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Comparing the Health of Populations: Methods to Evaluate and Tailor Population Management Initiatives in the Netherlands.
Hendrikx, Roy J P; Drewes, Hanneke W; Spreeuwenberg, Marieke; Ruwaard, Dirk; Baan, Caroline A.
Afiliação
  • Hendrikx RJP; 1 Tranzo Scientific Center for Care and Welfare, Tilburg School of Social and Behavioral Sciences, Tilburg University , Tilburg, the Netherlands .
  • Drewes HW; 2 Department for Quality of Care and Health Economics, Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment , Bilthoven, the Netherlands .
  • Spreeuwenberg M; 3 Department of Health Services Research, Faculty of Health, Medicine and Life Sciences, CAPHRI School for Public Health and Primary Care, Maastricht University , Maastricht, the Netherlands .
  • Ruwaard D; 4 Research Centre for Technology in Care, Zuyd University of Applied Sciences , Heerlen, the Netherlands .
  • Baan CA; 3 Department of Health Services Research, Faculty of Health, Medicine and Life Sciences, CAPHRI School for Public Health and Primary Care, Maastricht University , Maastricht, the Netherlands .
Popul Health Manag ; 21(5): 422-427, 2018 10.
Article em En | MEDLINE | ID: mdl-29091019
ABSTRACT
Health care no longer focuses solely on patients and increasingly emphasizes regions and their populations. Strategies, such as population management (PM) initiatives, aim to improve population health and well-being by redesigning health care and community services. Hence, insight into population health is needed to tailor interventions and evaluate their effects. This study aims to assess whether population health differs between initiatives and to what extent demographic, personal, and lifestyle factors affect these differences. A population health survey that included the Short Form 12 version 2 (SF12, physical and mental health status), Patient Activation Measure 13 (PAM13), and demographic, personal, and lifestyle factors was administered in 9 Dutch PM initiatives. Potential confounders were determined by comparing these factors between PM initiatives using analyses of variance and chi-square tests. The influence of these potential confounders on the health outcomes was studied using multivariate linear regression. Age, education, origin, employment, body mass index, and smoking were identified as potential confounders for differences found between the 9 PM initiatives. Each had a noteworthy influence on all of the instruments' scores. Not all health differences between PM initiatives were explained, as the SF12 outcomes still differed between PM initiatives once corrected. For the PAM13, the differences were no longer significant. Demographic and lifestyle factors should be included in the evaluation of PM initiatives and population health differences found can be used to tailor initiatives. Other factors beyond health care (eg, air quality) should be considered to further refine the tailoring and evaluation of PM initiatives.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nível de Saúde / Saúde da População Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Europa Idioma: En Revista: Popul Health Manag Assunto da revista: SAUDE PUBLICA / SERVICOS DE SAUDE Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nível de Saúde / Saúde da População Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Europa Idioma: En Revista: Popul Health Manag Assunto da revista: SAUDE PUBLICA / SERVICOS DE SAUDE Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Holanda