Evidence map›Paper›PMID 36609295›Full record

ArticleBMC public health2023

Household catastrophic health expenditure for COVID-19 during March-August 2021, in South India: a cross-sectional study.

Elumalai Rajalakshmi, Akhil Sasidharan, Bhavani Shankara Bagepally, Muthusamy Santhosh Kumar, Ponnaiah Manickam, T S Selva Vinayagam, P Sampath, K Parthipan

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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 7 papers.

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7citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Elumalai RajalakshmiICMR-National Institute of Epidemiology, Chennai, India.
Akhil SasidharanICMR-National Institute of Epidemiology, Chennai, India.
Bhavani Shankara BagepallyICMR-National Institute of Epidemiology, Chennai, India. bagepally.bs@gov.in.
Muthusamy Santhosh KumarICMR-National Institute of Epidemiology, Chennai, India.
Ponnaiah ManickamICMR-National Institute of Epidemiology, Chennai, India.
T S Selva VinayagamTamil Nadu Directorate of Public Health and Preventive Medicine, Chennai, India.
P SampathTamil Nadu Directorate of Public Health and Preventive Medicine, Chennai, India.
K ParthipanTamil Nadu Directorate of Public Health and Preventive Medicine, Chennai, India.

Funding

Dept. of Health Research Govt. of India funds the HTARC, ICMR-NIE T.11011/08/2017-HR(PART-1)/E-OFFICE -8025571 DATED 27TH NOV-2019
6 · The paper itself

Abstract

backgroundThe Coronavirus disease 2019 (COVID-19) pandemic increased the utilisation of healthcare services. Such utilization could lead to higher out-of-pocket expenditure (OOPE) and catastrophic health expenditures (CHE). We estimated OOPE and the proportion of households that experienced CHE by conducting a cross-sectional survey of 1200 randomly selected confirmed COVID-19 cases.

methodsA cross-sectional survey was conducted by telephonic interviews of 1200 randomly selected COVID-19 patients who tested positive between 1 March and 31 August 2021. We collected household-level information on demographics, income, expenditure, insurance coverage, direct medical and non-medical costs incurred toward COVID-19 management. We estimated the proportion of CHE with a 95% confidence interval. We examined the association of household characteristics; COVID-19 cases, severity, and hospitalisation status with CHE. A multivariable logistic regression analysis was conducted to ascertain the effects of variables of interest on the likelihood that households face CHE due to COVID-19.

resultsThe mean (95%CI) OOPE per household was INR 122,221 (92,744-1,51,698) [US$1,643 (1,247-2,040)]. Among households, 61.7% faced OOPE, and 25.8% experienced CHE due to COVID-19. The odds of facing CHE were high among the households; with a family member over 65 years [OR = 2.89 (2.03-4.12)], with a comorbid individual [OR = 3.38 (2.41-4.75)], in the lowest income quintile [OR = 1.82 (1.12-2.95)], any member visited private hospital [OR = 11.85 (7.68-18.27)]. The odds of having CHE in a household who have received insurance claims [OR = 5.8 (2.81- 11.97)] were high. Households with one and more than one severe COVID-19 increased the risk of CHE by more than two-times and three-times respectively [AOR = 2.67 (1.27-5.58); AOR = 3.18 (1.49-6.81)].

conclusionCOVID-19 severity increases household OOPE and CHE. Strengthening the public healthcare and health insurance with higher health financing is indispensable for financial risk protection of households with severe COVID-19 from CHE.

Indexed as

COVID-19Health ExpendituresCatastrophic IllnessCross-Sectional StudiesHumansIndiaSocioeconomic FactorsCOVID-19Household catastrophic health expenditureOut of pocket expenditure

Identifiers

PMID36609295
PMCPMC9821347

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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.