Filipe Prazeres1,2, Luiz Santiago3. 1. Faculdade de Ciências da Saúde, Universidade da Beira Interior, Covilhã, Portugal. 2. USF Beira Ria, Gafanha da Nazaré, Portugal. 3. Faculdade de Medicina da Universidade de Coimbra, Coimbra, Portugal.
Abstract
Background: The presence of multimorbidity in the family practice setting is as evident as it is hard to measure. Objective: The objective of this study was to describe the differences in the prevalence of multimorbidity in a single primary care population, through the use of the only two available lists of chronic conditions based on the International Classification for Primary Care coding system. Methods: This is a cross-sectional, analytical study. Secondary analysis of existing chronic conditions data involved 1279 women and 714 men attending primary care centres in mainland Portugal. Multimorbidity was measured by the presence in each patient of both ≥2 and ≥3 chronic conditions, from both a list of 147 conditions and another list of 75 conditions. Logistic regression analyses were conducted to study the association of multimorbidity with sex, age, education and income by the type of list of chronic conditions. Results: Multimorbidity prevalence estimates are modified by (i) the number of chronic conditions included in the lists used and (ii) the number of conditions necessary to define the cut-off for multimorbidity. The use of different lists of conditions modifies not only the multimorbidity prevalence estimates but also the evaluation of the determinants of multimorbidity. Conclusions: The use of different lists of chronic conditions produces different research results. Even the use of lists designed to be more general practice-oriented may underestimate the frequency of multimorbidity by limiting the number of conditions considered. Further research is still needed to grasp the full implications of using different lists of chronic conditions in multimorbidity research.
Background: The presence of multimorbidity in the family practice setting is as evident as it is hard to measure. Objective: The objective of this study was to describe the differences in the prevalence of multimorbidity in a single primary care population, through the use of the only two available lists of chronic conditions based on the International Classification for Primary Care coding system. Methods: This is a cross-sectional, analytical study. Secondary analysis of existing chronic conditions data involved 1279 women and 714 men attending primary care centres in mainland Portugal. Multimorbidity was measured by the presence in each patient of both ≥2 and ≥3 chronic conditions, from both a list of 147 conditions and another list of 75 conditions. Logistic regression analyses were conducted to study the association of multimorbidity with sex, age, education and income by the type of list of chronic conditions. Results: Multimorbidity prevalence estimates are modified by (i) the number of chronic conditions included in the lists used and (ii) the number of conditions necessary to define the cut-off for multimorbidity. The use of different lists of conditions modifies not only the multimorbidity prevalence estimates but also the evaluation of the determinants of multimorbidity. Conclusions: The use of different lists of chronic conditions produces different research results. Even the use of lists designed to be more general practice-oriented may underestimate the frequency of multimorbidity by limiting the number of conditions considered. Further research is still needed to grasp the full implications of using different lists of chronic conditions in multimorbidity research.