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

Abstract

BackgroundA sizeable fraction of tuberculosis (TB) cases go undiagnosed. By analysing data from enhanced demographic, microbiological and geospatial surveillance of TB registrations, we aimed to identify modifiable predictors of inequitable access to diagnosis and care.MethodsGovernmental community health workers (CHW) enumerated all households in 315 catchment areas during October–December 2015. From January 2015, government TB Officers routinely implemented enhanced TB surveillance at all public and private TB treatment registration centres within Blantyre (18 clinics in total). This included collection from registering TB patients of demographic and clinical characteristics, a single sputum sample for TB microscopy and culture, and geolocation of place of residence using an electronic satellite map application. We estimated catchment area annual TB case notification rates (CNRs), stratified by microbiological status. To identify population and area-level factors predictive of CHW catchment area TB case notification rates, we constructed Bayesian spatially autocorrelated regression models with Poisson response distributions. Worldpop data were used to estimate poverty.ResultsIn total, the 315 CHW catchment areas comprised 753,489 people (range 162 to 13,066 people/catchment area). Between 2015 and 2017, 6077 TB cases (61% male; 99% HIV tested; 67% HIV positive; 55% culture confirmed) were geolocated, with 3723 (61%) resident within a CHW catchment area. In adjusted models, greater distance to the nearest TB registration clinic was negatively correlated with TB CNRs, which halved for every 3.2-fold (95% CI 2.24–5.21) increase in distance. Poverty, which increased with distance from clinics, was negatively correlated with TB CNRs; a 23% increase (95% CI 17–34%) in the mean percentage of the population living on less than US$2 per day corresponded to a halving of the TB case notification rates.ConclusionsUsing enhanced surveillance of TB cases in Blantyre, we show an ecological relationship consistent with an ‘inverse care law’ whereby poorer neighbourhoods and those furthest from TB clinics have lower relative CNRs. If confirmed as low case detection, then pro-poor strategies to facilitate equitable access to TB diagnosis and treatment are required.Electronic supplementary materialThe online version of this article (10.1186/s12916-019-1260-6) contains supplementary material, which is available to authorized users.

Geospatial and hot spot analysis of paediatric tuberculosis infection in Bohol, Philippines

“…This funding bias was attributed by Canares [29] to the expenses of transporting healthcare supplies across the island, and in a similar manner, the expense of residents to get to the clinics for the same reasons. MacPherson et al (2019) also showed a geospatial relationship between an increase in TB transmission as there are increases in poorer neighbourhoods that are farther from the TB clinics [30]. We, too, found support for this phenomenon which is known as the 'inverse case law' coined by Hart (1971) in the Lancet [31].…”

“…MacPherson et al . (2019) also showed a geospatial relationship between an increase in TB transmission as there are increases in poorer neighbourhoods that are farther from the TB clinics [30]. We, too, found support for this phenomenon which is known as the ‘inverse case law’ coined by Hart (1971) in the Lancet [31].…”

“…We, too, found support for this phenomenon which is known as the 'inverse case law' coined by Hart (1971) in the Lancet [31]. 'Inverse case law' means that people often most in need of specialized care tend to live the farthest from their healthcare providers in rural areas [30,31]. However, more research is needed to better understand how other socioeconomic factors influence access to TB care and subsequently paediatric transmission.…”

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“…Thus, methods to robustly identify hotspots from routine reporting data would be valuable [15]. However, given that high local rates of tuberculosis notifications may reflect spatially aggregated risk for progression of infection, migration of individuals infected with tuberculosis into the area [16], or spatial heterogeneity in diagnostic capacity [17], finding new ways to probe routine surveillance data to find evidence of local transmission is a priority.…”

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“…In Bangladesh, it was estimated that only 52% of Nipah outbreaks were detected through passive surveillance systems and that the number of missed outbreaks increased with distance from the surveillance hospital [ 30 ]. This finding was supported by MacPherson et al [ 43 ], who found that the TB case notification rate halved for every 3.2–3.5 fold increase in distance from the nearest TB clinic. In a study carried out by Etyang et al [ 44 ], there was a significant decrease in infectious- and parasitic disease related hospital admissions for every 5 km increase in distance from the hospital.…”