No abstract

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.

ObjectivesPrevalence estimates of multimorbidity vary widely due to inconsistent definitions and measurement methods. This study examines the independent effects on prevalence estimates of how ‘disease entity’ is defined—as a single chronic condition or chapters/domains in the International Classification of Primary Care (V.2; ICPC-2), International Classification of Disease (10th revision; ICD-10) or the Cumulative Illness Rating Scale (CIRS), the number of disease entities required for multimorbidity, and the number of chronic conditions studied.DesignNational prospective cross-sectional study.SettingAustralian general practice.Participants8707 random consenting deidentified patient encounters with 290 randomly selected general practitioners.Main outcome measuresPrevalence estimates of multimorbidity using different definitions.ResultsData classified to ICPC-2 chapters, ICD-10 chapters or CIRS domains produce similar multimorbidity prevalence estimates. When multimorbidity was defined as two or more (2+) disease entities: counting individual chronic conditions and groups of chronic conditions produced similar estimates; the 12 most prevalent chronic conditions identified about 80% of those identified using all chronic conditions. When multimorbidity was defined as 3+ disease entities: counting individual chronic conditions produced significantly higher estimates than counting groups of chronic conditions; the 12 most prevalent chronic conditions identified only two-thirds of patients identified using all chronic conditions.ConclusionsMultimorbidity defined as 2+ disease entities can be measured using different definitions of disease entity with as few as 12 prevalent chronic conditions, but lacks specificity to be useful, especially in older people. Multimorbidity, defined as 3+, requires more measurement conformity and inclusion of all chronic conditions, but provides greater specificity than the 2+ definition. The proposed concept of “complex multimorbidity”, the co-occurrence of three or more chronic conditions affecting three or more different body systems within one person without defining an index chronic condition, may be useful in identifying high-need individuals.

Monitoring the prevalence and management of chronic conditions is of increasing importance. This study provided evidence for multifaceted definitions of chronicity. While all characteristics examined could be used by those interested in chronicity, the list has been designed to identify chronic conditions managed in Australian general practice, and is therefore not a nomenclature of all chronic conditions. Subsequent analysis of chronic conditions using pre-existing data sets will provide a baseline measure of chronic condition prevalence and management in general practice.

Results. There were 489,900 GP encounters at which OA was managed (rate of 26.4 per 1,000 encounters). OA-hip was managed at a rate of 2.3 per 1,000 encounters (n ‫؍‬ 1,106, 8.6% OA) and OA-knee at a rate of 6.2 per 1,000 (n ‫؍‬ 3,058, 23.7% OA). The encounter management rate per 1,000 for OA-hip was higher among non-metropolitan dwellers (2.85 per 1,000 versus 1.97 per 1,000) and lower for non-English-speaking people (1.53 per 1,000 encounters versus 2.39 per 1,000). The rate for OA-knee was higher for non-English-speaking background (8.50 per 1,000 encounters versus 6.24 per 1,000) and lower among indigenous people (3.16 per 1,000 encounters versus 6.46 per 1,000). Referral to an orthopedic surgeon was the most frequently used nonpharmacologic management (OA-knee 17.4 per 100 contacts and OA-hip 17.7 per 100), followed by advice, education, and counselling. As first-line treatment, medication prescription rates (OA-knee 78.7 per 100 contacts and OA-hip 73.2 per 100) were substantially higher than rates of lifestyle management (OA-knee 20.7 per 100 contacts and OA-hip 14.8 per 100). Conclusion. OA-hip and OA-knee encounters and management differ. Nonpharmacologic treatments as first-line management were low compared with pharmacologic management rates, and surgical referral rates were high. However, lack of longitudinal data limits definitive assessment of appropriateness of care.

No abstract

Objective: To collect data on incidents of potential or actual harm to general practice patients and to evaluate the possible causes of these incidents. Design: An observational study of incidents of potential harm based on a modified critical incidents technique. A non‐random sample of general practitioners (GPs) anonymously submitted incident reports contemporaneously. Setting and participants: Australian general practices between October 1993 and June 1995. During the study period, 324 GPs participated at some time. Main outcome measures: GP‐reported free‐text descriptions of incidents and structured responses for preventability, potential for harm, immediate consequences, predicted long‐term outcomes, type of incident, contributing factors, mitigating factors, and additional resource use. Results: 805 incidents were reported −76% were preventable; 27% had potential for severe harm. No long term harm was predicted for 66% of incidents. Incidents could relate to pharmacological management (51 per 100 incidents), non­ pharmacological management (42 per 100 incidents), diagnosis (34 per 100 incidents) or equipment (5 per 100 incidents). The most common contributory factors were poor communication between patients and healthcare professionals and actions of others (23 per 100 incidents each) and errors in judgement (22 per 100 incidents). Conclusion: Human errors and preventable system problems were identified. The incident monitoring technique provided useful data which could be applied to incident prevention strategies.

The usual care provided by GPs for LBP does not match the care endorsed in international evidence-based guidelines and may not provide the best outcomes for patients. This situation has not improved over time. The unendorsed care may contribute to the high costs of managing LBP, and some aspects of the care provided carry a higher risk of adverse effects.