Evidence map›Paper›PMID 37147648›Full record

ArticleBMC public health2023

Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa.

Innocent Maposa, Richard Welch, Lovelyn Ozougwu, Tracy Arendse, Caroline Mudara, Lucille Blumberg, Waasila Jassat

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 44% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

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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

7 authors at 3 institutions in 1 country.

Innocent MaposaDivision of Epidemiology & Biostatistics, School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa. innocent.maposa@wits.ac.za.
Richard WelchNational Institute for Communicable Diseases, Johannesburg, South Africa.
Lovelyn OzougwuNational Institute for Communicable Diseases, Johannesburg, South Africa.
Tracy ArendseNational Institute for Communicable Diseases, Johannesburg, South Africa.
Caroline MudaraNational Institute for Communicable Diseases, Johannesburg, South Africa.
Lucille BlumbergNational Institute for Communicable Diseases, Johannesburg, South Africa.
Waasila JassatNational Institute for Communicable Diseases, Johannesburg, South Africa.
Right to Care · ZANational Institute for Communicable DiseasesWestern Cape Department of Health · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors.

methodsCOVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian.

resultsThe risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts.

conclusionThe results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

Indexed as

COVID-19Bayes TheoremHospitalizationHospitalsHumansSARS-CoV-2South AfricaClustersCOVID-19DeathsHealth systemsHospitalizationsNonlinear effectsSpatial effects

Identifiers

PMID37147648
PMCPMC10161152
OpenAlexW4372337157

What OpenQuestion holds

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LicenceCC BY
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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.