Finding gaps in TB notifications: spatial analysis of geographical patterns of TB notifications, associations with TB program efforts and social determinants of TB risk in Bangladesh, Nepal and Pakistan

Mapping TB incidence across districts in Uganda to inform health program activities

BACKGROUND

Identifying spatial variation in TB burden can help national TB programs effectively allocate resources to reach and treat all people with TB. However, data limitations pose challenges for subnational TB burden estimation.

METHODS

We developed a small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016-2019. TB incidence was estimated using 1) cluster-level data from the national 2014-2015 TB prevalence survey transformed to incidence, and 2) case notifications adjusted for geospatial covariates of health system access. The case notification completeness surface was fit jointly using observed case notifications and estimated incidence.

RESULTS

Estimated pulmonary TB incidence among adults varied >10-fold across Ugandan districts in 2019. Case detection increased nationwide from 2016 to 2019, and the number of districts with case detection rates >70% quadrupled. District-level estimates of TB incidence were five times more precise than a model using TB prevalence survey data alone.

CONCLUSION

A joint spatial modeling approach provides useful insights for TB program operation, outlining areas where TB incidence estimates are highest and health programs should concentrate their efforts. This approach can be applied in many countries with high TB burden.

Effectiveness of Using AI-Driven Hotspot Mapping for Active Case Finding of Tuberculosis in Southwestern Nigeria

Background: Nigeria is among the top five countries that have the highest gap between people reported as diagnosed and estimated to have developed tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted active case finding (ACF) interventions. Leveraging community-level data together with granular sociodemographic contextual information can unmask local hotspots that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined with open-source high-resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value < 0.001) than the yield in other locations in all four states. Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.

Determinants of intention to conceal tuberculosis status among family members: an analysis of seven Sub-Saharan African countries

BACKGROUND

Tuberculosis (TB) remains a significant public health burden in Sub-Saharan Africa (SSA), accounting for about 25% of global TB cases. In several communities, TB diagnosis, treatment, and control have become a critical challenge, largely due to the intention to conceal TB status among family members. It is therefore crucial to understand the factors associated with the intentions to conceal TB status among family members in SSA.

METHODS

This quantitative study utilised data from the most recent Demographic and Health Surveys (DHS). The objective was to examine the factors associated with the intention to conceal the TB status of family members. The sample consisted of 58,849 individuals aged 10 years or older from seven SSA countries. Binary logistic regression was employed to assess the associations between TB status concealment and various socio-demographic and economic variables.

RESULTS

The overall prevalence of TB status concealment intentions for the seven countries was 28.0% (95% CI: 27.6-28.4). Malawi and Eswatini accounted for the highest (47.3%) and lowest (3.0%) prevalence of TB concealment intentions respectively. TB status concealment intentions decreased with increasing age (p < 0.001). Living in rural areas was associated with lower odds of intending to conceal the TB of family members compared to living in urban areas (aOR = 0.92; p = 0.008). Higher education levels were associated with lower odds of TB status concealment intentions (aOR = 0.50; p < 0.001) compared to lower education levels. As participants wealth index increased, the odds of TB status concealment intentions decreased (aOR = 0.83; p < 0.001). Country of residence also showed significant associations with individuals in Ghana (aOR = 4.51; p < 0.001), Lesotho (aOR = 2.08; p < 0.001), Malawi (aOR = 4.10; p < 0.001), Namibia (aOR = 4.40; p < 0.001), and Sao-Tome and Principe (aOR = 5.56; p < 0.001) showing higher odds of TB status concealment intentions compared to Eswatini.

CONCLUSIONS

The findings conclude that several social determinants of health, including age, urbanicity, education, and wealth contribute to TB status concealment intentions for family members. Considering these factors is important for designing targeted interventions to improve TB control in the sample. In light of the unavailability of cultural variables in the dataset, future research can leverage qualitative approaches to conduct a more comprehensive exploration of the cultural factors linked to TB status concealment intentions in the population.

Spatiotemporal Distribution of Tuberculosis in the Oromia Region of Ethiopia: A Hotspot Analysis

Tuberculosis (TB) is a major public health concern in low- and middle-income countries including Ethiopia. This study aimed to assess the spatiotemporal distribution of TB and identify TB risk factors in Ethiopia's Oromia region. Descriptive and spatiotemporal analyses were conducted. Bayesian spatiotemporal modeling was used to identify covariates that accounted for variability in TB and its spatiotemporal distribution. A total of 206,278 new pulmonary TB cases were reported in the Oromia region between 2018 and 2022, with the lowest annual TB case notification (96.93 per 100,000 population) reported in 2020 (i.e., during the COVID-19 pandemic) and the highest TB case notification (106.19 per 100,000 population) reported in 2019. Substantial spatiotemporal variations in the distribution of notified TB case notifications were observed at zonal and district levels with most of the hotspot areas detected in the northern and southern parts of the region. The spatiotemporal distribution of notified TB incidence was positively associated with different ecological variables including temperature (β = 0.142; 95% credible interval (CrI): 0.070, 0.215), wind speed (β = -0.140; 95% CrI: -0.212, -0.068), health service coverage (β = 0.426; 95% CrI: 0.347, 0.505), and population density (β = 0.491; 95% CrI: 0.390, 0.594). The findings of this study indicated that preventive measures considering socio-demographic and health system factors can be targeted to high-risk areas for effective control of TB in the Oromia region. Further studies are needed to develop effective strategies for reducing the burden of TB in hotspot areas.

Survey to measure the quality of life of patients with tuberculosis in Alexandria, Egypt: a cross-sectional study

BACKGROUND

Assessment of quality of life (QoL) in patients with tuberculosis (TB) may improve healthcare providers' understanding of the disease burden. This study aimed to investigate the QoL of patients with TB in Alexandria, Egypt.

METHODS

This cross-sectional study was conducted in chest clinics and main chest hospitals in Alexandria, Egypt. A structured interview questionnaire was used to collect data from participants through face-to-face interviews from November 20, 2021, until the June 30, 2022. We included all adult patients aged 18 years or above during the intensive or continuation phase of treatment. The World Health Organization (WHO) WHOQOL-BREF instrument was used to measure QoL, which includes the physical, psychological, social relationships, and environmental health domains. Using propensity score matching, a group of TB free population was recruited from the same setting and completed the questionnaire.

RESULTS

A total of 180 patients participated in the study: 74.4% were males, 54.4% were married, 60.0% were 18-40 years old, 83.3% lived in urban areas, 31.7% were illiterate, 69.5% reported insufficient income, and 10.0% had multidrug-resistant TB. The TB-free population group had higher QoL scores than the TB patients' group: (65.0 ± 17.5 vs. 42.4 ± 17.8) for the physical domain, (59.2 ± 13.6 vs. 41.9 ± 15.1) for the psychological domain, (61.8 ± 19.9 vs. 50.3 ± 20.6) for the social domain, (56.3 ± 19.3 vs. 44.5 ± 12.8) for the environment domain, (4.0(3.0-4.0) vs. 3.0(2.0-4.0)) for general health, and (4.0(3.0-4.0) vs. 2.0(2.0-3.0)) for the general QoL, P < 0.0001. Patients with TB aged 18-30 years had the highest environmental score compared with the other age groups (P = 0.021).

CONCLUSIONS

TB had a significant negative impact on QoL, with the physical and psychological domains being the most affected. This finding necessitates strategies to improve QoL of patients with to enhance their compliance to treatment.

A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan

Tuberculosis (TB) is the leading cause of avoidable deaths from an infectious disease globally and a large of number of people who develop TB each year remain undiagnosed. Active case-finding has been recommended by the World Health Organization to bridge the case-detection gap for TB in high burden countries. However, concerns remain regarding their yield and cost-effectiveness. Data from mobile chest X-ray (CXR) supported active case-finding community camps conducted in Karachi, Pakistan from July 2018 to March 2020 was retrospectively analyzed. Frequency analysis was carried out at the camp-level and outcomes of interest for the spatial analyses were mycobacterium TB positivity (MTB+) and X-ray abnormality rates. The Global Moran's I statistic was used to test for spatial autocorrelation for MTB+ and abnormal X-rays within Union Councils (UCs) in Karachi. A total of 1161 (78.1%) camps yielded no MTB+ cases, 246 (16.5%) camps yielded 1 MTB+, 52 (3.5%) camps yielded 2 MTB+ and 27 (1.8%) yielded 3 or more MTB+. A total of 79 (5.3%) camps accounted for 193 (44.0%) of MTB+ cases detected. Statistically significant clustering for MTB positivity (Global Moran's I: 0.09) and abnormal chest X-rays (Global Moran's I: 0.36) rates was identified within UCs in Karachi. Clustering of UCs with high MTB positivity were identified in Karachi West district. Statistically significant spatial variation was identified in yield of bacteriologically positive TB cases and in abnormal CXR through active case-finding in Karachi. Cost-effectiveness of active case-finding programs can be improved by identifying and focusing interventions in hotspots and avoiding locations with no known TB cases reported through routine surveillance.

Geographic accessibility to childhood tuberculosis care in Pakistan

Background

Tuberculosis (TB) in children is difficult to detect and often needs specialists to diagnose; the health system is supposed to refer to higher level of health care when diagnosis is not settled in a sick child. In Pakistan, the primary health care level can usually not diagnose childhood TB and will refer to a paediatricians working at a secondary or tertiary care hospital. We aimed to determine the health services access to child TB services in Pakistan.

Objective

We aimed to determine the geographical access to child TB services in Pakistan.

Method

We used geospatial analysis to calculate the distance from the nearest public health facility to settlements, using qGIS, as well as population living within the World Health Organization’s (WHO) recommended 5-km distance.

Result

At primary health care level, 14.1% of facilities report child TB cases to national tuberculosis program and 74% of the population had geographical access to general primary health care within 5-km radius. To secondary- and tertiary-level health care, 33.5% of the population had geographical access within 5-km radius. The average distance from a facility for diagnosis of childhood TB was 26.3 km from all settlement to the nearest child TB sites. The population of one province (Balochistan) had longer distances to health care services.

Conclusion

With fairly good coverage of primary health care but lower coverage of specialist care for childhood TB, the health system depends heavily on a good referral system from the communities.

Factors Affecting the Transition from Paper to Digital Data Collection for Mobile Tuberculosis Active Case Finding in Low Internet Access Settings in Pakistan

Between September 2020 and March 2021, Mercy Corps piloted hybrid digital (CAPI) and paper-based (PAPI) data collection as part of its tuberculosis (TB) active case finding strategy. Data were collected using CAPI and PAPI at 140 TB chest camps in low Internet access areas of Punjab and Khyber Pakhtunkhwa provinces in Pakistan. PAPI data collection was performed primarily during the camp and entered using a tailor-performed CAPI tool after camps. To assess the feasibility of this hybrid approach, quality of digital records were measured against the paper “gold standard”, and user acceptance was evaluated through focus group discussions. Completeness of digital data varied by indicator, van screening team, and month of implementation: chest camp attendees and pulmonary TB cases showed the highest CAPI/PAPI completeness ratios (1.01 and 0.96 respectively), and among them, all forms of TB diagnosis and treatment initiation were lowest (0.63 and 0.64 respectively). Vans entering CAPI data with high levels of completeness generally did so for all indicators, and significant differences in mean indicator completeness rates between PAPI and CAPI were observed between vans. User feedback suggested that although the CAPI tool required practice to gain proficiency, the technology was appreciated and will be better perceived once double entry in CAPI and PAPI can transition to CAPI only. CAPI data collection enables data to be entered in a more timely fashion in low-Internet-access settings, which will enable more rapid, evidence-based program steering. The current system in which double data entry is conducted to ensure data quality is an added burden for staff with many activities. Transitioning to a fully digital data collection system for TB case finding in low-Internet-access settings requires substantial investments in M&E support, shifts in data reporting accountability, and technology to link records of patients who pass through separate data collection stages during chest camp events.

Finding gaps in routine TB surveillance activities in Bangladesh

BACKGROUND :

TB was the leading cause of death from a single infectious pathogen globally between 2014 and 2019. Fine-scale estimates of TB prevalence and case notifications can be combined to guide priority-setting for strengthening routine surveillance activities in high-burden countries. We produce policy-relevant estimates of the TB epidemic at the second administrative unit in Bangladesh.

METHODS :

We used a Bayesian spatial framework and the cross-sectional National TB Prevalence Survey from 2015–2016 in Bangladesh to estimate prevalence by district. We used case notifications to calculate prevalence-to-notification ratio, a key metric of under-diagnosis and under-reporting.

RESULTS :

TB prevalence rates were highest in the north-eastern districts and ranged from 160 cases per 100,000 (95% uncertainty interval [UI] 80–310) in Jashore to 840 (UI 690–1020) in Sunamganj. Despite moderate prevalence rates, the Rajshahi and Dhaka Divisions presented the highest prevalence-to-notification ratios due to low case notifications. Resolving subnational disparities in case detection could lead to 26,500 additional TB cases (UI 8,500–79,400) notified every year.

CONCLUSION :

This study is the first to produce and map subnational estimates of TB prevalence and prevalence-to-notification ratios, which are essential to target prevention and treatment efforts in high-burden settings. Reaching TB cases currently missing from care will be key to ending the TB epidemic.

TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan

Pakistan's national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010-2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010-2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to subnational disease modelling. The NTP's use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

A role for community-level socioeconomic indicators in targeting tuberculosis screening interventions

Tuberculosis screening programs commonly target areas with high case notification rates. However, this may exacerbate disparities by excluding areas that already face barriers to accessing diagnostic services. We compared historic case notification rates, demographic, and socioeconomic indicators as predictors of neighborhood-level tuberculosis screening yield during a mobile screening program in 74 neighborhoods in Lima, Peru. We used logistic regression and Classification and Regression Tree (CART) analysis to identify predictors of screening yield. During February 7, 2019-February 6, 2020, the program screened 29,619 people and diagnosed 147 tuberculosis cases. Historic case notification rate was not associated with screening yield in any analysis. In regression analysis, screening yield decreased as the percent of vehicle ownership increased (odds ratio [OR]: 0.76 per 10% increase in vehicle ownership; 95% confidence interval [CI]: 0.58-0.99). CART analysis identified the percent of blender ownership (≤ 83.1% vs > 83.1%; OR: 1.7; 95% CI: 1.2-2.6) and the percent of TB patients with a prior tuberculosis episode (> 10.6% vs ≤ 10.6%; OR: 3.6; 95% CI: 1.0-12.7) as optimal predictors of screening yield. Overall, socioeconomic indicators were better predictors of tuberculosis screening yield than historic case notification rates. Considering community-level socioeconomic characteristics could help identify high-yield locations for screening interventions.

Assessing Factors Associated with TB Awareness in Nepal: A National and Subnational Study

Tuberculosis (TB) has still remained a serious global health threat in low- and middle-income countries in recent years. As of 2021, Nepal is one of the high TB burden countries, with an increasing prevalence of cases. This study evaluates factors associated with TB awareness in Nepal. This study uses data from the Nepal Demographic and Health Survey, a cross-sectional survey carried out from June 2016 to January 2017. Multilevel logistic regression is performed to examine the association of demographic and socioeconomic factors with TB awareness. Our findings show a high level of TB awareness in all seven provinces of Nepal. Province 5 has the highest level of awareness (98.1%) among all provinces, followed by provinces 3 and 4, while province 6 has the lowest awareness level (93.2%) compared to others. Socioeconomic factors such as wealth, education and owning a mobile phone are significantly associated with TB awareness. Socioeconomic determinants are influential factors associated with TB awareness in Nepal. The wide variation in the proportion of awareness at a regional level emphasizes the importance of formulating tailored strategies to increase TB awareness. For instance, the use of mobile phones could be an effective strategy to promote TB awareness at a regional level. This study provides valuable evidence to support further research on the contribution of information and communication technology (ICT) usage to improving TB awareness in Nepal.