Identifying population differences in lung function: results from the Allied Dunbar national fitness survey

In order to identify valid population differences in lung function (e.g. oc cupational, ethnic), it is necessary to adjust for known confounding variab les (e.g. age, body size). The present paper proposes appropriate methods f or analysing forced expiratory volume (FEV1), forced vital capacity (FVC) a nd maximum oxygen uptake (VO2 max), recorded as part of the Allied Dunbar n ational fitness survey (ADNFS). The ADNFS randomly selected subjects from 3 0 regional sites throughout England. Measurements of FEV1, FVC and complete records of other relevant information were available on 2672 subjects. Tra ditional analyses of co-variance (ANCOVA) were found to be inappropriate to investigate population differences in FEV1 and FVC, due to a significant i ncrease in error variance with age. However, by fitting a multiplicative mo del with allometric body size components to the FEV1 and FVC measurements u sing weighted log-linear regression, valid and plausible associations with body size, age, smoking, and physical activity, together with 'gender speci fic' regional differences in lung function were identified. Further insight was obtained when FEV1 and FVC were included into the multiplicative model to predict VO2 max. The apparent advantage of being taller when predicting VO2 max, was explained more accurately by the subjects' superior FVC. In s ummary, by fitting the multiplicative 'allometric' model using weighted log -linear regression, valid population differences in lung function were iden tified. Regions containing a higher proportion of working-class, unemployed or less affluent subjects were found to be associated with below average l ung function performances.