Objectives: To estimate the prevalence and patterns of multimorbidity in a sample of patients attending general practice, in the population who attended general practice in 2005, and in the Australian population. Design, setting and participants: Secondary analyses of data from a study of prevalence of selected conditions (a substudy of the BEACH [Bettering the Evaluation And Care of Health] program); data were provided by 305 general practitioners for 9156 patients seen in July–November 2005, based on knowledge of the patient, patient self‐report, and medical records. Listed conditions were classified according to the Cumulative Illness Rating Scale morbidity domains. Main outcome measures: Prevalence of morbidity in each domain; prevalence of specific patterns of multimorbidity (defined as presence of morbidity in two or more domains). Results: Prevalence of multimorbidity was estimated as 37.1% of surveyed patients, 29.0% of people who attended a GP in 2005, and 25.5% of the Australian population. Prevalence and complexity (number of domains present) increased with age: 83.2% of surveyed patients aged 75 years or older had multimorbidity, 58.2% had morbidity in three or more domains, and 33.4% in four or more. Prevalence of multimorbidity did not differ between the sexes. The most common morbidity combinations were arthritis/chronic back pain + vascular disease (15.0% of sample), a psychological problem + vascular disease (10.6%) and arthritis/chronic back pain + a psychological problem (10.6%). We estimate that 10.6% of people attending a GP in 2005 and 9.3% of the population have arthritis/chronic back pain + vascular disease (± other morbidity types studied), and this group accounted for about 15.2 million Medicare‐claimed general practice encounters in 2005. Conclusions: This study provides the first insight into prevalence and patterns of multimorbidity in Australia. Knowledge of the common combinations of multimorbidity may help in planning the health services needed in the future by an ageing population with an increasing burden of multimorbidity.
This new algorithm will enable the Australian community values to be reflected in future economic evaluations.
Objectives: To estimate prevalence of selected diagnosed chronic diseases among patients attending general practice, in the general practice patient population, and in the Australian population, and to compare population estimates with those of the National Health Survey (NHS). Design, setting and participants: In late 2005, 305 general practitioners each provided data for about 30 consecutive patients (total, 9156) as part of the BEACH (Bettering the Evaluation And Care of Health) program, a continuous national study of general practice activity. GPs used their knowledge of the patient, patient self‐report, and medical records as sources. Main outcome measures: Crude prevalence of each listed condition currently under management among surveyed patients, and adjusted prevalence for the general practice patient population, and the national population. Results: 39.6% of respondents had none of the listed conditions diagnosed; 30.0% had a cardiovascular problem (uncomplicated hypertension, 17.6%; ischaemic heart disease, 9.5%); 24.8% had a psychological problem (depression, 14.2%; anxiety, 10.7%); 22.8% had arthritis, mostly osteoarthritis (20.0%); 10.7% had asthma; and 8.3% had diabetes, mostly type 2 (7.2%). Adjustment to the population attending general practice resulted in lower estimates for cardiovascular disease, arthritis and diabetes but had little effect on prevalence of asthma and psychological problems. After adjusting for non‐attenders, about one in five people in the population had a cardiovascular problem, a similar proportion had a psychological problem, 14.8% had arthritis, and about 10% had asthma, hyperlipidaemia and gastro‐oesophageal reflux disease. Estimates were similar to NHS results for any arthritis, asthma, and malignant neoplasms; higher for any cardiovascular problem; far higher for specific cardiovascular diseases, cerebrovascular disease and hyperlipidaemia; and almost twice the NHS estimate for psychological problems (particularly depression and anxiety). Estimates for type 1 diabetes aligned with NHS results, but were far higher for “all diabetes” and type 2 diabetes. Conclusions: This study offers an alternative, perhaps more accurate, approach to measurement of disease prevalence than the NHS approach, which relies on respondent self‐report alone. It provides valid prevalence estimates with the help of GPs at a fraction of the cost of the NHS. This study could be repeated annually to augment other data sources and better define existing health needs in the population.
In this article, we describe the Stata command, which can be used to fit the generalized multinomial logit model and its special cases.
General practitioners provide fewer mental health services per capita in non-metropolitan areas. This difference could represent completely untreated psychological problems or fewer follow-up consultations. While non-metropolitan residents have limited access to specialists, rates of GP encounters for psychological problems are also very low.
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