Spatial distribution of Mycobacterium Tuberculosis in metropolitan Harare, Zimbabwe

Abstract

Introduction The contribution of high tuberculosis (TB) transmission pockets in propagating area-wide transmission has not been adequately described in Zimbabwe. This study aimed to describe the presence of hotspot transmission of TB cases in Harare city from 2011 to 2012 using geospatial techniques. Methods Anonymised TB patient data stored in an electronic database at Harare City Health department was analysed using geospatial methods. Confirmed TB cases were mapped using geographic information system (GIS). Global Moran's I and Anselin Local Moran's I (LISA) were used to assess clustering and the local Getis-Ord G i * was used to estimate hotspot phenomenon of TB cases in Harare City for the period between 2011 and 2012. Results A total of 12,702 TB cases were accessed and mapped on the Harare City map. In both 2011 and 2012, ninety (90%) of cases were new and had a high human immunodeficiency virus (HIV)/TB co-infection rate of 72% across all suburbs. Tuberculosis prevalence was highest in the Southern district in both 2011 and 2012. There were pockets of spatial distribution of TB prevalence across West South West, Southern, Western, South Western and Eastern health districts. TB hot spot occurrence was restricted to the West South West, parts of South Western, Western health districts. West South West district had an increased peri-urban population with inadequate social services including health facilities. These

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

“…The occurrence of TB hotspots and clusters in these areas may be associated with high population resulting in overcrowding hence ease transmission of the disease. These results are similar to findings by Chirenda [ 14 ] who found that TB hotspots and clusters are common in urban areas due to large populations and inadequate health care services. Consistently across the four years, and especially in 2017, the districts along the Great Dyke exhibited TB hotspots.…”

“…These studies have generated important information about the distribution of the disease and its transmission patterns. However, the major limitation of these studies is that they are biased towards smaller spatial scales such as a single urban area over short temporal durations [ 14 , 26 ]. By focussing on smaller spatial scales, these studies fail to capture the spatial pattern of TB at a spatial scale that is relevant for TB programming, for example district scale.…”

“…Without evidence of fine scale spatial heterogeneity in the occurrence of the disease [11], uniform interventions are implemented across different settings by NTP. Thus, a better understanding of the spatial epidemiology of TB may guide policy makers in formulating effective prevention and control strategies [12][13][14]. For instance, the identification of TB clusters and hotspots provides the basis for targeted control of the disease as well optimizing resource allocation [10].…”

Organizational Readiness for the Implementation of a Three-Month Short-Course TB Preventive Therapy Regimen (3HP) in Four Health Care Facilities in Zimbabwe in 2020: A Mixed Methods Study

“…A similar pattern of findings were obtained in Zhang et al's study suggesting that discrepancies between the current and desired conditions within an organization overshadowed the high change commitment for programs [26]. Notwithstanding, our study had limited time and resources therefore was conducted at one-point in time as compared to the aforementioned previous study that measured readiness at the pre-adoption, preimplementation and post-implementation stages of the program [26].…”

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

“…Anselin’s Local Moran’s I (ALMI ) identifies High-High clusters, Low-Low clusters, and spatial outliers (High-Low and Low-High) [ 47 ]. High-High clusters indicate areas with a high TB incidence rates surrounded by areas with similarly high TB rates, while Low-Low clusters designate areas with low TB incidence rates surrounded by areas with low rates; High-Low outliers specifies areas with high TB incidence rates surrounded by areas with low rates, while Low-High outliers indicate the opposite, i.e.…”