Self-assessed Health Measures Research Articles

Development and validation of an instrument to measure social capital in disadvantaged neighborhoods

Abstract Background Building social capital within communities is an integrated element and outcome of the Supersetting approach, but knowledge on how to measure it within a disadvantaged urban context remain scarce. The Objective To develop, test, and validate a questionnaire-based instrument to measure social capital among adult citizens living in a disadvantaged neighborhood. The study is done in Tingbjerg - a disadvantageous urban neighborhood. Methods/theory The development of the instrument is based on the social cohesion approach from within the theory of social capital and takes place in three phases. 1) Development including specification of a theoretical model of structural and cognitive social capital with indicators, development of items, and content validity of the items by experts. 2) Testing of developed items including face validity by conducting individual and focus groups interviews with Tingbjerg residents, translation into five languages and subsequent data collection among all 2567 adults living in Tingbjerg. 3) Statistical evaluation to test for dimensionality, reliability and validity. In case of suboptimal performance of the instrument, modifications may be applied, and the updated instrument re-evaluated. Findings Evaluations obtained from experts and residents will be analyzed and used to reduce and modify items included in the instrument. The questionnaire's dimensional structure will be explored with confirmatory factor analysis and its reliability with Cronbach alpha. Convergent validity will be tested through testing the association between social capital and a construct of social relations. Concurrent validity will be tested through testing the association between social capital and measures of self-assessed health and life quality. Conclusions An instrument to measure social capital in disadvantaged neighborhood will be presented at the conference. Recommendation of the instrument will depend on the evaluation of the study findings.

Saving behaviour and health: A high-dimensional Bayesian analysis of British panel data

We develop a two-part high-dimensional Bayesian modelling approach to analyse the relationship between saving behaviour and health. In contrast to the existing literature, our approach allows different data-generating processes for the decision to save and the amount saved, and therefore unveils a more detailed picture of the relationship between financial behaviour and health than previous work. We explore different measures of saving, including monthly saving behaviour and the stock of financial assets held. Further, we exploit British panel data, which includes an extensive range of biomarkers. Our second contribution lies in comparing the effects of these objective measures of health with commonly used self-assessed health measures. We find that health is a significant determinant of saving behaviour and financial asset holding, and that biomarker measures have differential impacts on saving behaviour compared to self-reported health measures.

Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis

BackgroundThe coronavirus disease 2019 (COVID-19) has resulted in an enormous dislocation of society especially in South Africa. The South African government has imposed a number of measures aimed at controlling the pandemic, chief being a nationwide lockdown. This has resulted in income loss for individuals and firms, with vulnerable populations (low earners, those in informal and precarious employment, etc.) more likely to be adversely affected through job losses and the resulting income loss. Income loss will likely result in reduced ability to access healthcare and a nutritious diet, thus adversely affecting health outcomes. Given the foregoing, we hypothesize that the economic dislocation caused by the coronavirus will disproportionately affect the health of the poor.MethodsUsing the fifth wave of the National Income Dynamics Study (NIDS) dataset conducted in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) dataset conducted in May/June 2020, this paper estimated income-related health inequalities in South Africa before and during the COVID-19 pandemic. Health was a dichotomized self-assessed health measure, with fair and poor health categorized as “poor” health, while excellent, very good and good health were categorized as “better” health. Household per capita income was used as the ranking variable. Concentration curves and indices were used to depict the income-related health inequalities. Furthermore, we decomposed the COVID-19 era income-related health inequality in order to ascertain the significant predictors of such inequality.ResultsThe results indicate that poor health was pro-poor in the pre-COVID-19 and COVID-19 periods, with the latter six times the value of the former. Being African (relative to white), per capita household income and household experience of hunger significantly predicted income-related health inequalities in the COVID-19 era (contributing 130%, 46% and 9% respectively to the inequalities), while being in paid employment had a nontrivial but statistically insignificant contribution (13%) to health inequality.ConclusionsGiven the significance and magnitude of race, hunger, income and employment in determining socioeconomic inequalities in poor health, addressing racial disparities and hunger, income inequality and unemployment will likely mitigate income-related health inequalities in South Africa during the COVID-19 pandemic.

The impact of the Affordable Care Act on health care access and self-assessed health in the Trump Era (2017-2018).

ObjectiveTo estimate the impact of the major components of the ACA (Medicaid expansion, subsidized Marketplace plans, and insurance market reforms) on health care access and self‐assessed health during the first 2 years of the Trump administration (2017 and 2018).Data SourceThe 2011‐2018 waves of the Behavioral Risk Factor Surveillance System (BRFSS), with the sample restricted to nonelderly adults. The BRFSS is a commonly used data source in the ACA literature due to its large number of questions related to access and self‐assessed health. In addition, it is large enough to precisely estimate the effects of state policy interventions, with over 300 000 observations per year.DesignWe estimate difference‐in‐difference‐in‐differences (DDD) models to separately identify the effects of the private and Medicaid expansion portions of the ACA using an identification strategy initially developed in Courtemanche et al (2017). The differences come from: (a) time, (b) state Medicaid expansion status, and (c) local area pre‐2014 uninsured rates. We examine ten outcome variables, including four measures of access and six measures of self‐assessed health. We also examine differences by income and race/ethnicity.Principal FindingsDespite changes in ACA administration and the political debate surrounding the ACA during 2017 and 2018, including these fourth and fifth years of postreform data suggests continued gains in coverage. In addition, the improvements in reported excellent health that emerged with a lag after ACA implementation continued during 2017 and 2018.ConclusionsWhile gains in access and self‐assessed health continued in the first 2 years of the Trump administration, the ongoing debate at both the federal and state level surrounding the future of the ACA suggests the need to continue monitoring how the law impacts these and many other important outcomes over time.

Evidencing the gap between health expectancy and life expectancy for ethnic groups in Scotland.

Background
 Recent evidence has shown that ethnic minorities live longer than the majority population in Scotland. This mortality advantage in ethnic minorities is not unique to Scotland. However, whether morbidity patterns by ethnicity align with mortality patterns by ethnicity is unknown. Thus, this study explores ethnic differences in health expectancies (HE) in Scotland and contrasts HE with life expectancy (LE) findings.
 Methods
 The Scottish Health and Ethnicity Linkage study anonymously links the Scottish Census 2001 for 4.6 million people to mortality records. The Scottish Census 2001 collected two measures of self-assessed health, self-declared ethnicity, age, and sex. Utilising the life tables used to calculate life expectancy by ethnicity and sex in Scotland, the Sullivan method was employed to calculate two measures of health expectancy (healthy life expectancy and disability-free life expectancy) by ethnicity and sex. 95% confidence intervals were calculated to detect significant differences compared to the majority White Scottish population, taken as reference.
 Results
 Longer health expectancies were found in males and females of Other White British, Other White, and Chinese origins as well as in Indian males compared to White Scottish populations. Any Mixed Background and Pakistani populations had the shortest healthy life expectancies. Patterns of health expectancy by ethnicity mostly aligned with patterns of life expectancy by ethnicity with the clear exception of the Pakistani population who showed among the longest life expectancies with the shortest health expectancies. Contrasting HE with LE findings, the number of years in an unhealthy state was greater in females than in males for each ethnic group. In relation to ethnicity, Pakistani and Indian populations had the highest number of years in an unhealthy state in Scotland. Pakistani females showed the strong-est disadvantage in this respect.
 Conclusion
 Pakistani populations had the shortest health expectancies contrasting with the longest life expectancies in Scotland. Future research should aim to understand why such a discrepancy occurs while policy makers ensure that fair and adapt-ed care is provided to offer better quality of life for the most vulnerable.

Measuring Weight-Specific Quality of Life in Adolescents: An Examination of the Concurrent Validity and Test-Retest Reliability of the WAItE

ObjectivesTo examine the concurrent validity of the Weight-specific Adolescent Instrument for Economic evaluation (WAItE) as compared with the generic, preference-based Child Health Utility 9D (CHU-9D) and the weight-specific Youth Quality of Life—Weight (YQOL-W) and also to examine the test-retest reliability of the WAItE. MethodsAn online survey was used to administer the 3 instruments on a sample of adolescents (aged 11-18 years). Individual responses were converted into either utility scores (CHU-9D) or health-related quality-of-life scores (WAItE and YQOL-W). A 10% subsample of the respondents also completed the WAItE 1 week after completion to assess test-retest reliability. ResultsOne thousand adolescents completed the online survey. There was a strong correlation between the WAItE and both the CHU-9D (0.731; P<.001) and the YQOL-W (0.747; P<.001). All 3 instruments were able to discriminate according to different weight status categories and a measure of self-assessed health. Unlike the CHU-9D or the YQOL-W, the WAItE did not show a substantial ceiling effect. The WAItE also showed acceptable levels of test-retest reliability. ConclusionsThe study results are encouraging, and illustrate that the WAItE can be used to reliably and accurately measure weight-specific outcomes in the younger population. The WAItE can also be used to assess outcomes in cost-effectiveness analysis of weight management interventions for young people, given the instrument is less likely to display ceiling effects and may thus be more sensitive in measuring change that results from interventions developed for this population.

An Ordinal Measure of Population Health

We propose a population-level health index that addresses two of the main concerns of existing health measures: (1) the biases in self-assessed health measures, and (2) the difficulties associated with making comparisons across populations afflicted by a variety of conditions in a context in which multi-morbidity is high. Starting from a set of general axioms, we derive a partial order index that ranks health across populations based on the relative prevalence of various conditions within these populations, and that can be readily applied to many existing health surveys. We illustrate the use of our health measure by applying it to data from the National Health Interview Survey in order to examine health differences across racial groups in the U.S.

PROTOCOL: Voluntary work for the physical and mental health of older volunteers.

PROTOCOL: Voluntary work for the physical and mental health of older volunteers.

Relationship between 8-year weight change, body size, and health inalarge cohort of adults in Thailand.

BackgroundOverweight and obesity have been shown to be risk factors for a range of non-communicable diseases, especially cardio-metabolic conditions, worldwide. But less is known about the effects of weight change on adults' overall health and wellbeing, particularly in transitional low- and middle-income countries. This study aimed to assess the relationship between 8-year weight change and measures of self-assessed health among Thai adults.MethodsData were collected from Thai adults aged 25–40 years (n = 27,003) enrolled in the Thai cohort Study and surveyed in 2005, 2009, and 2013. We used self-reported weight and height measurements at baseline and 2013, as well as three standard health questions regarding overall health, energy, and emotion asked at the two time points, to investigate the effects of weight change on health.ResultsBetween 2005 and 2013, 6.0% of participants lost more than 5% of their baseline weight; 38.5% were stable (<5% loss to 5% gain); 23.0% slightly gained weight (>5%–10%); 22.8% gained moderate weight (>10%–20%); and 9.4% had heavy weight gain (>20%). Moderate (>10%–20%) and heavy weight gain (>20%) were both associated with an increased risk of reporting ‘poor or very poor‘ overall health in 2013 among participants who had a normal body mass index (BMI) (adjusted odds ratio [AOR] 1.39; 95% confidence interval [CI], 1.13–1.71 and AOR 1.44; 95% CI, 1.09–1.90, respectively), were overweight (AOR 1.53; 955 CI, 1.01–2.29 and AOR 1.82; 95% CI, 1.04–3.19, respectively) or had obesity (AOR 2.47; 95% CI, 1.74–3.51 and AOR 3.20; 95% CI, 2.00–5.16, respectively) in 2005. Weight gain of over 20% also had a negative impact on energy level among cohort members with a normal BMI in 2005 (AOR 1.36; 95% CI, 1.11–1.65) and among participants with obesity in 2005 (AOR 1.93; 95% CI, 1.38–2.71). For those who were underweight, had a normal BMI, or had obesity at baseline, weight loss of more than 5% was associated with reporting emotional problems. Excessive weight gain adversely impacted participants who were underweight or had obesity at baseline.ConclusionOur study found that weight change, in particular weight gain, was associated with negative health outcomes, and this effect appeared to increase at higher levels of body size. The present findings may be useful to promote weight maintenance and healthy lifestyles.

Which long-term illnesses do patients find most limiting? A census-based cross-sectional study of 340,000 people

ObjectivesTo investigate associations between a widely used measure of self-assessed health (limiting long-term illness, LLTI) and 11 long-term health conditions.MethodsInformation on LLTI and health conditions was obtained from 2011 Census returns for a 28% representative sample of the Northern Ireland population (n = 342,868). Logistic regression was used to predict LLTI by sex and age group for each condition found in isolation, adjusting for marital status, social class, household car access, housing tenure, and educational attainment. The relationship between limitation and multimorbidity was also assessed.ResultsPrevalence of LLTI varied considerably among conditions when found in isolation; those with mobility problems were over 50 times more likely to report limitation than those with hearing loss. Women were less likely to report limitation than men [OR = 0.93 (0.90, 0.96)], but the pattern of associations with health conditions was similar for both sexes. Prevalence of LLTI increased with age and number of health conditions.ConclusionsLLTI was most closely associated with mobility problems. Limitation increased slightly with age, but patterns of LLTI across conditions were not sex dependent.

Is unemployment and low income harmful to health? Evidence from Britain

This study investigates how unemployment and income influence the length of time an individual remains in good health. This is a complex relationship since unemployment or low income deteriorates health but poor health can become a barrier to obtaining higher income or gaining re-employment. Data are from the British Household Panel Survey, using two measures of physical health: an index of mobility problems and a measure of self-assessed health. The results show that unemployment, low income and poor education adversely affect the time that people remain in good health. These results have important implications for public policy, particularly in an age of austerity when social protection mechanisms are under threat. In fact, the results suggest that to improve health and reduce health inequality, more investment needs to be directed at policies that enhance labour force participation, improve education and reduce income inequality.

Does self-assessed health measure health?

Despite concerns about reporting biases and interpretation, self-assessed health (SAH) remains the measure of health most used by researchers, in part reflecting its ease of collection and in part the observed correlation between SAH and objective measures of health. Using a unique Australian data set, which consists of survey data linked to administrative individual medical records, we present empirical evidence demonstrating that SAH indeed predicts future health, as measured by hospitalizations, out-of-hospital medical services and prescription drugs. Our large sample size allows very disaggregate analysis and we find that SAH predicts more serious, chronic illnesses better than less serious illnesses. Finally, we compare the predictive power of SAH relative to administrative data and an extensive set of self-reported health measures; SAH does not add to the predictive power of future utilization when the administrative data is included and improves prediction only marginally when the extensive survey-based health measures are included. Clearly there is value in the more extensive survey and administrative health data as well as greater cost of collection.

Inflated Responses in Measures of Self-Assessed Health

This paper focuses on the self-reported responses given to survey questions of the form “Overall, how would you rate your health?” with typical response items being on a scale ranging from poor to excellent. Usually, the overwhelming majority of responses fall in either the middle category or the one immediately to the “right” of this (for example, good and very good). However, based on a wide range of other medical indicators, such favorable responses appear to paint an overly rosy picture of true health. The hypothesis here is that these “middle” responses have been, in some sense, inflated. That is, for whatever reason, a significant number of responders inaccurately report into these categories. Our results do indeed suggest that such inflation is present in these categories. Adjusted responses to these questions could lead to significant changes in policy, and should be reflected upon when analyzing and interpreting these scales.

&lt;em&gt;The Health Consequences of Armed Conflict in Sub-Saharan Africa&lt;/em&gt;: How Much Do Conflict Intensity and Proximity Matter?

Armed conflict affects combatants and noncombatants directly through casualties, but other effects on health are less visible and more difficult to measure. Recent studies in the public health literature and in the comparative social sciences have increasingly reported on the negative effects of armed conflict on health. Most of these studies have relied on aggregate, macro-level data to compare effects between countries, but few have examined the micro-level foundations of this relationship. This study attempts to understand the extent of the effects of armed conflict on individual health and to explore whether conflict intensity and proximity present challenges to health that have not previously been documented. Using individual level survey data and a measure of self-assessed health improves our understanding of the links between health and armed conflict in sub-Saharan Africa.

Measuring Health Status: Self-, Interviewer, and Physician Reports of Overall Health

This study examines and compares respondent, interviewer, and physician ratings of overall health. Data are from the 2006 Social Environment and Biomarkers of Aging Study, a nationally representative survey of older adults in Taiwan. Ordered probit models are used to examine factors associated with self- and external assessments of health and discordant health ratings. Our results suggest similarities and differences in factors influencing health ratings across evaluators but a high level of interevaluator disagreement in ratings. Discrepancies in ratings between physicians and both respondents and interviewers are associated with the greater weight given to functional limitations and psychological well-being in interviewer and respondent ratings and to the importance of clinical measures or risk factors of illness and mortality in physician assessments. Interviewer and physician assessments may be complementary to self-assessed health measures. The importance and implications of these findings for future research are discussed.

Health care utilization and self-assessed health: specification of bivariate models using copulas

The discernment of relevant factors driving health care utilization constitutes one important research topic in health economics. This issue is frequently addressed through specification of regression models for health care use (y—often measured by number of doctor visits) including, among other covariates, a measure of self-assessed health (sah). However, the exogeneity of sah within those models has been questioned, due to the possible presence of unobservables influencing both y and sah, and because individuals’ health assessments may depend on the quantity of medical care received. This article addresses the possible simultaneity of (sah,y) by adopting a full information approach, through specification of the bivariate probability function (p.f.) of these discrete variables, conditional on a set of exogenous covariates (x). The approach is implemented with copula functions, which afford separate consideration of each variable margin and their dependence structure. The specification of the joint p.f. of (sah,y) enables estimation of several quantities of potential economic interest, namely features of the conditional p.f. of y given sah and x. The adopted models are estimated through maximum likelihood, with cross-sectional data from the Portuguese National Health Survey of 1998–1999. Estimates of the margins’ parameters do not vary much among different copula models, while, in accordance with theoretical expectations, the dependence parameter is estimated to be negative across the various joint models.

Can't We Just Live Together?: New Evidence on the Effect of Relationship Status on Health

An extensive literature suggests that marriage confers health benefits to men and women. However, several econometric issues raise doubts about this prevalent conclusion. First, the evolution of health is a strongly persistent dynamic process; yet extant research relies almost exclusively on static mean-differenced fixed effects estimators. Second, measures of morbidity vary across studies making comparisons difficult, and the often used categorical measure of self-assessed health raises difficult interpretation and econometric issues associated with non-linear models. Finally, existing research has not confirmed whether these benefits accrue to couples who cohabit. We employ the Blundell and Bond dynamic panel data estimator to control for selection into marriage and a multiple correspondence analysis to identify weights for a continuous health index that reflects the multiple facets of morbidity and alleviates the econometric issues associated with a discrete dependent variable. Contrary to extant literature, our analysis using a balanced panel of nearly 2,430 individuals over 17 waves of the British Household Panel Survey finds that the effect of cohabitation on health is not statistically different from the effect of marriage on health after controlling for both health dynamics and selection.

The influence of traffic‐related pollution on individuals' life‐style: results from the BRFSS

This paper employs the Behavioral Risk Factor Surveillance System (2001) data in conjunction with the Environmental Protection Agency's Air Quality System data to investigate how air pollution caused by motor vehicle emissions affects the likelihood of good health and the amount of health investments. Models are estimated using three different measures of overall health: a measure of self-assessed health and two health outcome indicators (asthma and blood pressure). A multivariate probit approach is used to estimate recursive systems of equations for self-assessed health, health outcomes and life-styles. The most interesting result concerns the influence of pollution on health-improving life-style choices: only if traffic pollution is in the 'satisfactory range' (AQI level at or below 100), individuals will have incentive to invest in health.

Work Disability, Work and Justification Bias in Europe and the U.S.

To analyze the effect of health on work, many studies use a simple self-assessed health measure based upon a question such as do you have an impairment or health problem limiting the kind or amount of work you can do? A possible drawback of such a measure is the possibility that different groups of respondents may use different response scales. This is commonly referred to as differential item functioning (DIF). A specific form of DIF is justification bias: to justify the fact that they don't work, non-working respondents may classify a given health problem as a more serious work limitation than working respondents. In this paper we use anchoring vignettes to identify justification bias and other forms of DIF across countries and socio-economic groups among older workers in the U.S. and Europe. Generally, we find differences in response scales across countries, partly related to social insurance generosity and employment protection. Furthermore, we find significant evidence of justification bias in the U.S. but not in Europe, suggesting differences in social norms concerning work.

The Health Penalty of China's Rapid Urbanization

textabstractRapid urbanization could have positive and negative health effects, such that the net impact on population health is not obvious. It is, however, highly pertinent to the human welfare consequences of development. This paper uses community and individual level longitudinal data from the China Health and Nutrition Survey to estimate the net health impact of China’s unprecedented urbanization. We construct an index of urbanicity from a broad set of community characteristics and define urbanization in terms of movements across the distribution of this index. We use difference-in-differences estimators to identify the treatment effect of urbanization on the self-assessed health of individuals. The results reveal important, and robust, negative causal effects of urbanization on health. Urbanization increases the probability of reporting fair or poor health by 5 to 15 percentage points, with a greater degree of urbanization having larger health effects. While people in more urbanized areas are, on average, in better health than their rural counterparts, the process of urbanization is damaging to health. Our measure of self-assessed health is highly correlated with subsequent mortality and the causal harmful effect of urbanization on health is confirmed using more objective (but also more specific) health indicators, such as physical impairments, disease symptoms and hypertension.