1. Contributions of Geographic, Socioeconomic, and Lifestyle Factors to Quality of Life, Frailty, and Mortality in Elderly in Hong Kong Prof. Jean Woo Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong

2. Background Increasing emphasis on collecting data on disparities in health outcomes Minimizing these disparities as part of public health improvement (Association of Public Health Observatories, 2009)

3. Background Contributory factors to disparities in health outcomes: Provision & accessibility of health services (Starfield et al, 2005) Social & psychological factors (Smith et al, 2008; Elstad, 2009) Physical environment, e.g. air pollution, open spaces (Sun et al, 2008; Mitchell et al, 2008) Neighbourhood factors, e.g. noise, constant bright light Personal factors, e.g. SES, lifestyle, life events (Huff et al, 1999; Khaw et al, 2008; Elstad, 2009)

4. Background Macro indicators e.g. mortality Individual health descriptors (more holistic indicator of health) e.g. self-rated health, degree of frailty Choices of health outcomes: vs

5. Background Few studies in older populations on disparities in frailty & other health outcomes contributions of individual & environmental factors to these outcomes

6. Aims of This Study To examine district variations in self-rated health, frailty & 4 year mortality in HK Chinese aged >=65 years To analyze the contributions of lifestyle, SES & geographical location of residence to these health outcomes in this population

7. Hypothesis Lifestyle, SES & regional characteristics directly & indirectly through interactions contribute to these health outcomes

8. Study participants

9. Methods General questionnaire Demographics Educational level Maximum life-time income Self-rated SES Smoking Alcohol use District of residence (18 districts in HK)

10. Methods Physical Activity Scale of the Elderly (PASE) (Washburn et al, 1993) 12-item scale no. of hours per day on leisure, household & occupational physical activities over past 1 week

11. Methods Dietary intake in past 12 months by validated Food Frequency Questionnaire (FFQ) (Woo et al, 1997) Calculate daily nutrient intake from overseas & Chinese food tables Calculate Dietary Quality Index- International (DQI) based on FFQ & calculated nutrient intake

12. Methods Dietary Quality Index-International (DQI) (Kim et al, 2003) an indicator of quality of diet covers 4 aspects variety, adequacy, moderation & overall balance scores between 0-100 high score represents high quality

13. Methods Self-rated health by validated Chinese version of SF-12 (Lam et al, 2005) SF-12 physical health SF-12 mental health 4 year mortality data from the Government Death Registry

14. Methods Frailty Index (FI) (Goggins et al, 2005) summation measure of deficits in physical, functional, psychological, nutritional & social domains low score represents less frailty health check questionnaire with list of deficits for FI calculation, e.g. self-reported medical history falls history in the past year body mass index <18.5 kg/m 2

15. Statistical Analysis Include districts with n>=100 participants Regression & path analysis To examine relationship between contributory factors & each health outcome, with adjustment for age & sex Use Shatin as reference district SAS version 9.1 p<0.05 as level of significance

16. Statistical analysis Contributory factors (independent variables) District of residence Self-rated SES Smoking Alcohol use PASE DQI Confounding variables Age Sex Health outcomes (dependent variables) SF-12 physical SF-12 mental FI (log transformed) 4 year mortality

17. Results 11 out of 18 districts with n>=100 3611 subjects for analysis (90.3% of original sample)

18. Results Path analysis model of SF-12 physical (adjusted for age & sex) Higher SES District (Ref: Shatin) DQI PASE Alcohol use Smoking SF12-Physical Kowloon City (0.039)* Eastern (0.076)* Yau Tsim Mong (0.038)* a b c d 0.031 0.014 -0.058* -0.034* 0.069* 0.041* 0.028 0.095* 0.099* Sham Shui Po (0.042)* Eastern (0.045)* a: Tsuen Wan (-0.04)*, Kowloon City (0.042)* b: Eastern (0.043)* c: Kowloon City (-0.058)*, Eastern (-0.082)* d: Kwai Tsing (-0.046)*, Yuen Long (-0.061)*, Kowloon City (-0.050)*, Kwun Tong (-0.045)*, Eastern (-0.052)*, Yau Tsim Mong (-0.057)* *p<0.05 Coefficients within path: standardized  from regression

19. Results Path analysis model of SF-12 mental (adjusted for age & sex) Higher SES in HK District (Ref: Shatin) DQI PASE Alcohol use Smoking SF12-Mental Kowloon City (0.039)* Eastern (0.076)* Yau Tsim Mong (0.038)* a b c d 0.031 0.014 -0.058* -0.034* 0.069* 0.038* -0.034 0.022 0.070* Tsuen Wan (0.05)* Kwai Tsing (0.039)* Yuen Long (0.037)* Sham Shui Po (0.069)* Eastern (0.062)* Yau Tsim Mong (0.043)* a: Tsuen Wan (-0.04)*, Kowloon City (0.042)* b: Eastern (0.043)* c: Kowloon City (-0.058)*, Eastern (-0.082)* d: Kwai Tsing (-0.046)*, Yuen Long (-0.061)*, Kowloon City (-0.050)*, Kwun Tong (-0.045)*, Eastern (-0.052)*, Yau Tsim Mong (-0.057)* *p<0.05 Coefficients within path: standardized  from regression

20. Results Path analysis model of FI(log) (adjusted for age & sex) a: Tsuen Wan (-0.04)*, Kowloon City (0.042)* b: Eastern (0.043)* c: Kowloon City (-0.058)*, Eastern (-0.082)* d: Kwai Tsing (-0.046)*, Yuen Long (-0.061)*, Kowloon City (-0.050)*, Kwun Tong (-0.045)*, Eastern (-0.052)*, Yau Tsim Mong (-0.057)* *p<0.05 Coefficients within path: standardized  from regression Higher SES in HK District (Ref: Shatin) DQI PASE Alcohol use Smoking Log (Frailty index) Kowloon City (0.039)* Eastern (0.076)* Yau Tsim Mong (0.038)* a b c d 0.031 0.014 -0.058* -0.034* -0.086* -0.08* -0.072* -0.107* -0.06* Sham Shui Po (-0.052)*

21. Results Path analysis model of Death (adjusted for age & sex) a: Tsuen Wan (-0.04)*, Kowloon City (0.042)* b: Eastern (0.043)* c: Kowloon City (-0.058)*, Eastern (-0.082)* d: Kwai Tsing (-0.046)*, Yuen Long (-0.061)*, Kowloon City (-0.050)*, Kwun Tong (-0.045)*, Eastern (-0.052)*, Yau Tsim Mong (-0.057)* *p<0.05 Coefficients within path: standardized  from regression Higher SES in HK District (Ref: Shatin) DQI PASE Alcohol use Smoking Death Kowloon City (0.039)* Eastern (0.076)* Yau Tsim Mong (0.038)* a b c d 0.031 0.014 -0.058* -0.034* -0.054* -0.013 0.011 -0.051* -0.036* Kowloon city (-0.055)* Eastern (-0.048)* Yau Tsim Mong (-0.052)*

22. Discussion Our findings District variation in health outcomes among Chinese elderly in HK District of residence, SES & lifestyle factors directly & indirectly affect the studied health outcomes Higher self-rated SES and better lifestyle (e.g. better diet quality, more physically active) contribute to better health outcomes

23. Discussion Support findings of previous studies Both health care systems & lifestyle contribute to variations in health outcomes (Avendano et al, 2009) Lower mortality rate with a healthier diet and higher physical activity level (Khaw et al, 2008) Higher SES is associated with decreased ill- health & disability (Siegrist et al, 2006)

24. Discussion District factor may have direct contribution to variations in health outcomes Neighbourhood deprivation is associated with worse health outcomes Social support, leisure facilities, safety, environmental pollution, crowdedness etc. (van Lenthe, 2006; Ko et al, 2007) Exert effect partly through psychological mechanisms mediated via neuroendocrine system (McEwen et al, 1999) Supported by our previous study of district variation in telomere length (Woo et al, 2009)

25. Limitations Cross sectional design Sampling bias either health conscious or with health problems higher educational level compared to the general HK population great variations in no. of participants from each district No data on life course dimension or detailed district factors

26. Conclusion District variations in health outcomes exist in the Hong Kong elderly population These variations result directly from district factors, & are indirectly mediated through SES position & lifestyle Future studies on district factors in reducing health disparities in the older population Reference: Woo J et al. (2010) Relative Contributions of Geographic, Socioeconomic, and Lifestyle Factors to Quality of Life, Frailty, and Mortality in Elderly. PLoS ONE 5(1): e8775. doi:10.1371/journal.pone.0008775