Disparities in access to diagnosis and care in Blantyre, Malawi, identified through enhanced tuberculosis surveillance and spatial analysis

A strong socio-economic gradient between poverty and incidence of tuberculosis (TB) has been demonstrated at both population and individual levels [1, 2], with the poorest in society having the greatest risk of developing active TB disease [3]. Poor housing quality, malnutrition, HIV and unhealthy behaviours such as smoking appear to be the major intermediary determinants [4]. People living in poverty also face the greatest difficulties in accessing TB diagnosis and treatment [5] and frequently experience catastrophic care-seeking costs [6]. The World Health Organization (WHO) End-TB strategy has called for ambitious policies to ensure all TB cases are detected and treated and to ensure that no person affected by TB faces socio-economic ruin [7]. However, in sub-Saharan Africa, a substantial ‘detection gap’ exists, with an estimated 51% of prevalent TB cases remaining undiagnosed in the community in 2016 [1].

There is likely substantial heterogeneity in the determinants of incident active TB and in access to TB diagnosis care within cities [8], meaning that preventive interventions and improved access to high-quality TB services targeted towards local neighbourhoods with the greatest need could plausibly result in improvements in TB control [9]. However, identifying these ‘hot-spots’ of inequitable access to TB care depends on integrating high-quality epidemiological and microbiological surveillance with geospatial technologies. Lower case notification rates from certain areas may reflect either worse access to care or lower TB burden. Where lower notification rates coincide with higher levels of known risk factors for TB (e.g. poverty), they may instead reflect barriers to access rather than a relative absence of TB.

We report on the implementation and evaluation of an enhanced TB surveillance programme in Blantyre, Malawi, where nearly one-in-five adults are HIV positive [10]. We aimed to use high-quality spatially resolved surveillance data to support public health practitioners and policymakers to identify the most important and modifiable barriers to TB diagnosis and treatment. We hypothesised that neighbourhood characteristics such as poverty rates, age and sex structure, and distance from TB registration clinics might be correlated with TB case notification rates, and so guide the implementation of interventions to improve access to TB diagnosis and prevention.

Through enhanced TB surveillance in Blantyre, Malawi, we found evidence supporting the ‘inverse case law’, with adjusted TB case notification rate ratios for the poorest neighbourhoods, and those furthest from TB registration clinics, being substantially lower than for less poor neighbourhoods and for those in closer proximity to TB diagnosis and care centres. Evidence was consistently strong in models investigating microbiologically confirmed TB disease and all TB cases, supporting our hypothesis that poor access to TB diagnosis and treatment is predominately determined by poverty, and disproportionately affects the most disadvantaged in society. If a high burden of undiagnosed TB is confirmed through future prevalence surveys, then policymakers should strongly consider prioritising the implementation of pro-poor interventions to improve access to TB services in these underserved neighbourhoods. This could include expanding or relocating primary health care centres with comprehensive TB and HIV services [19], periodic mobile TB screening camps [20] or targeted community-wide active case-finding interventions [21, 22]. There may also be a potential impact from wider development interventions, not specific to TB.

Cities—and in particular informal settlements—provide conditions highly conducive to the spread of infectious disease [23], although it is clear that there is considerable social and geographical heterogeneity in the prevalence and incidence of infections, including TB [2, 9]. The prevalence of undiagnosed active TB is known to be high in many urban Africa cities [21, 22]. However, understanding of how and where to best implement TB control strategies in resource-limited settings has been hampered by a lack of census data and TB case notification data disaggregated by age, sex and HIV status and at lower-level geospatial boundaries.

In Blantyre, we successfully implemented enhanced TB surveillance in an extremely densely populated setting, including routine sputum sample collection for mycobacterial culture and geolocation using an innovative electronic application. We believe this approach could be widely applied in other similar settings and would be beneficial to public health authorities, providing neighbourhood-level data on underserved areas to inform TB care and prevention strategies. Critical to achieving the high levels of case-capture were the partnerships we established with key stakeholders, including the Malawi National TB Programme and Blantyre District Health Office, and with the Blantyre TB Officers and community health workers who undertook most of the enhanced surveillance activities.

We additionally found strong evidence that neighbourhoods with lower male-to-female adult ratios (i.e. a higher proportion of women) had higher TB CNRs even after adjusting for other important determinants of access to care. Although we cannot speculate about the causal nature of the relationship between neighbourhood sex ratios and TB case notification rates, if confirmed by prevalence survey data, this suggests that lower case notification rates may, in part, be driven by male barrier to TB diagnosis. Community TB prevalence and case notifications are known to be substantially higher for men than women [24], perhaps driven by gendered patterns of social mixing and barriers in care-seeking [25]. Areas in Blantyre with the highest male-to-female ratios are centred around extremely densely populated trading areas, where young men migrate for work. For such individuals, intense economic pressure may mean that seeking care for symptoms of TB is not a priority, a hypothesis that is supported by our previous qualitative work with symptomatic men in the city [25]. Conversely, residents of neighbourhoods where sex ratios were equal, or where women outnumber men, may be relatively more stable, with social stability a prerequisite for prioritising individual or household health.

As a further potential marker of poor access and late presentation for care that could be investigated using routine programmatic surveillance data, we investigated associations between sputum smear positive to negative notification ratios (on samples collected by the routine TB programme) and neighbourhood TB case notification rates. Smear positivity is a marker of infectivity that increases with diagnostic delay but, once present, is considerably easier for routine programmes to diagnose than smear-negative TB; thus, greater diagnostic access barriers are likely to be reflected by a higher proportion of smear-positive patients. Here, we show that TB case notification rates halved with every 2.4-fold increase in the sputum smear positive to negative ratio. This supports our hypothesis that late and/or under-diagnosis of TB from the poorest, more distant neighbourhoods was explained by poor access to diagnosis rather than by less prevalent disease. However, confirming this would need neighbourhood-level data from TB prevalence surveys (and in particular prevalence-to-notification ratios), ideally combined with local data on other intermediary determinants.

There were a number of strengths and limitations to this study. Overall, we successfully recorded clinical and demographic data from 78% (6077/7799) of all registering TB cases at all 18 health facilities in urban Blantyre. We additionally completed microbiological surveillance by sputum culture from 71% of captured cases, and geolocated 70% of all TB cases registered at city health centres. We concentrated mapping activities on the largest and most deprived neighbourhoods of the city and excluded business districts and the most affluent neighbourhoods. Thus, we will have systematic under-sampled TB patients from middle-class homes. Reflecting these different underlying populations, non-CHW catchment area-resident cases did have different characteristics compared to CHW-resident cases. Additionally, because we could only collect geospatial data from TB cases who were resident within neighbourhoods mapped by our study activities, we are unable to include TB cases resident outwith mapped areas in our regression analysis. There therefore may be important predictors of access to TB diagnosis and care among populations excluded from the present analysis that we were unable to identify. Surveillance data were collected by routine Ministry of Health employees (TB Officers) using the electronic GPS application and not by research staff. Being programmatic data, we anticipate some imprecision in geolocating TB patients. The household enumeration data was limited to basic data on age and sex, and so we are unable to estimate correlations with important intermediary determinants—notably HIV prevalence; the relationship with sputum smear positivity rates at the population level remains uncertain without detailed HIV prevalence data. We relied on data from the Worldpop Project from for estimating neighbourhood-level poverty. TB cultures were not done on 27% of CHW-resident TB patients who did not submit sputum, including 17% of pulmonary TB cases: this is likely to include a bias against less gravely ill patients too sick to produce sputum, or started on TB treatment out-of-hours while inpatients. Overall, 58% of notified TB cases initiated on TB treatment by the National TB Programme in study health facilities were microbiologically confirmed by the study laboratory, reflecting the programmatic and diagnostic challenges in confirming TB disease in low-resource settings. Finally, without supporting epidemiological data, our hypothesis relating to under-diagnosis in the more remote neighbourhoods of Blantyre remains speculative.

Future priorities include investigation of the prevalence of undiagnosed TB in neighbourhoods with low CNRs but high poverty and poor access to care indicators, where TB may be underdiagnosed: if so, then the prevalence of undiagnosed infectious TB may in fact be very high in these areas. Community-level TB interventions including active case finding have tended to focus on high-density informal residential areas, guided by high CNRs and prevalence survey data that show urban settings to be high risk for TB [26]. Even within densely populated urban settings such as Blantyre, there may be substantial heterogeneity in TB prevalence and risk factors for transmission, meaning that the potential effects of community-wide interventions may become diluted. We note that across 3 years studied, there were consistent patterns of high TB case notifications arising from the same set of CHW catchment areas. It is possible that case-finding interventions directed towards areas with the greatest need (both in terms of risk factors for TB and in likely under-diagnosis) could have greater and quicker impact on controlling TB than broader population-based strategies. However, further epidemiological and modelling studies, as well as trials, are required.

In summary, we found evidence from statistical analysis of enhanced city-wide surveillance data that the poorest neighbourhoods of Blantyre, and those farthest from TB diagnosis and treatment clinics, had substantially lower relative TB CNRs. This remained true after adjusting for important epidemiological risk factors for TB, implying inequity in access to diagnosis and care. The innovative georeferenced surveillance system described here could be implemented widely in low-resource settings and potentially provide high-quality, real-time epidemiological and microbiological data to direct TB control efforts. In particular, the impact on TB burden of facilitated access to diagnosis and spatially directed active TB case-finding interventions should be examined further.