Search

Pakistan is among the top countries with the highest tuberculosis (TB) burden in the world. This study aims to identify and visualize spatiotemporal clusters of TB cases in South Punjab Province Pakistan during 2016–2020. We obtained TB data from the national surveillance of the District Health Information System from January 2016 to December 2020. We applied space-time scan statistics using the Discrete Poisson model to identify and investigate spatiotemporal patterns of TB at the tehsil level, which is a subunit of districts, in the study area. The region consistently experienced many new tuberculosis cases with Multan as the highest reporting division. A purely temporal clustering pattern was observed from December 2017 to February 2020 with a relative risk (RR) of 1.21 (P < 0.001). However, purely spatial scanning identified two significant high-risk (P < 0.001) clusters of TB within the South Punjab region comprising 36.5% and 6.8% of total cases, respectively. The first spatial cluster was located in the central part of the region (RR = 1.69), while the second cluster was observed in the northern area (RR = 1.46). Interestingly, the most significant spatial–temporal cluster (RR = 1.80) was also identified in the central part of the region, comprising 26.8% of the total TB cases, with the temporal accumulation of cases from October 2017 to March 2020. Our study revealed significant space–time clusters at the tehsil level, based on which public health interventions and strategies can be tailored to address the specific needs of these regions, potentially leading to more effective TB prevention and control measures. [ABSTRACT FROM AUTHOR]

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R 2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R 2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)