Mapping local hot spots with routine tuberculosis data: A pragmatic approach to identify spatial variability

Abstract

Objective To use routinely collected data, with the addition of geographic information and census data, to identify local hot spots of rates of reported tuberculosis cases. Design Residential locations of tuberculosis cases identified from eight public health facilities in Lima, Peru (2013–2018) were linked to census data to calculate neighborhood-level annual case rates. Heat maps of tuberculosis case rates by neighborhood were created. Local indicators of spatial autocorrelation, Moran’s I, were used to identify where in the study area spatial clusters and outliers of tuberculosis case rates were occurring. Age- and sex-stratified case rates were also assessed. Results We identified reports of 1,295 TB cases across 74 neighborhoods during the five-year study period, for an average annual rate of 124.2 reported TB cases per 100,000 population. In evaluating case rates by individual neighborhood, we identified a median rate of reported cases of 123.6 and a range from 0 to 800 cases per 100,000 population. Individuals aged 15–44 years old and men had higher case rates than other age groups and women. Locations of both hot and cold spots overlapped across age- and gender-specific maps. Conclusions There is significant geographic heterogeneity in rates of reported TB cases and evident hot and cold spots within the study area. Characterization of the spatial distribution of these rates and local hot spots may be one practical tool to inform the work of local coalitions to target TB interventions in their zones.

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

“…The study found that while the median rate of reported cases in the district was 123.6, the range among neighbourhoods varied from 0 to 800 cases per 100,000 population. While the study utilised notification registers alone, it highlights the possibility of local epidemics and relevance of geographic mapping of individual cases from their addresses to identify areas at risk of TB transmission and its practical implications for decision making [31].…”

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

“…The study found that while the median rate of reported cases in the district was 123.6, the range among neighbourhoods varied from 0 to 800 cases per 100,000 population. While the study utilised notification registers alone, it highlights the possibility of local epidemics and relevance of geographic mapping of individual cases from their addresses to identify areas at risk of TB transmission and its practical implications for decision making [35].…”

Detection Of Hotspots Of Tuberculosis Cases In Vietnam

“…This understanding can help inform public health actions, such as the customization and targeting of interventions (9). Finer spatial resolution of TB data is required in order to create successful interventions for local zones; this means moving away from solely examining nationally aggregated data and toward using pertinent local data (10). The better utilization of already-existing data, such as program reports of tuberculosis cases, and the extension of that routine data to incorporate spatial information are crucial to this change (9).…”