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--- multimorbidity_patterns [2026/08/12 14:23] – [Table] laura
+++ multimorbidity_patterns [2026/08/12 14:24] (current) – laura
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 For each general assessment, the number of affected disease domains was calculated by summing the 14 binary indicators. Participants with fewer than two affected domains were classified as having no multimorbidity, while those with two or more were classified as having multimorbidity.
-Latent class membership was estimated separately for the first and second assessment waves. The resulting variable describes membership in the latent disease configurations and includes a separate No NCDs category. The publication variable mcs combines the baseline latent class membership with the conventional multimorbidity definition. Participants with fewer than two disease domains are classified as No multimorbidity, regardless of their initial latent class. The mcs variable is therefore recommended for reproducing the baseline analyses reported in the publication.
+Latent class membership was estimated separately for the first and second assessment waves. The resulting variable describes membership in the latent disease configurations and includes a separate No NCDs category. The publication variable mcs combines the baseline latent class membership with the conventional multimorbidity definition. Participants with fewer than two disease domains are classified as No multimorbidity, regardless of their initial latent class. The mcs variable is therefore recommended for reproducing the baseline analyses reported in the publication.\\
 \\
 ===== Variables =====
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 |                                                                                                        |                  |                                    | mcs             |                  | 60+      |
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+===== Publications =====
+  * [[https://doi.org/10.1186/s12877-025-06029-x|Ogaz-González, R., Zou, Q., Du, Y., Gutiérrez-Robledo, L. M., Escamilla-Santiago, R., López-Cervantes, M., & Corpeleijn, E. (2025). Multimorbidity patterns in older adults from Northern Netherlands: Comparing factor analysis and latent class analysis solutions. BMC Geriatrics, 25, 381. DOI: https://doi.org/10.1186/s12877-025-06029-x]]