Observational studyBMC public health2025
Identifying high-risk subdistricts for targeted dengue interventions: a spatio-temporal study in Mueang Chonburi district, Thailand, 2011-2020.
Observational study in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn Thailand, the Department of Disease Control (DDC) under the Ministry of Public Health typically implements dengue interventions at the district level, especially in urban districts and those with recurrent outbreaks. However, such district-level assessments often encompass diverse subdistricts with varying levels of risk. Consequently, more granular, subdistrict-level interventions may offer greater precision and effectiveness than uniform, district-wide approaches.
methodsThis observational ecological study with spatio-temporal components analyzed daily dengue incidence data reported by hospitals nationwide from 2011 to 2020, using consecutive 5-year time frames to account for interannual variability in dengue transmission. It assessed dengue endemic intensity and identified spatio-temporal clusters at the subdistrict level within Mueang Chonburi district. Endemic intensity was measured by calculating the 5-year average incidence rate per one million population. Significant spatial and temporal clusters were detected using the spatial scan statistic method implemented via SaTScan software. Additionally, a key risk factor analysis was conducted based on five evidence-based factors: hidden population proportion, population density, number of temples and schools, number of cemeteries, and waste management performance.
resultsAll 18 subdistricts in Mueang Chonburi district exhibited dengue endemic characteristics across all consecutive 5-year intervals from 2011 to 2020, with varying intensity levels. Nong Mai Daeng, Nong Ri, and Nong Khang Khok were identified as the top three high-endemic subdistricts and consistently appeared as statistically significant spatial clusters (p < 0.05). Temporal clusters for these subdistricts primarily occurred during the rainy season, with extended periods in Nong Mai Daeng. The risk factor analysis revealed that subdistricts with multiple unfavorable conditions were more likely to correspond with high endemic levels and recurrent clusters.
conclusionsThe findings highlight the importance of timely, subdistrict-level interventions tailored to local outbreak dynamics and risk profiles. Combining spatial-temporal analysis with key environmental and demographic risk factors enables more precise and targeted dengue prevention efforts. Sustained public awareness campaigns and active community engagement remain vital for mitigating outbreaks in both high-risk subdistricts and the wider district.
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.