Data Sources & Issues for Health Inequalities Research. J. Dunn

Transcription

1 Data Sources & Issues for Health Inequalities Research J. Dunn

2 Background & Introduction major challenge to find secondary data sources that are compatible with research questions in many instances, data sources have to be adapted for use in health inequalities research the situation has improved greatly in the past decade, but there are still gaps it is important to know what kinds of questions can be asked with existing secondary data and what kinds of questions require primary data

3 Data Types, Study Designs & Inferences it is a combination of data and a study design that allow inferences about health inequalities Individual data cohort studies; case-control studies Aggregate data about population groups, sometimes geographical allows for so-called ecological studies Multi-level data individuals nested in contexts e.g., families, schools, neighbourhoods, etc.

4 Individual Data data concerning individuals useful when we have information on both independent variable (e.g., measure of SES) dependent variable (e.g., health status) allows one to make quite strong inferences about the relationship b/w SES & health making causal inferences is more difficult causal inferences require longitudinal data (+ control confounders) examples include surveys (e.g., CCHS, NPHS), but also administrative databases (e.g., BCLHDb) surveys sometimes hampered by inadequate samples for population sub-groups administrative data seldom has SES collected popular to use geographic proxies for SES from census data)

5 Aggregate Data this kind of data summarizes the health status or socio-economic status of groups of people these groups may be defined by some characteristic they share e.g., gender, age, users of psychiatric services, residents of the same neighbourhood typical summary measures include: incidence / prevalence rates, measures of central tendency (median / mean) causal inferences not possible, but this kind of analysis is useful for first steps in the study of contextual factors (like SES) and health

6 Multi-Level Data this kind of data is valuable because of: atomistic fallacy : the belief that individuals are islands and are unaffected by the contexts they occupy ecological fallacy : the belief that inferences about individuals can be drawn from aggregate data moreover, the association between SES and health (for example) may differ from place to place (different slopes in different places) multi-level or hierarchical modelling: can ask over and above individual attributes, what contribution does context make to the outcome of interest? data requirements are heavy: need fairly large number of people in a large number of contexts need individual SES, individual health outcome and contextual SES all properly linked in the same database not common

7 Examples Census of Canada this is individual data, but seldom can be used this way due to privacy concerns usually aggregated (levels) Statistics Canada and other surveys CCHS, NPHS provincially representative samples ESC, etc. random samples on another topic but contain some health variables and / or record linkage capacity Other secondary data sources DMTI (land use & commercial activity); mortality records, birth records, purpose-specific databases more examples coming from other speakers!

8 Examples from CRICH Access to preventive health care Immigrant health Weight of place Neighbour factors and diabetes

9 Access to preventive health care (1) Pap smears OHIP claims (~90% coverage) Toronto census tracts ecological Recent registrants individual Lofters A, Glazier RH, Agha MM, Creatore, MI, Moineddin R. Prev Med 2007;44:

10 Access to preventive health care (2) Mammography OHIP claims Toronto census tracts geographic autocorrelation target interventions Glazier RH, Creatore MI, Gozdyra P, Matheson F, Steele L, Boyle E, Moineddin R. J Gen Intern Med 2004;19:

11 Immigrant health Gender differences in depression by SES CCHS subgoups gender, immigration depression Smith KLW, Matheson FI, Moineddin R, Glazier RH. Can J Public Health 2007;98:

12 Weight of place CCHS measure of BMI nested in CTs deprivation index multilevel analysis Matheson FI, Moineddin R, Glazier RH. Soc Sci Med 2008; 66:

13 Neighbourhood factors and diabetes Diverse data sources census admin registry land use resources walkability network analysis Glazier RH, Booth G. Neighbourhood Environments and Resources for Healthy Living A Focus on Diabetes in Toronto: ICES Atlas. Toronto: Institute for Clinical Evaluative Sciences;

14 Your ideas, questions, comments