Did the health of the Dutch population improve between 2001 and 2008? Investigating age- and gender-specific trends in quality of life

Quality of Life in Patients With Definite Chronic Pancreatitis: A Nationwide Longitudinal Cohort Study

INTRODUCTION

Chronic pancreatitis (CP) has a negative impact on quality of life (QoL). Because CP is a chronic condition, multiple assessments of QoL are required to obtain a thorough understanding of its impact on patients. Such studies are currently lacking. This study aims to gain insight into the course and predictors of QoL in patients with CP using prospective longitudinal data from a large cohort of patients.

METHODS

Post hoc analysis of consecutive patients with definite CP registered in a prospective database between 2011 and 2019 in the Netherlands. Patient and disease characteristics, nutritional status, pain severity, medication usage, pancreatic function, and pancreatic interventions were assessed from medical records and through standard follow-up questionnaires. The physical and mental component summary scales of the Short-Form 36 were used to assess physical and mental QoL at baseline and during follow-up. The course of both physical and mental QoL and their associated factors were longitudinally assessed by using generalized linear mixed models.

RESULTS

Overall, 1,165 patients with definite CP were included for this analysis. During 10-year follow-up, generalized linear mixed model analyses revealed improvements in both physical (41.6-45.2, P < 0.001) and mental (45.9-46.6, P = 0.047) QoL. Younger age, current alcohol consumption, employment, no need for dietetic consultation, no steatorrhea, lower Izbicki pain score, and pain coping mechanism were positively associated with physical QoL ( P < 0.05). For mental QoL, a positive correlation was found between employment, nonalcoholic CP, no need for dietetic consultation, no steatorrhea, lower Izbicki pain score, pain coping mechanism, and surgical treatment. No association was observed between disease duration and longitudinal QoL per patient.

DISCUSSION

This nationwide study provides insight into the dynamics of physical and mental QoL in patients with CP over time. Important and potentially influenceable factors to improve QoL are nutritional status, exocrine pancreatic function, employment status, and patients' coping strategy.

Cost-Effectiveness of Cancer Screening: Health and Costs in Life Years Gained

INTRODUCTION

Studies reporting on the cost-effectiveness of cancer screening usually account for quality of life losses and healthcare costs owing to cancer but do not account for future costs and quality of life losses related to competing risks. This study aims to demonstrate the impact of medical costs and quality of life losses of other diseases in the life years gained on the cost-effectiveness of U.S. cancer screening.

METHODS

Cost-effectiveness studies of breast, cervical, and colorectal cancer screening in the U.S. were identified using a systematic literature review. Incremental cost-effectiveness ratios of the eligible articles were updated by adding lifetime expenditures and health losses per quality-adjusted life year gained because of competing risks. This was accomplished using data on medical spending and quality of life by age and disease from the Medical Expenditure Panel Survey (2011-2015) combined with cause-deleted life tables. The study was conducted in 2018.

RESULTS

The impact of quality of life losses and healthcare expenditures of competing risks in life years gained incurred owing to screening were the highest for breast cancer and the lowest for cervical cancer. The updates suggest that incremental cost-effectiveness ratios are underestimated by $10,300-$13,700 per quality-adjusted life year gained if quality of life losses and healthcare expenditures of competing risks are omitted in economic evaluations. Furthermore, cancer screening programs that were considered cost saving, were found not to be so following the inclusion of medical expenditures of competing risks.

CONCLUSIONS

Practical difficulties in quantifying quality of life losses and healthcare expenditures owing to competing risks in life years gained can be overcome. Their inclusion can have a substantial impact on the cost-effectiveness of cancer screening programs.

The marginal benefits of healthcare spending in the Netherlands: Estimating cost-effectiveness thresholds using a translog production function

New technologies may displace existing, higher-value care under a fixed budget. Countries aim to curtail adoption of low-value technologies, for example, by installing cost-effectiveness thresholds. Our objective is to estimate the opportunity cost of hospital care to identify a threshold value for the Netherlands. To this aim, we combine claims data, mortality data and quality of life questionnaires from 2012 to 2014 for 11,000 patient groups to obtain quality-adjusted life-year (QALY) outcomes and spending. Using a fixed effects translog model, we estimate that a 1% increase in hospital spending on average increases QALY outcomes by 0.2%. This implies a threshold of €73,600 per QALY, with 95% confidence intervals ranging from €53,000 to €94,000 per QALY. The results stipulate that new technologies with incremental cost effectiveness ratios exceeding the Dutch upper reference value of €80,000 may indeed displace more valuable care.

Relative contribution of various chronic diseases and multi-morbidity to potential disability among Dutch elderly

Background

The amount of time spent living with disease greatly influences elderly people’s wellbeing, disability and healthcare costs, but differs by disease, age and sex.

Methods

We assessed how various single and combined diseases differentially affect life years spent living with disease in Dutch elderly men and women (65+) over their remaining life course. Multistate life table calculations were applied to age and sex-specific disease prevalence, incidence and death rates for the Netherlands in 2007. We distinguished congestive heart failure, coronary heart disease (CHD), breast and prostate cancer, colon cancer, lung cancer, diabetes, COPD, stroke, dementia and osteoarthritis.

Results

Across ages 65, 70, 75, 80 and 85, CHD caused the most time spent living with disease for Dutch men (from 7.6 years at age 65 to 3.7 years at age 85) and osteoarthritis for Dutch women (from 11.7 years at age 65 to 4.8 years at age 85). Of the various co-occurrences of disease, the combination of diabetes and osteoarthritis led to the most time spent living with disease, for both men (from 11.2 years at age 65 to 4.9 -years at age 85) and women (from 14.2 years at age 65 to 6.0 years at age 85).

Conclusions

Specific single and multi-morbid diseases affect men and women differently at different phases in the life course in terms of the time spent living with disease, and consequently, their potential disability. Timely sex and age-specific interventions targeting prevention of the single and combined diseases identified could reduce healthcare costs and increase wellbeing in elderly people.

Quality-of-life: a many-splendored thing? Belgian population norms and 34 potential determinants explored by beta regression

PURPOSE

To identify determinants of health-related quality-of-life in the Belgian population and to provide age-specific population norms of health-related quality-of-life.

METHODS

Between September 2010 and February 2011, a representative sample of 1774 persons (age 0-99) was surveyed using the standard Euroqol questionnaire (EQ-5D-3L) with a Visual Analogue Scale (VAS). Significant determinants were identified using multivariate beta (VAS) and one-inflated beta (EQ-5D) regression, the latter modelling the probability to be in perfect health separately from the average EQ-5D score if not in perfect health.

RESULTS

Health-related quality-of-life depends largely on age and experience with severe disease. The probability to be in perfect health is highest for children. For 0-2 years children who are not in perfect health, proxies report EQ-5D and VAS scores as low as that of the elderly. Also smoking behaviour, educational attainment, pet ownership, working or having worked in health care, and potentially household size and 60+ living on their own (yes/no) are associated with health-related quality-of-life, whereas no association was found with gender, living in a single-parent home, educational attainment of mothers, alcohol consumption of 60+, having (grand-) children and the frequency of seeing them. The same determinants are significant for VAS and the probability to be in perfect health, but not for the average EQ-5D score if not in perfect health.

CONCLUSIONS

The population norms provided can be used directly as input in health economic evaluations. Estimating health-related quality-of-life in children and developing statistical tools capturing the particular features of health-related quality-of-life measures are important areas for future research.

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

Health inequalities in the Netherlands: trends in quality-adjusted life expectancy (QALE) by educational level

BACKGROUND

Quality-adjusted life expectancy (QALE) has been proposed as a summary measure of population health because it encompasses multiple health domains as well as length of life. However, trends in QALE by education or other socio-economic measure have not yet been reported. This study investigates changes in QALE stratified by educational level for the Dutch population in the period 2001-2011.

METHODS

Using data from multiple sources, we estimated mortality rates and health-related quality of life (HRQoL) as functions of age, gender, calendar year and educational level. Subsequently, predictions from these regressions were combined for calculating QALE at ages 25 and 65. QALE changes were decomposed into effects of mortality and HRQoL.

RESULTS

In 2001-2011, QALE increased for men and women at all educational levels, the largest increases being for highly educated resulting in a widening gap by education. In 2001, at age 25, the absolute QALE difference between the low and the highly educated was 7.4 healthy years (36.7 vs. 44.1) for men and 6.3 healthy years (39.5 vs. 45.8) for women. By 2011, the QALE difference increased to 8.1 healthy years (38.8 vs. 46.9) for men and to 7.1 healthy years (41.3 vs. 48.4) for women. Similar results were observed at age 65. Although the gap was largely attributable to widening inequalities in mortality, widening inequalities in HRQoL were also substantial.

CONCLUSIONS

In the Netherlands, population health as measured by QALE has improved, but QALE inequalities have widened more than inequalities in life expectancy alone.