Disruption of hospital care during the first year of the COVID-19 pandemic impacted socioeconomic groups differently: population based study using routine registration data.

BACKGROUND: During the COVID-19 pandemic, provision of non-COVID healthcare was recurrently severely disrupted. The objective was to determine whether disruption of non-COVID hospital use, either due to cancelled, postponed, or forgone care, during the first pandemic year of COVID-19 impacted socioeconomic groups differently compared with pre-pandemic use. METHODS: National population registry data, individually linked with data of non-COVID hospital use in the Netherlands (2017-2020). in non-institutionalised population of 25-79 years, in standardised household income deciles (1 = low, 10 = high) as proxy for socioeconomic status. Generic outcome measures included patients who received hospital care (dichotomous): outpatient contact, day treatment, inpatient clinic, and surgery. Specific procedures were included as examples of frequently performed elective and acute procedures, e.g.: elective knee/hip replacement and cataract surgery, and acute percutaneous coronary interventions (PCI). Relative risks (RR) for hospital use were reported as outcomes from generalised linear regression models (binomial) with log-link. An interaction term was included to assess whether income differences in hospital use during the pandemic deviated from pre-pandemic use. RESULTS: Hospital use rates declined in 2020 across all income groups. With baseline (2019) higher hospital use rates among lower than higher income groups, relatively stronger declines were found for lower income groups. The lowest income groups experienced a 10% larger decline in surgery received than the highest income group (RR 0.90, 95% CI 0.87 - 0.93). Patterns were similar for inpatient clinic, elective knee/hip replacement and cataract surgery. We found small or no significant income differences for outpatient clinic, day treatment, and acute PCI. CONCLUSIONS: Disruption of non-COVID hospital use in 2020 was substantial across all income groups during the acute phases of the pandemic, but relatively stronger for lower income groups than could be expected compared with pre-pandemic hospital use. Although the pandemic's impact on the health system was unprecedented, healthcare service shortages are here to stay. It is therefore pivotal to realise that lower income groups may be at risk for underuse in times of scarcity.

Systematic review of individual-level, community-level, and healthcare system-level factors contributing to socioeconomic differences in healthcare utilisation in OECD countries with universal health coverage.

OBJECTIVES: Countries with universal health coverage (UHC) strive for equal access for equal needs without users getting into financial distress. However, differences in healthcare utilisation (HCU) between socioeconomic groups have been reported in countries with UHC. This systematic review provides an overview individual-level, community-level, and system-level factors contributing to socioeconomic status-related differences in HCU (SES differences in HCU). DESIGN: Systematic review following the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines. The review protocol was published in advance. DATA SOURCES: Embase, PubMed, Web of Science, Scopus, Econlit, and PsycInfo were searched on 9 March 2021 and 9 November 2022. ELIGIBILITY CRITERIA: Studies that quantified the contribution of one or more factors to SES difference in HCU in OECD countries with UHC. DATA EXTRACTION AND SYNTHESIS: Studies were screened for eligibility by two independent reviewers. Data were extracted using a predeveloped data-extraction form. Risk of bias (ROB) was assessed using a tailored version of Hoy's ROB-tool. Findings were categorised according to level and a framework describing the pathway of HCU. RESULTS: Of the 7172 articles screened, 314 were included in the review. 64% of the studies adjusted for differences in health needs between socioeconomic groups. The contribution of sex (53%), age (48%), financial situation (25%), and education (22%) to SES differences in HCU were studied most frequently. For most factors, mixed results were found regarding the direction of the contribution to SES differences in HCU. CONCLUSIONS: SES differences in HCU extensively correlated to factors besides health needs, suggesting that equal access for equal needs is not consistently accomplished. The contribution of factors seemed highly context dependent as no unequivocal patterns were found of how they contributed to SES differences in HCU. Most studies examined the contribution of individual-level factors to SES differences in HCU, leaving the influence of healthcare system-level characteristics relatively unexplored.

From test to rest: evaluating socioeconomic differences along the COVID-19 care pathway in the Netherlands.

INTRODUCTION: The COVID-19 pandemic exacerbated healthcare needs and caused excess mortality, especially among lower socioeconomic groups. This study describes the emergence of socioeconomic differences along the COVID-19 pathway of testing, healthcare use and mortality in the Netherlands. METHODOLOGY: This retrospective observational Dutch population-based study combined individual-level registry data from June 2020 to December 2020 on personal socioeconomic characteristics, COVID-19 administered tests, test results, general practitioner (GP) consultations, hospital admissions, Intensive Care Unit (ICU) admissions and mortality. For each outcome measure, relative differences between income groups were estimated using log-link binomial regression models. Furthermore, regression models explained socioeconomic differences in COVID-19 mortality by differences in ICU/hospital admissions, test administration and test results. RESULTS: Among the Dutch population, the lowest income group had a lower test probability (RR = 0.61) and lower risk of testing positive (RR = 0.77) compared to the highest income group. However, among individuals with at least one administered COVID-19 test, the lowest income group had a higher risk of testing positive (RR = 1.40). The likelihood of hospital admissions and ICU admissions were higher for low income groups (RR = 2.11 and RR = 2.46, respectively). The lowest income group had an almost four times higher risk of dying from COVID-19 (RR = 3.85), which could partly be explained by a higher risk of hospitalization and ICU admission, rather than differences in test administration or result. DISCUSSION: Our findings indicated that socioeconomic differences became more pronounced at each step of the care pathway, culminating to a large gap in mortality. This underlines the need for enhancing social security and well-being policies and incorporation of health equity in pandemic preparedness plans.

Estimating healthcare expenditures after becoming divorced or widowed using propensity score matching.

Becoming divorced or widowed are stressful life events experienced by a substantial part of the population. While marital status is a significant predictor in many studies on healthcare expenditures, effects of a change in marital status, specifically becoming divorced or widowed, are less investigated. This study combines individual health claims data and registered sociodemographic characteristics from all Dutch inhabitants (about 17 million) to estimate the differences in healthcare expenditure for individuals whose marital status changed (n = 469,901) compared to individuals who remained married, using propensity score matching and generalized linear models. We found that individuals who were (long-term) divorced or widowed had 12-27% higher healthcare expenditures (RR = 1.12, 95% CI 1.11-1.14; RR = 1.27, 95% CI 1.26-1.29) than individuals who remained married. Foremost, this could be attributed to higher spending on mental healthcare and home care. Higher healthcare expenditures are observed for both divorced and widowed individuals, both recently and long-term divorced/widowed individuals, and across all age groups, income levels and educational levels.

Why does healthcare utilisation differ between socioeconomic groups in OECD countries with universal healthcare coverage? A protocol for a systematic review.

INTRODUCTION: Even in advanced economies with universal healthcare coverage (UHC), a social gradient in healthcare utilisation has been reported. Many individual, community and healthcare system factors have been considered that may be associated with the variation in healthcare utilisation between socioeconomic groups. Nevertheless, relatively little is known about the complex interaction and relative contribution of these factors to socioeconomic differences in healthcare utilisation. In order to improve understanding of why utilisation patterns differ by socioeconomic status (SES), the proposed systematic review will explore the main mechanisms that have been examined in quantitative research. METHODS AND ANALYSIS: The systematic review will follow the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines and will be conducted in Embase, PubMed, Scopus, Web of Science, Econlit and PsycInfo. Articles examining factors associated with the differences in primary and specialised healthcare utilisation between socioeconomic groups in Organisation for Economic Co-operation and Development (OECD) countries with UHC will be included. Further restrictions concern specifications of outcome measures, factors of interest, study design, population, language and type of publication. Data will be numerically summarised, narratively synthesised and thematically discussed. The factors will be categorised according to existing frameworks for barriers to healthcare access. ETHICS AND DISSEMINATION: No primary data will be collected. No ethics approval is required. We intend to publish a scientific article in an international peer-reviewed journal.

Socioeconomic differences in healthcare expenditure and utilization in The Netherlands.

BACKGROUND: Worldwide, socioeconomic differences in health and use of healthcare resources have been reported, even in countries providing universal healthcare coverage. However, it is unclear how large these socioeconomic differences are for different types of care and to what extent health status plays a role. Therefore, our aim was to examine to what extent healthcare expenditure and utilization differ according to educational level and income, and whether these differences can be explained by health inequalities. METHODS: Data from 18,936 participants aged 25-79 years of the Dutch Health Interview Survey were linked at the individual level to nationwide claims data that included healthcare expenditure covered in 2017. For healthcare utilization, participants reported use of different types of healthcare in the past 12 months. The association of education/income with healthcare expenditure/utilization was studied separately for different types of healthcare such as GP and hospital care. Subsequently, analyses were adjusted for general health, physical limitations, and mental health. RESULTS: For most types of healthcare, participants with lower educational and income levels had higher healthcare expenditure and used more healthcare compared to participants with the highest educational and income levels. Total healthcare expenditure was approximately between 50 and 150 % higher (depending on age group) among people in the lowest educational and income levels. These differences generally disappeared or decreased after including health covariates in the analyses. After adjustment for health, socioeconomic differences in total healthcare expenditure were reduced by 74-91 %. CONCLUSIONS: In this study among Dutch adults, lower socioeconomic status was associated with increased healthcare expenditure and utilization. These socioeconomic differences largely disappeared after taking into account health status, which implies that, within the universal Dutch healthcare system, resources are being spent where they are most needed. Improving health among lower socioeconomic groups may contribute to decreasing health inequalities and healthcare spending.