Absolute income is commonly used in studies of health inequalities, however it does not reflect spending patterns, debts, or expectations. These aspects are reflected in measures concerning perceived ...income inadequacy. While health inequities by absolute income or perceived income inadequacy are well established, few studies have explored the interplay of absolute income and perceived income inadequacy in relation to health.
Multiple data sources were linked into a nationally representative dataset (n = 445,748) of Dutch adults (18 +). The association between absolute income, perceived income inadequacy and health (self-reported health, chronic disease and psychological distress) was tested using logistic and Poisson regressions, controlling for various potential confounders (demographics, education) and mastery. Interactions were tested to check the association between perceived income inadequacy and health for different absolute income groups.
Perceived income inadequacy was reported at every absolute income group (with 42% of individuals in the lowest income group and 5% of individuals in the highest income group). Both absolute income and perceived income inadequacy were independently associated with health. The adjusted relative risk (RR) for lowest absolute income group is 1.11 (1.08-1.1.14) and 1.28 (1.24-1.32) for chronic disease and self-reported health respectively, and the Odds Ratio (OR) for psychological distress is 1.28 (1.16-1.42). For perceived income inadequacy the RR's were 1.41 (1.37-1.46) and 1.49 (1.44-1.54) and the OR for psychological distress is 3.14 (2.81-3.51). Mastery appeared to be an important mediator for the relationship between perceived income inadequacy, poor self-rated health and psychological distress.
Absolute income and perceived income inadequacy reflect conceptually different aspects of income and are independently associated with health outcomes. Perceived income inadequacy may be accounted for in health inequality studies, alongside measures of absolute income. In policy-making, targeting perceived income inadequacy might have potential to reduce health inequalities.
Type 2 diabetes mellitus (T2DM) is a common chronic disease that disproportionally affects disadvantaged groups. People with a low socioeconomic position (SEP) have increased risk of T2DM and people ...with a low SEP and T2DM have higher HbA
-levels compared to people with T2DM and high SEP. The aim of this study is to analyze longitudinal socioeconomic differences in health-related functioning in people with T2DM.
Longitudinal data from 1,537 participants of The Maastricht Study with T2DM were used (32.6% female, mean (SD) age 62.9 (7.7) years). SEP was determined by baseline measures of education, occupation and income. Health-related functioning (physical, mental and social) was measured with the Short-Form Health Survey and the Impact on Participation and Autonomy survey (all scored from 0 to 100). Associations of SEP and health-related functioning were studied annually over a 10-year period (median (IQR) 7.0 (5.0) years, baseline 2010-2018) using linear mixed methods adjusting for demographics, HbA
-levels and lifestyle factors.
Participants with a low SEP had significantly worse health-related functioning compared to those with a high SEP. For example, participants with low income had lower scores for physical (-4.49CI -5.77;-3.21), mental (-2.61-3.78,-1.44) and social functioning (-9.76-12.30;-7.23) compared to participants with high income on a scale from 0 to 100. In addition, participants with a low education significantly declined more over time in mental (score for interaction education with time - 0.23-0.37;-0.09) and social functioning (-0.44-0.77;-0.11) compared to participants with high education. Participants with low and intermediate incomes significantly declined more over time in physical functioning (-0.17 -0.34, -0.01 and - 0.18 -0.36, 0.00) compared to participants with high income.
Among people with T2DM, those with a lower SEP had worse health-related functioning in general than people with a higher SEP. Additionally, people with T2DM and low education developed poorer mental and social functioning over time compared to people with T2DM and high education. People with T2DM and low or intermediate income declined more in physical functioning over time than those with high incomes. In addition to HbA
-levels and lifestyle patterns, more attention is needed for socioeconomic differences in health-related functioning for people living with T2DM.
Objective:
We examined the association between low socioeconomic position (SEP) and Type 2 Diabetes Mellitus (T2DM), and the mediating role of psychosocial work environment by using counterfactual ...mediation analysis.
Methods:
Data from 8,090 participants of The Maastricht Study were analysed. SEP indicators (education, income, occupation), self-reported psychosocial work stressors, (pre)diabetes by oral glucose tolerance test were measured at baseline. Incident T2DM was self-reported per annum up to 9 years. Cox regression and causal mediation analyses were performed.
Results:
2.8% (
N
= 172) of the participants without T2DM at baseline reported incident T2DM. People with lower SEP more often had prevalent T2DM (e.g., education OR = 2.49, 95% CI: 2.16–2.87) and incident T2DM (e.g., education HR = 2.21, 95% CI: 1.53–3.20) than higher SEP. Low job control was associated with prevalent T2DM (OR = 1.44 95% CI: 1.25–1.67). Job control partially explained the association between income and prevalent T2DM (7.23%). Job demand suppressed the associations of education and occupation with prevalent T2DM. The mediation models with incident T2DM and social support were not significant.
Conclusion:
Socioeconomic inequalities in T2DM were present, but only a small part of it was explained by the psychosocial work environment.
Objectives:
Loneliness has been associated with unhealthy behavior, poorer health, and increased morbidity. However, the costs of loneliness are poorly understood.
Methods:
Multiple sources were ...combined into a dataset containing a nationally representative sample (
n
= 341,376) of Dutch adults (18+). The association between loneliness and total, general practitioner (GP), specialized, pharmaceutical, and mental healthcare expenditure was tested using Poisson and Zero-inflated negative binomial models, controlling for numerous potential confounders (i.e., demographic, socioeconomic, lifestyle-related factors, self-perceived health, and psychological distress), for four age groups.
Results:
Controlling for demographic, socioeconomic, and lifestyle-related factors, loneliness was indirectly (via poorer health) associated with higher expenditure in all categories. In fully adjusted models, it showed a direct association with higher expenditure for GP and mental healthcare (0.5 and 11.1%, respectively). The association with mental healthcare expenditure was stronger in younger than in older adults (for ages 19–40, the contribution of loneliness represented 61.8% of the overall association).
Conclusion:
Loneliness contributes to health expenditure both directly and indirectly, particularly in younger age groups. This implies a strong financial imperative to address this issue.
Loneliness is a growing public health issue. It is more common in disadvantaged groups and has been associated with a range of poor health outcomes. Loneliness may also form an independent pathway ...between socio-economic disadvantage and poor health. Therefore, the aim of this study was to explore the contribution of loneliness to socio-economic health inequalities. These contributions were studied in a Dutch national sample (n = 445,748 adults (≥19 y.o.)) in Poisson and logistic regression models, controlling for age, gender, marital status, migration background, BMI, alcohol consumption, smoking, and physical activity. Loneliness explained 21% of socioeconomic health inequalities between the lowest and highest socio-economic groups in self-reported chronic disease prevalence, 27% in poorer self-rated health, and 51% in psychological distress. Subgroup analyses revealed that for young adults, loneliness had a larger contribution to socioeconomic gaps in self-rated health (37%) than in 80+-year-olds (16%). Our findings suggest that loneliness may be a social determinant of health, contributing to the socioeconomic health gap independently of well-documented factors such as lifestyles and demographics, in particular for young adults. Public health policies targeting socioeconomic health inequalities could benefit from integrating loneliness into their policies, especially for young adults.
Regional differences in health further explained Meisters, Rachelle; Putrik, Polina; Westra, Daan ...
TSG - Tijdschrift voor gezondheidswetenschappen,
11/2022, Volume:
100, Issue:
4
Journal Article
Peer reviewed
Open access
Like in most Western countries, regional health inequalities are also present in the Netherlands. Explaining these inequalities is necessary for policymakers to target interventions to reduce them. ...Regional health inequalities are usually attributed to demographic and socio-economic factors, while lifestyle and psychosocial factors are increasingly shown to impact individuals’ health. Therefore, this study analyses the role of lifestyle, loneliness, and self-mastery in explaining regional inequalities, in addition to demographic factors and SES, for self-rated health, presence of chronic diseases, and psychological distress. Analyses are performed in the linked dataset from the Dutch Public Health Services, Statistics Netherlands, and the National Institute for Public Health and the Environment for the year 2016 (
n
= 334,721). The results show that lifestyle, loneliness and self-mastery contribute to the regional health inequalities in self-rated health and presence of chronic diseases. For psychological distress, both loneliness and self-mastery contribute to the regional health inequalities. Addressing lifestyle and psychosocial factors can offer policymakers additional pathways to bridge regional health inequalities. In this study, the region of Zuid-Limburg represents the reference region. Use compare regions for health and healthcare costs (Regiovergelijker gezondheid en zorgkosten
1
) in order to select all other Dutch regions as reference region.
Samenvatting
Zorgkosten nemen toe en variëren sterk tussen Nederlandse regio’s. Het verklaren van deze regionale verschillen kan beleidsmakers helpen om gericht te interveniëren en verdere stijgingen ...in zorgkosten te beperken. Bij het verklaren van regionale verschillen in zorgkosten wordt veelal gekeken naar regionale verschillen in demografische opbouw en sociaaleconomische status (SES). Gezondheid, leefstijl, eenzaamheid en zelfregie zouden echter ook met zorgkosten samenhangen. Daarom analyseert dit onderzoek, naast demografie en SES, wat gezondheid, leefstijl (BMI, alcoholconsumptie, roken en bewegen), eenzaamheid en zelfregie bijdragen aan de verklaring van regionale verschillen in zorgkosten. We analyseren gekoppelde data van de GGD, het CBS, het RIVM en Vektis (
n
= 334.721) met Poisson- en zero-inflated binomial regressies. Uit de resultaten blijkt dat gezondheid, leefstijl, eenzaamheid en zelfregie op verschillende wijzen bijdragen aan het verklaren van regionale verschillen in de diverse kostensoorten. Voor huisartsconsultkosten houden regionale verschillen na inclusie van alle verklarende factoren stand. De regionale verschillen wat betreft geestelijke gezondheidszorg-, farmacie- en medisch specialistische kosten komen minder vaak voor dan voor huisartsconsultkosten, en kunnen deels worden verklaard door leefstijl, eenzaamheid en zelfregie. Voor totale zorgkosten kunnen regionale verschillen grotendeels verklaard worden door gezondheid en leefstijl. Leefstijl, eenzaamheid en zelfregie kunnen beleidsmatige aanknopingspunten bieden om verdere stijgingen in zorgkosten tegen te gaan. In dit artikel is de regio Zuid-Limburg de referentieregio. Met de Regiovergelijker gezondheid en zorgkosten kunnen alle regio’s als referentieregio worden gekozen.
Samenvatting
Nederland kent gezondheidsverschillen tussen regio’s. Het verklaren van deze verschillen kan beleidsmakers helpen om gericht te interveniëren en deze verschillen te verkleinen. Bij het ...verklaren van deze regionale gezondheidsverschillen wordt veelal gekeken naar de bijdrage van verschillen in demografische opbouw en sociaaleconomische status (SES). Tegelijkertijd worden leefstijl en psychosociale factoren ook in verband gebracht met de gezondheid van het individu. Daarom analyseert dit onderzoek, naast demografie en SES, de bijdrage van leefstijl, eenzaamheid en zelfregie aan de verklaring van regionale verschillen in zelfervaren gezondheid, aanwezigheid van chronische ziekten en het risico op het ontwikkelen van een angststoornis of depressie. We gebruiken daarvoor een gekoppelde dataset van de GGD, het CBS en het RIVM voor het jaar 2016 (
n
= 334.721). Uit de resultaten blijkt dat leefstijl, eenzaamheid en zelfregie ook bijdragen aan het verklaren van regionale verschillen in zelfervaren gezondheid (prevalentieratio’s (PR) variërend van 0,72–0,93 tot 0,83–0,95) en chronische ziekten (PR’s van 0,81–0,95 tot 0,85–0,96). Voor het risico op een angststoornis of depressie blijken vooral eenzaamheid en zelfregie bij te dragen aan de verklaring van regionale gezondheidsverschillen (oddsratio’s van 0,65–1,27 tot 0,76–1,22). Leefstijl- en psychosociale factoren kunnen dan ook beleidsmatige aanknopingspunten bieden voor de aanpak van regionale gezondheidsverschillen. In dit artikel is de regio Zuid-Limburg de referentieregio. Met de Regiovergelijker gezondheid en zorgkosten kunnen alle regio’s als referentieregio worden gekozen.
Healthcare costs in the Netherlands are rising and vary considerably among regions. Explaining regional differences in healthcare costs can help policymakers in targeting appropriate interventions in ...order to restrain costs. Factors usually taken into account when analyzing regional differences in healthcare costs are demographic structure and socioeconomic status (SES). However, health, lifestyle, loneliness and mastery have also been linked to healthcare costs. Therefore, this study analyzes the contribution of health, lifestyle factors (BMI, alcohol consumption, smoking and physical activity), loneliness, and mastery to regional differences in healthcare costs. Analyses are performed in a linked dataset (
n
= 334,721) from the Dutch Public Health Services, Statistics Netherlands, the National Institute for Public Health and the Environment (year 2016), and the healthcare claims database Vektis (year 2017) with Poisson and zero-inflated binomial regressions. Regional differences in general practitioner consult costs remain significant even after taking into account health, lifestyle, loneliness, and mastery. Regional differences in costs for mental, pharmaceutical, and specialized care are less pronounced and can be explained to a large extent. For total healthcare costs, regional differences are mostly explained through the factors included in this study. Hence, addressing lifestyle factors, loneliness and mastery can help policymakers in restraining healthcare costs. In this study, the region of Zuid-Limburg represents the reference region. Use compare regions for health and healthcare costs (Regiovergelijker gezondheid en zorgkosten) in order to select all other Dutch regions as reference region.
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