Evidence map›Paper›PMID 39543597›Full record

ArticleEnvironmental health : a global access science source2024

Insights into relationship of environmental inequalities and multimorbidity: a population-based study.

Nina Rajovic, Nikola Grubor, Andja Cirkovic, Ravindra Maheswaran, Peter A Bath, Dan Green, Ilaria Bellantuono, Ognjen Milicevic, Selma Kanazir, Dragan Miljus and 8 more

Abstract read
In one paragraph

Article in Environmental health : a global access science source, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
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  5. 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

18 authors.

Nina RajovicInstitute for Medical Statistics and Informatics, University of Belgrade Faculty of Medicine, Belgrade, 11000, Serbia.
Nikola GruborInstitute for Medical Statistics and Informatics, University of Belgrade Faculty of Medicine, Belgrade, 11000, Serbia.
Andja CirkovicInstitute for Medical Statistics and Informatics, University of Belgrade Faculty of Medicine, Belgrade, 11000, Serbia.
Ravindra MaheswaranSchool of Medicine & Population Health, University of Sheffield, Sheffield, S1 4DA, UK.
Peter A BathInformation School, University of Sheffield, Sheffield, S10 2AH, UK.
Dan GreenCollege of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK.
Ilaria BellantuonoHealthy Lifespan Institute, University of Sheffield, Sheffield, S1 4DA, UK.
Ognjen MilicevicInstitute for Medical Statistics and Informatics, University of Belgrade Faculty of Medicine, Belgrade, 11000, Serbia.
Selma KanazirInstitute for Biological Research "Siniša Stanković" - National Institute of Republic of Serbia, University of Belgrade, Belgrade, 11000, Serbia.
Dragan MiljusInstitute of Public Health of Serbia "Dr Milan Jovanović Batut", Belgrade, 11000, Serbia.
Snezana ZivkovicInstitute of Public Health of Serbia "Dr Milan Jovanović Batut", Belgrade, 11000, Serbia.
Dragana VidojevicSerbian Environmental Protection Agency, Ministry of Environmental Protection, Belgrade, 11000, Serbia.
Natasa MickovskiInstitute of Public Health of Serbia "Dr Milan Jovanović Batut", Belgrade, 11000, Serbia.
Ivana RakocevicInstitute of Public Health of Serbia "Dr Milan Jovanović Batut", Belgrade, 11000, Serbia.
Ivan IvanovicInstitute of Public Health of Serbia "Dr Milan Jovanović Batut", Belgrade, 11000, Serbia.
Aleksandra MladenovicInstitute for Biological Research "Siniša Stanković" - National Institute of Republic of Serbia, University of Belgrade, Belgrade, 11000, Serbia.
Elizabeth GoyderSchool of Medicine & Population Health, University of Sheffield, Sheffield, S1 4DA, UK.
Natasa MilicInstitute for Medical Statistics and Informatics, University of Belgrade Faculty of Medicine, Belgrade, 11000, Serbia. natasa.milic@med.bg.ac.rs.ORCID http://orcid.org/0000-0001-7074-1187

Funding

Ministry of Science, Innovation and Technological Development of the Republic of Serbia 200007UKRI Global Challenges Research Fund 164294Wellcome Trust 200110
6 · The paper itself

Abstract

backgroundSubstantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators.

methodsThis ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency.

resultsThe overall prevalence of multimorbidity was 33.4% [32.6%-34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02-1.03; OR = 1.01, 95% CI 1.00-1.02 and OR = 1.03, 95% CI 1.02-1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA.

conclusionThere is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships.

Indexed as

Air PollutantsMultimorbidityAdolescentAdultAgedAir PollutionEnvironmental ExposureFemaleHumansMaleMiddle AgedPrevalenceSerbiaSocioeconomic FactorsWater QualityYoung AdultAir PollutantsAir pollutionDisease clustersInequalitiesLatent class analysisMultimorbidityWater pollution

Identifiers

PMID39543597
PMCPMC11566910

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.