Evidence map›Paper›PMID 34486234›Full record

ArticleTransboundary and emerging diseases2022

Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study.

Pandji Wibawa Dhewantara, Tities Puspita, Rina Marina, Doni Lasut, Muhammad Umar Riandi, Tri Wahono, Wawan Ridwan, Andri Ruliansyah

Open access · greenAbstract read
In one paragraph

Article in Transboundary and emerging diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 36% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Pandji Wibawa DhewantaraCentre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia.ORCID https://orcid.org/0000-0002-9461-0648
Tities PuspitaCentre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia.
Rina MarinaCentre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia.
Doni LasutCentre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia.
Muhammad Umar RiandiPangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia.
Tri WahonoPangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia.
Wawan RidwanPangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia.
Andri RuliansyahPangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia.
Ministry of Health · ID

Funding

Indonesian Ministry of HealthNational Institute of Health Research and Development
6 · The paper itself

Abstract

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID-19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID-19 incidence and related socio-demographic factors explaining the variations of COVID-19 incidence at local level. COVID-19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID-19 dashboard. Global and local spatial clustering of COVID-19 incidence was examined using the Moran's I and local Moran analysis. Socio-demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID-19 incidence was evaluated. The COVID-19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID-19 high-risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20-49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012-1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041-1.050), number of households (IRR = 1.196; 95% CI = 1.193-1.200), access to metered water for washing, and the main occupation of the residents were village level socio-demographic factors associated with the risk of COVID-19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high-risk COVID-19.

Indexed as

COVID-19AnimalsCitiesFamily CharacteristicsHumansIncidenceIndonesiaSpatial AnalysisCOVID-19Indonesiainequalitysocio-demographicsspatial analysis

Identifiers

PMID34486234
PMCPMC8661770
OpenAlexW3197040505

What OpenQuestion holds

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Registered trials

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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.