Spatial distribution of people diagnosed with tuberculosis through routine and active case finding: a community-based study in Kampala, Uganda

Abstract

Background: Routine tuberculosis (TB) notifications are geographically heterogeneous, but their utility in predicting the location of undiagnosed TB cases is unclear. We aimed to identify small-scale geographic areas with high TB notification rates based on routinely collected data and to evaluate whether these areas have a correspondingly high rate of undiagnosed prevalent TB. Methods: We used routinely collected data to identify geographic areas with high TB notification rates and evaluated the extent to which these areas correlated with the location of undiagnosed cases during a subsequent community-wide active case finding intervention in Kampala, Uganda. We first enrolled all adults who lived within 35 contiguous zones and were diagnosed through routine care at four local TB Diagnosis and Treatment Units. We calculated average monthly TB notification rates in each zone and defined geographic areas of "high risk" as zones that constituted the 20% of the population with highest notification rates. We compared the observed proportion of TB notifications among residents of these high-risk zones to the expected proportion, using simulated estimates based on population size and random variation alone. We then evaluated the extent to which these high-risk zones identified areas with high burdens of undiagnosed TB during a subsequent community-based active case finding campaign using a chi-square test.

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

“…Various studies have reported on the significance of patient risk groups and residency as key determinants in treatment adherence (11,50,68,69). In our modeling, we equally found the two characteristics to be predictive of whether a patient will adhere to treatment or not.…”

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

“…While some interventions are known to find TB cases, such as household contact investigations which identify individuals exposed at home to TB [1], those interventions will not find the majority of those sick with TB, since less than twenty percent of TB transmission is thought to take place within households [12,13]. Thus, geographic analyses on routinely collected data can be used to identify zones in a community where TB cases or risk factors for transmission are concentrated, in order to help public health workers discern where community-based screening efforts may be deployed [3,4,14]. Additionally, areas identified as cold spots may point to zones where there may be a lack of diagnostic services.…”

Spatial Distribution of Drug-Resistant Mycobacterium tuberculosis Infections in Rural Eastern Cape Province of South Africa

“…A better understanding of local geographic heterogeneity in routinely identified TB cases and the correlation of that heterogeneity with the location of undiagnosed prevalent cases may, therefore, be useful in directing active case-finding interventions to high-risk areas [ 10 ]. Furthermore, there is a need for accurate and early detection of drug-resistant TB (DR-TB) for minimizing the development of drug resistance, effective patient care, and preventing the spread of DR strains [ 11 ].…”