What ecological studies can & can’t tell us Justin O’Hagan 2 nd year Epi Doctoral Student

Ecological Studies Focus is on comparison of groups not individuals Types of measures: –Aggregate: summaries of individuals measures (e.g. proportion of smokers, median SES, average ab use on a ward) –Environmental: physical characteristics of place in which members of each group live (e.g. air pollution, hours of sunlight) –Global measures: attributes for groups for which there is no individual analogue (e.g. population density, level of social disorganisation) Rothman, Ch. 23, 2nd edition

Inferences from ecologic studies Biologic (individual) inference: effect of a factor on individual risk –E.g. effect of user’s antibiotic use on risk of resistant infection within themselves Contextual inference: group level effect on individual –E.g. effect of living in a poor area on the risk of disease independent of individual economic status, effect of antibiotic use on risk of resistant infection in non-users in hospital Cross-level inference: using ecologic data to make individual level inference or vice versa

Why use an ecologic design? Cheap & convenient Measurement limitations of individual studies –cannot accurately measure exposure at individual level Design limitations of individual studies –Exposure varies little within study area, can get ecologic data on variety of areas and levels Rothman, Ch. 23, 2 nd edition

Why use an ecologic design? Interested in ecologic effects – understand differences in disease rates among populations, effect of population intervention –Does not remove need for individual level data Ease of analysis & presentation

Individual level study of individual effects Can be confounded by contextual level Example: Bacteria for which acquired and primary resistance possible –Exposure = individual antibiotic use –Outcome = resistant infection Significant transmission of resistant infections causes null finding ON INDIVIDUAL LEVEL Ecologic analysis could still be significant for ab-resistance association

Ecologic study of individual effects Durkheim suicide example Examined religion and suicide in 4 groups of provinces in Germany, R 2 = 0.97

Ecologic study of individual/contextual effects Rate ratios of suicide comparing Protestants to others: –RR ecologic = 7.6 –RR ind = 2 Ecologic bias –Interaction at individual level between religion and religious makeup or region Ecologic tests of fit can be misleading

Ecologic bias Often cited weakness of ecologic studies Causes: –Within-group confounding Ecologic estimate even more biased –Confounding by group Unexposed rate of disease varies and is correlated with ecologic exposure –AIDS patients acquiring resistant infection from others –Effect modification by group on additive scale Rate difference for the exposure effect at individuals level varies across groups –AIDS patients more likely to get de novo resistance at low ab level? Not considered a bias at individual level

Basic problem of ecologic studies ?? ?? N1N2N1N2 M 1 M 2 Lose data by aggregating

Nondifferential misclassification Individual level studies: bias towards null Ecologic studies: bias away from null –Exposure usually continuous, Defined daily Dose/1000 people/day –Outcome usually dichotomous, resistant vs. susceptible Reliability subject to sensitivity and specificity of classification How are intermediate susceptibility bacteria classified?

Finnish Macrolide Study High erythromycin resistance in 1990 Nationwide effort to decrease outpatient macrolide prescriptions from 1991 on –Total antibiotic use remained stable Exposure = DDD Outcome = erythromycin resistant throat/pus sample –Used several methods to ensure reliability of results Seppala et al. NEJM, 2004

Total outpatient macrolide consumption in Finland Seppala et al. NEJM, 2004 Shows large and rapid decrease in prescribing possible on national level

Resistance frequency among Finnish GAS Samples Seppala et al. NEJM, 2004 P < Causal association? - Contemporaneous, rapid & large decrease - Erythromycin resistance not linked to resistance to other drugs

Why was decrease so quick? Erythromycin resistance not linked to resistance to any other drug –Fitness cost? –Increase in resistance to another drug sped erythromycin decrease?

S. pneumoniae resistance in Israel Nomenclature: Cross resistance between macrolides & also between cephalosporins & penicillins possible AntibioticClassHalf-life AzithromycinMacrolide, Erythromycin derivative Long ErythromycinMacrolideShort (Oral) cephalosporin Short Amoxicillin- clavulanate AminopenicillinShort Dagan et al. Pediat. Inf. Dis., 2006

Children treated for S. pneumoniae infection Individual level: –Reduced carriage of erythromycin resistant & MDR strains during & after treatment with amoxicillin-clavulanate –Increased carriage of erythromycin resistant & MDR strains during & after treatment with azithromycin & oral cephalosporin Dagan et al. Pediat. Inf. Dis., 2006

Children treated for S. pneumoniae infection Ecologic level: –30% decrease in overall antibiotic use in kids –But increase in oral cephalosporin & >11 fold increase in azithromycin use –No change in penicillin resistance –Increase in erythromycin resistance & MDR Reduction in overall ab use insufficient to reduce resistance –Likely must reduce specific classes –BUT only looked at ab use in children in 1 region Dagan et al. Pediat. Inf. Dis., 2006

Discrepancy due to ecologic vs. individual level design? Harbarth et al. CID, 2001 Examined antibiotic exposure (4 classes) and resistance in a US hospital Ecologic analysis – null for all 4 Individual level analysis – significant for all 4 Donnan et al. BMJ, 2004 Examined exposure to trimethoprim and resistance Practice level analysis – null Individual level analysis - significant

Individual/ecological comparisons Individual level –Support effort to reduce ab use –Show importance of collecting/analysing individual level data

So which to choose? Multi-level analysis –Incorporate individual and ecologic measures in same analysis –Separate individual and contextual effects 1.Contextual analysis – Use individual level outcomes & add both individual & group exposure predictors to model –Leibovici et al. & Bonten et al. 2.Mixed-effects modelling – account for clustering of data