Abstract
Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.
Highlights
In 2017, 10 million new cases of tuberculosis (TB) occurred worldwide [1] which constitute a major health burden that strains middle- and low-income countries
It is well known that TB is prone to spatial aggregation often in poor areas of cities and can even be associated with a higher risk of infection, as observed in Southern Ethiopia where the risk is 4.16 times higher inside a cluster [3]
Data sources Tuberculosis cases All new TB cases are reported in the 342 health centers of the Ministry of Health (MoH) in Lima; new cases diagnosed in hospitals are reported from their corresponding health center
Summary
In 2017, 10 million new cases of tuberculosis (TB) occurred worldwide [1] which constitute a major health burden that strains middle- and low-income countries. Many socio-economic factors within these countries are frequently associated with higher TB incidence such as poverty, unemployment, low income, overcrowding, and population density [2]. The use of geographical surveillance in public health allows for the detection of areas with a high prevalence or incidence of a particular disease in order to identify socio-economic factors associated with the phenomenon [4]. These methods have been applied to TB transmission [5]. Spatial information contributes to appropriate decision-making with a more efficient budget and human resources allocation and has been used previously in infectious diseases to detect hotspots and epidemics [6]
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