Health Insurance and Out-Of-Pocket Expenditure on Health and Medicine: Heterogeneities along Income

Abstract

Background: Achieving universal health coverage is an important objective enshrined in the 2015 global Sustainable Development Goals. However, the rising cost of healthcare remains an obstacle to the attainment of the universal health coverage. Health insurance is considered an option to reduce out-of-pocket (OOP) expenditure on health and medicine. Nevertheless, the relationship between insurance and the OOP along welfare distributions is not well understood. This study investigates the heterogeneous association between health insurance and OOP expenditure on health and medicine, along income, using data from the Kingdom of Saudi Arabia.Methods: This study used data of 8655 individuals drawn from the Saudi Family Health Survey conducted in 2018. The study adopts Tobit models to account for possible corner solution due to individuals with zero expenditure on health. We minimize the confounding effects of non-random selection into the insurance program by estimating the Tobit equations on a sample weighted by inverse propensity scores of insurance participation. In addition, we test whether the health insurance differently relates to OOP on health and medicine amongst people with access to free medical care as opposed to those without this privilege. The study estimates separate models for OOP expenditure on health and on medicines.Results: Health insurance reduces OOP expenditure on health by 2.0% and OOP expenditure on medicine by 2.4% amongst the general population while increasing the OOP expenditure on health by 0.2% and OOP expenditure on medicine by 0.2%, once income of the insured rises. The relationship between the insurance and OOP expenditure is robust only amongst the citizens, a sub-sample that also has access to free public healthcare. Specifically, the insurance reduces OOP expenditure on health by 3.6% and OOP on medicine by 5.2% and increases OOP expenditure on health by 0.4% and OOP expenditure on medicine by 0.5% once income of the insured increases amongst Saudi citizens. In addition, targeting medicines can lead to greater changes in OOP. The relationship between insurance and OOP is stronger for medicine relative to that observed on health expenditure.Conclusion: Our findings suggest that insurance induces different effects along the income spectrum. Hence, policy needs to be aware of the possible welfare distribution impacts of upscaling or downscaling the coverage of insurance amongst the populations, while pursuing universal healthcare coverage.

Economic Analysis of Inequality in Preventive Health Check-Ups Uptake in Saudi Arabia

“…These factors have also been identified as drivers of socioeconomic-related inequality in preventive health check-ups uptake in China (39) and India (40). One potential explanation might be that owing to the long waiting times at public healthcare facilities (41), those with better incomes may be in a position to support themselves to use private healthcare services or purchase health insurance, which has been found to contribute to easy access to the healthcare (18,42). Additionally, those with higher education may be much more aware about the benefits of carrying out preventive health check-ups.…”

Determinants of Turkish households' out‐of‐pocket expenditures on three categories of health care services: A multivariate probit approach

“…In terms of income‐related inequalities in healthcare expenditures, insurance, by implementing an interactive approach that combines an individual's income level and insurance status, has significantly reduced health expenditures among low‐income earners compared to high‐income earners 145 . In another study, health insurance was found to reduce out‐of‐pocket spending on healthcare and medicine at low‐income levels, while higher‐income among the insured increases out‐of‐pocket spending 146 …”

“…145 In another study, health insurance was found to reduce out-of-pocket spending on healthcare and medicine at low-income levels, while higher-income among the insured increases out-of-pocket spending. 146 Individuals with good health status incur healthcare expenditures less frequently than those with poor health. 145 Low-income people with poor health were found to spend as much as healthy individuals.…”

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

“…Furthermore, an Observational Medical Outcomes Partnership (OMOP) model can be used alongside the electronic healthcare system to efficiently provide data to institutions ( Gini et al, 2016 ; Michael et al, 2020 ). It was initially launched with the vision of using the system to determine the best practices by using healthcare data and helping patients ( Gini et al, 2016 ; Al-Hanawi et al, 2021 ).…”

“…EHRs can assist healthcare providers in efficiently diagnosing specific rare medical cases that may not be encountered often ( Knaup et al, 2007 ; Yeh et al, 2020 ; Wronikowska et al, 2021 ). In recent years, it has become more difficult to improve the health outcomes for a patient by a drastic margin without raising their out-of-pocket costs ( Al-Hanawi et al, 2021 ), thus making it difficult for timely progress toward better patient care ( Pastorino et al, 2019 ; Al-Hanawi et al, 2021 ). However, these obstacles can be overcome by using Big Data, which relies on its ability to recognize patterns and convert extremely high volumes of data into usable knowledge in the field of precision medicine ( Ahmed et al, 2019a ; Cammarota et al, 2020 ; Seyed Tabib et al, 2020 ).…”