Evidence map›Paper›PMID 39259738›Full record

ArticlePloS one2024

COVID-19's impact on hospital stays, mortality, and readmissions for poverty-related diseases, noncommunicable diseases, and injury groups in Thailand.

Satiti Palupi, Kyaw Ko Ko Htet, Vorthunju Nakhonsri, Chumpol Ngamphiw, Peerapat Khunkham, Sanya Vasoppakarn, Narumol Atthakul, Sissades Tongsima, Chantisa Keeratipusana, Watcharapot Janpoung and 1 more

Abstract read
In one paragraph

Article in PloS one, 2024. 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
–field-weighted citation impact
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.

  1. Observational
  2. Article
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

11 authors.

Satiti PalupiDepartment of Communicable Disease, East Java Provincial Health Office, Surabaya, Indonesia.ORCID 0000-0003-3949-8048
Kyaw Ko Ko HtetDepartment of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand.
Vorthunju NakhonsriNational Biobank of Thailand, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
Chumpol NgamphiwNational Biobank of Thailand, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
Peerapat KhunkhamNational Biobank of Thailand, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
Sanya VasoppakarnBureau of Service Quality Development, National Health Security Office, Lak Si, Bangkok, Thailand.
Narumol AtthakulBureau of Service Quality Development, National Health Security Office, Lak Si, Bangkok, Thailand.
Sissades TongsimaNational Biobank of Thailand, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
Chantisa KeeratipusanaNational Biobank of Thailand, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
Watcharapot JanpoungNational Biobank of Thailand, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
Virasakdi ChongsuvivatwongDepartment of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand.ORCID 0000-0002-9850-4463

Funding

TB/MDR-TB Research training and Capacity Strengthening for LMIC in Southeast Asia Phase II: Promotion of Diversity, Equity and Inclusion (DEI)D43TW009522 · FIC · PRINCE OF SONGKLA UNIVERSITY · PI Virasakdi Chongsuvivatwong · 2015 to 2026
$3.0M
FIC NIH HHS D43 TW009522
6 · The paper itself

Abstract

aimsThis study aims to compare the trends in the quality of hospital care for WHO's three disease groups pre-, during, and post-COVID-19 pandemic peak in Thailand.

methodsThe study utilized existing hospital admission data from the Thai Health Information Portal (THIP) database, covering the period from 2017 to 2022. We categorized WHO's three disease groups: poverty-related, noncommunicable, and injury groups using the International Classification of Diseases (ICD)-10 of initial admission of patients, and we analyzed three major outcomes: prolonged (≥ 90th percentile) length of stay (LOS), hospital mortality, and readmission pre-, during, and post-COVID-19 pandemic peak. Relative weight (RW) of hospital reimbursements was used as a surrogate measure of the severity of the diseases.

resultsThe average prolonged LOS of patients with poverty disease pre-, during, and post-COVID-19 pandemic peak were 7.1%, 10.8%, 9.05%, respectively. Respective hospital mortality rates were 5.02%, 6.22%, 6.05% and readmission were 6.98/1,000, 6.16/1,000, 5.43/1,000, respectively. For non-communicable diseases, the respective proportions in the prolonged LOS were 9.0%, 9.12%, and 7.58%, with respective hospital mortality being 10.65%, 8.86%, 6.62%, and readmissions were 17.79/1,000, 13.94/1,000, 13.19/1,000, respectively. The respective prolonged LOS for injuries were 8.75%, 8.55%, 8.25%. Meanwhile, respective hospital mortality were 4.95%, 4.05%, 3.20%, and readmissions were 1.99/1,000, 1.60/1,000, 1.48/1,000, respectively. The RW analysis reveals diverse impacts on resource utilization and costs. Most poverty-related and noncommunicable diseases indicate increased resource requirements and associated costs, except for HIV/AIDS and diabetes mellitus, showing mixed trends. In injuries, road traffic accidents consistently decrease resource needs and costs, but suicide cases show mixed trends.

conclusionsCOVID-19 had a more serious impact, especially prolonged LOS and hospital mortality for poverty-related diseases more than noncommunicable diseases and injuries.

Indexed as

COVID-19Hospital MortalityLength of StayNoncommunicable DiseasesPatient ReadmissionPovertyAdultAgedFemaleHumansMaleMiddle AgedPandemicsSARS-CoV-2ThailandWounds and Injuries

Identifiers

PMID39259738
PMCPMC11389936

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.