Immunization coverage,knowledge,satisfaction,and associated factors of non-National Immunization Program vaccines among migrant and left-behind families in China:evidence from Zhejiang and Henan provinces

Background Migrant and left-behind families are vulnerable in health services utilization,but little is known about their disparities in immunization of non-National Immunization Program(NIP)vaccines.This study aims to evaluate the immunization coverage,knowledge,satisfaction,and associated factors of non-NIP vaccines among local and migrant families in the urban areas and non-left-behind and left-behind families in the rural areas of China.Methods A cross-sectional survey was conducted in urban areas of Zhejiang and rural areas of Henan in China.A total of 1648 caregivers of children aged 1-6 years were interviewed face-to-face by a pre-designed online questionnaire,and their families were grouped into four types:local urban,migrant,non-left-behind,and left-behind.Non-NIP vaccines includedHemophilus influenza b(Hib)vaccine,varicella vaccine,rotavirus vaccine,enterovirus 71 vaccine(EV71)and 13-valent pneumonia vaccine(PCV13).Log-binomial regression models were used to calculate prevalence ratios(PRs)and 95%confidence intervals(CIs)for the difference on immunization coverage of children,and knowledge and satisfaction of caregivers among families.The network models were conducted to explore the interplay of immunization coverage,knowledge,and satisfaction.Logistic regression models with odds ratios(ORs)and 95%CIs were used to estimate the associated factors of non-NIP vaccination.Results The immunization coverage of all non-NIP vaccines and knowledge of all items of local urban families was the highest,followed by migrant,non-left-behind and left-behind families.Compared with local urban children,thePRs(95%CIs)for getting all vaccinated were 0.65(0.52-0.81),0.29(0.22-0.37)and 0.14(0.09-0.21)among migrant children,non-left-behind children and left-behind children,respectively.The coverage-knowledge-satisfaction network model showed the core node was the satisfaction of vaccination schedule.Non-NIP vaccination was associated with characteristics of both children and caregivers,including age of children(>2 years-OR:1.69,95%CI:1.07-2.68 for local urban children;2.67,1.39-5.13 for migrant children;3.09,1.23-7.76 for non-left-behind children);and below caregivers’characteristics:family role(parents:0.37,0.14-0.99 for non-left-behind children),age(≤35 years:7.27,1.39-37.94 for non-left-behind children),sex(female:0.49,0.30-0.81 for local urban children;0.31,0.15-0.62 for non-left-behind children),physical health(more than average:1.58,1.07-2.35 for local urban children)and non-NIP vaccines knowledge(good:0.45,0.30-0.68 for local urban children;7.54,2.64-21.50 for left-behind children).Conclusions There were immunization disparities in non-NIP vaccines among migrant and left-behind families compared with their local counterparts.Non-NIP vaccination promotion strategies,including education on caregivers,and optimization of the immunization information system,should be delivered particularly among left-behind and migrant families.

Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of research.In this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English databases.Previous studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy lifestyles.By summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

Timing and Magnitude of the Second Wave of the COVID-19 Omicron Variant—189 Countries and Territories,November 2021 to February 2023

Summary What is already known about this topic?The first nationwide wave of coronavirus disease 2019(COVID-19),driven by the Omicron variant,has largely subsided.However,subsequent epidemic waves are inevitable due to waning immunity and the ongoing evolution of the severe acute respiratory syndrome coronavirus 2.What is added by this report?Insights gleaned from other nations offer guidance regarding the timing and scale of potential subsequent waves of COVID-19 in China.What are the implications for public health practice?Understanding the timing and magnitude of subsequent waves of COVID-19 in China is crucial for forecasting and mitigating the spread of the infection.

Body mass index and survival after cancer diagnosis:A pan-cancer cohort study of 114430 patients with cancer

The recommendation encouraging patients with cancer to keep a normal body mass index(BMI)is largely extrapolated from data on risk of developing cancer.We tested the prospective association between peri-diagnostic(within 1 year post-diagnosis)BMI and all-cause mortality in patients with incident cancers.During 7.2 years of follow-up,42%(48,340)of the 114430 patients with cancer died.Spline analysis revealed that compared with a BMI of 22.5,a BMI lower than 22.5 was associated with increased risk of all-cause mortality across 24 cancer types.A BMI higher than 22.5 was associated with reduced all-cause mortality,while a non-linear association was observed;the lowest risk was found at a BMI of 29.6–34.2,and the risk started to return to and above unity at very high BMI values.The reduced mortality risk of high BMI was observed in 23 of 24 cancer types and maintained after attempts to remove potential selection bias,confounding by smoking and comorbidities,and reserve causality.

Smoking, smoking cessation, and survival after cancer diagnosis in 128,423 patients across cancer types

Dear Editor,Given the systemic and substantial health consequences of smoking and the significant cancer risk from smoking[1,2],it is expected that cancer patients would quit smoking after cancer diagnosis.However,the smoking rate among cancer survivors is only slightly lower than that among the general population[3,4],and 64%of smokers diagnosed with cancer continued to smoke even after they learned they had cancer[5].In addition,some former smokers may resume smoking after surviving cancer[6].A number of previous studies have investigated the association between smoking and clinical outcomes in cancer patients[1,7–10].Nevertheless,most studies were conducted in smoking-related cancers,and evidence for non-smoking-related cancers is limited.There is an urgent need for more convincing evidence showing the harms of smoking and the benefits of smoking cessation to promote smoking cessation in cancer patients and survivors.We systematically studied the associations of smoking status,smoking intensity,age at initiation,and smoking cessation at cancer diagnosis with all-cause mortality in a prospective cohort of 128,423 cancer patients across 23 cancer types from the MD Anderson Cancer Patients and Survivors Cohort.