Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China.

BACKGROUND: Most previous studies on women of childbearing age have focused on reproductive health and fertility intentions, and evidence regarding the comprehensive health status of women of childbearing age is limited. This study aimed to comprehensively examine the health status of women of childbearing age through a multi-method and multi-indicator evaluation, analyze the factors that influence their overall health, and provide sound recommendations for the improvement and promotion of healthy behaviors. METHODS: Data on women of childbearing age living in Shanxi Province were collected between September 2021 and January 2022 through online and offline surveys. The k-means algorithm was used to assess health-related patterns in women, and multivariate nonconditional logistic regression was used to assess the influencing factors of women's overall health. RESULTS: In total, 1,258 of 2,925 (43%) participants were classified as having a good health status in all five domains of the three health dimensions: quality of life, mental health, and illness. Multivariate logistic regression showed that education level, gynecological examination status, health status of family members, access to medical treatment, age, cooking preferences, diet, social support, hand washing habits, attitude toward breast cancer prevention, and awareness of reproductive health were significantly associated with different health patterns. CONCLUSIONS: The comprehensive health status of women of childbearing age in Shanxi Province is generally good; however, a large proportion of women with deficiencies in some dimensions remains. Since lifestyle greatly impacts women's health, health education on lifestyle and health-related issues should be strengthened.

The prevalence and burden of four major chronic diseases in the Shanxi Province of Northern China.

Background: Chronic non-communicable diseases constitute an important public health problem that is closely related to behavioral risk factors. The study examined the prevalence, burden, and behavioral risk factors relevant to four major chronic diseases in Shanxi Province, China. The results obtained could provide a basis for the formulation of chronic disease prevention and control strategies in north China. Methods: A multi-stage random sampling method was used to select 14,137 residents aged ≥15 years who completed a questionnaire survey and physical examination. The disease burden was evaluated using the disability-adjusted life years (DALY) index. The extent of disease burden attributable to smoking and drinking behavior was analyzed using counterfactual analysis. Results: The total DALYs due to the four major chronic diseases was 938,100. The years of life lost due to stroke accounted for 74.86%; the years of life lived with disabilities accounted for 54.0 and 68.1% of the total disease burden of coronary heart disease and diabetes. Coronary heart disease attributed to smoking (105,600) was the highest, followed by stroke (77,200), hypertension (6,000), and diabetes mellitus (5,900). Stroke attributed to drinking (30,700) was the highest followed by coronary heart disease (16,700) and diabetes (1,100). The disease burden caused by smoking and drinking was higher in men (164,000 and 40,700, respectively) than in women (30,700 and 7,300, respectively). Conclusion: There is a high prevalence and significant burden associated with major chronic diseases in Shanxi Province. Therefore, the need for the application of various interventions to control smoking and drinking (the major predisposing factors) should be applied to reduce this burden.

Coronavirus disease 2019 epidemic prediction in Shanghai under the "dynamic zero-COVID policy" using time-dependent SEAIQR model.

It's urgently needed to assess the COVID-19 epidemic under the "dynamic zero-COVID policy" in China, which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control. Here, we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-removed (SEAIQR) model with stage-specific interventions based on recent Shanghai epidemic data, considering a large number of asymptomatic infectious, the changing parameters, and control procedures. The data collected from March 1st, 2022 to April 15th, 2022 were used to fit the model, and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting. We then calculated the effective regeneration number (R t) and analyzed the sensitivity of different measures scenarios. Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai, and Pudong is the district with the most positive cases. The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th, 2022, with 1963 and 28,502 cases, respectively, and zero community transmission may be achieved in early to mid-May. The prediction errors for newly confirmed cases were considered to be reasonable, and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th. The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477 âˆ¼ 47,749 and 402,254 âˆ¼ 730,176, respectively. At the beginning of the outbreak, R t was 6.69. Since the implementation of comprehensive control, R t showed a gradual downward trend, dropping to below 1.0 on April 15th, 2022. With the early implementation of control measures and the improvement of quarantine rate, recovery rate, and immunity threshold, the peak number of infections will continue to decrease, whereas the earlier the control is implemented, the earlier the turning point of the epidemic will arrive. The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well, which can provide a reference for decision making of the "dynamic zero-COVID policy".