South Sulawesi’s reported tuberculosis burden shifts between districts
Reported tuberculosis rates rose after 2020 across a dataset covering 24 districts of South Sulawesi, Indonesia. High-rate districts changed over time, and socioeconomic associations varied by place and year. The single observational study measures notifications: disrupted diagnosis and later recovery of reporting could influence the trend, preventing a direct reading of changes in transmission.
Science··Evening
Notifications rose after the pandemic-period low
The 2020 district average was 23.1 tuberculosis notifications for every 100,000 people; by 2023 it had reached 44.9. The analysis covers 24 districts in Indonesia’s South Sulawesi. These are health-system notifications, not a direct measurement of every infection. The single peer-reviewed observational study covers 2018–2023. The median was lower than the mean: relatively few districts with high rates raised the average. A provincial mean therefore conceals substantial variation between the districts included in the analysis.[1]
High-rate districts changed over time
Districts classified in high-rate categories changed when each year was assessed against its own distribution. A persistent geographical cluster covering the province did not emerge. Education, unemployment and human development were the indicators most consistently associated with reported rates, but their relationships varied across place and year. Education and development were themselves correlated, limiting interpretation of their coefficients as independent effects. These district-level associations cannot identify why an individual patient became infected or establish that the socioeconomic indicators caused tuberculosis.[1]
Reporting capacity complicates the trend
Pandemic-period declines could reflect interrupted diagnosis and case-finding, while later increases could partly reflect the recovery of those services. Aggregated surveillance data cannot separate these possibilities from changes in transmission. The study combined linear tests with a regression approach allowing associations to vary geographically and over time. That flexibility reveals local differences but does not resolve the notification problem. Interpreting the rise as a matching rise in underlying transmission would go beyond the evidence available in this dataset.[1]
