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Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause‐specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non‐linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause‐specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01–1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03–1.15) during increased rainfall (13.21–16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03–1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31–1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05–1.27 for PM10) and respiratory infection‐related ED admissions (IRR: 2.78, 95% CI: 1.69–4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 μg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered. Plain Language Summary: The unpredictability and variability of emergency department (ED) admissions load pose significant challenges for healthcare systems, as it can lead to a strain in resources. Understanding risk factors leading to higher ED admission loads provide an opportunity to anticipate surges, identify potential trends, and allocate resources in a pre‐emptive manner, thereby enhancing the responsiveness and preparedness of healthcare systems. Our analyses revealed that: (a) high total daily rainfall was negatively associated with ED admissions (b) mean temperature, while positively associated with ED admissions, typically had delayed effects on ED admissions (c) increased wind speed contributed disproportionately to increased ED admissions due to respiratory infections (d) increased ambient air pollutant concentrations, while positively associated with ED admissions due to most categories, had the greatest impact on the categories belonging to cardiorespiratory system disorders. Key Points: High total daily rainfall was negatively associated with emergency department (ED) admissions, while mean temperature was positively associated with ED admissionsIncreased wind speed contributed disproportionately to increased ED admissions due to respiratory infectionsIncreased ambient air pollutant concentrations had the greatest impact on the categories belonging to the cardiorespiratory system disorders [ABSTRACT FROM AUTHOR]

Abstract Environmental tobacco smoke (ETS) exposure has been shown to be associated with a variety of diseases, but evidence regarding the association between it and urinary incontinence (UI) is limited. Cotinine, a metabolite of nicotine in the human body, can more accurately quantify the level of human exposure to tobacco smoke. The study utilized data from seven survey cycles (2007-March 2020 Pre-pandemic) of the National Health and Nutrition Examination Survey (NHANES) program. Weighted multivariable logistic regression analysis, subgroup analysis, interaction tests, smooth curve fitting, and threshold effect models were used to analyze the relationship between serum cotinine and UI. Additionally, a 1:1 nearest neighbor propensity score matching (PSM) method was employed to minimize the impact of confounding factors. Before and after PSM, serum cotinine levels were higher in individuals with UI than those without (P

Abstract Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause‐specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non‐linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause‐specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01–1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03–1.15) during increased rainfall (13.21–16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03–1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31–1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05–1.27 for PM10) and respiratory infection‐related ED admissions (IRR: 2.78, 95% CI: 1.69–4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 μg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered.

Singapore experiences endemic dengue. Vector control remains the primary means to reduce transmission due to the lack of available therapeutics. Resource limitations mean that vector-control tools need to be optimized, which can be achieved by studying risk factors related to disease transmission. We developed a statistical modelling framework which can account for a high-resolution and high-dimensional set of covariates to delineate spatio-temporal characteristics that are associated with dengue transmission from 2014 to 2020 in Singapore. We applied the proposed framework to two distinct datasets, stratified based on the primary type of housing within each spatial unit. Generalized additive models reveal non-linear exposure responses between a large range of ecological and anthropogenic factors as well as dengue incidence rates. At values below their mean, lesser mean total daily rainfall (Incidence rate ratio (IRR): 3.75, 95% CI: 1.00–14.05, Mean: 4.40 mm), decreased mean windspeed (IRR: 3.65, 95% CI: 1.87–7.10, Mean: 4.53 km/h), and lower building heights (IRR: 2.62, 95% CI: 1.44–4.77, Mean: 6.5 m) displayed positive associations, while higher than average annual NO2 concentrations (IRR: 0.35, 95% CI: 0.18–0.66, Mean: 13.8 ppb) were estimated to be negatively associated with dengue incidence rates. Our study provides an understanding of associations between ecological and anthropogenic characteristics with dengue transmission. These findings help us understand high-risk areas of dengue transmission, and allows for land-use planning and formulation of vector control policies. [ABSTRACT FROM AUTHOR]

Singapore experiences endemic dengue. Vector control remains the primary means to reduce transmission due to the lack of available therapeutics. Resource limitations mean that vector-control tools need to be optimized, which can be achieved by studying risk factors related to disease transmission. We developed a statistical modelling framework which can account for a high-resolution and high-dimensional set of covariates to delineate spatio-temporal characteristics that are associated with dengue transmission from 2014 to 2020 in Singapore. We applied the proposed framework to two distinct datasets, stratified based on the primary type of housing within each spatial unit. Generalized additive models reveal non-linear exposure responses between a large range of ecological and anthropogenic factors as well as dengue incidence rates. At values below their mean, lesser mean total daily rainfall (Incidence rate ratio (IRR): 3.75, 95% CI: 1.00–14.05, Mean: 4.40 mm), decreased mean windspeed (IRR: 3.65, 95% CI: 1.87–7.10, Mean: 4.53 km/h), and lower building heights (IRR: 2.62, 95% CI: 1.44–4.77, Mean: 6.5 m) displayed positive associations, while higher than average annual NO2 concentrations (IRR: 0.35, 95% CI: 0.18–0.66, Mean: 13.8 ppb) were estimated to be negatively associated with dengue incidence rates. Our study provides an understanding of associations between ecological and anthropogenic characteristics with dengue transmission. These findings help us understand high-risk areas of dengue transmission, and allows for land-use planning and formulation of vector control policies.

• This is the first to investigating the association between built environment around workplace and different walking behaviors. • It assesses the non-linear association between environment and walking behaviors. • Built environment around workplace is crucial for higher walking levels, but built environment features tend to have distinctive associations with different walking behaviors. Considering that most working adults spend nearly half their waking time at work, creating a supportive built environment around workplaces could be a feasible approach to maintain adequate levels of physical activity. However, the extent to which the built environment around workplaces influences walking behaviors in working adults remains unclear. Using survey data of 1009 full-time employees in Shanghai, China, this study assessed the nonlinear relationships between the built environment characteristics around workplaces and three domains of walking behaviors (commuting, utilitarian, and recreational walking). Using gradient boosting decision trees, our results showed that the built environment around workplaces is crucial for higher levels of walking behaviors, but built environment features tended to have distinctive associations with different domains of walking behaviors. Specifically, the number of physical activity facilities was positively associated with all three domains of walking behaviors, while a high floor area ratio was negatively associated with different domains of walking behaviors to some extent. Furthermore, several built environment characteristics, such as land use entropy, street view greenery, distance from home to the city center, and distance between the city center and workplaces had distinctive associations with different domains of walking behaviors. The findings of this study could provide nuanced guidance for creating pedestrian-friendly environments around workplaces to promote walking behaviors and overall physical activity levels in the working population. [ABSTRACT FROM AUTHOR]

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33–0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. Trial registration The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374 .

Background: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation.Methods: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model.Results: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown.Conclusion: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed.Trial Registration: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374. [ABSTRACT FROM AUTHOR]

Summary: A previously presented study investigated the impact of recent pesticide exposure on cytological signs of genotoxicity and on symptoms of intoxication in 71 male coffee workers in the Dominican Republic. An unexpected finding of this study was that conventional farming workers, among other symptoms, reported fewer children than controls working in organic farms without pesticide use. This study set out to investigate possible reasons for the latter difference. One statistical problem of this analysis is that the age of the workers is a strong predictor for the number of children and available data on the exposure determinants "duration of pesticide exposure" as well as "age at first pesticide exposure" are correlated with age. To correctly control statistics for these confounding parameters, different approaches to best control for age were explored. After careful elimination of the age-related confounding factors, a reduced number of children was still observed in exposed workers. The clearest effect is seen in those workers that reported first exposure before the age of 20 years. Socioeconomic factors could still confound that finding, but a direct effect of early life pesticide exposure is the most likely explanation of the observation. [ABSTRACT FROM AUTHOR]

Background: Impairments in social functioning are common in individuals with (sub)clinical levels of depression. One possible underlying mechanism of this relationship is low empathy. The present study examined linear and non-linear associations between empathy and depressive symptoms, and whether these associations were moderated by gender.Methods: Using the Empathy Quotient scale, cognitive and affective empathy were assessed using a large convenience sample from the general Dutch population (N = 3076). Depressive symptoms were indexed by the Depression, Anxiety and Stress Scale (DASS) and the Quick Inventory of Depressive Symptomatology (QIDS-SR16).Results: Low cognitive empathy was associated with more depressive symptoms. There was a weak positive relation between affective empathy and depressive symptoms, indexed by the QIDS rather than the DASS.Limitations: Generalizing results to the general population remains difficult with a convenience sample. Self-reported data may be affected by social-desirability or sex-stereotypical reporting biases.Conclusions: Our results support a linear relationship between cognitive empathy and symptoms of depression. There was some evidence for non-linear associations and a moderating role of gender, but these patterns were not observed across all types of analysis. [ABSTRACT FROM AUTHOR]

Background: Gene set analysis (GSA) aims to evaluate the association between the expression of biological pathways, or a priori defined gene sets, and a particular phenotype. Numerous GSA methods have been proposed to assess the enrichment of sets of genes. However, most methods are developed with respect to a specific alternative scenario, such as a differential mean pattern or a differential coexpression. Moreover, a very limited number of methods can handle either binary, categorical, or continuous phenotypes. In this paper, we develop two novel GSA tests, called SDRs, based on the sufficient dimension reduction technique, which aims to capture sufficient information about the relationship between genes and the phenotype. The advantages of our proposed methods are that they allow for categorical and continuous phenotypes, and they are also able to identify a variety of enriched gene sets. Results: Through simulation studies, we compared the type I error and power of SDRs with existing GSA methods for binary, triple, and continuous phenotypes. We found that SDR methods adequately control the type I error rate at the pre-specified nominal level, and they have a satisfactory power to detect gene sets with differential coexpression and to test non-linear associations between gene sets and a continuous phenotype. In addition, the SDR methods were compared with seven widely-used GSA methods using two real microarray datasets for illustration. Conclusions: We concluded that the SDR methods outperform the others because of their flexibility with regard to handling different kinds of phenotypes and their power to detect a wide range of alternative scenarios. Our real data analysis highlights the differences between GSA methods for detecting enriched gene sets. [ABSTRACT FROM AUTHOR]

Numerous time use studies consistently find that highly educated parents spend more time with their children and more time on developmental activities than do less educated parents. Nevertheless, few empirical studies have examined whether and how these inequalities in parental caring are related to children’s educational performance. This study aims to provide a more complete picture of how parental time spent with children is related to children’s educational performance than offered by prior studies. Unique couple-based time diaries from cohabiting/married couples in Denmark combined with register data on children’s final exams in ninth grade were used to examine different associations between parental time investment in children and children’s educational performance. The study shows a significant overall association between parental time spent on developmental care and children’s educational performance. Moreover, additional analyses reveal that this association is mainly driven by low socioeconomic status children who receive more than 20 minutes of developmental care daily. [ABSTRACT FROM AUTHOR]

Background: several studies have been pointing towards a non-linear relationship between serum 25(OH)D and cardiovascular disease. Next to vitamin D deficiency, also higher levels of 25(OH)D have been reported to be associated with increased cardiovascular risk. We aimed to investigate the nature of the relationship between serum 25(OH)D and measures of arterial stiffness and arteriosclerosis in an elderly population.Design: cross-sectional.Setting/subjects: a subgroup of the B-PROOF study was included to determine associations between serum 25(OH)D and arterial stiffness and atherosclerosis (n = 567, 57% male, age 72.6 ± 5.6 years, mean serum 25(OH)D 54.6 ± 24.1 nmol/l).Methods: carotid intima media thickness (IMT) was assessed using ultrasonography and pulse wave velocity (PWV) was determined with applanation tonometry. Associations were tested using multivariable restricted cubic spline functions and stratified linear regression analysis.Results: the associations between serum 25(OH)D and carotid IMT or PWV were non-linear. Spline functions demonstrated a difference between 25(OH)D deficient and sufficient individuals. In serum 25(OH)D sufficient participants (≥50 nmol/l; n = 287), a positive association with IMT and serum 25(OH)D was present (β 1.24; 95%CI [0.002; 2.473]). PWV levels were slightly lower in vitamin D deficient individuals, but the association with 25(OH)D was not significant.Conclusion: our study demonstrates that associations of serum 25(OH)D and PWV and IMT in an elderly population are not linear. In particular from serum 25(OH)D levels of 50 nmol/l and up, there is a slight increase of IMT with increasing 25(OH)D levels. [ABSTRACT FROM PUBLISHER]

Abstract Background Several studies have found a non-linear relationship between mental ill-health and BMI with higher rates in both the underweight and the obese. This study evaluated the shape of the relationship between BMI and distress, suicidal ideation and self-reported mental ill-health conditions in a large population sample. Methods Data were drawn from the South Australian Monitoring and Surveillance System (SAMSS) for the years 2002 to 2009 (n = 46,704). SAMSS monitors population trends in state and national risk factors and chronic diseases. Samples are drawn from all households with a functioning number in the Australian White Pages. Computer assisted telephone interviews collected information on self-reported height and weight, demographic and health behaviours. Respondents completed the Kessler Distress and suicidal ideation scales and reported specific mental ill-health conditions. BMI was categorized into deciles to allow for assessment of the shape of any associations with other variables. Logistic regression was used to examine associations between each mental ill-health condition and BMI-decile controlling for age in the base model. This was followed by a full model that added SES and the health-adverse coping behaviours of smoking, alcohol and physical activity to test for changes from the base model. Results Non-linear associations were observed between BMI-decile and mental ill-health but statistically significantly greater odds of mental ill-health were observed only in the obese and not in the underweight after controlling for age, health-adverse behaviours and socioeconomic status. The association between BMI and mental ill-health might best be described as 'threshold'. Elevated odds were apparent for middle-aged persons, whereas younger and older individuals had a significantly lower odds of having a mental ill-health condition. Conclusions In conclusion, this study has provided no support for the hypothesis of increased mental ill-health problems in the underweight but it has demonstrated the non-linear relationships between BMI and mental ill-health and between BMI and health-adverse behaviours. Non-linear relationships with BMI need to be recognized and addressed during analysis.

Objectives: To investigate the associations of the levels of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma glutamyltransferase (GGT), at baseline and their changes over time with mortality., Design: Cohort study., Setting, Participants and Outcome Measures: We analysed the data of 484 472 individuals from the National Health Insurance Service-National Health Screening Cohort (2002-2013). We used two exposure indices: (1) deciles of baseline ALT, AST and GGT levels measured in 2002 or 2003 and (2) deciles of changes in ALT, AST and GGT levels over a 4 year period (2002-2006 or 2003-2007). We constructed Cox models to evaluate the associations of these exposure indices with mortality (2008-2013)., Results: We found non-monotonic dose-response associations between the baseline levels of ALT and AST and all-cause mortality. We also found a monotonic non-linear association between the baseline levels of GGT and all-cause mortality (10th decile: HR=2.05, 95% CI: 1.93 to 2.18). Compared with the ninth, sixth and fourth deciles of changes in ALT (8-13 U/L), AST (1 U/L) and GGT (-3 to -2 U/L) over time, respectively, the risks of all-cause mortality increased in both the higher and lower deciles of changes in the corresponding liver enzyme levels (10th decile: HR=1.36, 95% CI 1.24 to 1.48; 1st decile: HR=1.46, 95% CI 1.34 to 1.59 for ALT; 10th decile: 1.55, 95% CI 1.40 to 1.71; 1st decile: HR=1.53, 95% CI 1.38 to 1.69 for AST; 10th decile: HR=1.71, 95% CI 1.56 to 1.88; 1st decile: HR=1.67, 95% CI 1.52 to 1.84 for GGT). These non-monotonic dose-response associations remained when analyses were stratified by the medians or quartiles of the baseline liver enzyme levels., Conclusions: The levels of liver enzymes at baseline and over time showed non-linear associations with mortality., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)