19 testing coverage was massively increased.

When samples are easy to obtain, providers are more likely to order diagnostic tests. As previously discussed, primary care providers empirically manage people with classic TB symptoms, rather than order microbiological sputum testing 8 . To change this behaviour, healthcare providers and patients must be offered more convenient testing options.

Even when a test is ordered, a sizeable fraction of people with suspected TB cannot produce sputum. Sputum collection is especially difficult in young children and people living with HIV. Thus, TB testing must move beyond sputum, to easier-to-collect, non-sputum specimens including tongue swabs, urine or bioaerosols 13 .

Imaging, of course, requires no sputum or any sample. Digital chest X-rays, combined with artificial intelligence-based software for interpretation, are currently WHO-endorsed options. However, X-ray hardware costs remain high, precluding broad adoption of digital chest X-rays at the primary care level. Development of affordable, portable digital X-ray systems could be hugely impactful. Cough and lung sound recordings are being explored as digital biomarkers for TB screening. Validation studies are ongoing to prove whether such markers could be transformed into a clinical TB test 13 .

Easy-to-collect samples would enable TB testing outside of traditional TB clinics, in primary care and community settings where most people seek care. Non-sputum-based tests could also help to detect subclinical disease, which is defined as microbiologically confirmed disease in individuals not reporting symptoms 14 . Non-sputum specimens such as oral swabs and urine might be more amenable to simpler specimen processing with fewer steps and without additional equipment, bypassing the complexities and costs of sputum processing for input into molecular assays. Easier specimen processing is one key reason why SARS-CoV-2 testing could be scaled up. Certainly, the use of specimens with simpler sample processing needs could reduce cost of molecular assays.

How can countries make this transition to non-sputum samples? The onus is on product developers to bring low-cost, non-sputum-based assays into validation trials. Thankfully, this is already happening, and can be accelerated by funders and product development partnerships.

Among non-sputum samples, tongue swabs seem most promising, but evidence is not yet available to support WHO guidance. Coordinated, multi-centric validation trials are urgently needed to generate evidence for policy development. For other samples, including high-sensitivity urine antigen tests, artificial intelligence-enabled cough and lung sound algorithms and bioaerosol sampling, continued research and development and funding support are essential 13 .

While we wait for WHO guidance, countries could start planning for simpler samples and decentralized testing. When non-sputum tests become available, countries will then be poised to reallocate funding to diverse testing strategies that replace existing diagnostic algorithms with better alternatives.

Active case finding must complement health facility-based, passive case finding

Tuberculosis testing today mainly relies on passive case finding among symptomatic people seeking care in health facilities. This approach misses a sizeable proportion of symptomatic people who do not seek care, and those with atypical symptoms. Global prevalence surveys report that half of sputum positive TB cases are asymptomatic. Subclinical TB is poorly characterized but may account for a meaningful proportion of TB transmission 14 . In order to detect undiagnosed symptomatic TB, and target subclinical TB, active case-finding approaches are required 15 .

How can countries make this transition? The WHO has released guidance on systematic screening 15 , and while these guidelines are expensive and challenging to implement in LMICs, there are efforts underway to identify new active case-finding modalities. For example, trials have reported promising results for intensified case finding in health facilities and community-wide screening strategies 15 . What is less clear is which of these screening methods is the most cost effective. During the COVID-19 pandemic, countries adopted many approaches to enable testing closer to where people live and work 5 . We need to explore similar approaches for TB, and reach people in both public and private healthcare sectors.

Policies must account for population diagnostic yield in addition to test accuracy

Much of the evidence base for TB diagnostics policy is focused on test accuracy, that is, sensitivity and specificity. While accuracy is critical, population coverage and yield also matter, especially when public health goals are considered. Testing more patients, especially at an earlier stage of the disease, is likely to yield more cases even if a test is only moderately sensitive.

For context, although rapid syphilis tests are less sensitive than conventional laboratory assays, they allowed maternal health programmes to massively increase population coverage of syphilis testing and treatment in pregnant mothers and newborns. Similarly, although COVID-19 antigen tests are less sensitive than molecular tests, rapid tests empowered citizens to test themselves at an unprecedented scale. In short, a test that is less sensitive can nonetheless be very useful if it can reach a much larger population.

How can this transition happen? Guideline development groups must consider evidence of population yield and public health impact, in addition to test accuracy. Pragmatic trials and implementation science are needed to measure population yield, once accuracy is established. Countries need to balance the public health imperative for case finding with the clinical imperative of diagnostic accuracy. When clinical suspicion remains high, more-sensitive diagnostic methods or empiric treatment must remain as secondary test options.

Affordable tests must replace expensive tests

The COVID-19 pandemic demonstrated that countries that had vaccine manufacturing capacity ended up with higher vaccine coverage. Diversified manufacturing is considered crucial for pandemic preparedness and response. Diversified manufacturing is relevant to diagnostics as well. Only 35% of COVID-19 tests worldwide were used in LMICs 5 . Lack of manufacturing and regulatory capacity in LMICs was one issue, but the main problem was that supply of tests and reagents were diverted to the highest payer.

Despite more than a decade of use and billions of dollars of public and philanthropic investments, products such as Xpert MTB/RIF (GeneXpert) continue to be overpriced and difficult to access and maintain in LMICs. More affordable options at higher volumes are needed, and increased competition is essential to get there.

How can tests become more affordable? Technology transfer and diversification in diagnostics manufacturing could make LMICs less reliant on donations and increase self-sufficiency 5 . There are several promising Asian-origin diagnostic platforms, with two Indian products included in WHO guidelines.

Diversified manufacturing could help to overcome monopolies, and transition from high-cost, low-volume products made in high-income countries, to more affordable, lower-cost, higher-volume products made in LMICs. Such a transition with generic anti-TB and antiretroviral drugs underpinned making treatments affordable and is required now for diagnostics. Just as affordable polymerase chain reaction kits by diverse manufacturers helped scale-up testing for SARS-CoV-2 in many countries, we need such affordable molecular testing kits for detection of Mycobacterium tuberculosis , made by diverse companies in many countries, especially LMICs with high TB burden. Reliance on premium-priced products from rich nations is not a sustainable strategy for countries with high TB burden.

Implementing all seven transitions will transform TB diagnosis

The massive gap in TB detection, made worse during the pandemic, has already cost lives, worsened transmission, and derailed years of progress in TB care and control. The seven transitions we describe could be truly transformative. They could close the diagnostic gap, and diagnose more people thereby enabling TB treatment, which would in turn reduce spread of TB in the community.

These transitions are inter-linked, and the biggest impact will come from their integration. A simple, non-sputum sample, combined with an affordable, multi-disease POC molecular technology, deployed in decentralized settings would reach a much larger population, close the case detection gap, and curb TB transmission at the population level. With political attention, resources and opportunities unlocked by the pandemic preparedness and response and UHC agendas, we believe the time has arrived to make these transitions.

Our ability to end the TB epidemic depends on it.

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McGill International TB Centre & McGill School of Population and Global Health, McGill University, Montreal, Quebec, Canada

• Madhukar Pai

Bill & Melinda Gates Foundation, Seattle, WA, USA

Puneet K. Dewan

MS Swaminathan Research Foundation, Chennai, India

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M.P. wrote the initial draft, and P.K.D. and S.S. revised the Comment. M.P. holds a Canada Research Chair award from the Canadian Institutes of Health Research.

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Correspondence to Madhukar Pai .

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The authors have no financial or industry-related conflicts. M.P. serves as an advisor to non-profits, namely the World Health Organization, Stop TB Partnership, Bill & Melinda Gates Foundation, and Foundation for Innovative New Diagnostics. P.K.D. is a Senior Program Officer with the Bill & Melinda Gates Foundation that has awarded grants to support new diagnostics. S.S. was previously Chief Scientist, World Health Organization.

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Pai, M., Dewan, P.K. & Swaminathan, S. Transforming tuberculosis diagnosis. Nat Microbiol 8 , 756–759 (2023). https://doi.org/10.1038/s41564-023-01365-3

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Tuberculosis: An Update on Pathophysiology, Molecular Mechanisms of Drug Resistance, Newer Anti-TB Drugs, Treatment Regimens and Host- Directed Therapies

Affiliations.

• 1 Pratiksha Institute of Pharmaceutical Sciences, Chandrapur Road, Panikhaiti, Guwahati-26, Assam, India.
• 2 Department of Pharmaceutical Sciences, Faculty of Pharmacy, Philadelphia University, PO Box 1, Amman 19392, Jordan.
• 3 Department of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
• 4 Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan.
• 5 Drug Discovery and Development Centre (H3D), University of Cape Town, Rondebosch, 7701, South Africa.
• 6 Neuroscience and Pain Research Lab, Department of Pharmaceutical Engineering &amp; Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221 005, India.
• 7 Department of Pharmaceutical Chemistry, Shri Vishnu College of Pharmacy, Vishnupur, Bhimavaram - 534 202, West Godavari Dist., Andhra Pradesh, India.
• PMID: 33319660
• DOI: 10.2174/1568026621999201211200447

Human tuberculosis (TB) is primarily caused by Mycobacterium tuberculosis (Mtb) that inhabits inside and amidst immune cells of the host with adapted physiology to regulate interdependent cellular functions with intact pathogenic potential. The complexity of this disease is attributed to various factors such as the reactivation of latent TB form after prolonged persistence, disease progression specifically in immunocompromised patients, advent of multi- and extensivelydrug resistant (MDR and XDR) Mtb strains, adverse effects of tailor-made regimens, and drug-drug interactions among anti-TB drugs and anti-HIV therapies. Thus, there is a compelling demand for newer anti-TB drugs or regimens to overcome these obstacles. Considerable multifaceted transformations in the current TB methodologies and molecular interventions underpinning hostpathogen interactions and drug resistance mechanisms may assist to overcome the emerging drug resistance. Evidently, recent scientific and clinical advances have revolutionised the diagnosis, prevention, and treatment of all forms of the disease. This review sheds light on the current understanding of the pathogenesis of TB disease, molecular mechanisms of drug-resistance, progress on the development of novel or repurposed anti-TB drugs and regimens, host-directed therapies, with particular emphasis on underlying knowledge gaps and prospective for futuristic TB control programs.

Keywords: Bedaquiline; Delamanid; Drug resistance; Host-directed therapy; MDR-TB; Mycobacterium tuberculosis; Tuberculosis.

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