The relationship between body mass index and hospitalisation rates, days in hospital and costs: findings from a large prospective linked data study.
BACKGROUND
Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation-admissions, days and costs-associated with above-normal BMI.


METHODS
Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated.


RESULTS
There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%).


CONCLUSIONS
The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would result in long-term savings to the health care system is not known from this study.
National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia.
Rosemary J
RJ
Korda
National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia.
Grace
G
Joshy
National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia.
Ellie
E
Paige
Australian Centre for Economic Research on Health, The Australian National University, Canberra, Australian Capital Territory, Australia.
James R G
JR
Butler
Centre for Health Research, The University of Western Sydney, Sydney, New South Wales, Australia.
Louisa R
LR
Jorm
School of Public Health and Community Medicine, The University of New South Wales, Sydney, New South Wales, Australia; The Sax Institute, Sydney, New South Wales, Australia.
Bette
B
Liu
School of Public Health, The University of Sydney, Sydney, New South Wales, Australia.
Adrian E
AE
Bauman
National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia; The Sax Institute, Sydney, New South Wales, Australia.
Emily
E
Banks
D000368:Aged; D000369:Aged, 80 and over; D015992:Body Mass Index; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000191:economics*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data; D011446:Prospective Studies; D011795:Surveys and Questionnaires