Multimorbidity and its measurement.

Multimorbidity is increasing in frequency. It can be quantitatively measured and is a major correlate of high use of health services resources of all types, especially over time. The ACG System for characterizing multimorbidity is the only widely used method that is based on combinations of different TYPES of diagnoses over time, rather than the presence or absence of particular conditions or numbers of conditions. It incorporates administrative data (as from claims forms or medical records) on all types of encounters and is not limited to diagnoses captured during hospitalizations or other places of encounter. It can be employed in any one or combination of analytic models, and can incorporate medication use if desired. It is being used in clinical care, management of health services resources, in health services research to control for degree of morbidity, and in understanding morbidity patterns over time. In addition to its research uses, it is being employed in many countries in various applications as a policy to better understand health needs of populations and tailor health services resources to health needs.

[1]  B. Starfield,et al.  Comorbidity and the Use of Primary Care and Specialist Care in the Elderly , 2005, The Annals of Family Medicine.

[2]  S. Salem-Schatz,et al.  The case for case-mix adjustment in practice profiling. When good apples look bad. , 1994 .

[3]  Sharada Weir,et al.  Case Selection for a Medicaid Chronic Care Management Program , 2008, Health care financing review.

[4]  A. Gittelsohn,et al.  Small Area Variations in Health Care Delivery , 1973, Science.

[5]  D M Steinwachs,et al.  Development and Application of a Population-Oriented Measure of Ambulatory Care Case-Mix , 1991, Medical care.

[6]  Norbert Goldfield,et al.  Journal of Ambulatory Care Management , 1992 .

[7]  Azeem Majeed,et al.  Comparison of specialty referral rates in the United Kingdom and the United States: retrospective cohort analysis , 2002, BMJ : British Medical Journal.

[8]  R. Reid,et al.  Assessing population health care need using a claims-based ACG morbidity measure: a validation analysis in the Province of Manitoba. , 2002, Health services research.

[9]  Chad Boult,et al.  Clinical features of high-risk older persons identified by predictive modeling. , 2006, Disease management : DM.

[10]  J A Knottnerus,et al.  Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. , 1998, Journal of clinical epidemiology.

[11]  R. Balicer,et al.  Assessing socioeconomic health care utilization inequity in Israel: impact of alternative approaches to morbidity adjustment , 2011, BMC public health.

[12]  E. Guinó,et al.  Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: A cross-sectional study of patient electronic records in primary care , 2008, BMC health services research.

[13]  A. Sicras-Mainar,et al.  Adjusted Clinical Groups use as a measure of the referrals efficiency from primary care to specialized in Spain. , 2007, European journal of public health.

[14]  C. Forrest,et al.  Health status of well vs ill adolescents. , 1996, Archives of pediatrics & adolescent medicine.

[15]  B. Starfield,et al.  Morbidity in childhood--a longitudinal view. , 1984, The New England journal of medicine.

[16]  B Starfield,et al.  Ambulatory care groups: a categorization of diagnoses for research and management. , 1991, Health services research.

[17]  B H Starfield,et al.  Johns Hopkins Ambulatory Care Groups (ACGs). A case-mix system for UR, QA and capitation adjustment. , 1992, HMO practice.

[18]  B. Starfield,et al.  Defining Comorbidity: Implications for Understanding Health and Health Services , 2009, The Annals of Family Medicine.

[19]  Taking Health Status Into Account When Setting Capitation Rates: A Comparison of Risk-Adjustment Methods , 1996 .

[20]  N. Powe,et al.  Systemwide provider performance in a Medicaid program. Profiling the care of patients with chronic illnesses. , 1996, Medical care.

[21]  A R Feinstein,et al.  THE PRE-THERAPEUTIC CLASSIFICATION OF CO-MORBIDITY IN CHRONIC DISEASE. , 1970, Journal of chronic diseases.

[22]  A. Halling,et al.  Validation of ACG Case-mix for equitable resource allocation in Swedish primary health care , 2009, BMC public health.

[23]  B. Starfield,et al.  Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. , 2002, Archives of internal medicine.

[24]  Sara A. Kreindler Lifting the burden of chronic disease: what has worked? what hasn't? what's next? , 2009, Healthcare quarterly.

[25]  Carl van Walraven,et al.  Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada , 2011, Medical care.

[26]  N. Powe,et al.  Costs vs quality in different types of primary care settings. , 1994, JAMA.

[27]  B. Starfield,et al.  Ambulatory Specialist Use by Nonhospitalized Patients in US Health Plans: Correlates and Consequences , 2009, The Journal of ambulatory care management.

[28]  J. Weiner,et al.  Morbidity Trajectories as Predictors of Utilization: Multi-year Disease Patterns in Taiwan's National Health Insurance Program , 2011, Medical care.

[29]  C. Black,et al.  Chronic conditions and co-morbidity among residents of British Columbia , 2005 .

[30]  R. Kaplan Disease, diagnoses, and dollars : , 2009 .

[31]  A. Hartz,et al.  A systematic review of studies comparing myocardial infarction mortality for generalists and specialists: lessons for research and health policy. , 2006, Journal of the American Board of Family Medicine : JABFM.

[32]  M. Hollander,et al.  Increasing value for money in the Canadian healthcare system: new findings on the contribution of primary care services. , 2009, Healthcare quarterly.

[33]  A. Beckman,et al.  Impact of comorbidity on the individual's choice of primary health care provider , 2011, Scandinavian journal of primary health care.