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BMC Med. 2019 Jan 29;17(1):21. doi: 10.1186/s12916-019-1260-6.

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

Author information

1
Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Queen Elizabeth Central Hospital, Blantyre, Malawi. peter.macpherson@lstmed.ac.uk.
2
Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK. peter.macpherson@lstmed.ac.uk.
3
Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Queen Elizabeth Central Hospital, Blantyre, Malawi.
4
Helse-Nord TB Programme, College of Medicine, University of Malawi, Blantyre, Malawi.
5
Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
6
MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.
7
School of Health and Related Research, University of Sheffield, Sheffield, UK.
8
Yale School of Public Health, Yale University, New Haven, USA.
9
Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK.

Abstract

BACKGROUND:

A 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.

METHODS:

Governmental 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.

RESULTS:

In 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.

CONCLUSIONS:

Using 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.

KEYWORDS:

Access to care; Bayesian regression analysis; Epidemiology; Gender; HIV; Inequality; Poverty; Spatial analysis; Surveillance; Tuberculosis

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