{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:22:13Z","timestamp":1791235333099,"version":"4.1.0"},"reference-count":22,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>\n                    A\n                    <jats:sc>BSTRACT<\/jats:sc>\n                  <\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Multimorbidity is a condition that refers to the coexistence of two or more chronic diseases or long-term medical conditions in an individual. It has become one of the key issues in the public health sector, particularly among older adults. So, this study aimed to estimate the prevalence of multimorbidity and its associated factors among older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology:<\/jats:title>\n                    <jats:p>\n                      The present study was a community-based cross-sectional study carried out on the elderly population (\u226560 years) in a rural area of the study district, Gujarat. The estimated sample size was 384, and the final sample size was taken as 420, considering the multistage sampling procedure for fair distribution. The eligibility criteria included elderly people \u226560 years old, who were permanent residents (living for \u22656 months) of the study district and willing to participate in the study. Elderly people who were not willing to participate in the study for their reasons were excluded. A multistage sampling procedure was used to choose the study participants. The data collection tools included a pretested, predesigned, semi-structured questionnaire. The questionnaire consisted of two parts: sociodemographic details and morbidity details. Categorical variables were described by frequency and percentage, and numerical variables were described by mean \u00b1 SD. Logistic regression analyses were applied to test the relationship of categorized independent and dependent variables, and all tests were two-tailed with statistical significance set at the probability value (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Out of the 420 study participants, 52.1% were male and 47.9% were female, with a mean age of 70.70 years (SD \u00b1 8.18 years). The prevalence of multimorbidity was 50%, with males having a mean number of morbidities of 1.38 \u00b1 1.06 and females having a mean number of morbidities of 1.44 \u00b1 0.99. As the age increased, the strength of association of multimorbidity also increased. Literacy level, marital status, occupation (business, farmer, housewife), and family history were statistically associated with multimorbidity in the study participants in both bivariate and multivariate analyses (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The study provides valuable insights into the health status of the elderly population in the rural area and can be used to inform healthcare policies and interventions aimed at improving the health outcomes of this population.<\/jats:p>\n                  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