How are lifestyle factors associated with socioeconomic differences in health care costs? Evidence from full population data in the Netherlands

Personality traits and quality of life: a cross-sectional study in a middle-aged Iranian general population

INTRODUCTION

Despite considerable research on the association between Personality Traits (PT) and Quality of Life (QoL) in patients and older adults, this association remains poorly understood among the middle-aged general population.This investigation examines how each PT is associated with total QoL and its dimensions.

METHODS

The present investigation utilized data collected from a cross-sectional survey involving 786 families in Isfahan (644 female/wife respondents), Iran. QoL and PT were assessed using the validated WHOQOL-BREF and NEO-FFI questionnaires. Other data including demographic and socioeconomic status (SES) were also collected. Statistical analyses included bivariate correlation and simple and multiple linear and logistic regressions.

RESULTS

Mean value of Psychological health [Mean difference = -4.34, P = 0.003], Physical health [mean difference = -3.93, P = 0.004], and total score of QoL [mean difference = -3.21, P = 0.049] were all significantly lower in women than men. Higher SES score was consistently associated with greater QoL scores (r > 0, P < 0.05). The physical domain and total QoL scores have been negatively correlated with the spouse's age (r < 0, P < 0.05). In crude and adjusted models, higher Neuroticism scores were inversly associated with higher scores of all QoL domains and total QoL (OR < 1, P < 0.05, for all models) while others personality traits except Openness showed a direct association (OR > 1, P < 0.05). Linear regression analysis also confirmed that higher Neuroticism scores were linked to poorer QoL scores (Betacoefficient < 0, P < 0.05), while all other traits, except Openness, showed a positive association (Betacoefficient > 0, P < 0.05).

CONCLUSION

This study provides robust evidence about the significant association of PT with QoL outcomes in middle aged people. This significant association highlights the importance of considering these traits in clinical applications, as tailored interventions based on personality profiles can effectively enhance the well-being of middle-aged individuals.

Differences in eHealth Access, Use, and Perceived Benefit Between Different Socioeconomic Groups in the Dutch Context: Secondary Cross-Sectional Study

BACKGROUND

There is a growing concern that digital health care may exacerbate existing health disparities. Digital health care or eHealth encompasses the digital apps that are used in health care. Differences in access, use, and perceived benefits of digital technology among socioeconomic groups are commonly referred to as the digital divide. Current research shows that people in lower socioeconomic positions (SEPs) use eHealth less frequently.

OBJECTIVE

This study aims to (1) investigate the association between SEP and eHealth access to, use of, and perceived benefit within the adult Dutch population and (2) evaluate disparities in eHealth access, use, and perceived benefit through three socioeconomic variables-education, standardized income, and the socioeconomic status of the neighborhood.

METHODS

A secondary analysis was conducted on data from the Nivel Dutch Health Care Consumer Panel (response rate 57%, 849/1500), to assess access to, use of, and perceived benefits from eHealth. These data were collected to monitor eHealth developments in the Netherlands. eHealth was examined through two concepts: (1) eHealth in general and (2) websites, apps, and wearables. Results were stratified into 9 SEP populations based on 3 indicators-education, standardized income, and socioeconomic status level of the neighborhood. Logistic regression analyses were performed to evaluate whether the outcomes varied significantly across different SEP groups. Age was included as a covariate to control for confounding.

RESULTS

This study confirms the association between eHealth and SEP and shows that low SEP respondents have less access (odds ratio [OR] 5.72, 95% CI 3.06-10.72) and use (OR 4.96, 95% CI 2.66-9.24) of eHealth compared to medium or high SEP respondents. Differences were most profound when stratifying for levels of education.

CONCLUSIONS

The access to and use of eHealth has a socioeconomic gradient and emphasizes that SEP indicators cannot be used interchangeably to assess eHealth access and use. The results underline the importance of activities and policies aimed at improving eHealth accessibility and usage among low SEP groups to mitigate disparities in health between different socioeconomic groups.

Mendelian Randomization Analysis of the Causal Effect of Cigarette Smoking on Hospital Costs

INTRODUCTION

Knowledge of the impact of smoking on health care costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error.

AIMS AND METHODS

We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome-wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300 045 individuals (mean age: 57 years at baseline, range 39-72 years) in the UK Biobank. We followed individuals up for a mean of 6 years.

RESULTS

Genetic liability to initiate smoking (ever vs. never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias.

CONCLUSIONS

Our findings have implications for the potential cost-effectiveness of smoking interventions.

IMPLICATIONS

We report the first Mendelian Randomization analysis of the causal effect of smoking on health care costs. Using two smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of antismoking interventions and to understand the scale of externalities associated with this behavior.

Impact of Healthy Lifestyle Factors on Life Expectancy and Lifetime Health Care Expenditure: Nationwide Cohort Study

BACKGROUND

The association between lifestyle risk factors and the risk of mortality and chronic diseases has been established, while limited research has explored the impact of healthy lifestyle factors on lifetime health care expenditure using longitudinal individual data.

OBJECTIVE

We aimed to determine the individual and combined effects of 5 healthy lifestyle factors on life expectancy and lifetime health care expenditure in Taiwan.

METHODS

Using data from the National Health Interview Survey cohort, 5 healthy lifestyle behaviors were defined and analyzed: nonsmoking, avoiding excessive alcohol consumption, engaging in sufficient physical activity, ensuring sufficient fruit and vegetable intake, and maintaining a normal weight. We used a rolling extrapolation algorithm that incorporated inverse probability of treatment weighting to estimate the life expectancy and lifetime health care expenditure of the study populations with and without healthy lifestyle factors.

RESULTS

A total of 19,893 participants aged ≥30 (mean age 48.8, SD 13.4) years were included, with 3815 deaths recorded during a median follow-up period of 15.6 years. The life expectancy and per capita estimated lifetime health care expenditures for the overall study population were 35.32 years and US $58,560, respectively. Multivariable-adjusted hazard ratios for all-cause mortality in participants adhering to all 5 healthy lifestyle factors, compared with those adhering to none, were 0.37 (95% CI 0.27-0.49). We found significant increases in life expectancy for nonsmokers (2.31 years; 95% CI 0.04-5.13; P=.03), those with sufficient physical activity (1.85 years; 95% CI 0.25-4.34; P=.02), and those with adequate fruit and vegetable intake (3.25 years; 95% CI 1.29-6.81; P=.01). In addition, nonsmokers experienced a significant reduction in annual health care expenditure (-9.78%; 95% CI -46.53% to -1.45%; P=.03), as did individuals maintaining optimal body weight (-18.36%; 95% CI -29.66% to -8.57%; P=.01). Overall, participants adhering to all 5 healthy lifestyle behaviors exhibited a life gain of 7.13 years (95% CI 1.33-11.11; P=.02) compared with those adhering to one or none, with a life expectancy of 29.19 years (95% CI 25.45-33.62). Furthermore, individuals adopting all 5 healthy lifestyle factors experienced an average annual health care expenditure reduction of 28.12% (95% CI 4.43%-57.61%; P=.02) compared with those adopting one or none.

CONCLUSIONS

Adopting a healthy lifestyle is associated with a longer life expectancy and a reduction of health care expenditure in Taiwanese adults. This contributes to a more comprehensive understanding of the impact of healthy lifestyle factors on the overall health and economic burden.

Do differences in sport participation contribute to socioeconomic health inequalities? Evidence from the Lifelines cohort study on all-cause mortality, diabetes and obesity

Little is known about the role of sport participation in socioeconomic health inequalities. We studied the association between different aspects of sport participation with all-cause mortality, type 2 diabetes mellitus (T2DM) and obesity, including inequalities between socioeconomic subpopulations. Using the Dutch Lifelines cohort study (n = 84,230), we assessed the associations of sport participation, as well as the amount, intensity, type and number of sports, with all-cause mortality, T2DM and obesity in individuals. We studied the effect of sport participation on health outcomes within and between educational categories. Outcomes were compared with moderate to vigorous physical activity (MVPA). Sport participation was significantly associated with lower mortality (HR = 0.81), T2DM (HR = 0.70), and obesity (HR = 0.77). No significant additional effects of the amount or intensity of sport participation were found, while participating in teams sport was associated with significantly lower mortality (HR = 0.53) compared with other types of sport. These effects were similar among educational categories. Sport participation explained between 11% (T2DM and obesity) and 22% (mortality) of health inequalities between educational categories. This was more than twice the effect size of MVPA. The sensitivity analysis with net income as the socioeconomic indicator showed similar results. Our results suggest that to reduce socioeconomic differences in health, public health policies should focus on increasing sport participation in groups with a low socioeconomic status, rather than increasing the amount or intensity of sport participation, or MVPA in general.

Impact of physical activity on healthcare costs: a systematic review

BACKGROUND

This systematic review aims to describe the relation between physical inactivity and healthcare costs, by taking into account healthcare costs of physical-inactivity-related diseases (common practice), including physical-activity-related injuries (new) and costs in life-years gained due to avoiding diseases (new), whenever available. Moreover, the association between physical inactivity and healthcare costs may both be negatively and positively impacted by increased physical activity.

METHODS

A systematic review was conducted, including records reporting on physical (in)activity in relation to healthcare costs for a general population. Studies were required to report sufficient information to calculate the percentage of total healthcare costs potentially attributable to physical inactivity.

RESULTS

Of the 264 records identified, 25 were included in this review. Included studies showed substantial variation in the assessment methods of physical activity and in type of costs included. Overall, studies showed that physical inactivity is related to higher healthcare costs. Only one study included costs of healthcare resources used in prolonged life when physical-inactivity-related diseases were averted, showing net higher healthcare costs. No study included healthcare costs for physical-activity-related injuries.

CONCLUSIONS

Physical inactivity is associated with higher healthcare costs in the general population in the short-term. However, in the long-term aversion of diseases related with physical inactivity may increase longevity and, as a consequence, healthcare costs in life-years gained. Future studies should use a broad definition of costs, including costs in life-years gained and costs related to physical-activity-related injuries.

Effects of Education and Income on Incident Type 2 Diabetes and Cardiovascular Diseases: a Dutch Prospective Study

BACKGROUND

Education and income, as two primary socioeconomic indicators, are often used interchangeably in health research. However, there is a lack of clear distinction between these two indicators concerning their associations with health.

OBJECTIVE

This study aimed to investigate the separate and combined effects of education and income in relation to incident type 2 diabetes and cardiovascular diseases in the general population.

DESIGN AND PARTICIPANTS

Participants aged between 30 and 65 years from the prospective Dutch Lifelines cohort study were included. Two sub-cohorts were subsequently created, including 83,759 and 91,083 participants for a type 2 diabetes cohort and a cardiovascular diseases cohort, respectively.

MAIN MEASURES

Education and income level were assessed by self-report questionnaires. The outcomes were incident type 2 diabetes and cardiovascular diseases (defined as the earliest non-fatal cardiovascular event).

KEY RESULTS

A total of 1228 new cases of type 2 diabetes (incidence 1.5%) and 3286 (incidence 3.6%) new cases of cardiovascular diseases were identified, after a median follow-up of 43 and 44 months, respectively. Low education and low income (<1000 euro/month) were both positively associated with a higher risk of incident type 2 diabetes (OR 1.24 [95%CI 1.04-1.48] and OR 1.71 [95%CI 1.30-2.26], respectively); and with a higher risk of incident cardiovascular diseases (OR 1.15 [95%CI 1.04-1.28] and OR 1.24 [95%CI 1.02-1.52], respectively); independent of age, sex, lifestyle factors, BMI, clinical biomarkers, comorbid conditions at baseline, and each other. Results from the combined associations of education and income showed that within each education group, a higher income was associated with better health; and similarly, a higher education was associated with better health within each income group, except for the low-income group.

CONCLUSIONS

Education and income were both independently associated with incident type 2 diabetes and cardiovascular diseases. The combined associations of these two socioeconomic indicators revealed that within each education or income level, substantial health disparities existed across strata of the other socioeconomic indicator. Education and income are two equally important socioeconomic indicators in health, and should be considered simultaneously in health research and policymaking.

The effects of health insurance and physical exercise participation on life satisfaction of older people in China-Based on CHNS panel data from 2006 to 2015

Background

In China, the problem of aging population has become more and more serious. The factors influencing life satisfaction of older people are becoming a significant issue. This study explores the effects of health insurance and physical exercise on life satisfaction of older people in China.

Method

This study used an unbalanced panel dataset (n = 6,393, T = 4) of older adults aged 60-80 years from the 2006 to 2015 China Health and Nutrition Survey (CHNS). A panel ordered logistic regression model was developed to examine the effects of health insurance and physical exercise on older people' life satisfaction. Mediation tests were used to examine the mediating role of physical exercise in the effect of health insurance on life satisfaction of older people.

Result

Life satisfaction of older people was positively associated with participation in health insurance (OR = 1.439) and physical exercise (OR = 1.033). Participation in government health insurance, urban employee health insurance (UEBMI), new rural cooperative health insurance (NRCMI), and other commercial health insurance all have positive effects on life satisfaction of older people. Physical exercise plays a masking role in the effect of health insurance on life satisfaction of older people.

Conclusion

Participation in health insurance and physical exercise are important means to promote life satisfaction among older people. Physical exercise affects the impact of health insurance on older people's life satisfaction.

Influence of socioeconomic variables on physical activity and screen time of children and adolescents during the COVID‑19 lockdown in Germany: the MoMo study

The COVID‑19 (coronavirus disease 2019) pandemic created a multitude of natural experiments about the change of human behavior in a widely unfamiliar situation. Besides physical and mental health, physical activity (PA) and people’s movement behaviors were of particular interest to researchers all over the world. In a recent study, we found that among youth in Germany, sports activity declined, whereas recreational screen time and habitual activity increased during the first COVID‑19 lockdown. In the present study, we analyze the influence of the socioeconomic status and the housing situation on the changes in PA behavior and recreational screen-time before and during the first COVID‑19 lockdown among children and adolescents living in Germany. We found an alignment of PA behavior among youth from families with different socioeconomic backgrounds during the first lockdown and identified the housing situation to be a meaningful predictor of the increase in habitual activity. We conclude that restriction policies, communities, and in the last instance parents need to enable access to nonorganized PA to all children and adolescents every day and especially during potential future lockdowns.

The impact of the Covid-19 crisis on socioeconomic differences in physical activity behavior: Evidence from the Lifelines COVID-19 cohort study

Covid-19 and measures to contain spreading the disease have led to changed physical activity behavior. This study aims to investigate the relationship between socioeconomic status (SES) and changes in the amount of moderate to vigorous physical activity (MVPA) during the Covid-19 crisis. Using the Dutch Lifelines Covid-19 cohort study (n = 17,749), the amount of MVPA was measured at 15 time-points between March and December 2020, and compared with the amount before the Covid19 pandemic. For SES, the population was stratified in three education and income levels. Logistic regression models were used to estimate the odds ratio (OR) and confidence interval (CI) of altered MVPA for low and high SES groups, with the middle SES category as the reference group. A clear socioeconomic gradient in changes in MVPA behavior was observed. Low educated individuals had significantly higher odds (OR = 1.14; CI: 1.03-1.27) of decreasing MVPA, while the high educated had significantly lower odds of decreased MVPA (OR = 0.84, CI: 0.79-0.90). Both low education (OR = 0.87; CI: 0.77-0.98) and low income (OR = 0.85; CI 0.78-0.92) had significantly lower odds to increase MVPA, while high education (OR = 1.21, CI: 1.12-1.30) and high income (OR = 1.17; CI: 1.07-1.28) had significantly higher odds to increase MVPA. Most findings were consistent over the full research period. Socioeconomic inequalities in MVPA have increased during the Covid-19 pandemic, even when Covid-19 containment measures were relaxed. Our findings suggest that future public health policies need to increase efforts to improve physical activity behavior with an even larger focus on low SES groups.

The health potential of neighborhoods: A population-wide study in the Netherlands

BACKGROUND

While differences in population health across neighborhoods with different socioeconomic characteristics are well documented, health disparities across neighborhoods with similar socioeconomic characteristics are less well understood. We aimed to estimate population health inequalities, both within and between neighborhoods with similar socioeconomic status, and assessed the association of neighborhood characteristics and socioeconomic spillover effects from adjacent neighborhoods.

METHODS

Based on Dutch whole-population data we determined the percentage of inhabitants with good or very good self-assessed health (SAH) and the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods. Neighborhoods were classified by quintiles of a composite neighborhoods socioeconomic status score (NSES). A set of spatial models was estimated accounting for spatial effects in the dependent, independent, and error components of the model.

RESULTS

Substantial population health disparities in SAH and CD both within and between neighborhoods NSES quintiles were observed, with the largest SAH variance in the lowest NSES group. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD. Projected impacts from the spatial regressions indicate how modest changes in NSES among the lowest socioeconomic neighborhoods can contribute to population health in both low- and high-SES neighborhoods.

CONCLUSION

Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socio-economic spillover effect.

Socioeconomic inequalities in health behaviors: exploring mediation pathways through material conditions and time orientation

BACKGROUND

Socioeconomic inequalities in health behaviors have been attributed to both structural and individual factors, but untangling the complex, dynamic pathways through which these factors influence inequalities requires more empirical research. This study examined whether and how two factors, material conditions and time orientation, sequentially impact socioeconomic inequalities in health behaviors.

METHODS

Dutch adults 25 and older self-reported highest attained educational level, a measure of socioeconomic position (SEP); material conditions (financial strain, housing tenure, income); time orientation; health behaviors including smoking and sports participation; and health behavior-related outcomes including body mass index (BMI) and self-assessed health in three surveys (2004, 2011, 2014) of the longitudinal GLOBE (Dutch acronym for "Health and Living Conditions of the Population of Eindhoven and surroundings") study. Two hypothesized pathways were investigated during a ten-year time period using sequential mediation analysis, an approach that enabled correct temporal ordering and control for confounders such as baseline health behavior.

RESULTS

Educational level was negatively associated with BMI, positively associated with sports participation and self-assessed health, and not associated with smoking in the mediation models. For smoking, sports participation, and self-assessed health, a pathway from educational level to the outcome mediated by time orientation followed by material conditions was observed.

CONCLUSIONS

Time orientation followed by material conditions may play a role in determining socioeconomic inequalities in certain health behavior-related outcomes, providing empirical support for the interplay between structural and individual factors in socioeconomic inequalities in health behavior. Smoking may be determined by prior smoking behavior regardless of SEP, potentially due to its addictive nature. While intervening on time orientation in adulthood may be challenging, the results from this study suggest that policy interventions targeted at material conditions may be more effective in reducing socioeconomic inequalities in certain health behaviors when they account for time orientation.

How is sport participation related to mortality, diabetes and prediabetes for different body mass index levels?

This study examined the association of sport participation with health outcomes and whether this relation differs between body mass index (BMI)‐level subpopulations. Research outcomes for sport participation were compared with other types of leisure‐time physical activity (PA). We used the Cox proportional hazards regression models to assess the associations of sport participation, and four other PA types (cycling, gardening, doing odd jobs, and walking), with the risk of prediabetes, type 2 diabetes mellitus (T2DM), and all‐cause mortality in 97,212 individuals (58.4% women; mean age: 46.5 years) in the Dutch LifeLines cohort. Outcomes were stratified by three BMI levels: healthy weight (BMI: 18.5‐24.9 kg/m²), overweight (BMI: 25.0‐29.9 kg/m²), and obesity (BMI: 30.0 kg/m² or above). Sport participation was associated with lower health risks, but only significantly so for prediabetes (HR = 0.86, 95% CI: 0.81‐0.92). For healthy weight persons, sport participation was associated with the largest risk reductions, with significantly lower risks of prediabetes (HR = 0.78, 95% CI: 0.68‐0.90) and all‐cause mortality (HR = 0.79, 95% CI 0.65‐0.96). Other PA types were not associated with significantly lower health risks, with the exception of cycling, for which significantly lower health risks for persons with overweight were found. Our findings show that sport participation is associated with lower health risks, especially prediabetes, but the effect varies between BMI levels, with the strongest link for persons with a healthy weight. Sport participation, together with cycling, is likely to be more effective in reducing health risks than other types of PA.