The relationship between body mass index and hospitalisation rates, days in hospital and costs: findings from a large prospective linked data study

• Published in: PloS one Vol. 10; no. 3; p. e0118599
• Main Authors: Korda, Rosemary J, Joshy, Grace, Paige, Ellie, Butler, James R G, Jorm, Louisa R, Liu, Bette, Bauman, Adrian E, Banks, Emily
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.03.2015; Public Library of Science (PLoS)
• Subjects: Adults; Age; Aged; Aged, 80 and over; Alcohol use; Alcoholic beverages; Analysis; Body mass; Body Mass Index; Body size; Body weight; Cost analysis; Costs; Economic aspects; Empirical analysis; Female; Health care; Health care costs; Health risk assessment; Hospitalization; Humans; Length of Stay - economics; Male; Middle Aged; Models, Statistical; Obesity; Overweight; Patient Admission - statistics & numerical data; Population studies; Prospective Studies; Regression analysis; Smoking; Studies; Surveys; Surveys and Questionnaires; Australia; United Kingdom > UK; United States > US; Canberra Australian Capital Territory Australia; Australian Capital Territory Australia; New South Wales Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0118599

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. 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. 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%). 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.