Measuring generic health using the minimum european health module: does it work and is it better than self-rated health?

BACKGROUND: Health is a fundamental aspect of many scientific disciplines and its definition and measurement is the analytical core of many empirical studies. Comprehensive measures of health, however, are typically precluded in survey research due to financial and temporal restrictions. Self-rated health (SRH) as a single indicator of health, on the other hand, exhibits a lack of measurement invariance by age and is biased due to non-health influences. In the three-item Minimum European Health Module (MEHM), SRH is complemented with questions on chronic health conditions and activity limitations, thus providing a compromise between single indicators and comprehensive measures. METHODS: Using data from the German Ageing Survey (2008 & 2014; n = 12,037), I investigated the feasibility to combine the MEHM into a generic health indicator and judged its utility in comparison to SRH as a benchmark. Additionally, I explored the option of an extended version of the MEHM by adding information on multimorbidity and the presence and intensity of chronic pain. RESULTS: The analyses showed that both versions of the MEHM had a good internal consistency and each represented a single latent variable that can be computed using generalized structural equation modeling. The utility of this approach showed great promise as it significantly reduced age-specific reporting behavior and some non-health biases present in SRH. CONCLUSIONS: Using the MEHM to measure generic (physical) health is a promising approach with a wide array of applications. Further research could extend these analyses to additional age groups, other countries, and establish standardized weights for greater comparability.

Stroke and embolism in patients with left ventricular hypertrabeculation/noncompaction.

OBJECTIVES: Left ventricular hypertrabeculation/noncompaction(LVHT) is characterized by extensive trabeculations. LVHT has been reported to be associated with stroke or embolism(S/E). Aim of the study was to compare characteristics and prognosis of LVHT-patients with and without S/E to identify potential risk factors for S/E. MATERIALS AND METHODS: Retrospectively included were consecutive patients with echocardiographically diagnosed LVHT in a cardiologic department in 1995-2020. Baseline characteristics and follow-up data were collected. The etiology of S/E was assessed by applying the Trial of Org 10172 in Acute Stroke Treatment(TOAST) criteria. RESULTS: The follow-up of 319 patients, mean age 53 years, 30% females, was 7.4 ± 6 years. In 49 patients(15%), either stroke(n = 44), peripheric embolism(n = 3) or both(n = 2) occurred. The etiology of S/E was cardioembolic(n = 32), atherothrombotic(n = 12), undetermined(n = 4) and intracerebral hemorrhage(n = 1). S/E occurred in 31 patients before, in 15 patients after and in 3 patients before as well as after the diagnosis of LVHT. Patients with S/E were older, suffered more frequently from arterial hypertension, diabetes mellitus, atrial fibrillation, neuromuscular disorders and heart transplantation than patients without S/E. Of the patients with S/E, only 8% were without risk factors for S/E. For the patients in whom S/E occurred after the diagnosis of LVHT, the rate of S/E was 0.74%/year. The death rate was 4.17%/year in patients with and without S/E. CONCLUSION: Cardiovascular risk factors are more prevalent in LVHT-patients with than without S/E. S/E in LVHT-patients is not always cardioembolic why investigations for etiology are useful. LVHT by itself seems to be only a minor risk factor for S/E.

Life Expectancy: Frequently Used, but Hardly Understood.

Period life expectancy is one of the most used summary indicators for the overall health of a population. Its levels and trends direct health policies, and researchers try to identify the determining risk factors to assess and forecast future developments. The use of period life expectancy is often based on the assumption that it directly reflects the mortality conditions of a certain year. Accordingly, the explanation for changes in life expectancy are typically sought in factors that have an immediate impact on current mortality conditions. It is frequently overlooked, however, that this indicator can also be affected by at least three kinds of effects, in particular in the situation of short-term fluctuations: cohort effects, heterogeneity effects, and tempo effects. We demonstrate their possible impact with the example of the almost Europe-wide decrease in life expectancy in 2015, which caused a series of reports about an upsurge of a health crisis, and we show that the consideration of these effects can lead to different conclusions. Therefore, we want to raise an awareness concerning the sensitivity of life expectancy to sudden changes and the menaces a misled interpretation of this indicator can cause.

[Importance of health indicators in the analysis of health effects on informal caregivers : Different indicator, different result?] / Bedeutung des Gesundheitsindikators bei der Analyse der Gesundheitsfolgen informeller Pflege : Anderer Indikator, anderes Ergebnis?

BACKGROUND: Analysis of the health of informal caregivers is gaining in importance. Research has shown negative effects of caregiving on mental health but the results regarding physical health were ambiguous. It remains unclear whether this can be traced back to the use of different health indicators. OBJECTIVE: Do the results on the relationship between informal caregiving and health vary depending on the care setting (domestic or external) and the outcome measure? MATERIAL AND METHODS: The relationship between informal caregiving inside and outside the domestic setting and the health of the caregiver was modelled using data from the Survey of Health, Ageing and Retirement in Europe (SHARE, waves 1, 2, 4-6). The direction and strength of the relationship between informal caregiving and eight indicators of physical and mental health were compared both cross-sectionally and longitudinally. RESULTS: For most health indicators and in the cross-sectional as well as longitudinal models, negative health effects could be observed. While caregivers in the domestic setting reported worse health than non-caregivers, the opposite was true for caregivers outside the household. The longitudinal model revealed that both negative and positive health changes during informal caregiving were evident depending on the health indicator used. CONCLUSION: The results confirmed health differences between caregivers inside and caregivers outside the domestic setting. For caregivers outside the household different health outcomes of caregiving were found depending on the chosen health indicator. This underlines that the chosen health indicator as well as the care population under study substantially affect the results of the analysis and the subsequent conclusions.

Rating Health and Rating Change: How Canadians Rate Their Health and Its Changes.

Objectives: We investigated the contribution of five health domains to self-rated health (SRH) cross-sectionally and longitudinally and whether these contributions differ by gender or age. Methods: Employing dominance analyses, we quantified the contributions of functioning, diseases, pain, mental health, and behavior to both SRH at a point in time and for changes in SRH using data from the Canadian National Population Health Survey (NPHS, 1994-2011). Results: Cross-sectionally and longitudinally, functioning was the most important health domain, followed by diseases and pain. There were no meaningful differences in the ranking by gender while functioning, diseases, and pain were more relevant in older cohorts. Discussion: Functioning, diseases, and pain systematically were the most important health domains in both cross-sectional and longitudinal analyses. While these results held for women and men, they were more salient for older adults. This points to a gender-invariant but age-graded process, confirming previous research with European data.

The gender health gap in Europe's ageing societies: universal findings across countries and age groups?

We provide a systematic country and age group comparison of the gender gap in several generic health indicators and more specific morbidity outcomes. Using data from the Survey of Health, Ageing and Retirement (SHARE), we examined the gender gap in the prevalence of poor self-rated health, chronic health conditions, activity limitations, multimorbidity, pain, heart attacks, diabetes, and depression in three age groups (50-64, 65-79, and 80+) based on linear probability models with and without adjustment for covariates. While women were typically disadvantaged regarding poor self-rated health, chronic health conditions, activity limitations, multimorbidity, pain, and depression, men had a higher prevalence of heart attacks and diabetes. However, the gender gap's magnitude and sometimes even its direction varied considerably with some age trends apparent. Regarding some health indicators, the gender gap tended to be higher in Southern and Eastern Europe than in Western and Northern Europe. All in all, the presence of a gender health gap cannot be regarded as a universal finding as the gap tended to widen, narrow or even reverse with age depending on the indicator and country.

Diverging ideas of health? Comparing the basis of health ratings across gender, age, and country.

BACKGROUND: Self-rated health (SRH) is arguably the most widely used generic health measurement in survey research. However, SRH remains a black box for researchers. In our paper, we want to gain a better understanding of SRH by identifying its determinants, quantifying the contribution of different health domains to explain SRH, and by exploring the moderating role of gender, age groups, and the country of residence. METHOD: Using data from 61,365 participants of the fifth wave (2013) of the Survey of Health, Ageing and Retirement in Europe (SHARE) living in fifteen European countries, we explain SRH via linear regression models. The independent variables are grouped into five health domains: functioning, diseases, pain, mental health, and behavior. Via dominance analysis, we focus on their individual contribution to explaining SRH and compare these contributions across gender, three age groups, and fifteen European countries. RESULTS: Our model explains SRH rather well (R2 = .51 for females/.48 for males) with functioning contributing most to the appraisal (.20/.18). Diseases were the second most relevant health dimension (.14/.16) followed by pain (.08/.07) and mental health (.07/.06). Health behavior (.02/.01) was less relevant for health ratings. This ranking held true for almost all countries with only little variance overall. A comparison of age groups indicated that the contribution of diseases and behavior to SRH decreased over the life-course while the contribution of functioning to R2 increased. CONCLUSION: Our paper demonstrates that SRH is largely based on diverse health information with functioning and diseases being most important. However, there is still room for idiosyncrasies or even bias.