ArticleInternational journal of environmental research and public health2020
Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015-2019.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Spatiotemporal trends in tuberculosis incidence in Thailand, 2012-2023: a nationwide, province-level analysis.Infectious diseases of poverty · 2026Article
- Spatial heterogenicity of tuberculosis and dengue in Nepal.Scientific reports · 2025Article
- Self-reported prevalence of tuberculosis: unveiling spatial representation in the districts of Tamil Nadu.International health · 2025Article
- Clustering and risk factor analysis of pulmonary tuberculosis in a district in Ethiopia: a population-based cohort study.BMJ open · 2025Article
- Geographic mapping and spatiotemporal patterns of tuberculosis in Libya within ten years' period (2015 to 2024).Frontiers in epidemiology · 2025Article
- Changes in incidence and clinical features of tuberculosis with regard to the COVID-19 outbreak in Southern Iran.BMC infectious diseases · 2024Article
- Depression care integration in tuberculosis services: A feasibility assessment in Pakistan.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018.BMC public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015-2019 to design effective interventions. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015-2016. The potential TB clusters in the remote rural part might be associated to the dry-cool climate and lack of access to the healthcare centers in the remote areas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.