BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time.
OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures.
METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes.
RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness.
CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study\'s application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Ensuring food security and sustainable resource management has become a paramount global concern, prompting significant attention to land suitability analysis for enhancing agricultural production. In this study, an AHP-weighted overlay method was employed to delineate rice cultivation suitability in Guilan province, Iran, a central hub for rice production. Sixteen climatic, topographic, and soil variables were integrated, and individual maps were reclassified to align with the specific requisites for rice production. The results revealed three suitability classes: including \'very suitable,\' \'suitable,\' and \'moderately suitable\', covering 91%, 6%, and 3% of the land, respectively. Soil attributes, particularly organic matter, significantly influenced suitability (weight value of 0.745), with topographic and soil factors outweighing climate in assessment. While salinity is generally absent, organic matter deficiency affects 44% of the land. Phosphorus imbalances are prevalent, with potassium toxicity observed in 10%. Microelement deficiencies, especially in iron and zinc, are noted. Additionally, the results indicated that topographic and soil attributes played a more significant role than climate-related factors in assessing land suitability for rice cultivation within the study area. This research provides a comprehensive spatial analysis of all variables in the study region, shedding light on the complexities of land suitability for rice cultivation. These findings contribute to the understanding of agricultural sustainability and resource management strategies in the context of food security.

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Today\'s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

BACKGROUND: In Nigeria, 97% of the population is at risk of contracting malaria. It is transmitted by female Anopheles mosquitoes carrying the Plasmodium parasite and can be lethal. An estimated 55 million illnesses and 80,000 deaths per year result from it. Children under five are more likely to contract malaria. Efforts to control malaria in Nigeria include indoor residual spraying, insecticide-treated bed nets, and quick detection and treatment of confirmed cases with effective antimalarial medications. These attempts have been impeded by limited healthcare access, poor financing, and drug-resistant parasites. Thus, the study of the relationship between malaria complications and housing for children under five is essential.
METHODS: The Demographic and Health Survey (DHS) Malaria Indicator Survey (MIS) 2021, a nationally representative data set from developing countries on population and health, was used for this study. A sample size of 13,727 was employed (n=13,727). Logistic regression analyses were conducted to test the association between the type of place of residence and malaria complications (outcome).
RESULTS: Overall, 4.2% (n=570, weight HV005) of participants in the sample reported malaria complications. The results of the logistic regression revealed that children residing in urban settlements (aOR 0.37, 95% CI 0.37-0.37, p-value <0.001), children from the poorest class families (aOR 11.63, 95% CI 1.62-1.63, p-value 0.004), children from poorer class families (aOR 7.56, 95% CI 7.55-7.57, p-value <0.001), children from middle-class families (aOR 4.05, 95% CI 4.03-9.06, p-value <0.001), children from richer class families (aOR 1.22, 95% CI 2.21-2.23, p-value <0.001), children of mothers with primary education (aOR 0.42, 95% CI 2.32-4.112, p-value 0.001), children of mothers with secondary education (aOR 0.24, 95% CI 3.21-3.22, p-value <0.001), children of mothers with higher education (aOR 0.08, 95% CI 0.72-0.80, p-value <0.001), and children of the female gender (aOR 0.65, 95% CI 0.65-0.66, p-value <0.001) are all associated with severe malaria complications.
CONCLUSIONS: In conclusion, the study examined malaria complications in Nigerian children under five by residency. The findings imply that rural children are more likely to have serious malaria complications than urban children. This emphasizes the necessity for targeted malaria therapies in rural areas with limited healthcare access.

Understanding gastrointestinal parasite distribution is crucial for effective control programs in horses. This study reports the prevalence of helminth infections in horses and selected risk factors (i.e., breed, age, climate, season) by analyzing 19,276 fecal samples from the Laboratory of Veterinary Clinical Parasitology, in Curitiba, Southern Brazil. The analyses were carried out from 2008 to 2019, coming from 153 stud farms located in 60 municipalities of nine Brazilian states. The parasite prevalence was 73.3%, with 72.1% present in the adult population and 80.6% in young horses. Strongyles were present in 100% horse farms. Strongyles had a prevalence of 72.1% with a mean FEC of 453.53 (+/- 717.6). Parascaris spp. had a prevalence of 5.8% and a FEC of 17.11 (+/- 149.2). The tropical wet/monsoon climate (Am) showed the lowest FEC for strongyles and Parascaris spp. when compared to the other climates. In the logistic regression analysis, young horses exhibited 4.6 times higher odds ratio (OR) (3.9-5.5) of Parascaris spp. and 1.2 (1.1-1.4) times higher OR of strongyles egg shedding when compared to adults (P < 0.001). Summer presented a higher risk for Parascaris spp. and Strongyles eggs when compared to the other seasons (P < 0.001). Mangalarga Marchador, Criollo, and Crossbred breeds were identified with higher OR of Parascaris spp. egg shedding than Thoroughbred. The extensive prevalence of strongyles across ages, seasons, breeds, and climates alerts for the risk of clinical manifestations in equines raised on pastures designing optimal health management and parasite control strategies worldwide.

UNASSIGNED: The ulnar collateral ligament (UCL) is the primary soft tissue stabilizer to valgus stress in the elbow and is placed under this valgus stress during the throwing motion. Although there are known risk factors for UCL injury, it is unknown whether the UCL undergoes adaptive changes in athletes from different climates.
UNASSIGNED: To compare elbow stress ultrasound (SUS) findings between professional baseball pitchers from warm climates versus cold climates and assess significant differences in adaptive and morphologic changes in the UCL.
UNASSIGNED: Cross-sectional study; Level of evidence, 3.
UNASSIGNED: Dynamic SUS evaluations were performed over 18 years on the dominant and nondominant arms of 643 professional pitchers from warm and cold climates as determined by the player\'s country/state of origin. Studies were compared with respect to relative UCL thickness (dominant arm vs nondominant arm), relative glenohumeral joint laxity (joint space distance under stress vs joint space distance at rest), and the presence of morphologic changes such as tears or calcifications. In addition, a subgroup analysis was performed to compare the progression of SUS findings over 3 years in players with sequential yearly data.
UNASSIGNED: Players from warmer climates had significantly greater relative UCL thicknesses than players from colder climates (1.75 vs 1.50 mm, respectively; P = .047). There were no differences between these 2 groups in terms of relative ulnohumeral joint laxity (P = .201), presence of morphologic changes (P = .433), 3-year progression of relative UCL thickness (P = .748), or relative joint laxity (P = .904).
UNASSIGNED: Professional pitchers from warm climates had a greater side-to-side difference in UCL thickness between the dominant and nondominant arms. This may be due to the potential for year-round throwing among baseball players from warm climates. There was no difference in laxity, thickness progression, laxity progression, or the presence of additional morphologic changes.

The health of honey bee queens is crucial for colony success, particularly during stressful periods like overwintering. To accompany a previous longitudinal study of colony and worker health, we explored niche-specific gut microbiota, host gene expression, and pathogen prevalence in honey bee queens overwintering in a warm southern climate. We found differential gene expression and bacterial abundance with respect to various pathogens throughout the season. Biologically older queens had larger microbiotas, particularly enriched in Bombella and Bifidobacterium. Both Deformed Wing Virus A and B subtypes were highest in the fat body tissue in January, correlating with colony Varroa levels, and Deformed Wing Virus titers in workers. High viral titers in queens were associated with decreased vitellogenin expression, suggesting a potential trade-off between immune function and reproductive capacity. Additionally, we found a complex and dynamic relationship between these viral loads and immune gene expression, indicating a possible breakdown in the coordinated immune response as the season progressed. Our study also revealed a potential link between Nosema and Melissococcus plutonius infections in queens, demonstrating that seasonal opportunism is not confined to just workers. Overall, our findings highlight the intricate interplay between pathogens, metabolic state, and immune response in honey bee queens. Combined with worker and colony-level metrics from the same colonies, our findings illustrate the social aspect of queen health and resilience over the winter dearth.

The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

Eolian dust and riverine discharge are identified as two key components of terrestrial input to the oceans. They supply micronutrients to the oceans and modify marine carbon biogeochemistry and global climate through dust-land-ocean interactions. However, it is challenging to accurately constrain regional terrestrial inputs in the past, with currently available models and geochemical proxies. The present study utilizes sedimentary wtCaCO3% records to estimate lithogenic fluxes. The depth-dependance of CaCO3 preservation in the Holocene and Last Glacial Maximum (LGM) sediments in two major basins of the tropical Northeast Atlantic Ocean is described using a carbonate dissolution model. Results show that during the LGM, reduced dust deposition and slight drops of fluvial input are found in the Canary Basin and Cape Verde margins, respectively. To supplement, carbonate deposition during the LGM indicates that the deep subtropical Northeast Atlantic is seized by more sluggish deep waters relative to today.