Measures of Health-Related Quality-of-Life Background presentation for IOM Committee on Women’s Health Research May 7, 2009 Dennis G. Fryback, PhD Professor Emeritus, Population Health Sciences University of Wisconsin-Madison

Measures of Health – a quick typology Mortality-based measures – death rates, life expectancies, etc. All the familiar stuff

Measures of Health – a quick typology Mortality-based measures – death rates, life expectancies, etc. Morbidity-based measures – indicators Indicators: – Single, countable things TB rate C-section rates % population who exercise – Examples: Healthy People 2010 “Leading Indicators” WHO “Core Health Indicators”

Measures of Health – a quick typology Mortality-based measures – death rates, life expectancies, etc. Morbidity-based measures – indicators – health status measures disease-, organ-specific Health Status Measures Disease-, organ-specific.... Created to be sensitive to changes in symptoms or functional impairment due to a particular disease process Examples: – Arthritis Impact Measurement System (AIMS) – Vision Function Questionnaire (VFQ-25) – McGill Pain Questionnaire – NY Heart Association Classification Some physician-reported, others patient-reported

Measures of Health – a quick typology Mortality-based measures – death rates, life expectancies, etc. Morbidity-based measures – indicators – health status measures disease-, organ-specific “generic” Generic Health Status Measures Most famous: SF-36 health profile One questionnaire with many questions  Several questions about each of 8 different domains of health multiple scales to cover broad scope of health, not tied to one disease or organ system Scoring: Arbitrary scales based on summing responses to multiple questions Separate scores for each subscale or health concept PF, RP, BP, GH, VT, SF, RE, MH PCS MCS

Measures of Health – a quick typology Mortality-based measures – death rates, life expectancies, etc. Morbidity-based measures – indicators – health status measures disease-, organ-specific

Measures of Health – a quick typology Mortality-based measures – death rates, life expectancies, etc. Morbidity-based measures – indicators – health status measures disease-, organ-specific “generic” – health-related quality-of-life (HRQoL) indexes HRQoL indexes Like generic health status – try to comprehensively cover conceptual basis of health with multiple questions about health Scoring – not simple sums--these are not psychometric scales! – econometric methods used to elicit utility weights (“preferences”) for health states – 0 = dead, 1= perfect health – average preference weights from community sample of people

Data Pyramid for Population Health (after Wolfson) more disaggregate measures more aggregated measures HRQoL Indexes preference-weighted aggregate scores summarizing overall health Generic Health Status Profiles Vector of health status domain scales Disease-specific Scales Do not necessarily cover all health domains Multitude of health indicators = summarization, evaluation = explanation, description

Domains addressed by a number of HRQoL indexes EuroQol EQ-5D – Mobility – Self-care – Usual activities – Pain/discomfort – Anxiety/depression SF-6D (from SF-36 questionnaire) – Physical function – Role limitation – Social function – Pain – Mental health – Vitality QWB-SA – Mobility – Physical activity – Social activity – Symptoms HUI3 – Vision – Hearing – Speech – Ambulation – Dexterity – Emotion – Cognition – Self-care – Pain HUI2 – Sensation – Mobility – Emotion – Cognition – Self-care – Pain HALex – Self-rated health – Physical activity limitations

Scale ranges for the HRQoL indexes EuroQol EQ-5D – Mobility – Self-care – Usual activities – Pain/discomfort – Anxiety/depression ( ) SF-6D (from SF-36 questionnaire) – Physical function – Role limitation – Social function – Pain – Mental health – Vitality 0...( ) QWB-SA – Mobility – Physical activity – Social activity – Symptoms 0...( ) HUI3 – Vision – Hearing – Speech – Ambulation – Dexterity – Emotion – Cognition – Self-care – Pain ( ) HUI2 – Sensation – Mobility – Emotion – Cognition – Self-care – Pain ( ) HALex – Self-rated health – Physical activity limitations 0...( )

Preference weights source for the HRQoL indexes EuroQol EQ-5D US national sample SF-6D (from SF-36 questionnaire) England national sample QWB-SA San Diego, CA HUI3 Ontario, Canada HUI2 Ontario, Canada HALex (ad hoc US for Healthy People 2000, 2010)

NHMS Design RDD survey of adults, non-institutionalized, – ages – continental US (not Alaska, Hawaii) – Fielded June 2005-Aug 2006 – designed to oversample African Americans and people aged 65+

NHMS: Data collected Indexes – EQ-5D, SF-36v2 TM, HUI2/3, QWB-SA, HALex EQ-5D, SF-36, HUI & QWB randomized US scoring EQ-5D SF-6D derived from SF-36 & SF-12 Other health & well-being – CDC QoL questions – 2 Psychology of Well-being scales – 11 health conditions (NHIS format + follow up questions) – 9 Perceived discrimination questions (everyday + lifetime) Personal data – Gender, age, race, height, weight, smoking status, marital status, education Socioeconomic data – household income, household assets, health insurance status Linked contextual data from US Census (2000) – Race, education, economic distributions in census tract – Census’ %urban %rural population in tract Now available as public data set: see

NHMS Sample Mean age 60.2 (SD=14)

SF-6DHUI 2HUI 3EQ-5DQWB-SA HUI HUI * EQ-5D QWB-SA HALex Correlations (weighted to US pop 35-89) *HUI2/3 are 2 different indexes scored from same questionnaire.

Means for women seem always a little lower than for men

D Cherepanov, in progress, Gender differences in HRQOL Table. Adjusted for Age Index Black s White s All (B+W) SF-6D EQ-5D HUI HUI QWB-SA *Bolded values have p<0.05 Table. Adjusted for Age + SES Index Black s White s All (B+W) SF-6D EQ-5D HUI HUI QWB-SA *SES is marital status, education, income *Bolded values have p<0.05

(more from Cherepanov, dissertation in progress) Similar results seen in other US national data sets for ages 35-89: US Valuation of the EQ-5D (n ~ 2,300) – EQ-5D, HUI2, HUI3 Medical Expenditure Panel Survey (MEPS) (n~13,200) – EQ-5D, SF-6D Joint Canada US Survey of Health (JCUSH) (n~3,200) – HUI3 Differences tend to disappear when incorporate added covariates education, marital status, household income Domain-by-domain analyses hint at differential gender effects Larger gender differences in pain & physical function domains; smaller on social-mental functioning, all sig. before adjustment After covariate adjustment, differences in pain & physical health remain weakly significant; no diff. on social-mental functioning. results only tentative so far as many complexities

SES & HRQoL in older adults At every age, significant gradient of differences in HRQoL associated with income, education, assets Each has independent association net of the others. Education and income differentials in HRQoL are relatively consistent across adult age groups controlling for other SES measures, race, and gender. HRQoL differentials associated with household assets are widest through midlife into old age. Robert SA, Cherepanov D, Palta M, Cross Dunham NC, Feeny D, Fryback DG. Socioeconomic status and age variations in health-related quality of life: Results from the National Health Measurement Study. Journal of Gerontology: Social Sciences, 2009 May; 64(3):378-89, [Epub 2009 Mar 23]

Gender differences in HRQoL As far as these indexes go, gender differences mostly because of differential distribution of strong health determinants between older women and men in US population: income, education, marital status?? Additional: do women and men answer self-report HRQoL questionnaires differently? (“Differential Item Functioning”, DIF) – JA Fleishman, WF Lawrence. Demographic variation in SF-12 scores: true differences or differential item functioning? Med Care 2003;41:III-75—III-86) Race x gender x instrument interactions??? Gender differences in weighting of health domains for aggregation?? Much work to do yet!

Summary Standardized HRQoL indexes offer a window into gender differences in health by aggregating across many facets of health as well as allowing disaggregation. Available in a number of important population surveys in US and elsewhere (depending on the measure)