Andrew Page 1 , Yang Gonghuan 2 , Alan Lopez 1 , Richard Taylor 1,3

1. Modernisation, socio-economic change and disease transition in China: Integrating structural and proximate determinants of cause-specific mortality Andrew Page1, Yang Gonghuan2, Alan Lopez1, Richard Taylor1,3 1. School of Population Health, University of Queensland 2. Chinese Centre for Disease Control, Beijing 3. School of Public Health and Community Medicine, University of New South Wales

2. Disclaimer... • A case study frozen in time, although questions still relevant • Case study written circa ≈ 2009 • Note slightly different format • need to respond to the requested format and word limits of the funder • ...but the same key headings we’ve been talking about are still covered

3. Background & rationale • Improvements in health outcomes internationally have been previously associated with economic growth and social development • Societies undergo an epidemiologic transition, where causes of death shift from communicable diseases and nutritional deficiencies to non-communicable diseases • demographic effects, e.g. increasing elderly pop • also late stage transitions are associated with newly emerging infectious disease (e.g. HIV/AIDS)

4. Background & rationale • People’s Republic of China (PRC) is reportedly undergoing disease transition • reported declines in infectious disease mortality, increases in non-communicable disease (IHD, stroke, injury) • differential rates of transition in urban/rural areas, re-emergence of infectious disease in rural areas? • Reportedly occurred at a faster rate (e.g. from 1970s to 2000s) than other contexts, due to rapid socio-economic development • compressed epi transition? artefactual effects in mortality registration? • extent to which socio-economic factors drive trends in NCD mortality? extent to which known antecedents account for secular trends? extent to which population prevention initiatives associated with secular trends? • extent to which effects differ by geographic area? (urban/rural, north/south)

5. Primary objectives • To investigate sex- and age-specific trends in mortality from coronary heart disease (CHD) and stroke and associated risk factors by socio-economic position, urban-rural residence, geography and measures of economic development. Risk factors include smoking, overweight and obesity, fat intake, alcohol consumption, salt consumption, and hypertension. • To investigate sex- and age-specific trends in lung cancer and associated risk factors (especially tobacco consumption) by socio-economic position, urban-rural residence, geography, and measures of economic development. • To investigate sex- and age-specific trends in road traffic accident mortality and associated risk factors by socio-economic position, urban-rural residence, geography, and measures of economic development. • To investigate sex- and age-specific trends in suicide and associated risk factors (social, demographic, psychiatric and psychological) by socio-economic position, urban-rural residence, geography, and measures of economic development

6. Question of study design • Descriptive questions, so descriptive design? • Also analytic dimensions (e.g. differences by geog area, SES, prevalence of risk factors), so analytic design? • Retrospective cohort study? case-control study? • ...well, these are largely broad-based population, ecological questions around trends over time. • c-c would be inappropriate. Maybe a retrospective cohort study at a pinch, but representative of China? • Aggregate, descriptive-analytic design seems the most reasonable option

7. Proposed study design • Partial-ecologic and ‘multi-level’ design of secular trends in mortality • ‘partial’ and ‘multi-level’ in that even though units of analysis may be ecological, there will be individual-level characteristics also available across multiple data sources (i.e. potentially individuals within areas) • various routinely collected data sources relating to distal, intermediate and proximal risk factors • DSP, MOH-VR, hospital admissions, periodic health surveys

8. Sufficient sample size? • ...well this is largely a descriptive study, based on routinely collected data. Sample size not strictly appropriate • But there are proposed analytic comparisons • need to ensure that data for the range of proposed comparisons (urban/rural, high/low SES, north/south, high/low prevalence) are obtained. • Preliminary data analysis, range and consistency checks to ensure that proposed comparisons are feasible and possible.

9. Recruitment procedure • Secondary data analysis, so no ‘recruitment’ per se • Arranging for data access and obtaining variables reflecting research questions • Disease Surveillance Points • MOH Vital Regsitration • Hospital admissions • Periodic Health Surveys • Guided by census of data sources, and assessment of reliability and validity • Acknowledgement of limitations

10. Ethical implications • Not so many • Secondary data analysis of de-identified (or aggregated) data • Institutional/ethical clearances to access datasets, development of user agreements with data custodians • Storage and access of data over the course of the study

11. e.g. Analytic strategy, objective 1 • “Trends in CHD and stroke by sex, age, and geographic area will be investigated to establish whether CHD and stroke mortality rates have increased over the study period (1970-2007). The unit of analysis using DSP data will be based on each of the 146 county-level collection points, however, broader aggregations (e.g. urban-rural residence) may also be the unit of analysis based on data availability and synthesis with other reported mortality data from other sources. Prevalence of proximate risk factors (smoking, overweight, hypertension, alcohol consumption, salt consumption, fat intake) based on corresponding sex, age, period and geographic strata will be derived from periodic health survey data and strata matched to mortality and population counts. Social and economic indicators (socio-economic status, unemployment rates, economic activity and development) will also be derived for strata corresponding to mortality for a specific period and geographic area. Risk factor prevalence data and other area-specific social and economic indicators will be correlated with area-specific mortality rates in a series of variously adjusted Poisson regression models, where proximate risk factors associated with health behaviours and other socio-demographic information will be considered as intermediaries between broader socio-economic and economic development factors and CHD and stroke mortality. The ecological unit of analysis will depend in part on the availability of proximate and distal risk factor information in areas corresponding to the availability of mortality and population data.”

12. Bias and confounding • Selection bias • evident in health surveys? • evident in mortality surveys? • effect on risk factor prevalence or mortality rates? • Measurement bias • misclassification bias in cause of death? • recall bias in health surveys? • measures of economic activity by geographic area? • appropriate characterisation of pop level health interventions? (e.g. tobacco control) • Cross-level bias • relating to ecological associations? partial or multi-level designs • Confounding • ...how long is a piece of string? • Age, sex, geographic area, and ethnicity will be adjusted for by multivariate modelling where applicable. Other factors? • Not where involved in a (proposed) causal chain (i.e. intermediaries)

13. Timeframe and budget • Timeframe • Budget • for later in the course

14. Outcomes and significance • First to systematically examine the role of modernisation and socio-economic change in China on proximate antecedent risk factors by geographic area for key non-communicable disease outcomes, and to explicitly incorporate the effects of distal socio-economic factors on proximate antecedents in a causal explanatory framework of health for China. • Evaluate the extent to which artefact in the enumeration and misallocation of mortality (and morbidity) data over time and between areas accounts for observed trends. Previous studies have investigated discrete non-communicable disease outcomes, either considering antecedent risk factors or socio-economic differentials, however none have considered multiple health outcomes simultaneously, or synthesised multiple data sources integrated within broader socio-economic trends. • Detailed retrospective information on the effects of health, social and economic policy and will directly inform future preventive activity and interventions relating to non-communicable disease outcomes in China, and provide an understanding of intersections between socio-economic factors and policy development and implementation. • The recent period of development in China presents unique opportunities in understanding social and economic determinants of health in populations, in that disease transition is occurring almost in ‘real’ time and is occurring differentially in different parts of China (e.g. urban compared to rural areas), during a period where multiple sources of health, social and economic data have been collected and are available for analysis.