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Climate and weather significantly influence the duration, timing, and intensity of disease outbreaks, reshaping the global landscape of infectious diseases. Rising temperatures and shifts in precipitation patterns driven by climate change can directly impact the survival and reproduction of pathogens and vector organisms. Moreover, climate change is expected to exacerbate extreme weather events, including floods and droughts, which can disrupt infrastructure and increase the risk of water- and foodborne diseases. There are potential shifts in the temporal and spatial patterns of infectious disease transmission owing to climate change. Furthermore, climate change may alter the epidemiology of vaccine-preventable diseases. These climatic variations not only affect the ecological characteristics of pathogens and vectors but also indirectly influence human behaviors and socioeconomic conditions, further amplifying disease transmission risks. Addressing this challenge requires an interdisciplinary collaboration and comprehensive public health strategies. This review aims to synthesize the current evidence on the impact of climate change on climate-sensitive infectious diseases and elucidate the underlying mechanisms and transmission pathways. Additionally, we explored adaptive policy strategies to mitigate the public health burden of infectious diseases in the context of climate change, offering insights for global health governance and disease control efforts.
Objective Burning solid cooking fuel contributes to household air pollution and is associated with frailty. However, how solid cooking fuel use contributes to the development of frailty has not been well illustrated. Methods This study recruited 8,947 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study, 2011–2018. Group-based trajectory modeling was employed to identify frailty trajectories. Multinomial logistic regression was used to assess the association between solid cooking fuel use and frailty trajectories. Population-attributable fractions were used to estimate the frailty burden from solid fuel use. Results We identified three frailty trajectories: low-stable (n = 5,789), moderate-increasing (n = 2,603), and fast-increasing (n = 555). Solid fuel use was associated with higher odds of being in the moderate-increasing (OR: 1.24, 95% CI: 1.08–1.42) and fast-increasing (OR: 1.48, 95% CI: 1.14–1.92) trajectories. These associations were strengthened by longer solid fuel use (P for trend < 0.001). Switching to clean fuel significantly reduced the risk of being in these trajectories compared with persistent solid fuel users. Without solid fuel, 8% of moderate- and 19% of fast-increasing trajectories demonstrated frailty development like the low-stable group. Conclusion Solid cooking fuel use is associated with frailty trajectories in middle-aged and older Chinese populations.
Objective We aimed to investigate the patterns of fasting blood glucose (FBG) trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers. Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort (TGOC) between 2017 and 2022. A group-based trajectory model was used to identify the FBG trajectories. Environmental risk scores (ERS) were constructed using regression coefficients from the occupational hazard model as weights. Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories. Results FBG trajectories were categorized into three groups. An association was observed between high temperature, noise exposure, and FBG trajectory (P < 0.05). Using the first quartile group of ERS1 as a reference, the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90 and 2.21 times, respectively (odds ratio [OR] = 1.90, 95% confidence interval [CI]: 1.17–3.10; OR = 2.21, 95% CI: 1.09–4.45). Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories. The risk of FBG trajectory levels increase with an increase in ERS.
Objective To investigate the association between ABO blood types and gestational diabetes mellitus (GDM) risk. Methods A prospective birth cohort study was conducted. ABO blood types were determined using the slide method. GDM diagnosis was based on a 75-g, 2-h oral glucose tolerance test (OGTT) according to the criteria of the International Association of Diabetes and Pregnancy Study Groups. Logistic regression was applied to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) between ABO blood types and GDM risk. Results A total of 30,740 pregnant women with a mean age of 31.81 years were enrolled in this study. The ABO blood types distribution was: type O (30.99%), type A (26.58%), type B (32.20%), and type AB (10.23%). GDM was identified in 14.44% of participants. Using blood type O as a reference, GDM risk was not significantly higher for types A (OR = 1.05) or B (OR = 1.04). However, women with type AB had a 19% increased risk of GDM (OR = 1.19, 95% CI = 1.05–1.34; P < 0.05), even after adjusting for various factors. This increased risk for type AB was consistent across subgroup and sensitivity analyses. Conclusion The ABO blood types may influence GDM risk, with type AB associated with a higher risk. Incorporating it—either as a single risk factor or in combination with other known factors—could help identify individuals at risk for GDM before or during early pregnancy.
Objective This study aimed to evaluate the relationships of white blood cell (WBC) count, platelet (PLT) count, and PLT-to-WBC ratio (PWR) with muscle mass in Chinese older adults. Methods This cross-sectional analysis involved 4,033 Chinese older adults aged ≥ 65 years from the Healthy Ageing and Biomarkers Cohort Study. Muscle mass and total skeletal muscle mass index (TSMI) were measured by bioelectric impedance analysis. WBC, PLT, and PWR were measured using standard methods. Multivariate linear regression was used to examine the associations of WBC count, PLT count, and PWR with TSMI. Results High WBC count, PLT count, and PWR were associated with low TSMI, with coefficients of −0.0091 (95% confidence interval [CI]: −0.0142 to −0.0041), −0.0119 (95% CI: −0.0170 to −0.0068), and −0.0051 (95% CI: −0.0102 to −0.0001). The associations between the three inflammatory indices and TSMI were linear. Stratified analyses indicated that the relationship between inflammatory markers and TSMI was more evident in male participants and in individuals aged < 80 years than in their counterparts. Conclusion Elevated WBC count, PLT count, and PWR correlated with muscle mass loss. This study highlights the importance of regular monitoring of inflammatory markers as a potential strategy for the screening and management of sarcopenia in older adults.
Objective This study aimed to explore the interplay between the life-course body mass index (BMI) trajectories and insulin resistance (IR) on incident diabetes. Methods This longitudinal cohort included 2,336 participants who had BMI repeatedly measured 3–8 times between 1989 and 2009, as well as glucose and insulin measured in 2009. BMI trajectories were identified using a latent class growth mixed model. The interplay between BMI trajectories and IR on diabetes was explored using the four-way effect decomposition method. Logistic regression and mediation models were used to estimate the interaction and mediation effects, respectively. Results Three distinct BMI trajectory groups were identified: low-stable (n = 1,625), medium-increasing (n = 613), and high-increasing (n = 98). Both interaction and mediation effects of BMI trajectories and IR on incident diabetes were significant (P < 0.05). The proportion of incident diabetes was higher in the IR-obesity than in the insulin-sensitivity (IS) obesity group (18.9% vs. 5.8%, P < 0.001). After adjusting for covariates, the odds ratios (95% confidence intervals) of the IR, IS-obesity, and IR-obesity groups vs. the normal group were 3.22 (2.05, 5.16), 2.05 (1.00, 3.97), and 7.98 (5.19, 12.62), respectively. IR mediated 10.7% of the total effect of BMI trajectories on incident diabetes (P < 0.001). Conclusion We found strong interactions and weak mediation effects of IR on the relationship between life-course BMI trajectories and incident diabetes. IS-obesity is associated with a lower risk of incident diabetes than IR-obesity.
Objective Epidemiological studies have shown that vitamin D status affects glycemic control in individuals with type 2 diabetes mellitus (T2DM). However, findings from intervention studies remain inconsistent. Therefore, a network meta-analysis was conducted to evaluate the comparative efficacy of various vitamin D supplementation strategies on glucose indicators in adults with T2DM. Methods Eligible studies published before September 12, 2024, were retrieved from PubMed, EMBASE, Cochrane Library, and Web of Science. A network meta-analysis of multiple dosage strategies—low (< 1,000 IU/day, LDS), medium (1,000–2,000 IU/day, MDS), high (2,000–4,000 IU/day, HDS), and extremely high (≥ 4,000 IU/day, EHDS)—was performed. Results The network meta-analysis of 40 RCTs indicated that, compared with placebo, vitamin D3 supplementation increased 25-hydroxyvitamin D [25-(OH)-D] levels, with pooled mean difference (MD) showing a stepwise increase from LDS to EHDS. Ranking probabilities showed a corresponding rise in 25-(OH)-D levels from LDS (46.7%) to EHDS (91.2%). EHDS reduced fasting blood glucose (FBG) relative to no treatment. LDS significantly decreased hemoglobin A1c (HbA1c), and vitamin D2 significantly affected FBG levels. MDS led to a significant change in fasting insulin (FIN) compared to both placebo (MD: −4.76; 95% CI −8.91 to −0.61) and no treatment (MD: −7.30; 95% CI −14.44 to −0.17). Conclusion The findings suggest that vitamin D supplementation may be a viable approach for improving glycemic control in adults with T2DM, with lower doses potentially offering benefit. The analysis also showed a dose-dependent increase in 25-(OH)-D levels.
Objective This study aimed to evaluate the association between susceptibility genes and non-alcoholic fatty liver disease (NAFLD) in children with obesity. Methods We conducted a two-step case-control study. Ninety-three participants were subjected to whole-exome sequencing (exploratory set). Differential genes identified in the small sample were validated in 1,022 participants using multiplex polymerase chain reaction and high-throughput sequencing (validation set). Results In the exploratory set, 14 genes from the NAFLD-associated pathways were identified. In the validation set, after adjusting for sex, age, and body mass index, ECI2 rs2326408 (dominant model: OR = 1.33, 95% CI: 1.02–1.72; additive model: OR = 1.22, 95% CI: 1.01–1.47), C6orf201 rs659305 (dominant model: OR = 1.30, 95% CI: 1.01–1.69; additive model: OR = 1.21, 95% CI: 1.00–1.45), CALML5 rs10904516 (pre-ad dominant model: OR = 1.36, 95% CI: 1.01–1.83; adjusted dominant model: OR = 1.40, 95% CI: 1.03–1.91; and pre-ad additive model: OR = 1.26, 95% CI: 1.04–1.66) polymorphisms were significantly associated with NAFLD in children with obesity (P < 0.05). Interaction analysis revealed that the gene-gene interaction model of CALML5 rs10904516, COX11 rs17209882, and SCD5 rs3733228 was optional (P < 0.05), demonstrating a negative interaction between the three genes. Conclusion In the Chinese population, the CALML5 rs10904516, C6orf201 rs659305, and ECI2 rs2326408 variants could be genetic markers for NAFLD susceptibility.
Objective This study explored the job choice preferences of Center for Disease Prevention and Control (CDC) workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies. Methods A discrete choice experiment was conducted in nine provinces across China. Seven key attributes were identified to analyze the job preferences of CDC workers. Mixed logit models, latent class models, and policy simulation tools were used. Results A valid sample of 5,944 cases was included in the analysis. All seven attributes significantly influenced the job choices of CDC workers. Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility. Income-prioritizers were concerned with income and opportunities for career development, whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits. The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives. Conclusion Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers. Heterogeneity in job preferences was also identified. Based on the preference characteristics of different subgroups, policy content should be skewed to differentiate the importance of incentives.
2015, 28(1): 57-71.
doi: 10.3967/bes2015.006
2022, 35(7): 573-603.
doi: 10.3967/bes2022.079
2018, 31(2): 87-96.
doi: 10.3967/bes2018.011
2023, 36(8): 669-701.
doi: 10.3967/bes2023.106
2012, 25(3): 317-324.
doi: 10.3967/0895-3988.2012.03.010
2019, 32(8): 559-570.
doi: 10.3967/bes2019.074
2014, 27(8): 606-613.
doi: 10.3967/bes2014.093
2003, 16(3): 246-255.
2018, 31(3): 208-214.
doi: 10.3967/bes2018.026
2019, 32(9): 659-672.
doi: 10.3967/bes2019.085
2022, 35(5): 381-392.
doi: 10.3967/bes2022.054
2016, 29(3): 212-218.
doi: 10.3967/bes2016.026
2018, 31(9): 637-644.
doi: 10.3967/bes2018.088
2022, 35(7): 648-651.
doi: 10.3967/bes2022.084
2019, 32(8): 578-591.
doi: 10.3967/bes2019.076
2019, 32(10): 769-778.
doi: 10.3967/bes2019.096
2017, 30(5): 384-389.
doi: 10.3967/bes2017.051
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