Journal of Global Health最新文献

Background: Given the limited understanding of individuals' positive gains, this study aimed to identify these gains that could be leveraged by policymakers to enhance future health and societal resilience.

Methods: We used a global qualitative approach to survey adults over 18 from 30 countries across six World Health Organization (WHO) regions, who detailed up to three personal positive gains from COVID-19 pandemic via an open-ended question. Inductive thematic analysis was employed to identify main themes, and quantitative methods were used for demographic and regional comparisons based on the percentage of responses for each theme.

Results: From 35 911 valid responses provided by 13 853 participants, six main themes (one negative theme), 39 subthemes, and 673 codes were identified. Five positive gain themes emerged, ordered by response frequency: 1) improved health awareness and practices; 2) strengthened social bonds and trust; 3) multi-dimensional personal growth; 4) resilience and preparedness building; 5) accelerated digital transformation. The percentage of responses under these themes consistently appeared in the same order across various demographic groups and economic development levels. However, there were variations in the predominant theme across WHO regions and countries, with either Theme 1, Theme 2, or Theme 3 having the highest percentage of responses. Although our study primarily focused on positive gains, unexpectedly, 12% of responses (4304) revealed 'negative gains', leading to an unforeseen theme: 'Distrust and emerging vulnerabilities.' While this deviates from our main topic, we retained it as it provides valuable insights. Notably, these 'negative gains' had a higher percentage of responses in areas like Burundi (94.1%), Rwanda (31.8%), Canada (26.9%), and in the African Region (37.7%) and low-income (43.9%) countries, as well as among non-binary individuals, those with lower education, and those facing employment challenges.

Conclusions: Globally, the identified diverse positive gains guide the domains in which health policies and practices can transform these transient benefits into enduring improvements for a healthier, more resilient society. However, variations in thematic responses across demographics, countries, and regions highlights need for tailored health strategies.

Background: Freely accessible data concerning modifiable risk factors for road traffic injury are critical for research and for evidence-based policymaking. This study investigated free-access availability and the major characteristics of nationally representative data on eight major risk factors for road traffic injury across 194 World Health Organization member countries/territories from 2000 to 2019.

Methods: We systematically searched and reviewed data sources from governmental departments, multi-country road safety research projects, and international organisations. Two researchers independently searched, screened, and extracted data. We assessed free-access availability of data for eight risk factors based on the presence of data from 2000 to 2019. Major data characteristics were evaluated for all included data sources, consisting of operational definitions, method of data collection, and sampling method.

Results: We identified 79 sources providing free-access available data on at least one of the eight risk factors. During 2000-2019, the number of countries/territories with freely-access data generally rose over time. However, only 134 of 194 countries/territories (69%) had at least one year of free-access data involving one or more risk factors, and 70% of those 134 countries/territories were high-income or upper middle-income countries. Large data heterogeneity existed across the data sources in terms of operational definitions used, method of data collection, years of data coverage, and sampling method. Operational definitions varied widely across the eight risk factors, ranging from 3 definitions used for fatigue driving to 17 definitions for seatbelts; and the proportion of data sources that adopted the recommended Global Road Safety Partnership (GRSP) definitions ranged from 25.5% for distracted driving to 77.8% for child restraint systems. Roadside observations were predominantly used to collect exposure data for six risk factors. Many free-access data sets were completely or partially based on non-probability sampling, and the sampling information was unknown for some additional data sources.

Conclusions: Availability of free-access data on road traffic injury risks generally improved globally, but was still absent for 60 countries/territories. The substantial heterogeneity of free-access data across the existing data sources warrants further research efforts and international coordination.

We examine the significant increase in staffing at the Chinese Center for Disease Control and Prevention (CDC) following the COVID-19 pandemic and compare it to the staffing changes post-2003 SARS outbreak. This analysis views the surge not only as compensation for long-term understaffing, but also as a response to the immediate demands of the COVID-19 crisis. We explore the implications of this increase, addressing the financial burden it imposes on the government, the challenges in maintaining a balanced human resource structure, and the potential long-term effects on public health infrastructure. Additionally, we propose strategic recommendations including reforming the income model for CDC employees, implementing strategic workforce planning, and making infrastructure improvements. These measures aim to support a more effective and resilient public health system in China.

Background: Our previous systematic review estimated the cumulative incidence of SARS-CoV-2 reinfections as 1.16% (95% CI = 1.01-1.33%) during the pre-Omicron period. The Omicron variant that emerged in November 2021 was significantly genetically distinct from the previous SARS-CoV-2 variants and thus, more transmissible and posed an increased risk of SARS-CoV-2 reinfections in the population. We, therefore, conducted a fresh systematic review and meta-analysis to estimate the SARS-CoV-2 reinfection burden during the Omicron period.

Methods: We searched CINAHL, Medline, Global Health, Embase, and WHO COVID-19 in October 2023 for studies reporting the SARS-CoV-2 reinfection incidence during the Omicron period. The quality of the included studies was assessed using the Joanna Briggs Institute checklists. Random effects meta-analyses were conducted to estimate the incidence, and requirement of hospitalisation of SARS-CoV-2 reinfections. Symptomatic severity of reinfections and case fatality rates were analysed narratively.

Results: Thirty-six studies were included. The reinfection cumulative incidence during the Omicron period was 3.35% (95% CI = 1.95-5.72%) based on data from 28 studies. The cumulative incidence was higher in 18-59-year-old adults (6.62% (95% CI = 3.22-13.12%)) compared to other age groups and in health care workers (9.88% (95% CI = 5.18-18.03%)) compared to the general population (2.48% (95% CI = 1.34-4.54%)). We estimated about 1.81% (95% CI = 0.18-15.87%) of the reinfected cases required hospitalisation based on limited and highly variable data.

Conclusions: There was an increased risk of reinfections during the Omicron period compared to the pre-Omicron period. The incidence was higher in 18-59-year-old adults and health care workers and generally less severe during the Omicron period. However, data were limited on disease severity and long-term outcomes.

Registration: PROSPERO: CRD42023482598.

Background: In 2018, China issued a set of criteria for effectively identifying and managing potentially inappropriate medication (PIM) use in older adults. However, there is currently a lack of evidence regarding the impact of these criteria on PIM use among older Chinese adults.

Methods: We used interrupted time series analysis on the prescription data of older outpatients from 59 hospitals in six major geographic regions of China to compare changes in the overall prevalence of PIM use, the prevalence of PIM use stratified by different numbers of PIMs, and the prevalence of top five PIMs (i.e. clopidogrel, estazolam, zolpidem, sliding-scale insulin, and alprazolam) from 2015 (before) to 2021 (after) the release of criteria in 2018.

Results: We included 982 605 older outpatients. Compared with trends prior to the publication of the criteria, there were significant decreases in the coefficient for change in the slope of the overall prevalence of PIM use (β = -0.607; 95% confidence interval (CI) = -0.881, -0.482; P < 0.001), the prevalence of single PIM use (β = -0.368; 95% CI = -0.465, -0.272; P < 0.001), the prevalence of multiple PIM use (β = -0.104; 95% CI = -0.173, -0.080; P = 0.019), the prevalence of clopidogrel (β = -0.342; 95% CI = -0.463, -0.227; P = 0.006), and the prevalence of estazolam (β = -0.077; 95% CI = -0.124, -0.037; P = 0.009) post-publication. Conversely, there was a significant increase in the prevalence of zolpidem, after the criteria were released (β = 0.030; 95% CI = 0.002, 0.057; P = 0.036).

Conclusions: We found that the release of criteria for effectively identifying and managing PIM use has had a positive effect on its prevalence among older outpatients in China.

Background: Insomnia characterised by difficulties in falling asleep and maintaining sleep, and early awaking, is a prevalent worldwide sleep disorder. While previous studies have suggested an association between insomnia and adverse glycaemic control, the evidence remains inconclusive. Therefore, this meta-analysis aims to explore this association.

Methods: Insomnia was assessed based on defined criteria, including related symptoms such as poor sleep quality and low sleep efficiency. Glycaemic control was evaluated using indicators such as fasting plasma glucose, haemoglobin A1c, and the presence of diabetes. A literature search was performed in PubMed, Web of Science, and Scopus. The quality of the included studied was assessed using The Newcastle-Ottawa Scale. Effect sizes, including odds ratio, relative risk, mean difference, and standard mean difference, were chosen based on data type. Forest plots visually displayed pooled effect sizes and corresponding 95% confidence intervals, while the I² test calculated heterogeneity. Meta-regression and subgroup analysis explored potential sources of heterogeneity. Leave-one-out sensitivity analysis assessed result robustness, and Begg's and Egger's tests evaluated publication bias.

Results: Ninety-one articles, comprising 84 are cross-sectional studies, (five are case-control studies, and two are cohort studies) with 2 217 521 participants, were included. Ten separate meta-analyses were conducted based on variable type (binary/continuous), study design (cross-sectional, case-control, or cohort), and measurement of exposures/outcomes. All meta-analyses indicated a positive association between insomnia (related symptoms) and adverse glycaemic control. However, three meta-analyses showed significant heterogeneity, and three lacked robustness. No publication bias was detected across any of the analyses.

Conclusions: Insomnia is likely associated with adverse glycaemic control. As the included studies are observational, future research should prioritise diverse methodologies and robust study designs to further explore this complex relationship.

Keywords: insomnia, insomnia related symptoms, glycaemic control, systematic review, meta-analysis.

Registration: PROSPERO CRD42024491688.

Background: Factors underlying the development of childhood underweight, overweight, and obesity are not fully understood. Traditional models have drawbacks in handling large-scale, high-dimensional, and nonlinear data. In this study, we aimed to identify factors responsible for underweight, overweight, and obesity using machine learning methods among Chinese children.

Methods: Our study participants were children aged 3-14 from 30 kindergartens and 26 schools in Beijing and Tangshan. Weight status was defined per the World Health Organization criteria. We implemented three ensemble learning algorithms and compared their performance and ranked the contributing factors by importance and identified an optimal set. A user-friendly web application was developed to calculate the predicted probability of childhood underweight, overweight, and obesity.

Results: We analysed data from 18 503 children aged 3-14, including 1798 underweight, 10 579 of normal weight, 3257 overweight, and 2869 with obesity. Of all algorithms, random forest performed the best, with the area under the receiver operating characteristic reaching 0.759 for underweight, 0.806 for overweight, and 0.849 for obesity, with other metrics also reinforcing this algorithm. Further cumulative analyses showed that, for underweight, the optimal set of six factors included maternal body mass index (BMI), age, paternal BMI, maternal reproductive age, paternal reproductive age, and birth weight. The optimal set for overweight comprised of five factors: age, fast food intake, maternal BMI, paternal BMI, and sedentary time. For obesity, the optimal set included six factors: age, fast food intake, maternal BMI, paternal BMI, sedentary time, and maternal reproductive age. Further logistic regression analyses confirmed the predictive capability of individual top factors.

Conclusions: Our findings indicate that random forest is the best ensemble learning algorithm for predicting underweight, overweight, and obesity in children aged 3-14 years. We identified the optimal set of significant factors for each malnutrition status and incorporated them into a web application to support the application of this study's findings.

Background: A wide spectrum of high-risk factors in pregnancy can lead to adverse pregnancy outcomes or short- or long-term health effects. Despite this, there has been no synthesis of findings on the measurement, potential causes, and health outcomes of multiple high-risk factors in pregnancy (MHFP). We aimed to address this gap by summarising the existing research on this topic.

Methods: We retrieved studies published up to 3 June 2024 through systematic database searches and used a narrative synthesis approach to summarise the measurement, patterns, causes, and outcomes of MHFP. We also estimated the pooled MHFP prevalence through meta-analysis with a random effects model and performed subgroup analyses and meta-regression to examine potential sources of between-study heterogeneity.

Results: We included 83 observational studies published between 2010 and 2024, of which 72% were from high-income countries. These studied factors can be grouped into four categories: physical conditions, mental conditions, sociobehavioural problems, and pregnancy history. We identified 16 MHFP patterns, among which co-existing multiple physical conditions were the most common pattern. The overall pooled prevalence of MHFP was 12% (95% confidence interval (CI) = 12-13), with an increasing trend and relatively higher levels in low- and middle-income countries (LMICs). We observed heterogeneity in the measurement of MHFP across the studies, possibly due to the number of risk factors in the definition of MHFP. About 78% of included studies investigated MHFP-associated health outcomes for women and offspring, with only two studies examining long-term maternal or offspring outcomes later in life.

Conclusions: Research into MHFP has been emerging over the past decade, but is far from complete. The burden of MHFP is increasing worldwide, particularly LMICs. Maternal healthcare systems must shift to a multidisciplinary and integrated framework so as to better design and implement prevention and intervention programmes and sustain the healthy development of the next generation.

Registration: PROSPERO: CRD42022358889.

Background: Post-viral issues following acute infection with coronavirus disease 2019 (COVID-19), referred to widely as long COVID, are associated with episodic, persistent, and disabling symptoms affecting quality of life and functional status. Evidence demonstrates a significant impairment and long disease course, but there remains limited empirical data to profile and determine the fluctuating symptom profile of long COVID.

Methods: We devised a 16-week, multicentre prospective cohort observation study to profile changes in patient-reported outcomes, and biological, physiological, psychological, and cognitive parameters following diagnosis and/or referral to an established long COVID clinic. Following baseline assessments, participants completed four face-to-face visits interspersed with telephone consultations. Face-to-face visits included physiological assessment, patient-reported outcome measures (PROMs), functional status, and respiratory function. Telephone consultations involved PROMs and symptom profiling.

Results: Patient-reported outcomes improved from baseline to week sixteen, but demonstrated between visit fluctuations in frequency and severity. Further findings highlight the severity and frequency of long COVID symptom profiles and the extent of quality of life and functional status impairment.

Conclusions: The data presented here highlight the episodic and relapsing nature and should be used to help characterise long COVID disability. They can inform the development of long COVID-specific guidelines and support services that can adequately respond to the reductions in patient well-being.