Research Articles (Statistics & Population Studies)

Permanent URI for this collectionhttps://hdl.handle.net/10566/16127

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Evaluating forecast accuracy: a comparative error-based analysis of ann and markov-switching models in inflation prediction
(Natural Sciences Publishing, 2026) Makatjane, Katleho; Moroke, Ntebogang; Shoko, Claris
In this study, we develop a hybrid modelling approach that integrates artificial neural networks with a Markov-switching autoregressive model to enhance the accuracy of inflation predictions, utilising monthly data from South Africa from 2009 to 2023. Ten statistical loss functions, including mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error (MASE), and Theil’s U, are used to measure how well the forecast works. The MS(2)-AR(1)-ANN(5,3) performed the best overall out of all the competing specifications. The model has a mean squared error of 0.9897, a mean absolute error of 0.7573, a root mean squared error of 0.9948, and a mean absolute scaled error of 0.115. The model further yields Theil’s U statistic of 3.568, where, out of the 100 loss functions used, it ranks first in eight, second in the mean prediction error, and third in the symmetric mean absolute percentage error, with an SMAPE score of 18.234. This score is slightly higher than some of the base learners, but scale-free measures like MASE are better at providing trustworthy advice when conditions change quickly. The findings show that percentage-based measures like MAPE have their limits and that MASE is a better predictor during times of structural change. In general, the results show that the hybrid MS-AR-ANN architecture produces inflation projections far more accurate than those from the separate base models. The suggested methodology offers valuable insights for policymakers and central banks seeking early-warning indicators and robust inflation-monitoring systems amid regime upheavals and economic turmoil.
COVID-19 vaccine uptake, barriers and facilitators among key populations living with HIV/AIDS in Rivers State, Nigeria: a cross-sectional quantitative study
(Oxford University Press, 2026) Korfii, Uebari; Owhonda, Golden; Kanee, Rogers Bariture; Wali, Ihuoma Aaron; Showers, Viome Amakuro; Aigbogun Jr, Eric; Neenwi, Mueka Edna; Akpona, Dennis; Ajaero, Ngozi; Ade-Dosumu, Adedamola; Sanusi, Fauwzia; Bashorun, Damilola; Uchechukwu, Etukokwu Ijeoma
The study evaluated COVID-19 vaccine uptake and the barriers and facilitators influencing COVID-19 vaccine uptake among key populations living with HIV/AIDS in Rivers State, Nigeria. A key population-based cross-sectional study employed purposive sampling to recruit 458 participants from one-stop shops between April–June 2024. Data collection tools were integrated into a Kobo database. SPSS version 27 (IBM, Armonk, USA) for descriptive and inferential statistics (Chi-square and bivariate logistic regression) analysed vaccine uptake and associated factors, with significance determined at p<0.05. The COVID-19 vaccine uptake was 54.1%, with 22.3% partially vaccinated and 31.8% fully vaccinated. Pfizer-BioNTech (43.5%) and Moderna (22.2%) were the most administered vaccines. Key barriers included lack of information (91.7%), vaccine side effects concerns (88.0%), and distrust in vaccine safety (95.2%). Younger participants, those with shorter antiretroviral therapy (ART) durations, single individuals, and unemployed participants showed significantly lower vaccine uptake (χ²=48.266, 37.689, 29.131, and 62.136; p<0.001). Moderate vaccine uptake highlights gaps in COVID-19 vaccination among key populations. To improve vaccine uptake, tailored interventions addressing stigma, misinformation, and access barriers are recommended. Leveraging community leaders and integrating vaccines into HIV/AIDS care programs can enhance acceptance and delivery.
A combined kalman filter–LSTM to forecast downside risk of BWP/USD returns: a bottom-up hierarchical approach
(MDPI, 2026) Makatjane, Katleho; Xaba, Diteboho
This paper offers a hybrid forecasting approach that merges a local-level state space Kalman filter with a Long-Short-Term Memory (LSTM) neural network to assess the downside risk of the Botswana Pula versus the US Dollar (BWP/USD). Inspired by the inability of conventional econometric models to capture complex latent structural shifts and nonlinear patterns, our architecure uses a bottom-up hierarchical methodology in which the smoothed level component of the exchange rate is isolated by the Kalman filter and subsequently fed into the LSTM architecture. Three key indicators for assessing downside risk—Maximum Drawdown (MDD), Conditional Drawdown-at-Risk (CDaR), and Downside Deviation—are used to assess model performance across various time-frames (7, 30, 90, 180, and 240 days). As confirmed by Kupiec and Christoffersen’s backtesting processes, the findings show a high degree of alignment between projected and actual values, with negligible downside deviation bias and robust calibration. Moreover, global economic and geopolitical shocks, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the 2015–2016 Shanghai Stock Exchange crash, are important factors that influence exchange rate volatility, according to explainable artificial intelligence techniques, particularly SHAP (SHapley Additive exPlanations) analysis. Downside risk is also greatly increased by regional currency links, especially the impact of the ZAR/BWP exchange rate. On the other hand, domestic temporal variables, such as week, quarter, and month, have very little impact. These results emphasise how Botswana’s currency rate is structurally vulnerable to external shocks and how crucial it is to include both global and regional considerations in risk analysis. The research concludes that the accuracy and transparency of projections for exchange rate risk significantly improve when practical filtering is combined with deep learning and explainable AI. To improve macroeconomic resilience and guide successful financial risk management plans in emerging market environments, policymakers are advised to employ AI-driven forecasting techniques, enhance regional monetary coordination, and set up real-set learning systems.
Statistical issues often overlooked when analyzing astronomical data
(American Astronomical Society, 2026) Koen Chris
The main topics covered in this paper are (1) controlling significance levels when applying the same hypothesis test to many (possibly millions) of datasets; (2) dealing with the fact that for very large datasets hypotheses are rejected for trivially small departures from the null; (3) in the presence of noise, extreme values selected from samples for follow-up studies are often biased; (4) inference conducted on models fitted to data routinely underestimate the parameter standard errors if the selected model was informed by the observations; (5) obtaining overall least-mean-squared error estimates of a group of observations (e.g. a collection of star cluster masses); and (6) the effects of the violation of mathematical regularity conditions on model selection statistics.
Real-time electricity load forecasting in South Africa using SOM-enriched deep learning ensembles
(AIMS, 2026) Makatjane Katleho; Sigauke Caston; Shoko Claris
Accurate short-term electrical demand forecasting is critical for maintaining operational efficiency and energy security, especially in power-constrained systems like South Africa’s Eskom. Statistical methods like autoregressive integrated moving average (ARIMA) and exponential smoothing often fail to represent nonlinear and regime-dependent trends in power demand. This study presents a dynamic ensemble that combines deep neural networks (DNN) and long short-term memory (LSTM) architectures, which are both augmented by self-organising maps (SOM)-based clustering. The proposed method divides historical hourly load data from the Drakensberg generation plant into discrete temporal regimes using SOM, then trains the DNN and LSTM architectures within each regime, and dynamically combines their predictions. Shapley additive explanations (SHAP) are used to improve the interpretability of the impact of each cluster and time hierarchies, while mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) measures are used to assess prediction performance. The ensemble architecture delivers a higher accuracy, lowering MAPE to 2.20% while consistently outperforming individual benchmark architectures. The deployment on Amazon Web Services (AWS) proves the model’s scalability and appropriateness for real-time applications. Although performance degrades in irregular demand clusters, adaptive re-clustering may alleviate this constraint. Overall, the combined DNN-LSTM-SOM strategy is a reliable, interpretable, and scalable solution for short-term load forecasting, enabling better operational planning and grid dependability in developing energy systems.
Hierarchical forecasting of COVID-19 cases in Africa using machine learning models
(Frontiers in Epidemiology, 2026) Shoko, Claris; Makatjane, Katleho; Sigauke, Caston
Introduction: The COVID-19 pandemic posed significant challenges for public health systems, especially in Africa, where data scarcity, inadequate healthcare infrastructure, and regional disparities hindered effective forecasting and response efforts. Conventional forecasting methods have faced challenges in adequately addressing the complexity and detail necessary for effective policy interventions at various administrative levels. This study examines the challenge of producing accurate and coherent forecasts of COVID-19 cases within the hierarchical structure of Africa, which includes the continental, regional, and national levels. Methods: To establish a comprehensive forecasting model that uses hierarchical time series forecasting through a bottom-up reconciliation approach augmented by machine learning algorithms. We employ extreme gradient boosting (XGBoost) and random forest models, subsequently improving predictive accuracy via a weighted average ensemble method. We produce forecasts at the national level and then aggregate them to ensure consistency across all hierarchical levels. The models are evaluated in comparison to conventional methods such as ARIMA and exponential smoothing. Results: Empirical findings indicate that XGBoost is the best among all the single forecast models used in this study, combining forecasts from the XGBoost with the random forest and assigning more weights to the XGBoost surpasses all other models in the area of mean absolute error, root mean square error, and mean absolute scale error. Results further revealed that Southern Africa, despite its low population density, reported the highest number of cases, indicating underlying health vulnerabilities and socioeconomic factors. In summary, the bottom-up HTSF method, when combined with machine learning, serves as an effective tool for forecasting in environments with limited data availability. Discussion: It is advisable to apply similar models to other infectious diseases and to expand their use to guide health interventions, resource allocation, and early warning systems in future pandemics.
Deep learning-based pairs trading: real-time forecasting of co-integrated cryptocurrency pairs
(Frontiers Media SA, 2026) Makatjane, Katleho; Tsoku, Johannes Tshepiso
Statistical arbitrage strategies, including pairs trading, rely on identifying co-movements and static long-term equilibrium relationships between assets, where conventional methods fail to capture non-stationary dynamics, hence reducing trading effectiveness. This study, therefore, addresses this challenge by employing a dynamic co-integration approach combined with deep learning techniques to select suitable cryptocurrency pairs and forecast spread dynamics. The study examines multiple cryptocurrencies, namely: BNB, Ethereum, Litecoin, Ripple, and USDT, using dynamic Johansen co-integration tests to identify pairs with time-varying equilibrium relationships, and model the spread through a Dynamic Weighted Ensemble of Deep Neural Network and Long Short-Term Memory. Forecasting accuracy, trading performance, and predictive uncertainty are evaluated using error metrics, trading outcomes, and 99% prediction intervals. The results indicate that only those cryptocurrencies with dynamically coherent relationships are suitable for mean-reversion strategies. Furthermore, the study found that the Dynamic Weighted Ensemble achieves the best predictive accuracy. At the same time, LSTM captures proportional temporal dynamics effectively, and the ensemble-driven trading signals generate timely buy and sell decisions with low-lag execution and robust management of market volatility. These findings, therefore, highlight the advantages of combining dynamic co-integration and adaptive deep learning for statistical arbitrage.
Impact of short-term external shocks on the world and European economies
(West Ukrainian National University, 2026) Barvinok, Alina; Mantsurov, Igor; Chernyshev, Igor
This study models the response of the global and European economies to short-term external shocks, including changes in trade and import tariffs, as well as geo-economic and geopolitical factors. The analysis examines the stochastic relationship between external events and variations in key macroeconomic indicators, including global and European GDP, global and European trade volumes, and the Composite Purchasing Managers’ Index (PMI) across major sectors of the world economy. A regression framework incorporating a formalized binary variable is employed to capture the effects of changes in tariff and sanctions policies on economic performance. The empirical results indicate statistically significant effects of external shocks on individual macroeconomic indicators of the global and European economies, as well as on an aggregated index constructed from these indicators. The findings reveal differences in the responses of the examined economies to external shocks, enabling a comparative assessment of their stability and resilience. The results suggest that the European economy demonstrates a higher degree of resistance to external shocks compared to the global economy as a whole and to the Chinese economy.
Charting the future of censuses: Insights, lessons and recommendations for the 2030 round
(SAGE Publications Ltd, 2025) Dindi, Pierre D; Stiegler, Nancy
Population censuses globally remain the primary source of ofﬁcial statistics despite the existence of sample surveys and administrative data sources, like population registers. The 2020 round of censuses was predominantly characterised by traditional approaches in about 69% of the countries, where data was obtained directly from respondents regardless of the push to explore alternative sources compelled by COVID-19. From the Babylonian times in 3800 BC to date, the principal purpose of a census has been to foster public administration. While the 1666 census in New France (now Quebec) marked the ﬁrst-ever scientiﬁcally sound enumeration, it still fell short of what presently typiﬁes a census. Besides, lack of globally standardised methods dwarfed the acceptability and comparability of results, leading to harmonisation efforts and the gradual adoption of modern censuses from the mid-1800s. Subsequently, the United Nations developed the maiden international standards on population censuses soon after World War II and established the decennial World Population and Housing Census Programme. Overtime, the census has evolved to what globally embodies universality, individual enumeration, simultaneity, periodicity and capacity to produce small area statistics. As countries transition towards the 2030 round, this paper reviews the global developments, lessons, and provides recommendations for future census implementation
Evaluating the impact of sanitation practices and clean drinking water on diarrheal prevalence among under-five children in South Africa: insight from the 2019 GHS
(Taylor and Francis Ltd., 2025) Duba, Vuyolethu
Diarrhoea is one of the primary causes of mortality in under-five-children in developing regions. Poor sanitation practices and the consumption of water from unsafe sources contribute to the prevalence of this preventable disease among children. This study aimed to investigate the factors associated with poor sanitation and drinking water facilities and their contribution to diarrheal-related infections among children in South Africa, utilizing data from the 2019 General Household Survey. This quantitative study employed chi-square and logistic regression analyses to examine the relationships among sanitation variables, water-related variables, and health outcomes, while accounting for demographic and socioeconomic factors. The study found that 3.3% of children under-five experienced diarrhoea within three months preceding the survey. Key factors associated with diarrheal prevalence included age, racial group, and poor handwashing practices. Logistic regression analysis revealed that handwashing practices were the strongest determinant. Handwashing after using the toilet was significantly associated with a reduced prevalence of diarrhoea. The study underscores the urgent need for health education initiatives that improve hand-washing practices to reduce diarrheal-related infections among under-five children in South Africa. Addressing poor sanitation practices and consumption of unimproved drinking water through targeted interventions could significantly lower diarrheal-related deaths and improve public health outcomes.
A review and comparison of methods of testing for heteroskedasticity in the linear regression model
(Taylor and Francis Ltd., 2025) Blignaut, Renette; Luus, Retha; Steel, Sarel
This study reviews inferential methods for diagnosing heteroskedasticity in the linear regression model, classifying the methods into four types: deflator tests, auxiliary design tests, omnibus tests, and portmanteau tests. A Monte Carlo simulation experiment is used to compare the performance of deflator tests and the performance of auxiliary design and omnibus tests, using the metric of average excess power over size. Certain lesser-known tests (that are not included with some standard statistical software) are found to outperform better-known tests. For instance, the best-performing deflator test was the Evans-King test, and the best-performing auxiliary design and omnibus tests were Verbyla's test and the Cook-Weisberg test, and not standard methods such as White's test and the Breusch-Pagan-Koenker test.
Quality of care offered by health care retail markets for medication abortion self-management: Findings from states in Nigeria and India
(Public Library of Science, 2025) Omoluabi, Elizabeth; Shankar, Mridula; OlaOlorun, Funmilola Morinoye
Dispensing of misoprostol and mifepristone by pharmacies and chemist shops for self-management of medication abortion (MA) fills a crucial gap in settings where abortion care by trained health professionals is not readily available. This promising service delivery pathway, endorsed by the World Health Organization (WHO), is hindered by concerns of poor-quality care. Simulated clients collected data on MA pill dispensing practices from 92 pharmacies and chemist shops in three Nigerian states and 127 pharmacies in an Indian state that we have anonymized. Guided by the WHO’s abortion guideline, we measured process-related quality indicators such as medication use instructions, warning signs, and respectful treatment among other aspects. We aggregated indicators under three domains: technical competence, information given to clients, and client experience. Overall, 51% of facilities in the Nigerian states and 32% in the Indian state offered MA pills. Most dispensing facilities offered the misoprostol-only regimen in Nigeria (68%) and the combination regimen in the Indian state (83%). Among facilities offering MA pills, 26% in Nigeria and 78% in the Indian state provided correct instructions on route of pill administration. Accurate information on the appropriate interval between pill type in the combination regimen was low in Nigeria (27%) and the Indian state (14%). Excessive bleeding as a warning sign was discussed more frequently in the Indian (56%) versus Nigerian states (32%); other abnormal bleeding patterns were rarely mentioned. Aggregate technical competency scores were low at 18% in Nigeria and 34% in the Indian state, with highest scores for client experience at 90% and 91% respectively. Findings suggest that people using MA pills purchased from the retail market are not given accurate and adequate information for most effective self-use. If MA self-management remains outside regulatory boundaries, technical quality will remain sub-standard, imposing unnecessary costs to people, their health, and health systems.
Surviving extremely low birth weight infants have a higher risk of ROP in Sub-Saharan Africa
(John Wiley and Sons Inc, 2025) Jordaan, Esmè; van der Lecq, Tshilidzi; Holmström, Gerd; Kali, Gugulabatembunamahlubi; Muloiwa, Rudzani; Rhoda, Natasha
Aim: Retinopathy of prematurity (ROP) risk factors have been investigated in population-based studies from most global regions. No such studies are available from Sub-Saharan Africa (SSA), where improved neonatal care is increasing the survival of preterm infants at risk of ROP. Methods: A population-based study was conducted in infants born in Cape Town, South Africa, from 1 May 2022 to 31 January 2023. The screening criteria were birth weight < 1250 g or gestational age < 32 weeks. The data were extracted from the Retinopathy of Prematurity South African register. Results: The study included 378 screened infants, 115 (30.4%) of whom developed ROP. In the multiple regression analyses, lower birth weight was an independent ROP risk factor, OR 1.3 95% CI 1.2–1.5, p < 0.001. Surgical necrotising enterocolitis (NEC) was the only other independent ROP risk factor, OR 5.8 95% CI 1.6–21.0, p = 0.007. Infants with birth weight < 1000 g were 39.4% (130/378) of those screened and more likely to develop ROP compared to larger infants, OR 2.4 95% CI 1.5–3.9, p < 0.001. Conclusion: Birth weight remained a significant ROP risk factor, especially for those born weighing less than 1000 g. These infants represented a larger proportion of screened infants compared to previous Sub-Saharan African studies.
Screening for retinopathy of prematurity in South Africa: are those developing severe ROP screened on time? Data from a prospective register
(BMJ Open Ophthalmology, 2025) Jordaan Esmè; van der Lecq Tshilidzi; Rhoda Natasha; Freeman Nicola
Background/Aims To determine whether retinopathy of prematurity (ROP) screening is initiated on time according to current South African (SA) guidelines, that is, before the onset of stage 3 and type 1 ROP. Methods A prospective study of preterm infants screened at five neonatal units between 1 May 2022 and 31 January 2023 in Cape Town, SA. Data on all infants screened with a birth weight <1250 g or gestational age (GA) <32 weeks were extracted from the ROP South African (ROPSA) register, including postnatal age (PNA) and postmenstrual age (PMA) at first screening. Results A total of 696 infants were included, 58.9% (n=410) of whom had an early ultrasound (EUS) for GA estimation. Overall, 220 (31.6%) infants developed ROP, 20 (2.9%) had stage 3 or type 1 and 7 (1.0%) required treatment. Screening was initiated on time according to SA criteria in 549 (78.9%) infants, none of whom had stage 3 or type 1 ROP at first screening. Stage 3 and type 1 ROP were first detected at PNA and PMA of 6.3 and 33.1 and 8.9 and 35.9 weeks, respectively. Most infants (319, 45.8%) were screened according to PNA only, and 78.9% of the 185 infants screened only once did not attend subsequent examinations. Conclusion Screening started on time in most infants and prior to the development of severe ROP. Due to the limited availability of EUS in our region and to promote complete screening, we recommend that screening be initiated using PNA alone at 4–6 weeks or prior to discharge, whichever is earliest. The low proportion of infants with stage 3 and type 1 ROP is a limitation in our study. Therefore, recommendations may not be generalisable to South African regions where neonatal care results in a higher proportion of infants developing type 1 ROP.
Population dynamics of religious affiliations in Nigeria
(University of the Western Cape, 2025) Garenne, Michel
The study presents an overview on population dynamics of the two main religious affiliations in Nigeria: Christian and Muslim religions. The population of the two groups was estimated at 30-year intervals (1930, 1960, 1990, 2020). Data used for the reconstruction came from three population censuses and from ten demographic sample surveys. Population growth rates of Christians and Muslims were compared with estimates of net fertility derived from the same demographic surveys over the 1960 to 2020 period. Results were overall consistent and showed major trends over time: the rise of Christian religions and the fluctuations of Muslim religions as main affiliation, and as a consequence the apparent decline of African traditional religions. Between 1980 and 2010, both monotheist religions shared approximately half of the population. However, since 1995 the growth rate of the Muslim population became higher than that of the Christian population. In particular, population growth in the Northern part of the country, mainly Muslim, was outstanding. Recent trends could have serious implications in the future, and in particular could lead to demographic imbalance between the two groups, could raise serious environmental issues, especially in the North, and could have numerous political and social consequences.
Statistical modelling in enrolment management: a higher education case study
(Elsevier, 2025) Brydon, Humphrey; Steel, Sarel; Mahlangu, Dineo
Enrolment management is important to institutions of higher learning. Administrators at these institutions are annually faced by the question: how many offers for a given academic programme should be made to applicants to meet the registration target set by the authorities? Data on past and new applicants are available at most institutions. In this paper, data from the Faculty of Natural Sciences at the University of the Western Cape are used to develop a statistical model that provides estimates of the likelihood of new applicants accepting registration offers from the Faculty. The paper therefore contributes to the important field of strategic enrolment management. The paper shows how a statistical model estimated from historical data can assist administrators to determine the number of offers that should be extended to applicants to reach a given registration target.
Exploring socio-demographic factors associated with poor school attendance among secondary school learners in South Africa
(Elsevier, 2025) Showers, Viome Amakuro; Abrams, Robynne Danielle; Nsengiyumva, Philomene
The Department of Basic Education in South Africa acknowledges that 99% of primary school-aged children attend school, but attendance at secondary school level is not yet universal. Low levels of secondary school attendance contribute to poverty and unemployment. We investigated the socio-demographic elements associated with school attendance among secondary school-aged learners in South Africa. We adopted a quantitative research approach and a cross-sectional design. The positivist research paradigm was applied, and the 2019 General Household Survey data (nationally representative survey) were used. The family socialisation theory and household production framework were embraced as the theoretical framework in this study. Descriptive analysis and cross-tabulations were conducted, and a Chi-square test was performed to measure the association between school attendance and learners’ characteristics. Furthermore, logistic regression was conducted to explore the factors associated with school attendance. Study findings agree with the assertions of the family socialisation theory and household production framework. We found that the overall school attendance level was 93.5% and older learners had significantly lower levels of school attendance. High levels of educational qualification of household heads inspired higher school attendance as the odds of school attendance for learners quadrupled when household heads attained secondary education relative to those with unschooled heads (OR = 4.1; p < 0.001). Being a Coloured learner, being part of a large household, being an orphan, and living in a low-income household were associated with reduced levels of school attendance. We recommend targeting and supporting learners who are over-aged for their grades, Coloured, and with poor or educationally low family backgrounds via conditional cash transfers to improve school attendance in the South African population.
Psychological support system for assize court jurors
(Elsevier B.V.,, 2025) Bouchard, Jean-Pierre; Masson, Josué; Mintoff, Karine
Assize court jurors, as lay citizens in the judicial system, are exposed to intense stress due to their participation in criminal trials. Given this specific context, the Emergency Medical-Psychological Unit of the Marne (CUMP 51) has implemented an innovative support system aimed at preventing and addressing potential psychopathological reactions. This article describes the foundations, structure, and clinical evaluation of this three-component system: a preventive and psychoeducational intervention, immediate psychological support in the form of debriefing, and post-immediate follow-up for jurors presenting with persistent symptoms. Clinical observations confirm the psychological impact of the juror's role and highlight the relevance of such a support system, while also identifying areas for improvement. In this interview with Jean-Pierre Bouchard, Josué Masson, Karine Mintoff, Émilie Philippe, and Mélanie Hermand discuss the development and implementation of the support psychological system for assize court jurors, its perception by jurors, and its future perspectives. They present the system, its benefits, limitations, and the challenges they have encountered throughout their experience.
Increased number of symptoms during the acutephase of SARS-cov- infection in athletes is associated with prolonged time to return to full sports performance—AWAREVIII
(ElsevierB.V., 2025) Snyders Carolette; Jordaan Esme; Dyer Marlise; Sewry Nicola
The aim of the study was to identify factors associated with prolonged time to return to full performance (RTFP) in athletes with recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods Prospective cohort study with cross sectional analysis. A total of 84 athletes with confirmed SARS-CoV-2 infection assessed at a coronavirus disease 2019 recovery clinic gave a history of age, sex, type/level of sport, co-morbidities, pre-infection training hours, and 26 acute SARS-CoV-2 symptoms from 3 categories (“nose and throat”, “chest and neck”, and “whole body”/systemic). Data on days to RTFP were obtained by structured interviews. Factors associated with RTFP were demographics, sport participation, history of co-morbidities, pre-infection training history, and acute symptoms (type, number). Outcomes were: (a) days to RTFP (median, interquartile range (IQR)) in asymptomatic (n = 7) and symptomatic athletes (n = 77), and (b) hazard ratios (HRs; 95% confidence interval) for symptomatic athletes with vs. without a factor (univariate, multiple models). HR < 1 was predictive of higher percentage chance of prolonged RTFP. Significance was p < 0.05. Results Days to RTFP were 30 days (IQR: 23–40) for asymptomatic and 64 days (IQR: 42–91) for symptomatic participants (p > 0.05). Factors associated with prolonged RTFP (univariate models) were: females (HR = 0.57; p = 0.014), endurance athletes (HR = 0.41; p < 0.0001), co-morbidity number (HR = 0.75; p = 0.001), and respiratory disease history (HR = 0.54; p = 0.026). In symptomatic athletes, prolonged RTFP (multiple models) was significantly associated with increased “chest and neck” (HR = 0.85; p = 0.017) and “nose and throat” (HR = 0.84; p = 0.013) symptoms, but the association was more profound between prolonged RFTP and increased total number of “all symptoms” (HR = 0.91; p = 0.001) and “whole body”/systemic HR = 0.82; p = 0.007) symptoms. Conclusion A larger number of total symptoms and specifically “whole body”/systemic symptoms during the acute phase of SARS-CoV-2 infection in athletes is associated with prolonged RTFP.
Climate Change and Violence Against Women
(Elsevier Masson SA, 2025) Stiegler, Nancy; Fapohunda, Tomiwa; Bouchard, Jean Pierre
Climate change is the significant alteration in the weather conditions of a place, which include wetter, drier or warmer conditions over several years or decades. Human activities are the core cause of climate change. Research has stated that the unmitigated release of carbon will cause global warming of several degrees Celsius by 2100, causing high-impact risks to human society. The average global temperature has increased by 0.06° C per decade since 1855. The global warming trend since 1982 is about three times faster than 0.20° C per decade. Since the global temperatures’ increase began, 2023 was discovered to be the warmest year in history. The World Health Organisation (WHO) stated that one in three women has experienced violence in their lifetime. Because the very important stakes for human societies of these two phenomena, this study's objective was to comprehensively investigate the multifaceted effects of climate change on violence against women (VAW) in diverse societal contexts. Our findings indicate that climate change significantly exacerbates Gender Based Violence (GBV). Studies in several countries have demonstrated that climate change has led to displacements, economic disruptions, lack of access to water and education, food insecurity, public health issues, which all increase violence against women. It is crucial to develop a strategic plan for protecting girls and women during climatic disasters as part of comprehensive climate action to spur the Sustainable Development Goals (SDG) that are related to poverty alleviation, quality education and health, building sustainable cities and climate action. Similarly, adhering to climate action as suggested by the Paris Agreement in 2016 and reducing greenhouse gas emissions via better energy, food and transport choices can result in great gains. This will also avert several adverse effects of climate change on humans such as undernutrition, forced migration, forced marriage, forced abortion, xenophobic attacks, economic disruptions, and of course, violence against women.

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