Prediction of COVID-19 patients’ participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches

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Abstract

Background

Informal care plays an essential role in managing the COVID-19 pandemic. Expanding health insurance packages that reimburse caregivers' services through cost-sharing policies could increase financial resources. Predicting payers' willingness to contribute financially accurately is essential for implementing such a policy. This study aimed to identify the key variables related to WTP/WTA of COVID-19 patients for informal care in Sanandaj city, Iran.

Methods

This cross-sectional study involved 425 COVID-19 patients in Sanandaj city, Iran, and 23 potential risk factors. We compared the performance of three classifiers based on total accuracy, specificity, sensitivity, negative likelihood ratio, and positive likelihood ratio.

Results

Findings showed that the average total accuracy of all models was over 70%. Random trees had the most incredible total accuracy for both patient WTA and patient WTP(0.95 and 0.92). Also, the most significant specificity (0.93 and 0.94), sensitivity (0.91 and 0.87), and the lowest negative likelihood ratio (0.193 and 0.19) belonged to this model. According to the random tree model, the most critical factor in patient WTA were patient difficulty in personal activities, dependency on the caregiver, number of caregivers, patient employment, and education, caregiver employment and patient hospitalization history. Also, for WTP were history of COVID-19 death of patient's relatives, and patient employment status.

Conclusion

Implementing of a more flexible work schedule, encouraging employer to support employee to provide informal care, implementing educational programs to increase patients' efficacy, and providing accurate information could lead to increased patients' willingness to contribute and finally promote health outcomes in the population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-024-11250-2.

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Most cited references 27

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Health system performance in Iran: a systematic analysis for the Global Burden of Disease Study 2019

(2022)

Summary Background Better evaluation of existing health programmes, appropriate policy making against emerging health threats, and reducing inequalities in Iran rely on a comprehensive national and subnational breakdown of the burden of diseases, injuries, and risk factors. Methods In this systematic analysis, we present the national and subnational estimates of the burden of disease in Iran using the Global Burden of Disease Study 2019. We report trends in demographics, all-cause and cause-specific mortality, as well as years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) caused by major diseases and risk factors. A multi-intervention segmented-regression model was used to explore the overall impact of health sector changes and sanctions. For this analysis, we used a variety of sources and reports, including vital registration, census, and survey data to provide estimates of mortality and morbidity at the national and subnational level in Iran. Findings Iran, which had 84·3 million inhabitants in 2019, had a life expectancy of 79·6 years (95% uncertainty interval 79·2–79·9) in female individuals and 76·1 (75·6–76·5) in male individuals, an increase compared with 1990. The number of DALYs remained stable and reached 19·8 million (17·3–22·6) in 2019, of which 78·1% were caused by non-communicable diseases (NCDs) compared with 43·0% in 1990. During the study period, age-standardised DALY rates and YLL rates decreased considerably; however, YLDs remained nearly constant. The share of age-standardised YLDs contributing to the DALY rate steadily increased to 44·5% by 2019. With regard to the DALY rates of different provinces, inequalities were decreasing. From 1990 to 2019, although the number of DALYs attributed to all risk factors decreased by 16·8%, deaths attributable to all risk factors substantially grew by 43·8%. The regression results revealed a significant negative association between sanctions and health status. Interpretation The Iranian health-care system is encountering NCDs as its new challenge, which necessitates a coordinated multisectoral approach. Although the Iranian health-care system has been successful to some extent in controlling mortality, it has overlooked the burden of morbidity and need for rehabilitation. We did not capture alleviation of the burden of diseases in Iran following the 2004 and 2014 health sector reforms; however, the sanctions were associated with deaths of Iranians caused by NCDs. Funding Bill & Melinda Gates Foundation.

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Willingness-to-Pay for Community-Based Health Insurance among Informal Workers in Urban Bangladesh

Sayem Ahmed, Mohammad Hoque, Abdur Sarker … (2016)

Introduction Reliance on out-of-pocket payment for healthcare may lead poor households to undertake catastrophic health expenditure, and risk-pooling mechanisms have been recommended to mitigate such burdens for households in Bangladesh. About 88% of the population of Bangladesh depends on work in the informal sector. We aimed to estimate willingness-to-pay (WTP) for CBHI and identify its determinants among three categories of urban informal workers rickshaw-pullers, shopkeepers and restaurant workers. Methods The bidding game version of contingent valuation method was used to estimate weekly WTP. In three urban locations 557 workers were interviewed using a structured questionnaire during 2010 and 2011. Multiple-regression analysis was used to predict WTP by demographic and household characteristics, occupation, education level and past illness. Results WTP for a CBHI scheme was expressed by 86.7% of informal workers. Weekly average WTP was 22.8 BDT [Bangladeshi Taka; 95% confidence interval (CI) 20.9–24.8] or 0.32 USD and varied significantly across occupational groups (p = 0.000) and locations (p = 0.003). WTP was highest among rickshaw-pullers (28.2 BDT or 0.40 USD; 95% CI: 24.7–31.7), followed by restaurant workers (20.4 BDT 0.29 USD; 95% CI: 17.0–23.8) and shopkeepers (19.2 BDT or 0.27 USD; 95% CI: 16.1–22.4). Multiple regression analysis identified monthly income, occupation, geographical location and educational level as the key determinants of WTP. WTP increased 0.196% with each 1% increase in monthly income, and was 26.9% lower among workers with up to a primary level of education versus those with higher than primary, but less than one year of education. Conclusion Informal workers in urban areas thus are willing to pay for CBHI and socioeconomic differences explain the magnitude of WTP. The policy maker might think introducing community-based model including public-community partnership model for healthcare financing of informal workers. Decision making regarding the implementation of such schemes should consider worker location and occupation.

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What do community-dwelling people with dementia need? A survey of those who are known to care and welfare services.

Aaltje Jansen, Robrecht van der Wel, Hannie C. Comijs … (2009)

The aging society will bring an increase in the number of people with dementia living in the community. This will mean a greater demand on care and welfare services to deliver efficient and customized care, which requires a thorough understanding of subjective and objective care needs. This study aims to assess the needs of community-dwelling people with dementia as reported by themselves and by their informal carers. The study also aims to give insight into the service use and gaps between needs and the availability of services. 236 community-dwelling people with dementia and 322 informal carers were interviewed separately. (Un)met needs were assessed using the Camberwell Assessment of Needs for the Elderly (CANE). Most unmet needs were experienced in the domains of memory, information, company, psychological distress and daytime activities. People with dementia reported fewer (unmet) needs than their carers. Type and severity of dementia, living situation and informal carer characteristics were related to the number of reported needs. This study showed a large number of unmet needs in dementia. Reasons for unmet needs are lack of knowledge about the existing service offer, a threshold to using services and insufficient services offer. These results provide a good starting point for improving community care for people with dementia.

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Author and article information

Contributors

Omid Hamidi: omid_hamidi@hut.ac.ir

Journal

Journal ID (nlm-ta): BMC Health Serv Res

Journal ID (iso-abbrev): BMC Health Serv Res

Title: BMC Health Services Research

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6963

Publication date (Electronic): 10 July 2024

Publication date PMC-release: 10 July 2024

Publication date Collection: 2024

Volume: 24

Electronic Location Identifier: 796

Affiliations

[1 ]Department of Health Management and Economics, School of Public Health, Hamadan University of Medical Sciences, ( https://ror.org/02ekfbp48) Hamadan, Iran

[2 ]GRID grid.411950.8, ISNI 0000 0004 0611 9280, Modeling of Noncommunicable Diseases Research Center, , Hamadan University of Medical Sciences, ; Hamadan, Iran

[3 ]Department of Industrial Engineering, Kermanshah University of Technology, ( https://ror.org/05hkxne09) Kermanshah, Iran

[4 ]Department of Clinical Pharmacy, School of Pharmacy, Hamadan University of Medical Sciences, ( https://ror.org/02ekfbp48) Hamadan, Iran

[5 ]Department of Science, Hamedan University of Technology, ( https://ror.org/01hgb6e08) Hamedan, Iran

Article

Publisher ID: 11250

DOI: 10.1186/s12913-024-11250-2

PMC ID: 11234787

PubMed ID: 38987739

SO-VID: 931b39f0-80bd-4322-9630-a7e16900386b

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 22 October 2023

Date accepted : 25 June 2024

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ScienceOpen disciplines: Health & Social care

Keywords: healthcare financings,informal care,patient preferences,covid-19,machine learning

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ScienceOpen disciplines: Health & Social care

Keywords: healthcare financings, informal care, patient preferences, covid-19, machine learning

Prediction of COVID-19 patients’ participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches

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Abstract

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Conclusion

Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 27

Health system performance in Iran: a systematic analysis for the Global Burden of Disease Study 2019

Willingness-to-Pay for Community-Based Health Insurance among Informal Workers in Urban Bangladesh

What do community-dwelling people with dementia need? A survey of those who are known to care and welfare services.

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