Literature DB >> 22014855

[Comparison of three methods for measuring multiple morbidity according to the use of health resources in primary healthcare].

Antoni Sicras-Mainar, Soledad Velasco-Velasco, Ruth Navarro-Artieda, Milagrosa Blanca Tamayo, Alba Aguado Jodar, Amador Ruíz Torrejón, Alexandra Prados-Torres, Concepción Violan-Fors.   

Abstract

OBJECTIVE: To compare three methods of measuring multiple morbidity according to the use of health resources (cost of care) in primary healthcare (PHC).
DESIGN: Retrospective study using computerized medical records.
SETTING: Thirteen PHC teams in Catalonia (Spain). PARTICIPANTS: Assigned patients requiring care in 2008. MAIN MEASUREMENTS: The socio-demographic variables were co-morbidity and costs. Methods of comparison were: a) Combined Comorbidity Index (CCI): an index itself was developed from the scores of acute and chronic episodes, b) Charlson Index (ChI), and c) Adjusted Clinical Groups case-mix: resource use bands (RUB). The cost model was constructed by differentiating between fixed (operational) and variable costs. STATISTICAL ANALYSIS: 3 multiple lineal regression models were developed to assess the explanatory power of each measurement of co-morbidity which were compared from the determination coefficient (R(2)), p< .05.
RESULTS: The study included 227,235 patients. The mean unit of cost was €654.2. The CCI explained an R(2)=50.4%, the ChI an R(2)=29.2% and BUR an R(2)=39.7% of the variability of the cost. The behaviour of the ICC is acceptable, albeit with low scores (1 to 3 points), showing inconclusive results.
CONCLUSIONS: The CCI may be a simple method of predicting PHC costs in routine clinical practice. If confirmed, these results will allow improvements in the comparison of the case-mix.
Copyright © 2011 Elsevier España, S.L. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 22014855      PMCID: PMC7025198          DOI: 10.1016/j.aprim.2011.05.010

Source DB:  PubMed          Journal:  Aten Primaria        ISSN: 0212-6567            Impact factor:   1.137


  37 in total

Review 1.  How to measure comorbidity. a critical review of available methods.

Authors:  Vincent de Groot; Heleen Beckerman; Gustaaf J Lankhorst; Lex M Bouter
Journal:  J Clin Epidemiol       Date:  2003-03       Impact factor: 6.437

2.  Prevalence of morbidity and multimorbidity in elderly male populations and their impact on 10-year all-cause mortality: The FINE study (Finland, Italy, Netherlands, Elderly).

Authors:  A Menotti; I Mulder; A Nissinen; S Giampaoli; E J Feskens; D Kromhout
Journal:  J Clin Epidemiol       Date:  2001-07       Impact factor: 6.437

3.  Ambulatory care groups: a categorization of diagnoses for research and management.

Authors:  B Starfield; J Weiner; L Mumford; D Steinwachs
Journal:  Health Serv Res       Date:  1991-04       Impact factor: 3.402

4.  Relationship between multimorbidity and health-related quality of life of patients in primary care.

Authors:  Martin Fortin; Gina Bravo; Catherine Hudon; Lise Lapointe; José Almirall; Marie-France Dubois; Alain Vanasse
Journal:  Qual Life Res       Date:  2006-02       Impact factor: 4.147

5.  Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale.

Authors:  M D Miller; C F Paradis; P R Houck; S Mazumdar; J A Stack; A H Rifai; B Mulsant; C F Reynolds
Journal:  Psychiatry Res       Date:  1992-03       Impact factor: 3.222

6.  The importance of classifying initial co-morbidity in evaluating the outcome of diabetes mellitus.

Authors:  M H Kaplan; A R Feinstein
Journal:  J Chronic Dis       Date:  1974-09

7.  Cumulative illness rating scale.

Authors:  B S Linn; M W Linn; L Gurel
Journal:  J Am Geriatr Soc       Date:  1968-05       Impact factor: 5.562

8.  Comorbidity in psychiatry: its impact on psychopharmacological treatment.

Authors:  Slobodan Loga; Svjetlana Loga-Zec
Journal:  Psychiatr Danub       Date:  2009-09       Impact factor: 1.063

9.  Comorbidity: implications for the importance of primary care in 'case' management.

Authors:  Barbara Starfield; Klaus W Lemke; Terence Bernhardt; Steven S Foldes; Christopher B Forrest; Jonathan P Weiner
Journal:  Ann Fam Med       Date:  2003 May-Jun       Impact factor: 5.166

10.  An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

Authors:  Hsien-Yen Chang; Jonathan P Weiner
Journal:  BMC Med       Date:  2010-01-18       Impact factor: 8.775

View more
  4 in total

1.  Relationship between efficiency and clinical effectiveness indicators in an adjusted model of resource consumption: a cross-sectional study.

Authors:  Concepción Violán; Oleguer Plana-Ripoll; Quintí Foguet-Boreu; Bonaventura Bolíbar; Alba Aguado; Ruth Navarro-Artieda; Soledad Velasco-Velasco; Antoni Sicras-Mainar
Journal:  BMC Health Serv Res       Date:  2013-10-18       Impact factor: 2.655

2.  Burden of multimorbidity, socioeconomic status and use of health services across stages of life in urban areas: a cross-sectional study.

Authors:  Concepción Violán; Quintí Foguet-Boreu; Albert Roso-Llorach; Teresa Rodriguez-Blanco; Mariona Pons-Vigués; Enriqueta Pujol-Ribera; Miguel Ángel Muñoz-Pérez; Jose M Valderas
Journal:  BMC Public Health       Date:  2014-05-29       Impact factor: 3.295

3.  Comparison of count-based multimorbidity measures in predicting emergency admission and functional decline in older community-dwelling adults: a prospective cohort study.

Authors:  Emma Wallace; Ronald McDowell; Kathleen Bennett; Tom Fahey; Susan M Smith
Journal:  BMJ Open       Date:  2016-09-20       Impact factor: 2.692

4.  [Impact of cardiovascular risk factors on the consumption of resources in Primary Care according to Clinical Risk Groups].

Authors:  Magdalena Millá Perseguer; Natividad Guadalajara Olmeda; David Vivas Consuelo
Journal:  Aten Primaria       Date:  2018-06-13       Impact factor: 1.137

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.