Age-related difference in health care use and costs of patients with chronic kidney disease and matched controls: analysis of Dutch health care claims data.

BACKGROUND: The financial burden of chronic kidney disease (CKD) is increasing due to the ageing population and increased prevalence of comorbid diseases. Our aim was to evaluate age-related differences in health care use and costs in Stage G4/G5 CKD without renal replacement therapy (RRT), dialysis and kidney transplant patients and compare them to the general population. METHODS: Using Dutch health care claims, we identified CKD patients and divided them into three groups: CKD Stage G4/G5 without RRT, dialysis and kidney transplantation. We matched them with two controls per patient. Total health care costs and hospital costs unrelated to CKD treatment are presented in four age categories (19-44, 45-64, 65-74 and ≥75 years). RESULTS: Overall, health care costs of CKD patients ≥75 years of age were lower than costs of patients 65-74 years of age. In dialysis patients, costs were highest in patients 45-64 years of age. Since costs of controls increased gradually with age, the cost ratio of patients versus controls was highest in young patients (19-44 years). CKD patients were in greater need of additional specialist care than the general population, which was already evident in young patients. CONCLUSION: Already at a young age and in the earlier stages of CKD, patients are in need of additional care with corresponding health care costs far exceeding those of the general population. In contrast to the general population, the oldest patients (≥75 years) of all CKD patient groups have lower costs than patients 65-74 years of age, which is largely explained by lower hospital and medication costs.

Healthcare costs of patients on different renal replacement modalities - Analysis of Dutch health insurance claims data.

BACKGROUND: The aim of this study is to present average annual healthcare costs for Dutch renal replacement therapy (RRT) patients for 7 treatment modalities. METHODS: Health insurance claims data from 2012-2014 were used. All patients with a 2014 claim for dialysis or kidney transplantation were selected. The RRT related and RRT unrelated average annual healthcare costs were analysed for 5 dialysis modalities (in-centre haemodialysis (CHD), home haemodialysis (HHD), continuous ambulatory peritoneal dialysis (CAPD), automated peritoneal dialysis (APD) and multiple dialysis modalities in a year (Mix group)) and 2 transplant modalities (kidney from living and deceased donor, respectively). RESULTS: The total average annual healthcare costs in 2014 ranged from €77,566 (SD = €27,237) for CAPD patients to €105,833 (SD = €30,239) for patients in the Mix group. For all dialysis modalities, the vast majority (72-84%) of costs was RRT related. Patients on haemodialysis ≥4x/week had significantly higher average annual costs compared to those dialyzing 3x/week (Δ€19,122). Costs for kidney transplant recipients were €85,127 (SD = €39,679) in the year of transplantation and rapidly declined in the first and second year after successful transplantation (resp. €29,612 (SD = €34,099) and €15,018 (SD = €16,186)). Transplantation with a deceased donor kidney resulted in higher costs (€99,450, SD = €36,036)) in the year of transplantation compared to a living donor kidney transplantation (€73,376, SD = €38,666). CONCLUSIONS: CAPD patients have the lowest costs compared to other dialysis modalities. Costs in the year of transplantation are 25% lower for patients with kidneys from living vs. deceased donor. After successful transplantation, annual costs decline substantially to a level that is approximately 14-19% of annual dialysis costs.

Healthcare utilisation and expenditure of overweight and non-overweight children.

BACKGROUND: Quantification of the burden of overweight on the healthcare system is becoming increasingly urgent for health policy, but accurate estimates are hard to obtain. METHODS: In order to assess healthcare utilisation and expenditure of overweight and non-overweight children, we linked, on an individual basis, data on body mass index from a birth cohort study with administrative health insurance claims data. In children aged 14-15 years, we compared utilisation and expenditure on general practice care, dental care, hospital/specialist care, prescribed medication, allied healthcare and mental healthcare of overweight children (overweight at age 11 and 14, n=80) and non-overweight children (no overweight at age 11 and 14, n=1253). RESULTS: For overweight children, mean 1-year healthcare expenditure was €837 per child and for non-overweight children €616. This difference was mainly due to significant differences in utilisation of hospital care (49% vs 37%) and mental healthcare (14% vs 7%) and to a lesser extent to higher expenditure per user. CONCLUSION: Our results indicate the potential value of linking survey data to claims data in order to obtain insight into the healthcare costs of childhood overweight. Further studies should elucidate whether the observed differences are causally related to overweight.

Cost Variation in Diabetes Care across Dutch Care Groups?

OBJECTIVE: The introduction of bundled payment for diabetes care in the Netherlands led to the origination of care groups. This study explored to what extent variation in health care costs per patient can be attributed to the performance of care groups. Furthermore, the commonly applied simple mean aggregation was compared with the more advanced generalized linear mixed model (GLMM) to benchmark health care costs per patient between care groups. DATA SOURCE: Dutch 2009 nationwide insurance claims data of diabetes type 2 patients (104,544 patients, 50 care groups). STUDY DESIGN: Both a simple mean aggregation and a GLMM approach was applied to rank care groups, using two different health care costs variables: total treatment health care costs and diabetes-specific specialist care costs per diabetes patient. PRINCIPAL FINDINGS: Care groups varied slightly in the first and mainly in the second indicator. Care group variation was not explained by composition. Although the ranking methods were correlated, some care groups' rank positions differed, with consequences on the top-10 and the low-10 positions. CONCLUSIONS: Differences between care groups exist when an appropriate indicator and a sophisticated aggregation technique is used. Currently applied benchmarking may have unfair consequences for some care groups.

You have to be there to enjoy it? Neighbourhood social capital and health.

BACKGROUND: Several studies have shown the positive effect of neighbourhood social capital on health. Existing research, however, has hitherto not studied whether the duration and intensity of exposure to neighbourhood social capital conditions and its effect on health. The aim of this study was to examine whether neighbourhood social capital affects individual's health immediately and equally. METHODS: We used two waves of the Dutch cross-sectional 'Housing and Living Survey'. One (from 2009) as individual data (n = 65,990), and the other (from 2006) to estimate with ecometric measurements a social capital measure for 3001 Dutch neighbourhoods. We assessed by means of multilevel regression models the combined effect of exposure and amount of neighbourhood social capital on self-rated health. RESULTS: Duration of exposure, measured by the length of stay in the same neighbourhood is not linearly associated with individual health. Health of people who live up to 6 years or >22 years in the same neighbourhood is not affected by neighbourhood social capital. People with young children in the household or elderly were assumed to be more intensively exposed. However, exposure intensity was only found to have an effect for households with young children. CONCLUSIONS: Duration and intensity of exposure to neighbourhood social capital, a social aspect of the environment, matters for people's health. Interventions focusing on the health of people with young children may want to stimulate the creation of neighbourhood social capital.

Health-related behavior as a mechanism behind the relationship between neighborhood social capital and individual health--a multilevel analysis.

BACKGROUND: Although various studies have found a positive association between neighborhood social capital and individual health, the mechanism explaining this direct effect is still unclear. Neighborhood social capital is the access to resources that are generated by relationships between people in a friendly, well-connected and tightly knit neighborhood community. We expect that the resources generated by cohesive neighborhoods support and influence health -improving behaviors in daily life. We identify five different health-related behaviors that are likely to be affected by neighborhood social capital and test these behaviors separately as mediators. METHODS: The data set pertaining to individual health was taken from the 'health interview' in the 'Second Dutch national survey of general practice' (DNSGP-2, 2002). We combine these individual-level data with data from the 'Dutch housing demand survey' (WBO, 1998 and WoON, 2002) and statistical register information (1995-1999). Per neighborhood 29 WBO respondents, on average, had answered questions regarding social capital in their neighborhood. These variables have been aggregated to the neighborhood level by an ecometric methodology. In the main analysis, in which we tested the mediation, multilevel (ordered) logistic regressions were used to analyze 9253 adults (from the DNSGP-2 data set) from 672 Dutch neighborhoods. In the Netherlands, on average, neighborhoods (4-digit postcodes) comprise 4,000 inhabitants at highly variable population densities. Individual- and neighborhood-level controls have been taken into account in the analyses. RESULTS: In neighborhoods with a high level of social capital, people are more physically active and more likely to be non-smokers. These behaviors have positive effects on their health. The direct effect of neighborhood social capital on health is significantly and strongly reduced by physical activity. This study does not support nutrition and sleep habits or moderate alcohol intake as possible explanations of the effects of neighborhoods on health. CONCLUSIONS: This study is one of the first to test a mechanism explaining much of the direct effect of small-area social capital on individual health. Neighborhood interventions might be most successful at improving health if they stimulate both social interaction and physical activity.

Neighborhood social capital and individual health.

Neighborhood social capital is increasingly considered to be an important determinant of an individual's health. Using data from the Netherlands we investigate the influence of neighborhood social capital on an individual's self-reported health, while accounting for other conditions of health on both the level of the neighborhood and the individual. We use national representative data ('The Housing and Living Survey', 2006) on the Netherlands with 61,235 respondents in 3273 neighborhoods. The cross-sectional data were combined with information provided by Statistics Netherlands on neighborhoods, i.e., the percentage of residents in the highest income quintile per neighborhood and the municipality's degree of urbanity. The association of neighborhood social capital with individual health was assessed by multilevel logistic regression analysis. Our results show that neighborhood social capital is positively associated with health. Interestingly, residents in urban neighborhoods benefit particularly from their neighborhood social capital.

The average age of smoking onset in Germany--trends and correlates.

OBJECTIVES: Germany is rated among the countries with the highest prevalence of tobacco use in Europe. This paper analyzes whether the age of smoking onset has decreased in recent years. METHODS: Multivariable event data analyses were performed on the basis of the representative national cross-sectional study "Drug Affinity among Young People in the Federal Republic of Germany 2004". The survey involved a total net sample of 3032 individuals aged 12 to 25. RESULTS: Socioeconomic groups starting to smoke at significantly earlier age include those from the economically deprived areas in eastern Germany, low educational achievers and subjects in households with adult smokers. CONCLUSIONS: The average age of smoking onset has decreased further in the 1978-1992 birth cohorts.