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Hong Kong Medical Journal = Xianggang... Jun 2024
Topics: Humans; Hong Kong
PubMed: 38918067
DOI: 10.12809/hkmj245162 -
BMJ Evidence-based Medicine Jun 2024
PubMed: 38918061
DOI: 10.1136/bmjebm-2024-113095 -
American Journal of Epidemiology Jun 2024There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a...
There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS' ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with "true" expected incidence proportions and compared these to imprecise incidence proportions derived from sparse control samples. We varied parameters impacting Type I error and statistical power (exposure group size, outcome's incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of Type I error, and removal of very rare outcomes for TBSS analysis due to "zero" background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts.
PubMed: 38918039
DOI: 10.1093/aje/kwae151 -
BMJ (Clinical Research Ed.) Jun 2024
Topics: Humans; Sports; Paris; COVID-19; Anniversaries and Special Events
PubMed: 38918027
DOI: 10.1136/bmj.q1263 -
Nicotine & Tobacco Research : Official... Jun 2024Pictorial health warning labels (HWLs) can communicate the harms of tobacco product use, yet little research exists for cigars. We sought to identify the most effective...
INTRODUCTION
Pictorial health warning labels (HWLs) can communicate the harms of tobacco product use, yet little research exists for cigars. We sought to identify the most effective types of images to pair with newly developed cigar HWLs.
AIMS AND METHODS
In September 2021, we conducted an online survey experiment with US adults who reported using little cigars, cigarillos, or large cigars in the past 30 days (n = 753). After developing nine statements about health effects of cigar use, we randomized participants to view one of three levels of harm visibility paired with each statement, either: (1) an image depicting internal harm not visible outside the body, (2) an image depicting external harm visible outside of the body, or (3) two images depicting both internal and external harm. After viewing each image, participants answered questions on perceived message effectiveness (PME), negative affect, and visual-verbal redundancy (VVR). We used linear mixed models to examine the effect of harm visibility on each outcome, controlling for warning statement.
RESULTS
Warnings with both and external harm depictions performed significantly better than the internal harm depictions across all outcomes, including PME (B = 0.21 and B = 0.17), negative affect (B = 0.26 and B = 0.25), and VVR (B = 0.24 and B = 0.17), respectively (all p < .001). Compared to both, the external depiction of harm did not significantly change PME or negative affect but did significantly lower VVR (B = -0.07, p = .01).
CONCLUSIONS
Future cigar pictorial HWLs may benefit from including images depicting both or external harm depictions. Future research should examine harm visibility's effect for other tobacco pictorial HWLs.
IMPLICATIONS
The cigar health warning labels (HWLs) proposed by the US Food and Drug Administration are text-only. We conducted an online survey experiment among people who use cigars to examine the effectiveness of warnings with images depicting different levels of harm visibility. We found HWLs with images depicting both an internal and external depiction of cigar harm, or an external depiction of harm alone, performed better overall than images portraying internal depictions of harm. These findings provide important regulatory evidence regarding what type of images may increase warning effectiveness and offer a promising route for future cigar HWL development.
PubMed: 38918001
DOI: 10.1093/ntr/ntae113 -
American Journal on Intellectual and... Jul 2024This study examines the intervention effect of a culturally tailored parent education program in reducing depressive symptoms among Latina mothers of autistic children.... (Randomized Controlled Trial)
Randomized Controlled Trial
This study examines the intervention effect of a culturally tailored parent education program in reducing depressive symptoms among Latina mothers of autistic children. In this two-site randomized waitlist-control study (n = 109 mother-child dyads), a peer-to-peer mentoring (promotora) model was used to deliver an intervention that was designed to increase mothers' self-efficacy and use of evidence-based strategies. We assessed mothers' depressive symptom (CES-D) scores at three time points and used linear mixed models to determine whether their scores significantly changed from baseline to postintervention (Time 2) and at 4 months postintervention (Time 3). Results show that mothers in the intervention group reported a significant decrease in mean depressive symptom scores at Time 2 and that the effect was maintained at Time 3 with intermediate to medium effect sizes. There were no differences in results across sites. Findings suggest that Parents Taking Action, a culturally tailored intervention led by peer mentors, showed a significant effect both immediately after the intervention and 4 months postintervention in reducing depressive symptoms among Latina mothers of autistic children.
Topics: Humans; Hispanic or Latino; Female; Mothers; Depression; Adult; Child; Male; Autistic Disorder; Child, Preschool; Self Efficacy
PubMed: 38917994
DOI: 10.1352/1944-7558-129.4.294 -
Journal of Physical Activity & Health Jun 2024Identifying factors related to physical activity in university students can aid the development of health promotion interventions, but there is limited research...
BACKGROUND
Identifying factors related to physical activity in university students can aid the development of health promotion interventions, but there is limited research regarding the influence of university environments. This study examined the relationship between level of provision for university environments that aim to promote physical activity and self-reported physical activity patterns of students.
METHODS
An environmental audit tool was completed by universities (n = 28) on the island of Ireland to acquire information about physical activity opportunities, resources, and supports offered. Students (N = 6951; 50.7% male; 21.51 [5.55] y) completed an online survey, providing responses about their active transport and recreational physical activity behaviors. Binary logistic regressions were used to examine the associations between environmental factors that support physical activity and clustered physical activity patterns, while controlling for gender, age, and university size.
RESULTS
Universities with a high provision for organizational structures and internal partnerships, indoor facilities, and sport clubs increase the odds of their students having more active physical activity patterns. Increased provision of investment and personnel was seen to have a mixed relationship with students' physical activity engagement, highlighting the need to understand where resources are needed and not just increase them.
CONCLUSIONS
It is important for universities to have adequate organizational structures with internal partnerships to understand how resources can be maximized to support physical activity engagement across the whole student population. University campuses hold the potential for increasing student engagement in physical activity, and these findings can help inform campus-wide initiatives that foster active student populations for improving overall long-term health.
PubMed: 38917991
DOI: 10.1123/jpah.2023-0409 -
Ageing Research Reviews Jun 2024To evaluate the trends and cross-country inequalities of global osteoarthritis (OA) burden over the last 30 years, and further predicted its changes to 2035. (Review)
Review
OBJECTIVE
To evaluate the trends and cross-country inequalities of global osteoarthritis (OA) burden over the last 30 years, and further predicted its changes to 2035.
METHODS
The estimates and 95% uncertainty intervals (UIs) for incidence, prevalence, and disability-adjusted life-years (DALYs) of OA were extracted from Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. We described OA epidemiology at global, regional, and national levels, analyzed 1990-2019 trends in OA burden from overall, local, and multi-dimension scopes, decomposed OA burden according to population size, age structure, and epidemiologic changes, quantified cross-country inequalities in OA burden using standard health equity methods recommended by World Health Organization, and predicted changes of OA burden to 2035.
RESULTS
GBD 2019 estimated 527,811,871 (95% UIs: 478,667,549 to 584,793,491) prevalent cases, 41,467,542 (95% UIs: 36,875,471 to 46,438,409) incident cases and 18,948,965 (95% UsI:9,571,298 to 37,659,660) DALYs cases of OA worldwide in 2019, with the highest cases in East Asia and highest age-standardized rate (ASR) in high-income North America. The global burden of OA increased overall from 1990 to 2019 with the fastest growth observed in the first decade of the 21st century. Decomposition analysis revealed that OA knee (62.78%), women (60.47%), and middle sociodemographic index (SDI) quintile (32.35%) were responsible for the most significant DALYs, whose changes were primarily driven by population growth and aging. A significant increase in SDI-related inequalities was detected, and the gap in DALYs between the highest SDI country and the lowest SDI country increased from 179.5 (95% CI: 149.3 to 209.8) per 100,000 in 1990 to 341.9 (95% CI: 309.5 to 374.4) per 100,000 in 2019. Notably, although the ASR of incidence, prevalence, and DALYs of OA was predicted to decrease annually from 2020 to 2035, the case number of these metrics was predicted to keeping increasing, with predicted values of 52,870,737 (95% UI: 39,330,063 to 66,411,411), 727,532,373 (95% UI: 542,765,783 to 912,298,962), and 25,986,983 (95% UI: 19,216,928 to 32,757,038) in 2035, respectively.
CONCLUSIONS
As a major public health issue, the global burden of OA showed an overall increasing trend from 1990 to 2019, which was primarily driven by population growth and aging. Countries with high SDI shouldered disproportionately high OA burden, and the SDI-related inequalities across countries exacerbated over time. This study highlighted great challenges in the control and management of OA, including both growing case number and distributive inequalities worldwide, which may be instructive for better making public health policy and reasonably allocating medical source.
PubMed: 38917934
DOI: 10.1016/j.arr.2024.102382 -
The Science of the Total Environment Jun 2024Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in...
Multi-isotopes (δD, δO, Sr/Sr, δS and δO) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China.
Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δO, δD, Sr/Sr, δO and δS) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.
PubMed: 38917902
DOI: 10.1016/j.scitotenv.2024.174181 -
A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction.The Science of the Total Environment Jun 2024Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies...
Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies to mitigate adverse impacts. This study develops a novel hybrid deep learning model, named wind direction-based dynamic spatio-temporal graph network (WDDSTG-Net), for hourly ozone concentration prediction. The model uses a dynamic directed graph structure based on hourly changing wind direction data to capture evolving spatial relationships between air quality monitoring stations. It applied the graph attention mechanism to compute dynamic weights between connected stations, thereby aggregating neighborhood information adaptively. For temporal modeling, it utilized a sequence-to-sequence model with attention mechanism to extract long-range temporal dependencies. Additionally, it integrated meteorological predictions to guide the ozone forecasting. The model achieves a mean absolute error of 6.69 μg/m and 18.63 μg/m for 1-h prediction and 24-h prediction, outperforming several classic models. The model's IAQI accuracy predictions at all stations are above 75 %, with a maximum of 81.74 %. It also exhibits strong capabilities in predicting severe ozone pollution events, with a 24-h true positive rate of 0.77. Compared to traditional static graph models, WDDSTG-Net demonstrates the importance of incorporating short-term wind fluctuations and transport dynamics for data-driven air quality modeling. In principle, it may serve as an effective data-driven approach for the concentration prediction of other airborne pollutants.
PubMed: 38917895
DOI: 10.1016/j.scitotenv.2024.174229