De vraag of gezondheidsverschillen over tijd groter of kleiner worden is complex. Wij beschrijven een methode om veranderingen over de tijd te toetsen, toegepast op verschillen in overgewicht tussen opleidingsgroepen. De jaarlijkse gezondheidsenquêtes (1999–2023) zijn gebruikt voor het berekenen van de Slope Index of Inequality (SII) voor absolute gezondheidsverschillen en de Relative Index of Inequality (RII) voor relatieve gezondheidsverschillen. Hiervoor wordt de relatieve opleidingspositie gebruikt. De keuze voor de SII en RII kwam tot stand na advies van Nederlandse experts op het gebied van gezondheidsonderzoek. Tussen 1999 en 2023 nam overgewicht in alle opleidingsgroepen toe. De analyse met de SII laat zien dat het aantal mensen met overgewicht sneller steeg in de groep laagst opgeleiden dan in de groep hoogst opgeleiden (SIItrend 0,005; 95

BACKGROUND:Reimbursement decisions for new health interventions focus on maximizing health gains, with limited attention to who benefits from these gains or the impact on income related health inequalities. This study aimed to examine the preferences of Dutch citizens regarding the distribution of health gains of new interventions across income groups. METHODS:A discrete choice experiment (DCE) was completed by 614 Dutch adults. Respondents were presented with 12 choice tasks. In each choice task, they were asked to choose between two health interventions that differed on the following attributes: total healthy life years gained, distribution of healthy life years gained across income groups, additional costs in terms of health insurance premium increases and whether the intervention was curative or preventive. Preferences were estimated using multinomial logit (MNL) models, relative attribute importance, willingness-to-pay, and willingness-to-trade total health gains. Preference heterogeneity was examined using latent class (LC) analyses. RESULTS:Respondents found the distribution of health gains by income the most important attribute in their decision between health interventions (relative importance [RI] = 40.5%, 95% CI: 38.3%-42.7%). Overall, respondents preferred an equal distribution of healthy life years gained across income groups (βhigher income groups = -1.427, 95% CI: -1.547--1.307; βlower-income groups = -0.315, 95% CI: -0.395--0.235). A health intervention should yield 14 283 (95% CI: 10 463-18, 102) additional healthy life years or reduce the yearly health insurance premium by €39.96 (95% CI: €29.03-€50.89) if it mainly favors lower-income groups. Preventive interventions were generally preferred over equally effective or more effective curative interventions (βprevention = 0.270, 95% CI: 0.204-0.336). While preferences displayed a similar direction across LCs, the classes differed in the RI assigned to the attributes. CONCLUSION:Our findings suggest societal support for interventions that prioritize preventive programs over equally effective or more effective curative interventions and prioritize interventions that provide equal benefits across different income groups.

BackgroundLower socioeconomic status is associated with lower self-rated health and higher healthcare expenditure. This study identifies which chronic conditions and social determinants contribute most to socioeconomic differences in self-rated health and healthcare expenditure.MethodsRegistry and survey data combining 3 socioeconomic indicators (income, education, and financial welfare), 26 social determinants, 20 chronic conditions, age, sex, self-rated health, and healthcare expenditure for 135,183 Dutch individuals aged 25-65, were linked at individual level. Oaxaca-Blinder decomposition analyses were conducted to quantify the relative contributions of chronic conditions and social determinants to socioeconomic differences in self-rated health and healthcare expenditure.ResultsPoorer self-rated health and higher healthcare expenditure among lower income groups were partly attributable to a higher prevalence of chronic conditions (33% and 70%, respectively). Acid-related disorders, cardiovascular diseases and psychological disorders contributed most to both differences in self-rated health and healthcare expenditure. Social determinants almost completely accounted for income differences in self-rated health. Social determinants explained more than the observed difference in healthcare expenditure between income groups, suggesting that, when adjusted for social determinants, lower income groups would have lower healthcare expenditure than higher income groups. Including both chronic conditions and social determinants in a single decomposition indicated that income security & social protection (28%), social & human capital (26%), and chronic conditions (23%) were equally important to income differences in self-rated health. For healthcare expenditure, chronic conditions and social determinants each accounted for approximately half of the socioeconomic differences.ConclusionsSocial determinants outside the healthcare sector accounted for almost all of the socioeconomic differences in self-rated health. This highlights the need for integrated policies across multiple domains, such as the social, economic and healthcare sector, to reduce avoidable health inequalities. Given that socioeconomic differences in healthcare expenditure were primarily associated with chronic conditions, prioritizing prevention of chronic conditions among lower socioeconomic groups can potentially reduce healthcare spending within this group and improve the healthcare system's sustainability and affordability.

The stage of the pandemic significantly affects people’s preferences for (the societal impacts of) COVID-19 policies. No discrete choice experiments were conducted when the COVID-19 pandemic was in a transition phase. This is the first study to empirically investigate how citizens weigh the key societal impacts of pandemic policies when the COVID-19 pandemic transitions into an endemic. We performed two discrete choice experiments among 2181 Dutch adults that included six attributes: COVID-19 deaths, physical health problems, mental health problems, financial problems, surgery delays and the degree to which individual liberties are restricted. We used latent class choice models to identify heterogeneous preferences for the impacts of COVID-19 measures across different groups of respondents. A large majority of the participants in this study was willing to accept deaths to avoid that citizens experience physical complaints, mental health issues, financial problems and the postponement of surgeries. The willingness to tolerate COVID-19 deaths to avoid these societal impacts differed substantially between participants. When participants were provided with information about the stringency of COVID-19 measures, they assigned relatively less value to preventing the postponement of non-urgent surgeries for 1–3 months across all classes. Having gone through a pandemic, most Dutch citizens clearly prefer pandemic policies that consider citizens’ financial situations, physical problems, mental health problems and individual liberties, alongside the effects on excess mortality and pressure on healthcare.

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.

ObjectivesCountries 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).DesignSystematic review following the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines. The review protocol was published in advance.Data sourcesEmbase, PubMed, Web of Science, Scopus, Econlit, and PsycInfo were searched on 9 March 2021 and 9 November 2022.Eligibility criteriaStudies that quantified the contribution of one or more factors to SES difference in HCU in OECD countries with UHC.Data extraction and synthesisStudies 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.ResultsOf 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.ConclusionsSES 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.

Abstract 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.

BackgroundVaccine uptake differs between social groups. Mobile vaccination units (MV-units) were deployed in the Netherlands by municipal health services in neighbourhoods with low uptake of COVID-19 vaccines.AimWe aimed to evaluate the impact of MV-units on vaccine uptake in neighbourhoods with low vaccine uptake.MethodsWe used the Dutch national-level registry of COVID-19 vaccinations (CIMS) and MV-unit deployment registrations containing observations in 253 neighbourhoods where MV-units were deployed and 890 contiguous neighbourhoods (total observations: 88,543 neighbourhood-days). A negative binomial regression with neighbourhood-specific temporal effects using splines was used to study the effect.ResultsDuring deployment, the increase in daily vaccination rate in targeted neighbourhoods ranged from a factor 2.0 (95% confidence interval (CI): 1.8-2.2) in urbanised neighbourhoods to 14.5 (95% CI: 11.6-18.0) in rural neighbourhoods. The effects were larger in neighbourhoods with more voters for the Dutch conservative Reformed Christian party but smaller in neighbourhoods with a higher proportion of people with non-western migration backgrounds. The absolute increase in uptake over the complete intervention period ranged from 0.22 percentage points (95% CI: 0.18-0.26) in the most urbanised neighbourhoods to 0.33 percentage point (95% CI: 0.28-0.37) in rural neighbourhoods.ConclusionDeployment of MV-units increased daily vaccination rate, particularly in rural neighbourhoods, with longer travel distance to permanent vaccination locations. This public health intervention shows promise to reduce geographic and social health inequalities, but more proactive and long-term deployment is required to identify its potential to substantially contribute to overall vaccination rates at country level.

Objective: Decision-making about breast cancer screening requires balanced and understandable information that takes prior beliefs of screening invitees into account. Methods: In qualitative interviews with 22 Dutch women who were invited for screening for the first time (49-52 years of age, varying health literacy levels), we gained insight in their beliefs on breast cancer and breast cancer screening, and explored how the current screening information matched these beliefs. Results: Breast cancer was perceived as an unpredictable, severe, and uncontrollable disease. Women considered screening as self-evident and an important mean to gain some control over breast cancer. Information on benefits of screening was in line with women's prior beliefs and confirmed women's main reasons to participate. Information about false-positive outcomes, overtreatment, and false negative outcomes did not correspond to women's prior beliefs and this information was generally not considered relevant for decision-making. Preferences for additional information merely concerned practical information on the screening procedure. Conclusion: Complex information on the harms of screening does not match women's beliefs and is not taken into account in their decision-making. Practice Implications: Information regarding breast cancer screening could be further aligned to prior beliefs by taking into account values, filling knowledge gaps and correct misconceptions.

Background Although risk factors for differences in SARS-CoV-2 infections between migrant and non-migrant populations in high income countries have been identified, their relative contributions to these SARS-CoV-2 infections, which could aid in the preparation for future viral pandemics, remain unknown. We investigated the relative contributions of pre-pandemic factors and intra-pandemic activities to differential SARS-CoV-2 infections in the Netherlands by migration background (Dutch, African Surinamese, South-Asian Surinamese, Ghanaians, Turkish, and Moroccan origin). Methods We utilized pre-pandemic (2011–2015) and intra-pandemic (2020–2021) data from the HELIUS cohort, linked to SARS-CoV-2 PCR test results from Public Health Service of Amsterdam (GGD Amsterdam). Pre-pandemic factors included socio-demographic, medical, and lifestyle factors. Intra-pandemic activities included COVID-19 risk aggravating and mitigating activities such as physical distancing, use of face masks, and other similar activities. We calculated prevalence ratios (PRs) in the HELIUS population that was merged with GGD Amsterdam PCR test data using robust Poisson regression (SARS-CoV-2 PCR test result as outcome, migration background as predictor). We then obtained the distribution of migrant and non-migrant populations in Amsterdam as of January 2021 from Statistics Netherlands. The migrant populations included people who have migrated themselves as well as their offspring. We used PRs and the population distributions to calculate population attributable fractions (PAFs) using the standard formula. We used age and sex adjusted models to introduce pre-pandemic factors and intra-pandemic activities, noting the relative changes in PAFs. Results From 20,359 eligible HELIUS participants, 8,595 were linked to GGD Amsterdam PCR test data and included in the study. Pre-pandemic socio-demographic factors (especially education, occupation, and household size) resulted in the largest changes in PAFs when introduced in age and sex adjusted models (up to 45%), followed by pre-pandemic lifestyle factors (up to 23%, especially alcohol consumption). Intra-pandemic activities resulted in the least changes in PAFs when introduced in age and sex adjusted models (up to 16%). Conclusion Interventions that target pre-pandemic socio-economic status and other drivers of health inequalities between migrant and non-migrant populations are urgently needed at present to better prevent infection disparities in future viral pandemics.

Background: Mobile vaccination units (MV-units) were deployed by municipal health services in neighbourhoods with low uptake of COVID-19 vaccines. This study estimates their impact on vaccine uptake in those typically more socially vulnerable neighbourhoods.Methods: Dutch national-level register of COVID-19 vaccinations (CIMS) and MV-Unit deployment registrations were used to assess the increase of first dose COVID-19 vaccines in 253 neighbourhoods where MV-units were deployed and 890 contiguous neighbourhoods (total observations = 88,543 neighbourhood-days). A Negative Binomial regression with neighbourhood-specific temporal effects using splines was used to assess the effect of MV-units on vaccine uptake.Findings: During deployment, the daily uptake in targeted neighbourhoods increased with a factor 2·0 (95% CI: 1·8 - 2·2) in urbanised neighbourhoods and a factor 14·5 (95% CI:11·6 - 18·0) in rural neighbourhoods. Effects were larger in neighbourhoods with more voters for right-wing Christian parties and positive but smaller in neighbourhoods with a higher proportion people with migration backgrounds. The absolute increase in uptake over the complete intervention period ranged from 0·22 percentage points (95% CI: 0·18-0·26) in the most urbanised neighbourhoods to 0·33 percentage point (95% CI: 0·28-0·37) in rural neighbourhoods.Interpretation: Deployment of MV-units substantially increased daily vaccine uptake, particularly in rural neighbourhoods, with longer travel distance to permanent locations. This public health intervention shows substantial promise, also to reduce geographic and social health inequalities, but more proactive and long-term deployment is required to identify its potential to substantially contribute to overall vaccination rates at country level.Funding: The research was funded by the Dutch ministry of Public Health, Welfare and Sport.Declaration of Interest: All authors declare no conflict of interests.Ethical Approval: All methods in this study were carried out in accordance with relevant guidelines and regulations of the Declaration of Helsinki. The Centre for Clinical Expertise at the RIVM assessed the above-mentioned research proposal and verified whether the work complies with the specific conditions as stated in article 1 of the Dutch law on Medical Research Involving Human Subjects (WMO) (https://wetten.overheid.nl/BWBR0009408/2022-07-01). They are of the opinion that the research does not fulfil one or both of the conditions and therefore conclude that approval from the ethical research committee was deemed unnecessary, as the study was based on ecological data. Data was only included for vaccinated individuals who have consented for this information to be registered in CIMS. No administrative permissions were required to access the raw data used in our study, since the data about vaccine uptake from CIMS for which consent was given are owned by RIVM and data on determinants were publicly available.

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.

Background: Although it is known that health literacy (HL) plays an explanatory role in educational inequalities in health, it is unknown whether this role varies across age groups. Objective: The purpose of this study was to investigate whether the mediating role of HL in educational inequalities in four health outcomes varies across age groups: age 46 to 58 years, age 59 to 71 years, and age 72 to 84 years. Methods: We used data from the Dutch Doetinchem Cohort Study, which included 3,448 participants. We included years of education as predictor, chronic illness prevalence and incidence, mental and self-perceived health as outcomes, and HL, based on self-report, as mediator. We used multiple-group mediation models to compare indirect effects across age groups. Key Results: In the complete sample without age stratification, HL partly mediated the effect of education on all health outcomes except for incidence of chronic diseases. These indirect effect estimates were larger for subjective (self-perceived health, proportion mediated [PM] = 37%, and mental health, PM = 37%) than for objective health outcomes (prevalence of chronic disease, PM = 17%). For the prevalence of chronic disease, the indirect effect estimate was significantly larger among individuals age 46 to 58 years compared to individuals age 59 to 71 years and for incidence of chronic disease also compared to individuals age 72 to 84 years. All other indirect effect estimates did not differ significantly between age groups. Using an alternative cut-off point for HL or adjusting for cognitive functioning did not meaningfully change the results. Conclusions: Overall, we found that the explanatory role of HL in educational inequalities in mental and subjective health was stable but that it varied across age groups for chronic diseases, where it was largest among individuals age 46 to 58 years. Future studies may investigate the benefits of starting to intervene on HL from a younger age but means to improve HL may also benefit the subjective health of older adults with lower education. [ HLRP: HL Research and Practice . 2023;7(1):e26–e38.] Plain Language Summary: This study examined age-group differences in the mediating role of HL in the relationship between education and health. Overall, we found that the explanatory role of HL in educational inequalities in mental and subjective health was stable but that it varied across age groups for chronic diseases, where it was largest among individuals age 46 to 58 years compared to individuals age 59 to 71 years and individuals age 72 to 84 years.

Behavioural sciences have complemented medical and epidemiological sciences in the response to the SARS-CoV-2 pandemic. As vaccination uptake continues to increase across the EU/EEA - including booster vaccinations - behavioural science research remains important for both pandemic policy, planning of services and communication. From a behavioural perspective, the following three areas are key as the pandemic progresses: (i) attaining and maintaining high levels of vaccination including booster doses across all groups in society, including socially vulnerable populations, (ii) informing sustainable pandemic policies and ensuring adherence to basic prevention measures to protect the most vulnerable population, and (iii) facilitating population preparedness and willingness to support and adhere to the reimposition of restrictions locally or regionally whenever outbreaks may occur. Based on mixed-methods research, expert consultations, and engagement with communities, behavioural data and interventions can thus be important to prevent and effectively respond to local or regional outbreaks, and to minimise socioeconomic and health disparities. In this Perspective, we briefly outline these topics from a European viewpoint, while recognising the importance of considering the specific context in individual countries.

BACKGROUND:The COVID-19 outbreak early 2020 was followed by an unprecedented package of measures. The relative calmness of the pandemic early 2022 provides a momentum to prepare for various scenarios. OBJECTIVES:As acceptance of COVID-19 measures is key for public support we investigated citizens' preferences towards imposing measures in four scenarios: 1) spring/summer scenario with few hospitalizations; 2) autumn/winter scenario with many hospitalizations; 3) a new contagious variant, the impact on hospitalizations is unclear; 4) a new contagious variant, hospitalizations will substantially increase. METHODS:Study 1 comprised a Participatory Value Evaluation (PVE) in which 2011 respondents advised their government on which measures to impose in the four scenarios. Respondents received information regarding the impact of each measure on the risk that the health system would be overloaded. To triangulate the results, 2958 respondents in Study 2 evaluated the acceptability of the measures in each scenario. RESULTS:Measures were ranked similarly by respondents in Study 1 and 2: 1) the majority of respondents thought that hygiene measures should be upheld, even in the spring/summer; 2) the majority supported booster vaccination, working from home, encouraging self-testing, and mandatory face masks from scenario 2 onwards; 3) even in scenario 4, lockdown measures were not supported by the majority. Young respondents were willing to accept more risks for the health system than older respondents. CONCLUSION:The results suggest that policies that focus on prevention (through advising low-impact hygiene measures) and early response to moderate threats (by scaling up to moderately restrictive measures and boostering) can count on substantial support. There is low support for lockdown measures even under high-risk conditions, which further emphasizes the importance of prevention and a timely response to new threats. Our results imply that young citizens' concerns, in particular, should be addressed when restrictive COVID-19 measures are to be implemented.

SamenvattingVaccinatie is een belangrijk onderdeel in de bestrijding van het COVID-19-virus. Een voorspeller van het aandeel mensen dat daadwerkelijk een vaccinatie zal nemen is de vaccinatiebereidheid onder de bevolking. Uit buitenlandse literatuur blijkt dat de vaccinatiebereidheid onder mensen met een lagere sociaaleconomische status lager ligt dan onder andere groepen. In deze bijdrage beschrijven we in hoeverre dit ook in Nederland het geval is en laten we zien hoe risicoperceptie, vertrouwen in de werking en veiligheid van het vaccin en gezondheidsvaardigheden hier mogelijk mee samenhangen. Tot slot belichten we een aantal interventiestrategieën die positief aan de vaccinatiebereidheid onder laagopgeleiden kunnen bijdragen.

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.

Abstract 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.

INTRODUCTION:It is increasingly considered important that people make an autonomous and informed decision concerning colorectal cancer (CRC) screening. However, the realisation of autonomy within the concept of informed decision-making might be interpreted too narrowly. Additionally, relatively little is known about what the eligible population believes to be a 'good' screening decision. Therefore, we aimed to explore how the concepts of autonomous and informed decision-making relate to how the eligible CRC screening population makes their decision and when they believe to have made a 'good' screening decision.METHODS:We conducted 27 semi-structured interviews with the eligible CRC screening population (eighteen CRC screening participants and nine non-participants). The general topics discussed concerned how people made their CRC screening decision, how they experienced making this decision and when they considered they had made a 'good' decision.RESULTS:Most interviewees viewed a 'good' CRC screening decision as one based on both reasoning and feeling/intuition, and that is made freely. However, many CRC screening non-participants experienced a certain social pressure to participate. All CRC screening non-participants viewed making an informed decision as essential. This appeared to be the case to a lesser extent for CRC screening participants. For most, experiences and values were involved in their decision-making.CONCLUSION:Our sample of the eligible CRC screening population viewed aspects related to the concepts of autonomous and informed decision-making as important for making a 'good' CRC screening decision. However, in particular the existence of a social norm may be affecting a true autonomous decision-making process. Additionally, the present concept of informed decision-making with its strong emphasis on making a fully informed and well-considered decision does not appear to be entirely reflective of the process in practice. More efforts could be made to attune to the diverse values and factors that are involved in deciding about CRC screening participation.

Abstract It is unclear to what extent self‐employed choose to become self‐employed. This study aimed to compare the health care expenditures—as a proxy for health—of self‐employed individuals in the year before they started their business, to that of employees. Differences by sex, age, and industry were studied. In total, 5,741,457 individuals aged 25–65 years who were listed in the tax data between 2010 and 2015 with data on their health insurance claims were included. Self‐employed and employees were stratified according to sex, age, household position, personal income, region, and industry for each of the years covered. Weighted linear regression was used to compare health care expenditures in the preceding (year x–1) between self‐employed and employees (in year x). Compared with employees, expenditures for hospital care, pharmaceutical care and mental health care were lower among self‐employed in the year before they started their business. Differences were most pronounced for men, individuals ≥40 years and those working in the industry and energy sector, construction, financial institutions, and government and care. We conclude that healthy individuals are overrepresented among the self‐employed, which is more pronounced in certain subgroups. Further qualitative research is needed to investigate the reasons why these subgroups are more likely to choose to become self‐employed.