Association of smoking status and nicotine dependence with multi-morbidity in China: A nationally representative crosssectional study

Prevalence and patterns of multimorbidity in China during 2002-2022: A systematic review and meta-analysis

BACKGROUND

Multimorbidity is common, particularly among elderly people. Restructuring health service systems to better manage this public health issue requires knowledge regarding disease prevalence and patterns. We quantified the epidemiology characteristics of multimorbidity among adults in China to inform policy-making and resource allocation.

METHODS

We searched 10 databases for studies (January 2000-October 2023) reporting primary epidemiological multimorbidity data for adults in China. We included observational studies; we excluded duplicate publications and studies investigating a single comorbidity pattern, focused on specific population categories, using medical insurance reimbursement data, and with unclear/incomplete data. We assessed risk of bias using the STROBE checklist and estimated heterogeneity among studies. The prevalence was pooled using the random-effects method and sample size as weight.

FINDINGS

Of 13,998 records retrieved, 67 studies (30 in English, 37 in Chinese) were included. The prevalence (95% confidence interval) of multimorbidity was 25.4% (15.1%, 35.7%) among Chinese adults. Among 42 studies reporting age-specific prevalence, multimorbidity prevalence increased rapidly with age: 3.3% (0%, 15.2%) for age 18-29 years, 5.9% (0%, 12.9%) for 30-44 years, 17.6% (6.1%, 29.1%) for 45-59 years, 32.4% (16.1%, 48.7%) for 60-69 years, 38.5% (23.6%, 53.4%) for 70-79 years, and 40.2% (20.8%, 59.6%) for age ≥ 80 years. Overall prevalence of multimorbidity has increased in recent years, with regional disparity. The most common patterns included hypertension with hearing impairment (10.4% [95% CI: 4.3%, 16.5%]), dyslipidemia (8.9% [4.1%, 13.6%]), and diabetes (8.7% [3.7%, 13.8%]).

CONCLUSION

Multimorbidity was present nearly one in four Chinese adults, with hypertensive diseases and other comorbidities being the most-observed pattern; the prevalence increased rapidly with increased age. There is huge variation in the prevalence of multimorbidity across China. Coordinated, comprehensive strategies are urgently needed to control the ongoing impact of multimorbidity.

Findings from the Indonesian family life survey on patterns and factors associated with multimorbidity

The prevalence of multimorbidity tends to increase with age, but it is now also reported in the middle-aged population, which has a negative impact on healthcare systems and health outcomes. This study aims to analyze the patterns and factors associated with multimorbidity in Indonesia. This national cross-sectional population-based survey used publicly available data from the Indonesian Family Life Survey (IFLS-5) for 2014 among middle-aged (40-59 years old) and elderly (≥ 60 years old) respondents. Information on all chronic diseases was assessed using a self-reported questionnaire. Sociodemographic and health-related behavioral factors were obtained from self-reported data. Binary logistic regression analysis was used to identify the factors associated with multimorbidity. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. The study recruited 11,867 respondents. The prevalence of multimorbidity was 18.6% (95% CI 17.9-19.3) with which 15.6% among middle age (95% CI 14.95-16.25) and 24.9% among the elderly (95% CI 24.12-25.68). Hypertension was the most commonly reported disease (23.2%) in all combinations of multimorbidity and among all age groups. Socio-demographic factors: elderly (AOR: 1.66; 95% CI 1.46-1.89), female (AOR: 1.42; 95% CI 1.20-1.69), living in the urban area (AOR: 1.22; 95% CI 1.09-1.38), higher educational level (AOR: 2.49; 95% CI 1.91-3.26), unemployed (AOR: 1.63; 95% CI 1.44-1.84), and higher economic level (AOR: 1.41; 95% CI 1.18-1.68) were associated with multimorbidity. Poor health behavior factors: being former smokers (AOR: 2.03; 95% CI 1.65-2.51) and obesity (AOR: 1.53; 95% CI 1.35-1.75) were also associated with multimorbidity. The prevalence of multimorbidity in the middle-aged and elderly population in Indonesia is relatively high, particularly in populations with poor health behaviors. Therefore, healthcare professionals should integrate more patient-specific factors when designing and implementing tailored interventions to manage multimorbidity in Indonesia.

How much disease risk is due to old age and established risk factors?

Improved healthcare is leading to older populations and increasing numbers of individuals experiencing multiple diseases, possibly concurrently (multimorbidity). This article asks whether the observed number of new diseases is more than expected based on age and established risk factors alone, assuming that disease risk is unchanged by prior or pre-existing disease. This is accomplished by designing a new epidemiological approach, where the expected number of disease types are estimated for individuals without prior disease, by combining individual risk predictions with a "Poisson-Binomial" model to estimate the expected number of new diseases and its confidence interval. For 123 diseases in men and 99 diseases in women, the expected number of new diseases based on age and established risk factors was approximately 2 / 3 of that observed, with the observed number of new diseases approximately 1.5 times that predicted. The differences could not be explained by natural statistical variation, and provide a rigorous statistical demonstration of lower disease risk for individuals without any previous disease. The multiple of 1.5 was sufficiently consistent across different diseases to prevent its use for classification of disease types, but there were differences for subgroups such as smokers with high body mass index, and for some classes of disease (as defined by the International Classification of Diseases, version 10). The results suggest that empirical modeling might allow reliable predictions of future hospital admissions, and confirm the value of conventional epidemiological approaches that study disease risk in healthy individuals. The implications and future possibilities of this new approach are discussed.