Universidade Federal de Alfenas. Instituto de Ciências da Motricidade. Alfenas, MG, Brasil.
Universidade de São Paulo. Faculdade de Saúde Pública. São Paulo, SP, Brasil.
Rev Saude Publica. 2024 Jul 26;58:26. doi: 10.11606/s1518-8787.2024058006058. eCollection 2024.
To identify empirical patterns of multimorbidity and quantify their associations with socioeconomic, behavioral characteristics, and health outcomes in the megacity of São Paulo.
This was a cross-sectional study conducted through household interviews with residents aged 20 years or older in urban areas (n = 3,184). Latent class analysis was used to identify patterns among the co-existence of 22 health conditions. Age-adjusted prevalence ratios were estimated using Poisson regression.
The analysis of latent classes showed 4 patterns of multimorbidity, whereas 58.6% of individuals were classified in the low disease probability group, followed by participants presenting cardiovascular conditions (15.9%), respiratory conditions (12.8%), and rheumatic, musculoskeletal, and emotional conditions (12.8%). Older individuals, with lower schooling and lower household income, presented higher multimorbidity prevalence in cardiovascular, respiratory, rheumatic, musculoskeletal, and emotional conditions patterns compared with the low disease probability pattern.
The results showed four distinct patterns of multimorbidity in the megacity population, and these patterns are clinically recognizable and theoretically plausible. The identification of trends between patterns would make it feasible to estimate the magnitude of the challenge for the organization of health care policies.
在圣保罗大都市识别多种疾病的经验模式，并量化其与社会经济、行为特征和健康结果的关联。
这是一项横断面研究，通过对城市地区 20 岁及以上居民（n=3184）进行家庭访谈进行。使用潜在类别分析来识别 22 种健康状况共存的模式。使用泊松回归估计年龄调整后的患病率比。
潜在类别分析显示存在 4 种多种疾病模式，而 58.6%的个体被归类为低疾病概率组，其次是心血管疾病（15.9%）、呼吸系统疾病（12.8%）和风湿、肌肉骨骼和情绪疾病（12.8%）。与低疾病概率模式相比，年龄较大、受教育程度较低和家庭收入较低的个体在心血管、呼吸、风湿、肌肉骨骼和情绪疾病模式中呈现出更高的多种疾病患病率。
结果表明，大都市人口存在四种不同的多种疾病模式，这些模式在临床上是可识别的，在理论上也是合理的。识别模式之间的趋势将使评估卫生保健政策组织所面临挑战的规模成为可能。
