SF‐6D POPULATION NORMS

The derivation of population norms using simple generic health‐related quality of life measures to inform policy has been recommended in the literature. This letter illustrates the derivation of population norms for the SF‐6D in the United Kingdom. It uses a sample of 22,166 respondents from the 2010 wave of the study Understanding Society. Understanding Society is a national representative sample of British citizens. The survey of this study contains the SF‐12. It is possible to derive health state utilities from the SF‐12 (and from the SF‐36) using a relatively new instrument, the SF‐6D. The SF‐12 and the SF‐36 belong to the most widely used generic health‐related quality of life measures. Mean SF‐6D utility scores for males and females are 0.81 and 0.79, respectively. Especially the older age categories have lower utility scores. The younger age categories have slightly higher utility scores. From a list of 17 conditions, people with congestive heart failure had the lowest (0.60) and people with diabetes the highest (0.76) SF‐6D scores. This letter encourages the health economics research community to derive SF‐6D population norms to inform policy. Copyright © 2012 John Wiley & Sons, Ltd.     

Physical activity and body mass shape quality of life trajectories in mid‐age women

Objective: To determine the combined longitudinal effect of body mass index (BMI) and physical activity (PA) on health‐related quality of life (HrQoL), using the SF‐6D (SF‐36) utility measure. Methods: Five waves of self‐reported data from the 1946–51 cohort (n=5,200; data collection, 2001–2013) of the Australian Longitudinal Study on Women’s Health were used. Mixed effect models were employed to address the objective. Results: Women with high PA experienced higher HrQoL regardless of BMI group, however, for those healthy or overweight, there was a very small decline in HrQoL over time. Women reporting no PA levels experienced the lowest baseline mean SF‐6D score within each BMI group, with decreasing trajectories over the follow‐up period. The rate of decline was greatest in women with obesity. Within each BMI group, there was a large, increasing gap in HrQoL between those who reported no and low PA over time. Women with obesity and high PA experienced similar HrQoL trajectories to women with normal weight or overweight with low PA levels. Overweight women with moderate PA experienced similar HrQoL to those with low PA but normal weight. Conclusions: PA may mitigate the adverse effect of overweight and obesity on HrQoL at mid‐life, at higher activity levels. Implications for public health: PA benefits HrQoL regardless of body mass, with larger gains for those currently not physically active. Moderate to high PA may mitigate the effect of overweight and obesity.     

The SF‐36v2 and SF‐12v2 health surveys in New Zealand: norms,scoring coefficients and cross‐country comparisons

Objective : To provide New Zealand population norms for version 2 of the SF‐36 and SF‐12 health surveys and report scoring coefficients that enable the construction of Physical and Mental Component Summary scores from New Zealand SF‐36v2 and SF‐12v2 data. Approach : Norms for the SF‐36v2 and scoring coefficients for the Physical and Mental Component Summary scores are estimated using 2006/07 New Zealand Health Survey data, which included 12,488 adults (aged 15 years and over). Norms for the SF‐12v2 are derived from 2008 New Zealand General Social Survey data, including 8,721 adults. Comparisons are made between New Zealand norms for versions 1 and 2 of the SF‐36 instrument. In addition, New Zealand SF‐36v2 and SF‐12v2 norms and the scoring coefficients are compared with those for the United States and South Australia. Conclusion : Differences between: 1) New Zealand population norms for the SF‐36 versions 1 and 2; and 2) SF‐36v2 and SF‐12v2 population norms for New Zealand and those for the United States and South Australia highlight the importance of using version‐specific and country‐specific population norms. Implications: The analysis reported here allows for the appropriate use of the SF‐36v2 and SF‐12v2 instruments in New Zealand.     

Income‐related health inequalities in working age men and women in Australia and New Zealand

Objective : To examine income‐related inequalities in health in working age men and women in Australia and New Zealand. Methods : We used data from two longitudinal surveys, Wave 8 (2008) of the Household Income and Labour Dynamics in Australia (HILDA) Survey and Wave 7 (2008/2009) of the New Zealand Survey of Family Income and Employment (SoFIE). We compared concentration indices (a measure of income‐related health inequality) that examined the distribution of general and mental health‐related quality of life scores (from the SF‐36) across income in working age (20–65 year old) men and women. Decomposition analyses of the concentration indices were done to identify the relative contribution of various determinants to the income‐related health inequality. Results : General health (GH) scores generally decline with age, and mental health (MH) scores increase with age, in both surveys. Income‐related health inequalities were present in both the HILDA and SoFIE samples, with better health in high income groups. Decomposition analyses found that income, area deprivation and being inactive in the labour force were major contributors to income‐related health inequality, in both surveys, and for both health outcomes. Conclusions and implications : Despite some baseline differences in income‐related health inequalities using Australian and New Zealand surveys, we found similar modifiable determinants, which could be targeted to improve health inequalities in both countries.     

Population norms for the AQoL derived from the 2007 Australian National Survey of Mental Health and Wellbeing

Objective : To provide Australian health‐related quality of life (HRQoL) population norms, based on utility scores from the Assessment of Quality of Life (AQoL) measure, a participant‐reported outcomes (PRO) instrument. Methods: The data were from the 2007 National Survey of Mental Health and Wellbeing. AQoL scores were analysed by age cohorts, gender, other demographic characteristics, and mental and physical health variables. Results: The AQoL utility score mean was 0.81 (95%CI 0.81–0.82), and 47% obtained scores indicating a very high HRQoL (>0.90). HRQoL gently declined by age group, with older adults’ scores indicating lower HRQoL. Based on effect sizes (ESs), there were small losses in HRQoL associated with other demographic variables (e.g. by lack of labour force participation, ES_median: 0.27). Those with current mental health syndromes reported moderate losses in HRQoL (ES_median: 0.64), while those with physical health conditions generally also reported moderate losses in HRQoL (ES_median: 0.41). Conclusions: This study has provided contemporary Australian population norms for HRQoL that may be used by researchers as indicators allowing interpretation and estimation of population health (e.g. estimation of the burden of disease), cross comparison between studies, the identification of health inequalities, and to provide benchmarks for health care interventions.     

Health Utility Assessment Using EQ-5D among Caregivers of Children with Autism

ObjectivesHealth utility of caregivers of children with autism was assessed by using the EuroQol five-dimensional (EQ-5D) questionnaire. Utility scores of autism caregivers were compared with norms for the general adult US population. Predictors of health utility were identified.MethodsA cross-sectional online survey design was used. Caregivers registered with the Interactive Autism Network were approached for participation in the online survey. Three hundred and sixteen usable responses were received. Health utility among caregivers was calculated and compared with the US population norms by using Student’s t test. Problems in EQ-5D questionnaire domains and utility scores were analyzed by study characteristics by using Kruskal-Wallis analysis of variance. Factors predicting health utility were identified by using ordinary least square regression.ResultsRoughly 94% of the caregivers who participated in the study were females. As compared to their counterparts in the general US population, caregivers who were aged 18 to 44 years and were females had lower utility scores (P < 0.001). Significant differences in utility scores were observed among caregivers. When compared to males, females had lower health utility. Caregivers of lower socioeconomic status had lower utility scores and reported more problems in EQ-5D questionnaire domains than did those from higher socioeconomic status. Caregiver burden was inversely correlated with health utility. Caregiver physical and mental health status, objective strain, education, and relationship with the care recipient were found to significantly predict health utility (adjusted R² ~57%).ConclusionsAutism caregivers had lower health utility than did the general adult US population. There is an immediate need to address health concerns among this growing population.     

The SF36 Version 2: critical analyses of population weights, scoring algorithms and population norms

Background The SF36 Version 2 (SF36V2) is a revision of the SF36 Version 1, and is a widely used health status measure. It is important that guidelines for interpreting scores are available. Method A population sample of Australians (n = 3015) weighted to achieve representativeness was administered the SF36V2. Comparisons between published US weights and sample derived weights were made, and Australian population norms computed and presented. Major findings Significant differences were observed on 7/8 scales and on the mental health summary scale. Possible causes of these findings may include different sampling and data collection procedures, demographic characteristics, differences in data collection time (1998 vs. 2004), differences in health status or differences in cultural perception of the meaning of health. Australian population norms by age cohort, gender and health status are reported by T-score as recommended by the instrument developers. Additionally, the proportions of cases within T-score deciles are presented and show there are important data distribution issues. Principal conclusions The procedures reported here may be used by other researchers where local effects are suspected. The population norms presented may be of interest. There are statistical artefacts associated with T-scores that have implications for how SF36V2 data are analysed and interpreted.     

Measuring the health of Scottish drug users

Information on how individuals perceive their own health is important for providing appropriate health‐related support and monitoring health over time. The Short Form 36 (SF‐36) Health Status Questionnaire is one of the most widely used generic measures of self‐perceived health status. Despite this, there has been no large‐scale research documenting the SF‐36 scores of problem drug users in the UK. The present paper has two aims: (1) to compare the self‐perceived health of a sample of problem drug users with that of a sample from the general population; and (2) to investigate differences between the self‐perceived health of various subgroups of problem drug users. In total, 1179 individuals starting a new episode of drug misuse treatment in Scotland were invited to complete a structured questionnaire that included the SF‐36. Out of these, 1033 (87.6%) agreed and 990 successfully completed all SF‐36 questions. The 990 completers were more likely than the 43 partial completers to be male [odds ratio (OR) = 0.45; 95% confidence interval (95% CI) = 0.24–0.85] and not in prison (OR = 0.36; 95% CI = 0.19–0.68). The reliability of the SF‐36 – measured by internal consistency – was very good (Cronbach's α > 0.7 for all eight dimensions). Comparing the respondents’ mean dimension scores with those of a sample from the UK general population revealed that the drug users’ health was consistently worse than that of the general population (> 20 points on seven out of the eight scales). Differences between the mean SF‐36 scores of various subcategories of respondents were analysed using t‐tests or an analysis of variance, as appropriate. The factors considered were: sex; age; imprisonment; relationship status; homelessness; and recent drug injection. Significant differences between particular groups of respondents were identified. Despite limitations with the data presented, the paper highlights the utility of the SF36 for drug misuse treatment providers.     

Comparing SF-6D and EQ-5D utilities across groups differing in health status

Objectives  To compare the EQ-5D and SF-6D within socio-demographic and clinical groups in a representative sample (n = 1,005) of the Greek general population and to examine mean utility differences across groups differing in health in this population and in a highly morbid disease sample (diabetes, n = 215). Methods  Association and level of agreement between instruments were estimated with Pearson’s r and the intraclass correlation coefficient (ICC), respectively. Paired-samples t-test was used to identify significant score differences, which were regarded as minimally important differences (MID) when they exceeded 0.03. The EQ-VAS was used to classify individuals into health status groups, covering the range from very poor to very good health, and the same classification was used in the diabetes sample. Results  EQ-5D and SF-6D were in agreement and strongly correlated over the entire sample (ICC = 0.536, P < 0.001 and r = 0.662, P < 0.001), but correlation varied according to socio-demographic factors and clinical conditions. In healthier responders, EQ-5D scores were significantly higher than SF-6D scores (P < 0.001) and differences constituted MIDs. Contrarily, in individuals with clinical conditions, SF-6D scores were predominantly higher than EQ-5D. The pattern of results was replicated in the disease sample as well. Conclusions  The hypotheses that EQ-5D generates higher scores in healthier populations and the SF-6D in less healthier groups were confirmed. Based on the evidence provided here, EQ-5D and SF-6D measuring discrepancies generate utility differences across VAS-based health groups, which warrant further within-sample investigation.     

Lowering the 'floor' of the sf-6d scoring algorithm using a lottery equivalent method

This paper presents a new scoring algorithm for the SF‐6D, one of the most popular preference‐based health status measures. Previous SF‐6D value sets have a minimum (a floor), which is substantially higher than the lowest value generated by the EQ‐5D model. Our algorithm expands the range of SF‐6D utility scores in such a way that the floor is significantly lowered. We obtain the wider range because of the use of a lottery equivalent method through which preferences from a representative sample of Spanish general population are elicited. Copyright © 2011 John Wiley & Sons, Ltd.     

Empirical evidence suggests adverse climate events have not affected Australian women's health and well‐being

Objective: To compare the health and well‐being of women by exposure to adverse climate events. An Exceptional Circumstance declaration (EC) was used as a proxy for adverse climate events. The Australian government may provide financial support to people living in EC areas, i.e. areas experiencing a one in 20–25 year event (drought, flood or fire) that results in a severe, extended downturn in farm or farm‐related income. Methods: Data from 6,584 53–58 year old non‐metropolitan women participating in the 2004 survey of the Australian Longitudinal Study on Women's Health (ALSWH) were linked to EC data. Generalised linear models were used to analyse differences in SF‐36 General Health (GH) and Mental Health (MH) and perceived stress by EC for all women. Models were adjusted for demographic, health‐related and psychosocial factors potentially on the pathway between EC and health. Given that the effects on health were expected to be greater in vulnerable people, analyses were repeated for women with worse socioeconomic circumstances. Results: GH, MH and stress did not differ for the 3,366 women in EC areas and 3,218 women in non‐EC areas. GH, MH and stress were worse among vulnerable women (who had difficulty managing on available income) regardless of EC. Conclusion and implications: This research adds to the existing literature on climate change, associated adverse climate events and health, by suggesting that multiple resources available in high income countries, including government support and individual psychosocial resources may mitigate some of the health impacts of adverse climate events, even among vulnerable people.     

Relationship Among Sociodemographic Factors,Clinical Conditions,and Health-related Quality of Life: Examining the EQ-5D in the U.S. General Population

Introduction: Health-related quality of life (HRQL) measures are used increasingly in evaluations of clinical and population-based outcomes and in economic analyses. We investigate the influence of demographic, socioeconomic, and chronic disease factors on the HRQL of a representative U.S. sample. Methods: We examined data from 13,646 adults in the 2000 Medical Expenditure Panel Survey, a nationally representative sample of the U.S. general population, who completed a self-administered questionnaire containing the EQ-5D, a preference-based measure. We assessed the relationships between EQ-5D scores and sociodemographic variables, including age, sex, race/ethnicity, income and education, and six common chronic conditions. Results: In fully adjusted models, EQ-5D scores decreased with increasing category of age and were lower for persons with a lower income and educational attainment as well as each of the six conditions. Although the EQ-5D scores were lower for females and Whites compared with Blacks such differences were not of a magnitude considered to be clinically important. Conclusions: In the U.S., sociodemographic factors and clinical conditions are strongly associated with scores on the EQ-5D. Population health studies and risk-adjustment models should account and adjust for these factors when assessing the performance of health programs and clinical care.     

Health‐related quality of life of medical students

Medical Education 2010: 44 : 227–235 Objectives Mental problems such as stress, anxiety and depression have been described among medical students and are associated with poor academic and professional performance. It has been speculated that these problems impair students’ quality of life (QoL). The authors aimed to assess the health‐related QoL (HRQL) of medical students throughout their 6 years of training at a school with a traditional curriculum. Methods Of a total of 490 students attending our institution’s medical school, 38 were surveyed in February 2006 (incoming Year 1 group, surveyed when students were in the second week of Year 1 classes) and 352 were surveyed in February 2007 (students in Years 1–6). Students self‐reported their HRQL and depressive symptoms using the Short‐Form Health Survey (SF‐36) and the Beck Depression Inventory (BDI). Comparisons were performed according to year in training, presence of depressive symptoms, gender, living arrangements and correlations with family income. Results The students’ ages ranged from 18 to 31 years (median 22.3 years). Students in Years 2, 3, 4 and 6 had lower scores for mental and physical dimensions of HRQL compared with the incoming Year 1 group (P < 0.01), with the largest difference observed for Year 3 students. Students with depressive symptoms had lower scores in all domains of the SF‐36 (P < 0.01). Female students had lower HRQL scores than males (P < 0.01). No differences were observed for students living with versus without family and no correlation with family income was found. Conclusions Major impairments in HRQL were observed among Year 3 students, students with depressive symptoms and women. Medical schools should institute efforts to ensure that students’ HRQL and emotional support are maintained, particularly during critical phases of medical training.     

Psychological distress is associated with a range of high‐priority health conditions affecting working Australians

Background: Psychological distress is growing in prevalence in Australia. Comorbid psychological distress and/or depressive symptoms are often associated with poorer health, higher healthcare utilisation and decreased adherence to medical treatments. Methods: The Australian Work Outcomes Research Cost‐benefit (WORC) study cross‐sectional screening dataset was used to explore the association between psychological distress and a range of health conditions in a sample of approximately 78,000 working Australians. The study uses the World Health Organization Health and Productivity Questionnaire (HPQ), to identify self‐reported health status. Within the HPQ is the Kessler 6 (K6), a six‐item scale of psychological distress which strongly discriminates between those with and without a mental disorder. Potential confounders of age, sex, marital status, number of children, education level and annual income were included in multivariate logistic regression models. Results: Psychological distress was significantly associated with all investigated health conditions in both crude and adjusted estimates. The conditions with the strongest adjusted association were, in order from highest: drug and alcohol problems, fatigue, migraine, CVD, COPD, injury and obesity. Conclusions: Psychological distress is strongly associated with all 14 health conditions or risk factors investigated in this study. Comorbid psychological distress is a growing public health issue affecting Australian workers.     

Socioeconomic status and quality of life in population‐based Australian men: data from the Geelong Osteoporosis Study

Objective : To investigate the relationship between socioeconomic status (SES) and reported perceptions of quality of life (QOL) in a cross‐sectional population‐based analysis of a representative sample of Australian men. Methods : In 917 randomly recruited men aged 24–92 years, we measured QoL in the domains of physical health, psychological health, environment and social relationships, using the Australian World Health Organization Quality of Life Instrument (WHOQOL‐BREF). Residential addresses were cross‐referenced with Australian Bureau of Statistics 2006 census data to ascertain SES. Participants were categorised into lower, mid, or upper SES based on the Index of Relative Socioeconomic Disadvantage and Advantage (IRSAD), the Index of Economic Resources (IER), and the Index of Education and Occupation (IEO). Lifestyle and health information was self‐reported. Results : Males of lower SES reported poorer satisfaction with physical health (OR=0.6, 95%CI 0.4–0.9, p=0.02), psychological health (OR=0.4, 95%CI 0.3–0.7, p<0.001) and environment (OR=0.5, 95%CI 0.3–0.7, p<0.001), although not social relationships (p=0.59). The poorest QOL for each domain was observed in the lower and upper SES groups, representing an inverse U‐shaped pattern of association; however, statistical significance was only observed for psychological health (OR=0.5, 95%CI 0.4–0.7, p<0.001). These relationships were similar for IEO and IER. Conclusions : Men from lower and upper SES groups have lower QOL compared to their counterparts in the mid SES group.     

Social inequalities in health‐related use of time in Australian adolescents

Objective: Young people's socioeconomic position and time use behaviours – including physical activity, sedentary behaviours, social engagement, sleep and cognitive activities – have been associated with health outcomes. This study aimed to describe how time use varies with household income in a representative sample of 9–16 year old Australians. Methods: A random sample of 2,071 9–16 year old Australian children provided household income data and four days’ use‐of‐time data. Average daily minutes spent in various types of activities were calculated. Kruskal‐Wallis and Mann Whitney U tests were used to compare time use across the income bands. Results: Higher income participants spent significantly more time playing sport (p<0.0001), including team sports (p=0.0005), and in cognitively demanding behaviours such as school routine (p<0.0001), doing homework (p<0.0001) and playing music (p=0.001) than their low‐income counterparts. Conversely, low‐income participants spent significantly more time watching television (p<0.001) and playing videogames (p<0.0002). There were no differences in sleep or social interaction. Screen time and school‐related activities were the major locations of differences. Conclusions: Time use differences in the areas of sport, school‐related and screen activities may be associated with various health and wellbeing outcomes, and thus be a source of health inequalities. Implications : Socioeconomic‐related time use behaviour differences could be used to develop specific interventions to address health inequalities via interventions addressing time use or income inequalities.     

Estimating Utility Values for Health States of Overweight and Obese Individuals Using the SF-36

Objective: To use health-related quality-of-life (HRQoL) data from the Australian 1995 National Health Survey to estimate the impact of obesity (as measured by body mass index or BMI) on utility and quality-adjusted life expectancy (QALE).Method: SF-36 responses from 12,661 individuals in the general population were transformed into utility values using the SF-6D algorithm developed by Brazier and colleagues. Separate regression analyses for males and females were used to examine the impact of BMI and five obesity-related medical conditions (diabetes, coronary heart disease, depression, musculoskeletal disorders, and cancer) on utility. The utility estimates were used to provide indicative estimates of the decrease in QALE associated with being overweight or obese.Results: There was a statistically significant negative relationship between BMI and utility for males and females. For males (females), the marginal effect of a one-unit increase in BMI was associated with a −0.0024 (−0.0034) decrement in utility. Based on these estimates, a non-smoking male (female) aged 40 years who is obese can expect 7.2 (8.7) years less of QALE over their remaining lifetime.Conclusions : Results suggest that BMI is negatively associated with utility. Evaluation of policies designed to prevent or treat obesity should capture HRQoL as an outcome.     

Impact of low‐weight severity and menstrual status on bone in adolescent girls with anorexia nervosa

Clinicians currently use different low‐weight cut‐offs both to diagnose anorexia nervosa (AN) and to determine AN severity in adolescent girls. The purpose of this study was to evaluate the clinical utility of existing cut‐offs and severity criteria by determining which are most strongly associated with risk for low bone mineral density (BMD). Height adjusted BMD Z scores were calculated for 352 females: 262 with AN and 90 healthy controls (controls) (12–20.5 years), using data from the BMD in Childhood Study, for the lumbar spine, whole body less head, and total hip. For most cut‐offs used to define low weight (5th or 10th BMI percentile, BMI of 17.5 or 18.5, and 85 or 90% of median BMI), AN had lower BMD Z scores than controls. AN at >85 or >90% expected body weight for height (EBW‐Ht) did not differ in BMD Z scores from controls, but differed significantly from AN at ≤85 or ≤90% EBW‐Ht. Among AN, any amenorrhea was associated with lower BMD. AN had lower BMD than controls across DSM‐5 and The Society for Adolescent Health and Medicine (SAHM) severity categories. The SAHM moderate severity classification was differentiated from the mildly malnourished classification by lower BMD at hip and spine sites. Amenorrhea and %EBW‐Ht ≤ 85 or ≤ 90% are markers of severity of bone loss within AN. Among severity categories, BMI Z scores (SAHM) may have the greatest utility in assessing the degree of malnutrition in adolescent girls that corresponds to lower BMD.     

Socio‐economic status and survival from breast cancer for young,Australian, urban women

Objective: To estimate the association between measures of socio‐economic status (SES) and breast cancer (BC) survival for young, urban Australian women. Methods: We used a population‐based sample of 1,029 women followed prospectively for a median of 7.9 years. SES was defined by education and area of residence. Hazard ratios (HRs) associated with SES measures were estimated for (i) distant recurrence (DR) and (ii) all‐cause mortality as end‐points. Results: HRs for area of residence were not significantly different from unity, with or without adjustment for age at diagnosis and education level. The univariable HR estimate of DR for women with university education compared with women with incomplete high school education was 1.51 (95% CI = 1.08 – 2.13, p = 0.02), which reduced to 1.20 (95% CI = 0.85 – 1.72, p = 0.3) after adjusting for age at diagnosis and area of residence. Adjusting for prognostic factors differentially distributed across SES groups did not substantially alter the association between survival and SES. Conclusions: Among young, urban Australian women there is no association between SES and BC survival. Implications: This lack of estimates of association may be partly attributed to universal access to adequate breast cancer care in urban areas.