Contributions of Geographic, Socioeconomic, and Lifestyle Factors to Quality of Life, Frailty, and Mortality in Elderly in Hong Kong

Prof. Jean WooDepartment of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong

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)

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)

Background

Macro indicatorse.g. mortality

Individual health descriptors (more holistic indicator of health)e.g. self-rated

health, degree of frailty

• Choices of health outcomes:

vs

Background

Few studies in older populations ondisparities in frailty & other health outcomes contributions of individual & environmental

factors to these outcomes

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

Hypothesis

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

Study participants

Methods

General questionnaireDemographicsEducational levelMaximum life-time incomeSelf-rated SESSmokingAlcohol useDistrict of residence (18 districts in HK)

Methods

Physical Activity Scale of the Elderly (PASE) (Washburn et al, 1993)

12-item scaleno. of hours per day on leisure, household &

occupational physical activities over past 1 week

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

Methods

Dietary Quality Index-International (DQI) (Kim et al, 2003)

an indicator of quality of dietcovers 4 aspects

variety, adequacy, moderation & overall balancescores between 0-100

high score represents high quality

Methods

Self-rated health by validated Chinese version of SF-12 (Lam et al, 2005)

SF-12 physical healthSF-12 mental health

4 year mortality data from the Government Death Registry

Methods

Frailty Index (FI) (Goggins et al, 2005)

summation measure of deficits in physical, functional, psychological, nutritional & social domains

low score represents less frailtyhealth check questionnaire with list of

deficits for FI calculation, e.g.self-reported medical history falls history in the past yearbody mass index <18.5 kg/m2

Statistical Analysis

Include districts with n>=100 participantsRegression & path analysis

To examine relationship between contributory factors & each health outcome, with adjustment for age & sex

Use Shatin as reference district

SAS version 9.1p<0.05 as level of significance

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

Results

11 out of 18 districts with n>=1003611 subjects for analysis (90.3% of

original sample)

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)*

ab

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<0.05Coefficients within path: standardized from regression

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<0.05Coefficients within path: standardized from regression

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<0.05Coefficients 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)*

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<0.05Coefficients 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)*

Discussion

Our findingsDistrict variation in health outcomes among

Chinese elderly in HKDistrict 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

Discussion

Support findings of previous studiesBoth 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)

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)

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

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