Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research.Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool.Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable.Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

ObjectiveTo examine the self-reported prevalence of 13 chronic conditions and poor health among Chinese adults aged 45 years and older with and without self-reported vision impairment.DesignCross-sectional study from the China Health and Retirement Longitudinal Study 2018, a nationally representative survey of Chinese adults aged 45 years and older involving 19 374 participants.MethodsWe used logistic regression to assess the association between vision impairment and 13 common chronic conditions and between vision impairment and poor health for those with any of these chronic conditions.ResultsOlder people with self-reported vision impairment were significantly more likely to report all 13 chronic conditions (all p<0·05). After controlling for age, gender, education, residential status (rural vs urban), smoking and BMI, the highest adjusted odds were for hearing impairment (OR=4.00 (95% CI 3·60 to 4·44]) and depression (OR=2.28 (95% CI 2.06 to 2.51)). The lowest risk, though still significant, was for diabetes (OR=1·33 (95% CI 1.11 to 2.05)) and hypertension (OR=1.20 (95% CI 1.04 to 1.38)). After controlling for these potential confounding factors, among older people with chronic conditions, those with vision impairment were 2.20 to 4.04 times more likely to have poor health, compared with those without vision impairment (all p<0.001), with the exception of cancer (p=0.595).ConclusionsHigher prevalence of chronic conditions is strongly associated with vision impairment among older Chinese adults and poor health is strongly associated with vision impairment among people with chronic conditions.

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Background China has experienced a continuing increase in hypertension prevalence over the past few decades, especially in rural areas. The paper aims to examine the variation of urban–rural disparities in hypertension prevalence, awareness, treatment, and control among Chinese middle-aged and older adults between 2011 and 2015. Methods Our team extracted data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of residents aged 45 years and older. In this study, we used the 2011 wave and the 2015 wave of CHARLS. We calculated crude rates and age-adjusted rates of hypertension prevalence, awareness, treatment, and control for the general, urban, and rural populations in each wave and performed chi-square tests to examine urban–rural disparities. We used logistic regression to further confirm these disparities by controlling confounding factors in each wave. We then used generalized estimating equation (GEE) to further examine whether urban–rural disparities changed between 2011 and 2015. Results We included 11,129 records in the 2011 wave and 8916 records in the 2015 wave in this study. The mean age was 59.0 years and 5359 (48.2%) participants were male in the 2011 wave. Age-adjusted hypertension prevalence, awareness, treatment, control, and control among treated in the total population were 38.5%, 70.6%, 59.2%, 27.4%, and 46.4% in 2015. Urban–rural disparities in the indicators mentioned above were 5.7%, 13.4%, 15.3%, 9.4% and 5.6% in 2011; which decreased to 4.8%, 2.7%, 5.2%, 4.9% and 3.8% in 2015. Urban–rural disparities in prevalence, awareness and treatment were statistically significant in 2011 but not significant in 2015 adjusted for confounding factors, yet control disparities were statistically significant in both waves. Finally, urban–rural disparities in awareness and treatment had narrowed from 2011 to 2015. Conclusions Awareness, treatment, and control rates were sub-optimal among both urban and rural adults. Prevention and management of hypertension among both urban and rural adults should be further strengthened. Awareness and treatment increased more rapidly among rural adults, indicating some achievement had been made in enhancing the healthcare system in rural areas. More efforts are needed in attaining urban–rural equity of healthcare services.

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