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2022
- doi: 10.1016/j.ssmph.2022.101063
- issn: 2352-8273
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ssmph.2022.101063
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info:eu-repo/semantics/altIdentifier/pmid/35308585
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info:eu-repo/semantics/altIdentifier/pissn/2352-8273
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info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_AF0972C7210C7
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Mots-clés
Public Health, Environmental and Occupational Health; Health Policy; Health (social science)Sujets proches
Care, Continuum of Continuity of patient care Continuity of care Group dynamics Association Groups, Social Populations, Human Human populations Population growth Human population Prepaid medical care Medical care, Prepaid Health plans, Prepaid Medical insurance Insurance, Health Prepaid health plans Sickness insurance Assurance (Insurance) Coverage, Insurance Insurance protection Insurance coverage Insurance industry Indemnity insurance Mutual insurance Underwriting War claims Claims--Law and legislation International claims Private claims Demography Demographics illnessCiter ce document
A. Nicolet et al., « Continuity of care and multimorbidity in the 50+ Swiss population: An analysis of claims data », Serveur académique Lausannois, ID : 10.1016/j.ssmph.2022.101063
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Résumé
To assess the relationship between continuity of care (COC) and multimorbidity in the older general population in Switzerland, accounting for relevant determinants of COC, and to apply various expressions of multimorbidity derived from claims data. We used data on 240'000 insured individuals aged 50+ for the period 2015-2018, received from one of the largest Swiss health insurance company. We calculated Bice-Boxerman index based on all doctor visits (overall COC) and visits to the general practitioners (COC GP). We analyzed the relationship between COC and multimorbidity using generalized linear and probit models. To express multimorbidity, we applied three approaches based on pharmacy-cost groups (PCGs) assigned to an individual. First, we used simple PCG counts. Second, we expressed multimorbidity via clinically relevant disease groups derived from PCGs. Finally, a data-driven approach allowed defining distinct clusters representing different patient complexities. The association between overall COC and multimorbidity expressed in PCG counts was modest: COC among individuals with 3+ PCGs was 2 percentage points higher than COC among individuals with 0 PCGs. The approach of clinically relevant disease groups showed larger variation in COC and its association with multimorbidity. The data-driven approach showed that most complex ("high-cost high-need") individuals tended to have higher overall COC. Additionally, 70% of the sample visited exclusively one general practitioner (COC GP = 1.0). Other important factors associated with COC in the Swiss context were insurance model with gatekeeping, level of deductibles, and region of residence. Multimorbid patients require regular medical attention often involving multiple healthcare providers, which can lead to varying COC, depending on types of doctors seen and specific condition of the patient. Insurance models with gatekeeping may facilitate COC, prompting developments of better-designed models of care. This represents important implications for policymakers, health insurance representatives, medical professionals and hospital managers.