Influence of biological, behavioural, health service and social risk factors on the trend towards more frequent.
OBJECTIVE
To analyse the trend in rising acute hospital admission rates in the Renfrew Paisley MIDSPAN cohort and assess the influence of baseline risk factor data, morbidity patterns, deprivation category and characteristics of GP practice on the increase.


METHODS
Cohort analysis which, using a linked data set covering a 23 year follow-up period, combined original 'risk'-related data with subsequent routine hospital admissions data. A multiple logistic regression model predicted changes in hospital admissions patterns.


METHODS
Renfrew and Paisley, two post-industrial towns in Scotland.


METHODS
Eight thousand three hundred and fifty four women and 7,052 men, aged 45-64 in the early 1970s.


METHODS
The contribution that each of the factors investigated made to the likelihood of admission over time.


RESULTS
While risk status in middle life, diagnosis reached after admission, deprivation category and characteristics of GP practice influence the absolute chance of being admitted to hospital, changes in these factors do not explain much, if any, of the quite marked increase in admission rates observed during the last 10 year of the follow-up period.


CONCLUSIONS
Whatever the reasons for the trend of rising admission, the most likely explanation appears to be a combination of social and health service related factors. For the Paisley-Renfrew cohort, factors like smoking status, FEV1, deprivation category and GP practice remain important predictors of admission throughout the time period but changes in these factors explain little of the rising trend in admissions.
Hanlon
P
P
Department of Public Health, University of Glasgow.
Walsh
D
D
Whyte
B W
BW
Scott
S N
SN
Hole
D
D
Lightbody
P
P
Gilhooly
M L
ML
D000328:Adult; D035841:Behavioral Research; D015331:Cohort Studies; D005260:Female; D006779:Hospitals, Public / Q000706:statistics & numerical data*; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D010343:Patient Admission / Q000706:statistics & numerical data / Q000639:trends*; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D012307:Risk Factors; D012606:Scotland / Q000453:epidemiology; D012959:Socioeconomic Factors; D035862:Vulnerable Populations