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

Table 2 Posterior distributions of population-level covariates in a Bayesian spatial model for predicting tuberculosis case notification rates in Blantyre, Malawi: 2015–2017

	Adjusted relative rate	Lower 95% credible interval	Upper 95% credible interval
Analysis 1: all TB cases
Mean number of people per household	1.00	0.78	1.27
Log₁₀ population density (people/km²)	0.90	0.70	1.16
Log₁₀ distance to nearest TB clinic (m)	0.60	0.42	0.86
Mean proportion of population living in poverty	0.97	0.96	0.98
Adult male-to-female ratio	0.28	0.11	0.72
Proportion of population aged ≥15 years	1.00	0.98	1.02
Sputum smear positive to negative ratio	0.79	0.65	0.97
Analysis 2: microbiologically confirmed TB cases
Mean number of people per household	0.93	0.70	1.24
Log₁₀ population density (people/km²)	0.95	0.70	1.29
Log₁₀ distance to nearest TB clinic (m)	0.55	0.36	0.84
Mean proportion of population living in poverty	0.97	0.95	0.98
Adult male-to-female ratio	0.24	0.08	0.74
Proportion of population aged ≥ 15 years	0.99	0.97	1.02
Sputum smear positive to negative ratio	1.18	0.95	1.48

Estimated by fitting a Bayesian spatial regression model with Poisson response, a k nearest-neighbours conditional spatial autocorrelation prior (with k = 6 for analysis 1 and k = 4 for analysis 2), with linear terms fitted for community health worker catchment area log₁₀ population density, adult M:F ratio, mean number of people per household, log₁₀ Cartesian distance from geographical centroid to the nearest TB clinic, percentage of population aged 15 years or older, mean percentage living on less than US $2 per day, offset term for log₁₀ population size, and with weakly informative prior on the population-level effects intercept (Gaussian: mean = 0, sd = 10), and predictor intercept (Gaussian, mean = 0, sd = 10)

ISSN: 1741-7015