An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health.
Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between "specific" (measures of association) and "general" (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health.



We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics.



For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%.



Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood "effects" are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level.
Unit for Social Epidemiology, Faculty of Medicine, Lund University, Malmö, Sweden.
Juan
0000-0001-8379-9708
J
Merlo
Unit for Social Epidemiology, Faculty of Medicine, Lund University, Malmö, Sweden.|Centre for Clinical Research Västmanland, Uppsala University, Uppsala, Sweden.
Philippe
P
Wagner
Unit for Social Epidemiology, Faculty of Medicine, Lund University, Malmö, Sweden.|Research Unit of Chronic Conditions, Bispebjerg University Hospital, Copenhagen, Denmark.
Nermin
N
Ghith
Centre for Multilevel Modelling, University of Bristol, Bristol, United Kingdom.
George
G
Leckie
D000328:Adult; D005260:Female; D058005:General Practitioners; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D055361:Multilevel Analysis / Q000379:methods; D011619:Psychotropic Drugs / Q000627:therapeutic use; D011634:Public Health / Q000706:statistics & numerical data*; D012372:ROC Curve; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D013548:Sweden
D011619:Psychotropic Drugs