National Data Research Articles

A multisectoral approach to medication safety in Kuwait using the take-back campaign.

The take-back campaign, which focuses on enhancing public awareness, safe medication disposal practices, stakeholder engagement and patient participation was used to promote medication safety in Kuwait. To evaluate the effectiveness of the take-back campaign in promoting medication safety in Kuwait. The campaign was implemented systematically through several stages of planning, communication and coordination among several stakeholders. This social media campaign encouraged individuals from the participating healthy cities to bring unwanted medications to designated collection centres. Returned medications were categorized based on the British National Formulary and data analysis was conducted using Excel. Consultations were held at the collection sites to increase patient confidence in medication compliance and safety. The medications were sorted and disposed safely. The take-back campaign lasted 56 hours over a period of one month. It successfully engaged 405 households and collected 1005 kg of medication, comprising 7648 items, over a period 6 working days. Fifty-seven percent of the medications collected through the campaign originated from the Ministry of Health and 43% from the private sector. Fifty-two percent had expired and 59% were in solid dosage form. Painkillers comprised the largest group (18%) among the returned medications. The take-back campaign effectively raised awareness about medication safety and provided a safe disposal mechanism for unused and expired medications. This campaign has provided a foundation for future initiatives and contributed significantly to improving medication safety and public health outcomes in Kuwait.

An updated analysis of retransplantation following living donor liver transplantation in the US: insights from the latest UNOS database.

There is no recent update on the clinical course of retransplantation(re-LT) after living-donor liver transplantation (LDLT) in the US using recent national data. The UNOS database (2002-2023) was used to explore patient characteristics in initial LT, comparing deceased-donor liver transplantation (DDLT) and LDLT for graft survival (GS), reasons for graft failure, and GS after re-LT. It assesses waitlist dropout and re-LT likelihood, categorizing re-LT cohort based on time to re-listing as acute or chronic (≤ or >1mo). Of 132,323 DDLT and 5,955 LDLT initial transplants, 3,848 DDLT and 302 LDLT recipients underwent re-LT. Of the 302 re-LT following LDLT, 156 were acute and 146 chronic. Primary non-function (PNF) was more common in DDLT, although the difference was not statistically significant (17.4%vs14.8% for LDLT; p=0.52). Vascular complications were significantly higher in LDLT (12.5%vs8.3% for DDLT; p<0.01). Acute re-LT showed a larger difference in PNF between DDLT and LDLT (49.7%vs32.0%; p<0.01). Status 1 patients were more common in DDLT (51.3%vs34.0% in LDLT; p<0.01). In the acute cohort, Kaplan-Meier curves indicated superior GS post-re-LT for initial LDLT recipients in both short-term and long-term (p=0.02 and <0.01, respectively), with no significant difference in the chronic cohort. No significant differences in waitlist dropout were observed, but the initial LDLT group had a higher re-LT likelihood in the acute cohort (sHR 1.40, p<0.01). A sensitivity analysis focusing on the most recent 10-year cohort revealed trends consistent with the overall study findings. LDLT recipients had better GS in re-LT than DDLT. Despite a higher severity of illness, the DDLT cohort was less likely to undergo re-LT.

Trajectories of State-Level Sepsis-Related Mortality by Race and Ethnicity Group in the United States.

Background: Recent reports on the national temporal trends of sepsis-related mortality in the United States (US) suggested improvement of outcomes in several race and ethnicity groups. However, it is unknown whether national data reflect state-level trajectories. Methods: We used the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research Multiple Cause of Death data set to identify all decedents with sepsis in the US during 2010-2019. Negative binomial regression models were fit to estimate national and state-level trends of age-adjusted sepsis-related mortality rates within race and ethnicity groups. Results: There were 1,852,610 sepsis-related deaths in the US during 2010-2019. Nationally, sepsis-related mortality rates decreased among Blacks and Asians, were unchanged among Hispanics and Native Americans, and rose among Whites. The percent of states with similar trends were 30.0% among Blacks, 32.1% among Asians, 74.3% among Hispanics, 75.0% among Native Americans, and 66.7%% among Whites, while trending in opposite direction from 3.6% among Asians to 15.0% among Blacks. Conclusions: National trends in sepsis-related mortality in the US did not represent state-level trajectories in race ethnicity groups. Gains in sepsis outcomes among race and ethnicity groups at the national level were not shared equitably at the state level.

Contemporary Predictors of Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention: A Nationally Representative US Sample.

Background: Patient outcomes after percutaneous coronary intervention (PCI) have improved over the last 30 years due to better techniques, therapies, and care processes. This study evaluated contemporary predictors of post-PCI major adverse cardiovascular events (MACE) and summarized risk in a parsimonious risk prediction model. Methods: The Cardiovascular Patient-Level Analytical Platform (CLiPPeR) is an observational dataset of baseline variables and longitudinal outcomes from the American College of Cardiology's CathPCI Registry® and national claims data. Cox regression was used to evaluate 2-6 years of patient follow-up (mean: 2.56 years), ending in December 2017, after index PCI between 2012 and 2015 (N = 1,450,787), to examine clinical and procedural predictors of MACE (first myocardial infarction, stroke, repeat PCI, coronary artery bypass grafting, and mortality). Cox analyses of post-PCI MACE were landmarked 28 days after index PCI. Results: Overall, 12.4% (n = 179,849) experienced MACE. All variables predicted MACE, with cardiogenic shock, cardiac arrest, four diseased coronary vessels, and chronic kidney disease having hazard ratios (HRs) ≥ 1.50. Other major predictors of MACE were in-hospital stroke, three-vessel disease, anemia, heart failure, and STEMI presentation. The index revascularization and discharge prescription of aspirin, P2Y12 inhibitor, and lipid-lowering medication had HR ≤ 0.67. The primary Cox model had c-statistic c = 0.761 for MACE versus c = 0.701 for the parsimonious model and c = 0.752 for the parsimonious model plus treatment variables. Conclusions: In a nationally representative US sample of post-PCI patients, predictors of longitudinal MACE risk were identified, and a parsimonious model efficiently encapsulated them. These findings may aid in assessing care processes to further improve care post-PCI outcomes.

Research on Photovoltaic Development in Northwestern China using Remote Sensing Images

Abstract. Photovoltaics, a clean energy source, have received widespread attention worldwide recently. Many countries are carrying out photovoltaic construction, while also compiling and analyzing their photovoltaic development status. The same goes for China. In northwestern China, a considerable number of cities lack electricity. Meanwhile, its vast plains and abundant sunlight are conducive to the construction of photovoltaics. Therefore, the northwestern China has vigorously carried out photovoltaic construction nowadays. With the support of high-resolution and multi-temporal remote sensing images, we are able to analyze the development status of photovoltaics in these regions. We chose six provinces in northwestern China as our research areas and took three steps to complete our studies. Firstly, we extracted patterns of photovoltaics using deep learning methods. Secondly, based on the patterns and national land use survey data, we calculated the distribution and development status of photovoltaics in each province. Thirdly, we present the statistical results in figures and charts, showing the photovoltaic construction status and its development trend. We finally made conclusions and discussions about our insufficiency in work and future plans for further study.

Effect of bathymetry data on tsunami wave ray tracing in the western Banten sea

The lack of tsunami wave warning during the Anak Krakatau collapse in 2018 resulted in devastating damage to coastal areas. The tsunami wave arrived in the coastal areas approximately half an hour after the collapse. As a tsunami wave can travel abruptly, an early warning system should warn faster. However, a warning system based on a sophisticated hydrodynamic model in real-time would take time to conduct the numerical tsunami wave simulation. Hence, this study proposes a fast and reliable estimation of tsunami wave propagation through a classic ray tracing analysis. We use two ray-tracing methods to investigate the tsunami wave propagation from Anak Krakatau to the western Banten Sea. The first method follows Snell’s law, considering dispersive waves, and the second assumes non-dispersive waves based on the ray tracing equations, considering the Earth’s sphericity. Both methods are quantitatively evaluated by comparing the travel time measured at Anyer and Marina Jambu. This study finds that non-dispersive wave tracing performs a shorter computational time and slightly better prediction of tsunami wave propagation than dispersive-based wave tracing, with a relative absolute difference of the travel time of 17.9–26.7% in Anyer and 3.6–5.2% in Marina Jambu. This study also confirms the importance of bathymetry validity in wave ray tracing. Two regional bathymetry datasets with a mean difference of less than 5 m result in different wave ray tracing, in which one dataset does not produce the wave ray path towards the Panaitan Island. Based on bathymetric surveys in Anyer and Marina Jambu, the national bathymetry data (BATNAS) has shown its superiority to being used as a bathymetry in the ray-tracing process, with correlations of 81% and 93% in both areas compared to other available bathymetry datasets. We summarize that reliable bathymetry data and the non-dispersive ray tracing method can be used as an initial estimation of tsunami wave propagation efficiently.

Botulism Cases in Romania-An Overview of 14-Year National Surveillance Data.

Botulism is a priority disease worldwide because it has a very severe course of evolution that can lead to death. This paper aims to describe the main epidemiological characteristics of botulism cases confirmed in Romania over 14 years (2007-2020). We performed a retrospective study using the publicly available national surveillance data and reported to the National Institute of Public Health. A total of 325 cases of foodborne botulism were reported in Romania, with no infant or wound botulism. Most of the cases (125, 38.5%) were reported among young adults (25-44 years old), over half (205, 63%) of them living in rural areas. The incriminated food item was identified in 161 cases; in most cases (145, 90%) the food item was prepared in the household. The main food category was represented by meat and meat-based products (94, 68.6%). In almost all cases the identified type was BoNT/B (230/231, 99.5%). Fifteen deaths were recorded, and the case fatality rate was 4.6%. Botulism cases were reported annually in Romania. Surveillance data are essential for implementing control measures and adapting educational campaigns according to existing needs.

Moderately reducing N input to mitigate heat stress in maize

In a warming climate, high temperature stress greatly threatens crop yields. Maize is critical to food security, but frequent extreme heat events coincide temporally and spatially with the period of kernel number determination (e.g., flowering stage), greatly limiting maize yields. In this context, how to increase or at least maintain maize yield has become more important. Nitrogen fertilizer (N) is widely used to improve maize yields, but its effect in heat stress is unclear. For this, we collected 1536 pairs of comparisons from 113 studies concerning N conducted in the past 20 years over China. We classified the data into two groups – without high temperature stress (NHT) and with high temperature stress during the critical period for maize kernel number determination (HT) – based on the national meteorological data. We comprehensively evaluated N effects on grain yield under HT and NHT using meta-analysis. The effect of N on maize yield became significantly smaller in HT than that in NHT. In NHT, soil characteristics, crop management practices, and climatic conditions all significantly affected N effects on maize yield, but in HT, only a few factors such as soil organic matter and mean annual precipitation significantly affected N effects. Hence, it is difficult to improve N effect by improving soil characteristics and crop management when meeting with high temperature stress during flowering. On average, N effect increased with increased N input, but there were respective N input thresholds in NHT and HT, beyond which N effects on maize yield remained stable. According to the thresholds, it is speculated that moderately reducing N input (~20 %) likely increased high temperature tolerance of maize during flowering. These findings have important implications for the optimization of N management under a warming climate.

Per- and polyfluoroalkyl substances (PFAS) in construction and demolition debris (CDD): discerning sources and fate during waste management

As regulatory frameworks for per- and polyfluoroalkyl substances (PFAS) evolve, the solid waste community seeks to manage PFAS risks effectively. Despite extensive research on PFAS in municipal solid waste (MSW) and wastewater sludge, there is limited information on a major global waste stream which seldom gleans regulatory oversight — construction and demolition debris (CDD). This study sampled a CDD processing facility to provide material-specific information on the PFAS profile within CDD. The bulk CDD accepted by this facility was separated into major categories, representatively sampled, then characterized for total available PFAS (∑92PFAS). As reprocessed CDD is ultimately recycled or landfilled, often unencapsulated or in unlined landfills, the PFAS leaching potential was also examined using two leaching procedures. Among the categories assessed for total PFAS, carpeting, carpet padding, and gypsum drywall showed elevated concentrations compared to other components, with most of the PFAS mass contributed by precursor species. However, materials with the highest total PFAS, such as carpeting, did not necessarily exhibit the highest leaching, and leachate was predominantly composed of terminal species rather than precursors. Extrapolating these findings with national CDD generation and management data inventories suggests that despite MSW having higher total available PFAS concentrations, the leachability of PFAS from landfilled CDD is comparable, raising legitimate concerns with CDD disposal practices, particularly in unlined CDD landfills.

Inter-agency coordination and digital platform regulation: lessons from the Whatsapp case in Brazil

ABSTRACT This article examines the drivers and obstacles behind the first large-scale cross-agency coordination in digital platform regulation in Brazil. Drawing on elite semi-structured interviews and document analysis, it investigates how the Brazilian National Data Protection Authority (ANPD) and other federal agencies worked together to scrutinize the legal implications of WhatsApp’s privacy policy and terms of service changes announced in January 2021, including concerns regarding transparency, legitimate interest, and consent. The study reveals that while formal institutional arrangements such as cooperation agreements existed, informal factors like personal relationships played a significant role in facilitating coordination. However, capacity and autonomy issues within the involved bodies hindered more substantive coordination and may explain divergences in the interpretation of data protection legislation. The findings emphasize the importance of enhancing mechanisms for inter-agency coordination in digital platform regulation, especially considering capacity and autonomy issues within and across regulatory ecosystems, offering insights to other jurisdictions, particularly for developing countries facing public governance constraints similar to those experienced in Brazil.

Generalized machine learning models to predict significant wave height utilizing wind and atmospheric parameters

Significant wave height (SWH) is a key parameter for wave energy extraction, ship navigation, oil and gas extraction, coastal structure construction, etc. Direct measurements of SWH using buoys are expensive and accurate over a limited area while numerical models are computationally expensive, inaccurate, with limited generalizability. Thus, this work focuses on developing generalizable machine learning models for predicting SWH from wind parameters (speed, direction, and gust) and atmospheric parameters (temperature and pressure). Two deep learning models (Artificial Neural Network (ANN) and Self Normalizing Neural Network (SNN) and two gradient boosting tree-based models (XGBoost and LightGBM) have been used in this study. Three different data sets were collected from the National Data Buoy Center: Data Set-1 (DS1): 12 years of data from 47 stations; Data Set-2 (DS2): 14 months of additional data from 6 stations randomly selected from DS1; Data Set-3 (DS3): 13 years data from completely 6 new stations. DS1 was split into training, testing, and validation datasets. Training and hyper-parameter tuning was done on the train and validation dataset, while the performance of the models was evaluated on the DS1 test dataset, DS2, and DS3, employing three error metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-square score (R2). The collected data underwent data cleaning, preprocessing, and exploratory data analysis before modeling. The deep learning models have demonstrated superior fitting capacity to the tree-based models, achieving the lowest MSE (0.047), MAE (0.153), and highest R2 score (0.953) on test data. However, the gradient boosting models demonstrate better generalizing capacity than the deep learning models on DS2 and DS3. This study helps further Sustainable Development Goal 7 (SDG 7) by allowing fast and cheap assessment of wave height for ocean energy site development.

Children and young people's body mass index measures derived from routine data sources: A national data linkage study in Wales.

Routine monitoring of Body Mass Index (BMI) in general practice, and via national surveillance programmes, is essential for the identification, prevention, and management of unhealthy childhood weight. We examined and compared the presence and representativeness of children and young people's (CYPs) BMI recorded in two routinely collected administrative datasets: general practice electronic health records (GP-BMI) and the Child Measurement Programme for Wales (CMP-BMI), which measures height and weight in 4-5-year-old school children. We also assessed the feasibility of combining GP-BMI and CMP-BMI data for longitudinal analyses. We accessed de-identified population-level GP-BMI data for calendar years 2011 to 2019 for 246,817 CYP, and CMP-BMI measures for 222,772 CYP, held within the Secure Anonymised Information Linkage Databank. We examined the proportion of CYP in Wales with at least one GP-BMI record, its distribution by child socio-demographic characteristics, and trends over time. We compared GP-BMI and CMP-BMI distributions. We quantified the proportion of children with a CMP-BMI measure and a follow-up GP-BMI recorded at an older age and explored the representativeness of these measures. We identified a GP-BMI record in 246,817 (41%) CYP, present in a higher proportion of females (54.2%), infants (20.7%) and adolescents. There was no difference in the deprivation profile of those with a GP-BMI measurement. 31,521 CYP with a CMP-BMI had at least one follow-up GP-BMI; those with a CMP-BMI considered underweight or very overweight were 87% and 70% more likely to have at least one follow-up GP-BMI record respectively compared to those with a healthy weight, as were males and CYP living in the most deprived areas of Wales. Records of childhood weight status extracted from general practice are not representative of the population and are biased with respect to weight status. Linkage of information from the national programme to GP records has the potential to enhance discussions around healthy weight at the point of care but does not provide a representative estimate of population level weight trajectories, essential to provide insights into factors determining a healthy weight gain across the early life course. A second CMP measurement is required in Wales.

Predicted impact of banning nonessential, energy-dense food and beverages in schools in Mexico: A microsimulation study.

Childhood obesity is a growing concern worldwide. School-based interventions have been proposed as effective means to improve nutritional knowledge and prevent obesity. In 2023, Mexico approved a reform to the General Education Law to strengthen the ban of sales and advertising of nonessential energy-dense food and beverages (NEDFBs) in schools and surroundings. We aimed to predict the expected one-year change in total caloric intake and obesity prevalence by introducing the ban of NEDFBs sales in schools, among school-aged children and adolescents (6 to 17 years old) in Mexico. We used age-specific equations to predict baseline fat-free mass (FFM) and fat mass (FM) and then estimated total energy intake (TEI) per day. The TEI after the intervention was estimated under 4 scenarios: (1) using national data to inform the intervention effect; (2) varying law compliance; (3) using meta-analytic data to inform the intervention effect size on calories; and (4) using national data to inform the intervention effect by sex and socioeconomic status (SES). We used Hall's microsimulation model to estimate the potential impact on body weight and obesity prevalence of children and adolescents 1 year after implementing the intervention in Mexican schools. We found that children could reduce their daily energy intake by 33 kcal/day/person (uncertainty interval, UI, [25, 42] kcal/day/person), reducing on average 0.8 kg/person (UI [0.6, 1.0] kg/person) and 1.5 percentage points (pp) in obesity (UI [1.1, 1.9] pp) 1 year after implementing the law. We showed that compliance will be key to the success of this intervention: considering a 50% compliance the intervention effect could reduce 0.4 kg/person (UI [0.3, 0.5] kg/person). Our sensitivity analysis showed that the ban could reduce body weight by 1.3 kg/person (UI [0.8, 1.8] kg/person) and up to 5.4 kg/person (UI [3.4, 7.5] kg/person) in the best-case scenario. Study limitations include assuming that obesity and the contribution of NEDFBs consumed at school remain constant over time, assuming full compliance, and not considering the potential effect of banning NEDFBs in stores near schools. Even in the most conservative scenario, banning sales of NEDFBs in schools is expected to significantly reduce obesity, but achieving high compliance will be key to its success. - School-based interventions have been recognized as effective means to improve nutritional knowledge and prevent obesity-related diseases.- In December 2023, the Chamber of Representatives of Mexico approved an amendment that strengthens and updates the General Education Law (Article 75) and nutritional guidelines to ban the sales and advertising of nonessential energy-dense food and beverages (NEDFBs) in schools. - We used age-specific equations to predict baseline fat-free mass (FFM) and fat mass (FM) and total energy intake (TEI) per day.- We used microsimulation modeling to predict body weight and obesity prevalence of children and adolescents 1 year after implementing the intervention in Mexican schools.- Our modeling study suggests that an important impact on obesity prevalence can be expected if the law is implemented and enforced as intended. - If successful, this law could serve as an example beyond Mexico on how to achieve changes in body weight through school food regulation.- An important limitation of our main scenario is that we assumed full compliance of schools with the law, yet lower compliance will reduce its impact. We also did not consider historical trends on obesity or NEDFBs consumed in schools during our 1 year simulation, and we considered only the ban impact inside schools, excluding effects near and outside schools.

Predicting unplanned hospitalisations in older adults using routinely recorded general practice data.

Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. Retrospective study using general practice electronic health records of 243,129 community-dwelling adults aged ≥65 years linked with national administrative data. The dataset was geographically split into a development (58.7%) and validation (41.3%) sample to predict unplanned hospitalisations within 6 months. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). We used logistic regression with backward selection for model development. The models were validated internally and externally. We assessed predictive performance by area under the curve (AUC) and calibration plots. In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration plots showed good calibration. Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

Social Representativeness and Intervention Adherence-A Systematic Review of Clinical Physical Activity Trials in Breast Cancer Patients.

Representativeness in physical activity randomised controlled trials (RCT) in breast cancer patients is essential to analyses of feasibility and validity considering privileged- social groups. A step-by-step exclusion of less privileged groups through the trial process could reinforce health inequality. This study aimed at examining representativeness in breast cancer (BC) physical activity trials, investigate associations between socio-economic status (SES) and intervention adherence, and explore associations between representativeness and the relationship between SES and intervention adherence. Systematic, computerised searches were performed in PubMed, CINAHL, AMED, EMBASE and PsycINFO. Additional citation-based searches retrieved 37 articles. Distributions of education level, ethnicity, and marital status in study samples were compared to national populations data to estimate representativeness in less privileged groups. A preponderance of studies favoured educated, married and white patients. Only six studies reported SES-adherence associations, hampering conclusions on this relationship and possible associations between representativeness and an SES-adherence relationship. Less educated, unmarried and non-white individuals may be underrepresented in BC physical activity RCTs, while SES-adherence associations in such trials are inconclusive. Unintentional social misrepresentations may indicate that disguised inequity warrants revived attention.

Assessing landscape-level effects of permanent grassland management and landscape configuration on open-land butterflies based on national monitoring data

AbstractHalting and reversing the ongoing insect decline requires in-depth knowledge on key drivers. Due to their sensitivity to habitat quality, butterflies are valuable indicators for grassland management intensity, including mowing. However, most studies examining mowing regime impacts on butterflies are limited to small spatial extents. Here, we tested the potential of citizen science butterfly monitoring data for assessing landscape-level effects of mowing regimes (number of mowing events and timing of the first event) and edge density (density of boundaries between different land-cover types) on butterfly richness, abundance, and community composition. We used generalised linear mixed-effects models to relate nationwide data from the German Butterfly Monitoring Scheme (DEBMS) to high-resolution satellite imagery on mowing events in permanent grasslands (grasslands excluded from crop rotation). As butterfly transects may not consistently be located within grasslands, we ran our models for different thresholds from 0 to 50%, representing increasing shares of the transect route situated within permanent grasslands (10% intervals). We did not find significant associations between mowing regimes and butterflies when focussing on species richness and abundance of all species inhabiting open land. However, we found strong positive associations of delayed mowing with the abundance of grassland specialists with increasing grassland shares per transect. Further, we found negative associations of delayed mowing with the annual number of generations and of more frequent mowing with the abundance of specialists, depending on the share of grassland per transect. Edge density had a positive association with species richness and abundance of species inhabiting open land, as well as abundance of grassland indicator species and grassland specialists in landscapes with a low grassland share per transect. Our findings underscore the importance of low-intensity managed permanent grasslands at the landscape scale for specialised butterflies. Additionally, we highlight the importance of a high density of boundaries for open-land and specialised butterflies, particularly in landscapes with highly fragmented permanent grasslands. To improve future analyses of grassland management impacts, we recommend expanding DEBMS monitoring sites to cover a larger grassland management intensity gradient and to place more transects within grasslands.

Resilience in the time of crisis: A review of the maternal, perinatal and reproductive health effects of COVID-19 in South Africa

Background. The COVID‐19 pandemic had a profound effect on the health sector globally and in South Africa (SA). Objective. To review the effects of COVID‐19 on maternal, perinatal and reproductive health outcomes and service utilisation in SA. Methods. Three routine national data collection systems were sourced: the District Health Information System, the Saving Mothers reports of the National Committee on Confidential Enquiry into Maternal Deaths and the Saving Babies reports from the National Perinatal Morbidity and Mortality Committee using data from the Perinatal Problem Identification Program. Results. There were 35% and 8% increases in maternal and stillbirth mortality rates, respectively, in 2020 and 2021, which correlated with the COVID‐19 waves. However, in 2022, rates returned to pre‐COVID levels. Antenatal visits and facility births showed little change, but there was a shift to more rural provinces. The use of oral and injectable contraceptives and termination of pregnancy services decreased markedly in 2020 and 2021, with a sustained shift to long‐acting reversible contraceptives. The increase in maternal deaths was predominantly due to COVID‐19 respiratory complications, but also an increase in obstetric haemorrhage. Stillbirths increased significantly (10%) for birthweights between 2 000 g and 2 499 g, categorised mostly as unexplained stillbirths or preterm labour, but no increase in neonatal deaths was observed. Administrative avoidable factors increased by 24% in the 2020 ‐ 2022 triennium, but there was no increase in patient/community level or healthcare provider‐related avoidable factors during the pandemic years. Conclusion. COVID‐19 caused a marked increase in maternal death and stillbirth rates in 2020 and 2021 due to both direct effects of the virus and indirect effects on functioning of the health system. The continued, although modified, health‐seeking behaviour of women and the rapid return to pre‐COVID‐19 mortality rates demonstrates enormous resilience in women and the health system.

Survival after live donor versus deceased donor liver transplantation: propensity score-matched study.

For individuals with advanced liver disease, equipoise in outcomes between live donor liver transplant (LDLT) and deceased donor liver transplant (DDLT) is uncertain. A retrospective cohort study was performed using data extracted from the Scientific Registry of Transplant Recipients. Adults who underwent first-time DDLT or LTDL in the United States between 2002 and 2020 were paired using propensity-score matching with 1:10 ratio without replacement. Patient and graft survival were compared using the model for end-stage liver disease (MELD) score for stratification. After propensity-score matching, 31 522 DDLT and 3854 LDLT recipients were included. For recipients with MELD scores ≤15, LDLT was associated with superior patient survival (HR = 0.92; 95% c.i. 0.76 to 0.96; P = 0.013). No significant differences in patient survival were observed for MELD scores between 16 and 30. Conversely, for patients with MELD scores >30, LDLT was associated with higher mortality (HR 2.57; 95% c.i. 1.35 to 4.62; P = 0.003). Graft survival was comparable between the two groups for MELD ≤15 and for MELD between 21 and 30. However, for MELD between 16 and 20 (HR = 1.15; 95% c.i. 1.00 to 1.33; P = 0.04) and MELD > 30 (HR = 2.85; 95% c.i. 1.65 to 4.91; P = 0.001), graft survival was considerably shorter after LDLT. Regardless of MELD scores, re-transplantation rate within the first year was significantly higher after LDLT. In this large propensity score-matched study using national data, comparable patient survival was found between LDLT and DDLT in recipients with MELD scores between 16 and 30. Conversely, for patients with MELD > 30, LDLT was associated with worse outcomes. These findings underscore the importance of transplant selection for patients with high MELD scores.

The Spatial–Racial Patterns of U.S. Dam Removals Since 2010

Dam removal in the United States has grown exponentially, yet we do not know whether the pattern of such removals comports with principles of environmental justice. This exploratory study investigates the spatial pattern of dam removals across the United States to ascertain whether there were any geographic areas where the probability of removal was correlated with the racial or ethnic composition of the environs. We analyze dam removals since 2010 using national data on existing dams, removed dams, and demographics. We estimate multivariate probability models of dam removal stratified by census region and dam ownership to pinpoint contexts where significant spatial-racial patterns occur that cannot be attributed to dam characteristics. Our exploration reveals only a few such contexts. After controlling for dam purpose, construction type, age, and height, the probability of a dam being removed since 2010 is positively associated with the proportion of nearby White residents for dams owned by local or state governments in the South. The probability of removal is negatively associated with the proportion of nearby White residents for dams owned privately or by state or local governments in the West. Future case studies should probe these contexts of clear spatial–racial patterns from an environmental justice perspective.

Welfare at the Statehouse Democracies: Assessing the Impact of Public Opinion on Welfare Policies at the State Level

A frequent question in discussions about democracy is whether input from the public is ever considered and to what extent by politicians. This influence of public opinion on the realm of welfare policies has not been extensively explored, and most analyses are less precise for being conducted before the passage of the national welfare reform in 1996, better known as the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA). Bringing the analysis to a period after the reform to account for contextual changes since its passage, this study uses the multilevel and poststratification (MRP) model considered superior in analysis of subnational opinion using national survey data to assess the influence of public opinion on welfare policies at the state level. Collecting data from the 2014 CCES and a new developed welfare generosity index, I find that public opinion does not have any influence on how generous welfare programs turn out in their states, unless it is interacted with state government ideology. It seems that the ideology of the state government and the state poverty rate are the major determinants on welfare policies outcomes in the states, although the latter had different effects for TANF and SNAP.