Research Article| Volume 138, P201-210, April 2018

Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: The utility of a cluster analysis approach

Published:February 09, 2018DOI:



      We aimed to characterise use of general practitioners (GP) simultaneously across multiple attributes in people with diabetes and examine its impact on diabetes related potentially preventable hospitalisations (PPHs).


      Five-years of panel data from 40,625 adults with diabetes were sourced from Western Australian administrative health records. Cluster analysis (CA) was used to group individuals with similar patterns of GP utilisation characterised by frequency and recency of services. The relationship between GP utilisation cluster and the risk of PPHs was examined using multivariable random-effects negative binomial regression.


      CA categorised GP utilisation into three clusters: moderate; high and very high usage, having distinct patient characteristics. After adjusting for potential confounders, the rate of PPHs was significantly lower across all GP usage clusters compared with those with no GP usage; IRR = 0.67 (95%CI: 0.62–0.71) among the moderate, IRR = 0.70 (95%CI 0.66–0.73) high and IRR = 0.76 (95%CI 0.72–0.80) very high GP usage clusters.


      Combination of temporal factors with measures of frequency of use of GP services revealed patterns of primary health care utilisation associated with different underlying patient characteristics. Incorporation of multiple attributes, that go beyond frequency-based approaches may better characterise the complex relationship between use of GP services and diabetes-related hospitalisation.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Diabetes Research and Clinical Practice
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Van Loenen T.
        • Faber M.J.
        • Westert G.P.
        • Van den Berg M.J.
        The impact of primary care organization on avoidable hospital admissions for diabetes in 23 countries.
        Scand J Prim Health Care. 2016; 34: 5-12
        • Tamayo T.
        • Rosenbauer J.
        • Wild S.H.
        • Spijkerman A.M.
        • Baan C.
        • Forouhi N.G.
        • et al.
        Diabetes in Europe: an update.
        Diabetes Res Clin Pract. 2014; 103: 206-217
      1. Centers for disease control and prevention. 2014 National diabetes statistics report. Centers for disease control and prevention; 2014.

      2. AIHW. How many Australians have diabetes? Canberra Australian Institute of Health and Welfare; 2017.

      3. Erny-Albrecht K, Bywood P, Oliver-Baxter J. The role of primary care in primary and secondary prevention of diabetes and related complications: PHCRIS Policy Issue Review. Adelaide primary health care research & information service; 2015.

        • Comino E.J.
        • Islam M.D.F.
        • Tran D.T.
        • Jorm L.
        • Flack J.
        • Jalaludin B.
        • et al.
        Association of processes of primary care and hospitalisation for people with diabetes: a record linkage study.
        Diabetes Res Clin Pract. 2015; 108: 296-305
        • Gibson O.R.
        • Segal L.
        • McDermott R.A.
        A systematic review of evidence on the association between hospitalisation for chronic disease related ambulatory care sensitive conditions and primary health care resourcing.
        BMC Health Serv Res. 2013; 13: 336
        • van Loenen T.
        • van den Berg M.J.
        • Westert G.P.
        • Faber M.J.
        Organizational aspects of primary care related to avoidable hospitalization: a systematic review.
        Fam Pract. 2014; 31: 502-516
        • Einarsdottir K.
        • Preen D.B.
        • Emery J.D.
        • Kelman C.
        • Holman C.D.
        Regular primary care lowers hospitalisation risk and mortality in seniors with chronic respiratory diseases.
        J Gen Intern Med. 2010; 25: 766-773
        • Wolters R.J.
        • Braspenning J.C.C.
        • Wensing M.
        Impact of primary care on hospital admission rates for diabetes patients: a systematic review.
        Diabetes Res Clin Pract. 2017; 129: 182-196
        • Gibson D.
        • Moorin R.
        • Preen D.
        • Emery J.
        • Holman D.A.
        Effects of the Medicare enhanced primary care program on primary care physician contact in the population of older Western Australians with chronic diseases.
        Australian Health Rev. 2011; 35: 334-340
        • Liao M.
        • Li Y.
        • Kianifard F.
        • Obi E.
        • Arcona S.
        Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis.
        BMC Nephrol. 2016; 17: 25
        • Conry M.C.
        • Morgan K.
        • Curry P.
        • McGee H.
        • Harrington J.
        • Ward M.
        • et al.
        The clustering of health behaviours in Ireland and their relationship with mental health, self-rated health and quality of life.
        BMC Public Health. 2011; 11: 692
        • Clatworthy J.
        • Buick D.
        • Hankins M.
        • Weinman J.
        • Horne R.
        The use and reporting of cluster analysis in health psychology: a review.
        Brit J Health Psychol. 2005; 10: 329-358
        • Eisen M.B.
        • Spellman P.T.
        • Brown P.O.
        • Botstein D.
        Cluster analysis and display of genome-wide expression patterns.
        PNAS. 1998; 95: 14863-14868
        • Holman C.D.
        • Bass A.J.
        • Rouse I.L.
        • Hobbs M.S.
        Population-based linkage of health records in Western Australia: development of a health services research linked database.
        Aust N Z J Public Health. 1999; 23: 453-459
        • Ha N.T.
        • Harris M.
        • Robinson S.
        • Preen D.
        • Moorin R.
        Stratification strategy for evaluating the influence of diabetes complication severity index on the risk of hospitalization: a record linkage data in Western Australia.
        J Diabetes Complications. 2017; 31: 1175-1180
        • Australian Institute of Health and Welfare
        A set of performance indicators across the health and aged care system.
        Australian Institute of Health and Welfare, Canberra2008
        • Davis W.A.
        • Knuiman M.W.
        • Hendrie D.
        • Davis T.M.
        Determinants of diabetes-attributable non-blood glucose-lowering medication costs in type 2 diabetes: the Fremantle Diabetes Study.
        Diabetes Care. 2005; 28: 329-336
        • Hughes A.M.
        Strategic database marketing.
        McGraw-Hill Pub. Co., 2005
        • Lee E.W.
        Data mining application in customer relationship management for hospital inpatients.
        Healthcare Inform Res. 2012; 18: 178-185
      4. Australian Bureau of Statistics. Census of population and housing: socio-economic indexes for areas Canberra: Australian Bureau of Statistics; 2001 2006 & 2011.

      5. Australian Bureau of Statistics. Census of population and housing: socio-economic indexes for areas. Canberra: Australian Bureau of Statistics; 1981–2006.

        • Young B.A.
        • Lin E.
        • Von Korff M.
        • Simon G.
        • Ciechanowski P.
        • Ludman E.J.
        • et al.
        Diabetes complications severity index and risk of mortality, hospitalization, and healthcare utilization.
        Am J Manage Care. 2008; 14: 15-23
        • Holman C.D.
        • Preen D.B.
        • Baynham N.J.
        • Finn J.C.
        • Semmens J.B.
        A multipurpose comorbidity scoring system performed better than the Charlson index.
        J Clin Epidemiol. 2005; 58: 1006-1014
        • Caliński T.
        • Harabasz J.
        A dendrite method for cluster analysis.
        Commun Stat. 1974; 3: 1-27
        • Chamberlain G.
        Multivariate regression models for panel data.
        J Economet. 1982; 18: 5-46
        • Mundlak Y.
        On the pooling of time series and cross section data.
        Econometrica. 1978; 46: 69-85
        • Nuti L.A.
        • Lawley M.
        • Turkcan A.
        • Tian Z.
        • Zhang L.
        • Chang K.
        • et al.
        No-shows to primary care appointments: subsequent acute care utilization among diabetic patients.
        BMC Health Serv Res. 2012; 12: 304
      6. Bywood P, Katterl R, Lunnay B. Disparities in primary health care utilisation: Who are the disadvantaged groups? How are they disadvantaged? What interventions work?. PHCRIS Policy Issue Review. Adelaide: Primary Health Care Research & Information Service; 2011.

        • Davy C.
        • Harfield S.
        • McArthur A.
        • Munn Z.
        • Brown A.
        Access to primary health care services for Indigenous peoples: a framework synthesis.
        Int J Equity Health. 2016; 15: 163
        • Struijs J.N.
        • Baan C.A.
        • Schellevis F.G.
        • Westert G.P.
        • van den Bos G.A.
        Comorbidity in patients with diabetes mellitus: impact on medical health care utilization.
        BMC Health Serv Res. 2006; 6: 84
        • van Oostrom S.H.
        • Picavet H.S.J.
        • de Bruin S.R.
        • Stirbu I.
        • Korevaar J.C.
        • Schellevis F.G.
        • et al.
        Multimorbidity of chronic diseases and health care utilization in general practice.
        BMC Family Pract. 2014; 15: 61
        • Fisher K.
        • Griffith L.
        • Gruneir A.
        • Panjwani D.
        • Gandhi S.
        • Sheng L.
        • et al.
        Comorbidity and its relationship with health service use and cost in community-living older adults with diabetes: a population-based study in Ontario, Canada.
        Diabetes Res Clin Pract. 2016; 122: 113-123
        • Booth G.L.
        • Hux J.E.
        Relationship between avoidable hospitalizations for diabetes mellitus and income level.
        Arch Intern Med. 2003; 163: 101-106
        • Grossman M.
        On the concept of health capital and the demand for health.
        J Polit Econ. 1972; 80: 223-255
        • Tian W.-H.
        • Chen C.-S.
        • Liu T.-C.
        The demand for preventive care services and its relationship with inpatient services.
        Health policy (Amsterdam, Netherlands). 2010; 94: 164-174
        • Hsiao C.
        Panel data analysis—advantages and challenges.
        TEST. 2007; 16: 1-22
        • Calderón-Larrañaga A.
        • Abad-Díez J.M.
        • Gimeno-Feliu L.A.
        • Marta-Moreno J.
        • González-Rubio F.
        • Clerencia-Sierra M.
        • et al.
        Global health care use by patients with type-2 diabetes: does the type of comorbidity matter?.
        Eur J Internal Med. 2015; 26: 203-210
        • Youens D.
        • Preed D.
        • Harris M.
        • Moorin R.
        The importance of historical address information in longitudinal studies using administrative health data.
        Int J Epidemiol. 2017; (In press)