Analysis Of Longitudinal Data Research Articles

Treatment patterns and characteristics of headache in patients in Japan: A retrospective cross-sectional and longitudinal analysis of health insurance claims data.

The present study aimed to investigate prescription patterns for patients aged over 17 years with headaches in the REZULT database. We conducted a cross-sectional study (Study 1) of the proportion of over-prescription of acute medications (≥30 tablets/90 days for triptans, combination non-steroidal anti-inflammatory drugs (NSAIDs) and multiple types; ≥45 tablets/90 days for single NSAIDs) among patients with headache diagnosed in 2020. We longitudinally studied (Study 2) patients for >2 years from initial headache diagnosis (July 2010 to April 2022). The number of prescribed tablets was counted every 90 days. In Study 1, headache was diagnosed in 200,055 of 3,638,125 (5.5%) patients: 13,651/200,055 (6.8%) received acute medication. Single NSAIDs were prescribed to 12,297/13,651 (90.1%) patients and triptans to 1710/13,651 (12.5%). Over-prescription was found in 2262/13,651 (16.6%) patients and 1200/13,651 (8.8%) patients received prophylactic medication. In Study 2, 408,183/6,840,618 (6.0%) patients were first diagnosed with headaches, which persisted for ≥2 years. Over time, the proportion of patients over-prescribed acute medications increased. Over 2 years, 37,617/408,183 (9.2%) patients were over-prescribed acute medications and 29,313/408,183 (7.2%) patients were prescribed prophylaxis at least once. According to real-world data, prophylaxis remains poorly prescribed, and both acute and prophylactic treatment rates for headaches have increased over time.

Promoting healthful and diverse eating behaviours through an extracurricular culinary skills intervention in Philadelphia.

In the current study we evaluated an afterschool nutrition education programme, called Vetri Cooking Lab (VCL), for promoting healthy and diverse eating habits among at-risk children in the Greater Philadelphia area. To understand potential programme impacts, we conducted a longitudinal analysis of survey data collected before and after participation in VCL. Main study included cooking confidence, cooking knowledge, changes in dietary consumption behaviours, and changes in vegetable preferences. Participants included students in grades 3-11 enrolled in VCL during the 2018-19 school year at VCL sites (n = 60) throughout Philadelphia, PA, and Camden, NJ. Eligible participants completed surveys both before and after participating in the programme. We found that students' confidence and knowledge increased (P < 0.001) after the cooking intervention. Knowledge and confidence were positively associated (r = 0.55; P < 0.001). Confidence was correlated with consumption behaviour changes (r = 0.18; P = 0.022). Confidence was positively associated with consumption changes in both our adjusted (OR = 1.81; P < 0.001) and unadjusted models (aOR = 1.88; P = 0.013). Compared to Black students, White students were more likely to report consumption changes (aOR = 5.83; P = 0.013). Hispanic/Latino participants and participants who spoke Spanish had nearly three times higher odds of consumption behaviour changes (Hispanic/Latino OR = 2.55; P = 0.007; Spanish OR = 3.04; P = 0.005). Student age and gender were not associated with behaviour changes. Our research demonstrates that programmes integrating practical cooking skills education along with nutrition, food, and cooking education can improve confidence and knowledge about healthy food choices amongst children driving an overall improvement in children's eating habits.

Analysis of Hearing Monitoring in Children during Annual Visits to an Outpatient Hearing Health Service Program

Preserving the integrity of the auditory system is crucial for language development, learning, and social interaction. Any interruption in these processes during development can result in significant functional impairments. The objective was to carry out a longitudinal analysis of audiological data from children who underwent an initial assessment at SASA in 2018 and were followed up for 4 years at the outpatient's annual consultations. This is a retrospective cross-sectional study with children who underwent initial assessment for hearing loss and follow-up at a reference service for the Unified Health System (SUS), from January 2018 to December 2022. Children aged 0 to 12 years old who underwent an initial assessment for hearing loss and follow-up in the service's database, where secondary data analyses will be carried out. Data from 127 subjects with an average age of 20 months were analyzed. 60.62% of the subjects were diagnosed with some degree of hearing loss, and the etiology of the change remained unknown in 29.90% of the cases. A considerable percentage of subjects remained under follow-up during annual consultations. There was good adherence to annual returns and follow-ups at the outpatient clinic, indicating a high attendance rate.

Exploring the Influence of Structured Familiarization to an Adjustable, Passive Load-Bearing Exoskeleton on Oxygen Consumption and Lower Limb Muscle Activation During Walking.

Walking patterns are modified during load carriage, resulting in an increased activation of lower limb muscles and energy expenditure. Negative effects of load carriage could be minimized by wearing an exoskeleton, but evidence on the effects are conflicting. The objectives of this study were to describe the influence of an adjustable, passive load-bearing exoskeleton on the metabolic cost of walking (MCW) and associated muscle activations, and to explore changes in MCW after a familiarization process. Thirteen participants walked on a treadmill with a 22.75 kg payload at six preselected speeds (from 0.67 to 1.56 m/s) under three walking conditions: 1) without exoskeleton (NoExo); 2) with exoskeleton before familiarization (ExoPre); and 3) with exoskeleton after familiarization (ExoPost). Metabolic data was normalized to walking speed to provide MCW. Multi-muscle surface electromyography (EMG) was time and amplitude normalized to the gait cycle to provide muscle activation patterns. The familiarization occurred over three weeks including exposure to the exoskeleton. Differences in MCW and muscle activations were compared using a nonparametric analysis of longitudinal data. There were statistically significant increases in MCW for all speeds in the ExoPre and ExoPost conditions compared the NoExo. The average muscle activation showed an increase during ExoPre and ExoPost for the three speeds evaluated. Post-hoc analysis showed no significant effect of the familiarization period on metabolic data. In conclusion, a first exposure to the adjustable exoskeleton increased MCW and muscle activations, but the familiarization process did not provide any benefits toward a reduction in MCW or reduction in muscle activations at all speeds evaluated.

Age and sex dynamics in the incidence of diabetes in Contributory Health Services Scheme beneficiaries: A retrospective cohort study in Mumbai

ABSTRACT Introduction: A comprehensive body of literature addresses self-reported diabetes prevalence, yet a notable gap exists in research investigating the clinically ascertained incidence of diabetes in India through rigorous longitudinal data analysis. This study aimed to determine the incidence of clinically diagnosed diabetes in a nondiabetic cohort. Materials and Methods: The research gathered data from 1669 participants (aged 30 years and above) enrolled in a government hospital’s Contributory Health Services Scheme, utilizing electronic medical records. Clinical diagnosis of diabetes relied on three laboratory tests. A cohort of initially diabetes-free individuals in 2011–2012 was tracked for 10 years to assess diabetes incidence. Age-adjusted incidence rates were determined through survival analysis techniques. Results: Over a decade-long observational period, 552 beneficiaries within the study cohort were clinically diagnosed with diabetes, yielding an age-adjusted incidence rate of 38.9 cases per 1000 person-years (PYs) spanning from 2013 to 2021. Stratifying by gender, age-adjusted incidence rates were notably elevated in males compared to females, with rates of 41.5 versus 38.5 cases per 1000 PYs, respectively. Further analysis revealed the highest incidence rates among males aged 55–59 years (60.5 per 1000 PYs) and females aged 65–69 years (83.4 per 1000 PYs). Conclusion: This extended follow-up investigation transpired in a setting characterized by uniform health-care provision, devoid of discernible access differentials, or inequalities, thereby enhancing the credibility of the ascertained diabetes incidence rates.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories. We design an autoencoder with a latent space where an orthogonal constraint is imposed to separate the space of global trajectories from individual trajectories. We also devise a cross-reconstruction loss to ensure consistency of global trajectories and enhance the orthogonality between representation spaces. To evaluate UOMETM, we conducted simulation experiments on images to verify that every component functions as intended. Furthermore, we evaluated its performance and robustness using longitudinal brain cortical thickness from two Alzheimer's disease (AD) datasets. Comparative analyses with state-of-the-art methods revealed UOMETM's superiority in identifying global and individual longitudinal patterns, achieving a lower reconstruction error, superior orthogonality, and higher accuracy in AD classification and conversion forecasting. Remarkably, we found that the space of global trajectories did not significantly contribute to AD classification compared to the space of individual trajectories, emphasizing their clear separation. Moreover, our model exhibited satisfactory generalization and robustness across different datasets. The study shows the outstanding performance and potential clinical use of UOMETM in the context of longitudinal data analysis.

Evaluating the validity and reliability of the Tswana adaptation of the MOS-HIV tool for health-related quality of life among HIV Sub-populations in Botswana: A study protocol.

The lack of culturally and contextually appropriate adaptations of health-related quality of life (HRQoL) tools hinders HIV patient outcomes. This study aims to assess the validity and reliability of a Tswana version of the Medical Outcome Survey-HIV (MOS-HIV) tool among diverse HIV sub-populations in Botswana. In terms of the methodology the study will comprise of several steps. Firstly, forward and back-translation of the original U.S. English MOS-HIV tool into Setswana, followed by the review of the translated tool. Phase 1 will evaluate content, construct validity, and reliability of the newly developed tool among HIV outpatients at Gaborone Infectious Diseases Clinics (IDCC). A Cronbach's alpha coefficient >0.7 across the 35 items and 11 dimensions of the MOS-HIV tool will indicate internal consistency reliability. Phase 2 will employ the use of logistic regression models to identify predictors of poor HRQoL among randomly selected IDCC sites, both in urban and rural centers. Phase 3 will investigate predictors of poor HRQoL among inpatients receiving treatment for HIV-associated cryptococcal meningitis (CM) using longitudinal data analysis methods. Ethical approval has been obtained from the University of Botswana, Walter Sisulu University, Human Research and Development Unit, Ministry of Health, and Princess Marina Hospital. Prospective participants will provide written informed consent, with proxy consent explored when feasible. Voluntary participation and confidentiality will be ensured during data collection and analysis. Data will be securely stored under lock-and-key. Dissemination of study findings will adhere to strict privacy protocols, avoiding the sharing of personal identifiers.

유창성 장애의 언어치료 관련 경시적 계수자료의 유연한 분석

In this study, the goal was to apply a flexible and easy-to-manage analysis method through a model that includes a dispersion parameter in the longitudinal analysis method of count data. In the analysis of count data, it may be inconvenient to change the statistical model depending on whether there is under- or over-dispersion. To compensate for these weaknesses, We have applied a statistical method including a dispersion parameter to the statistical model. The count data covered were included in the diagnosis of fluency disorders and the evaluation of the effectiveness of speech therapy. but research on speech-language therapy is limited by being conducted on small numbers of subjects for several reasons. Therefore, statistical analysis methods that work only on large samples, are complex, or require excessive calculations in the model fitting process were not appropriate for the field. With this background, we applied the Conway-Maxwell Poisson model to longitudinal data analysis of fluency disorders. We evaluated whether the model that reflects the level of data dispersion in the model was consistent with the speech therapy data for fluency disorders by comparing it to the existing Poisson mixed model and linear mixed model. In particular, the Conway-Maxwell Poisson model had the advantage of reflecting well when the degree of dispersion was different for each group or specific subgroup.

Dynamic Structural Equation Models: Promising Yet Concerning

Dynamic Structural Equation Model (DSEM) is a powerful statistical modeling approach that has recently gained popularity among researchers studying intensive longitudinal data. Despite its exciting potential, the stability and replicability of DSEM is yet to be closely examined. This study empirically investigates DSEM using recently published data to explore its strengths and potential limitations. The results show that while some of its parameter estimates are stable, others are characterized by substantial variation as a function of seemingly innocuous initial model estimation conditions. Indeed, some parameters fluctuate between significance and non-significance for the same model estimated using the same data. The instability of DSEM estimates poses a serious threat to the internal and external validity of conclusions drawn from its analyses, challenging the reproducibility of findings from applied research. Given the recent focus on the replication crisis in psychology, it is critical to address these issues as the popularity of DSEM in psychological research continues to rise. Several potential solutions are investigated to address this problem and recommendations of best practice are offered to applied researchers who plan to use DSEM in intensive longitudinal data analysis. KEYWORDS: Dynamic Structural Equation Model; Bayesian; Robust Estimation; Intensive Longitudinal Data

The Role of Age in Adult ADHD Quality Care: A Longitudinal Analysis of Electronic Health Record Data

Objective: Several studies have shown that Adult ADHD presents differently in younger and older adults. We sought to assess the difference in care between these two groups using previously identified quality measures (QMs). Methods: Using electronic health record data, we matched a younger group of ADHD patients to an older group. We then assessed the achievement of the QMs using probit models with and without interaction terms. Results: The majority of QMs shown an increase in achievement for both groups over time. However, significant differences in quality of care between younger and older adult ADHD patients persisted. By the end of the study period, with the exception of three QMs, younger patients achieved the QMs more. Conclusion: While, in general, the quality of care for adult ADHD increased from 2010 to 2020, there were still differences in care between younger and older adult ADHD patients.

Multivariable reference centiles for maximum grip strength in childhood to young adults

ObjectivesMaximum grip strength (mGS) is a useful predictor of health-related outcomes in children and adults. The aim of the study was to generate sex- and age-adjusted reference centiles for mGS for children, adolescents and young adults, while adjusting for body height and body mass index (BMI).MethodsA retrospective analysis of longitudinal data from children and young adults participating in the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study (single center, open cohort study) from 2004 to 2022 was conducted. To generate sex-, age-, height- and BMI-adjusted reference centiles, a new algorithm combining multiple linear regression and the LMS method was conducted.ResultsOverall, 3325 measurements of mGS of 465 females and 511 males were eligible. The mean age at measurement of females was 12.6 ± 3.9 years, mean age of males was 12.4 ± 4.7 years. The median of number of repeated measurements per individual was 3 (range 1–8). The mGS was significantly (p < 0.001) correlated to body height and BMI (r = 0.303–0.432). Additional reference centiles for the change of z-scores of mGS were generated for children and young adults from 8 to 20 years.ConclusionsWe proposed to evaluate mGS in children, adolescents and young adults with the presented reference centiles adjusted to sex, age, height and BMI. The method presented may also be applicable to other biological variables that depend more than just on sex and age. For the first time, also reference centiles to assess the change of mGS in repeated measurements were presented.

Household Health-Related Social Needs in Newborns and Infant Behavioral Functioning at 6 Months

Dysfunctional patterns of behavior during infancy can predict the emergence of mental health disorders later in childhood. The Baby Pediatric Symptom Checklist (BPSC) can identify indicators of behavioral disorders among children aged 0 to 18 months. Understanding the association of early health-related social needs (HRSNs) with poor infant behavioral functioning can inform interventions to promote early childhood mental well-being. To examine the association between household HRSNs in the first 4 months of life and BPSC results at 6 months. This was a retrospective cohort analysis of longitudinal electronic health record data. Covariates were selected based on the biopsychosocial ecological model. Logistic regression analyses examined the association of HRSN domains and the number of HRSNs with the 6-month BPSC outcomes. Participants were recruited from 6 primary care clinics within 1 health system. Children aged 5 to 8 months who were evaluated for 6-month well-child visits between March 30, 2021, and June 30, 2022, were included in the study. Responses to the first HRSN screening tool that a caregiver completed for infants between 0 and 4 months of age. HRSN domains were examined individually and as the number of positive HRSNs. BPSC screen identified for clinical review due to 1 or more elevated subscales (inflexibility, irritability, and difficulty with routines) at 6 months. A total of 1541 children (mean [SD] age, 6.1 [0.5] months; 775 female [50.3%]) were included in the study. A total of 405 children (26.3%) had a BPSC screen identified for clinical review, and 328 caregivers (21.3%) reported at least 1 HRSN. Food insecurity (174 [11.3%]) and benefits issues (169 [11.0%]) were the most frequently reported HRSN. Children in households with food insecurity had statistically significant higher odds of inflexibility (adjusted odds ratio [aOR], 1.73; 95% CI, 1.14-2.63), difficulty with routines (aOR, 1.64; 95% CI, 1.05-2.57), and irritability (aOR, 1.86; 95% CI, 1.13-3.08) than children in households without food insecurity. Children in households with benefits issues had statistically significant higher odds of difficulty with routines (aOR, 1.70; 95% CI, 1.10-2.65) and irritability (aOR, 1.70; 95% CI, 1.03-2.82). Children in households with 2 or more HRSNs had consistently higher odds of having a BPSC screen identified for clinical review (aOR, 2.16; 95% CI, 1.38-3.39) compared with children with no HRSNs. Results of this cohort study suggest that household food insecurity, benefits issues, and the number of HRSNs were significantly associated with a BPSC screen identified for clinical review at 6 months of age. These findings highlight the urgency of intervening on HRSNs in the newborn period to prevent adverse infant behavioral outcomes.

Impact of COPD on mortality: An 8-year observational retrospective healthcare claims database cohort study

BackgroundChronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality. Here we present a large observational study on the association of COPD and exacerbations with mortality (AvoidEx Mortality). MethodsA real-world, observational cohort study with longitudinal analyses of German healthcare claims data in patients ≥40 years of age with a COPD diagnosis from 2011 to 2018 (n = 250,723) was conducted. Patients entered the cohort (index date) upon the first COPD diagnosis. To assess the impact of COPD on all-cause death, a propensity score-matched control group of non-COPD patients was constructed. The number and severity of exacerbations during a 12-month pre-index period were used to form subgroups. For each exacerbation subgroup the exacerbations during 12 months prior to death were analysed. ResultsCOPD increases the all-cause mortality risk by almost 60% (HR 1.57 (95% CI 1.55–1.59)) in comparison to matched non-COPD controls, when controlling for other baseline covariates. The cumulative risk of death after 8 years was highest in patients with a history of more than one moderate or severe exacerbation. Among all deceased COPD patients, 17.2% had experienced a severe, and 34.8% a moderate exacerbation, within 3 months preceding death. Despite increasing exacerbation rates towards death, more than the half of patients were not receiving any recommended pharmacological COPD therapy in the year before death. ConclusionOur study illustrates the impact of COPD on mortality risk and highlights the need for consequent COPD management comprising exacerbation assessment and treatment.

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.

Multivariate contaminated normal mixture regression modeling of longitudinal data based on jointmean‐covariancemodel

AbstractOutliers are common in longitudinal data analysis, and the multivariate contaminated normal (MCN) distribution in model‐based clustering is often used to detect outliers and provide robust parameter estimates in each subgroup. In this paper, we propose a method, the mixture of MCN (MCNM), based on the joint mean‐covariance model, specifically designed to analyze longitudinal data characterized by mild outliers. Our model can automatically detect outliers in longitudinal data and provide robust parameter estimates in each subgroup. We use iteratively expectation‐conditional maximization (ECM) algorithm and Aitken acceleration to estimate the model parameters, achieving both algorithm acceleration and stable convergence. Our proposed method simultaneously clusters the population, identifies progression patterns of the mean and covariance structures for different subgroups over time, and detects outliers. To demonstrate the effectiveness of our method, we conduct simulation studies under various cases involving different proportions and degrees of contamination. Additionally, we apply our method to real data on the number of people infected with AIDS in 49 countries or regions from 2001 to 2021. Results show that our proposed method effectively clusters the data based on various mean progression trajectories. In summary, our proposed MCNM based on the joint mean‐covariance model and MCD of covariance matrices provides a robust method for clustering longitudinal data with mild outliers. It effectively detects outliers and identifies progression patterns in different groups over time, making it valuable for various applications in longitudinal data analysis.

Routine health data describe adherence and persistence patterns for oral diabetes medication for a virtual cohort in the Khayelitsha sub-district of Cape Town, South Africa.

Type 2 diabetes mellitus (T2DM) is managed with combined lifestyle modifications and antidiabetic drugs, but people on treatment often fail to reach glycaemic control. Adherence is important for achieving optimal glycaemic control, and management of diabetes with drugs is a lifelong process, so understanding adherence through analysis of longitudinal medications data is important. Using retrospective routine health data and metformin dispensing records as a proxy for medication use, we describe longitudinal persistence and adherence to oral diabetes medication in a virtual cohort of 10541 people with diabetes (PLWD) in Khayelitsha subdistrict, Cape Town. Adherence was measured in 120-day sliding windows over two years and used to estimate metformin adherence trajectories. Multinomial logistic regression identified factors influencing these trajectories. Analysis of pharmacy dispensing records showed varying medication refill patterns: while some PLWD refilled prescriptions consistently, others had treatment gaps with periods of non-persistence and multiple treatment episodes-from one to five per individual across two years. There was a general trend of decreasing adherence over time across all sliding windows in the two-year period, with only 25% of the study population achieved medication adherence (> = 80% adherence) after two years. Four adherence trajectories; 'low adherence gradual decline (A), 'high adherence rapid decline' (B), 'low adherence gradual increase (C) and 'adherent' (D) were identified. Only trajectory D represented participants who were adherent at treatment start and remained adherent after two years. Taking HIV antiretroviral treatment before or concurrently with diabetes treatment and taking metformin in combination with sulphonylurea and/or insulin were associated with the long-term adherence (trajectory D). Routine data shows real life medication implementation patterns which might not be seen under controlled study conditions. This study illustrates the utility of these data in describing longitudinal adherence patterns at both an individual and population level.

DATA ANALYTICS FOR HEALTH-RELEVANT EVENTS DETECTION BASED UPON LONGITUDINAL FITBIT HEART RATE DATA

Abstract The increasing prevalence of wearable devices enables low-cost, long-term collection of health relevant data such as heart rate, exercise, and sleep signals. Currently these data are used to monitor short term changes with limited interpretation of their relevance to health. These data provide an untapped resource to monitor daily and long-term activity patterns. Changes and trends identified from such data can provide insights and guidance to the management of many chronic conditions that change over time. In this study we conducted a machine learning based analysis of longitudinal heart rate data collected over multiple years from Fitbit devices. We built a multi-resolutional pipeline for time series analysis, using model-free clustering methods inspired by statistical conformal prediction framework. With this method, we were able to detect health relevant events, their interesting patterns (e.g., daily routines, seasonal differences, and anomalies), and correlations to acute and chronic changes in health conditions. We present the results, lessons, and insights learned, and how to address the challenge of lack of labels. The study confirms the value of long-term heart rate data for health monitoring and surveillance, as complementary to extensive yet intermittent examinations by health care providers.

REDUCING HOSPITAL ADMISSIONS FOR PEOPLE LIVING WITH DEMENTIA—RESULTS FROM THE GERMAN DEMWG-STUDY

Abstract People living with dementia (PlwD) have a high risk of being admitted to hospital, especially in advanced stages of dementia – with often severe negative consequences. The DemWG-study therefore aimed to reduce the number of hospital admissions for long-term care dependent PlwD by a complex intervention - including motor and cognitive training for PlwD and education of caregivers and general practitioners. By means of a cluster randomised longitudinal design, data on hospital admissions as well as quality of life, risk of falls, challenging behaviours and cognitive functioning were assessed in 97 shared-housing arrangement (SHA) in Germany with a total of 341 PlwD at baseline and after a 6 months intervention. Results of the of longitudinal data analysis showed a significant decline in hospital admissions for the intervention period and less challenging behaviours. Quantitative and additionally qualitative data were also collected 12 and 18 months after baseline. Caregivers perceived improved social interaction, which in turn has a positive effect on living together in the SHA. Based on the results and in light of the urgent need for easy to implement non-pharmacological and/or psychosocial therapy approaches recommendations for better care and support can be derived and the intervention can be implemented in everyday practice.

Effect of the thermal environment on mortality: analysis of longitudinal data from Cyprus (2009–2018)

Exposure to a suboptimal outdoor thermal environment is associated with increased morbidity and mortality. This study aimed to examine the effect of the thermal environment on all-cause mortality in Cyprus. Daily mortality data were obtained from 2009 to 2018. Hourly meteorological data were retrieved from the ERA5-Land reanalysis database and air pollutant concentrations were obtained from the official air quality network of the Republic of Cyprus. Air temperature (Tair), the physiologically equivalent temperature (PET), and the universal thermal climate index (UTCI) were used to assess the thermal environment. Associations of Tair, PET, and UTCI with daily mortality were examined using negative binomial regression while adjusting for air quality. Mortality in the cold period (December to March) increased by 3.3% for each 1 °C decrease in Tair and by up to 1.8% for each 1 °C decrease in PET and UTCI (p value < 0.001). In the warm period (April to November), a 1 °C increase in Tair or in PET and UTCI was associated with an increase in mortality of up to 1.4% and 1.1% (p value < 0.001), respectively. The effect (increase in mortality) was more pronounced when the analysis focused on winter (3.6% per 1 °C decrease in Tair, 2.4% per 1 °C decrease in PET, and 2% per 1 °C decrease in UTCI) and summer (4.1% per 1 °C increase in Tair, 2.4% per 1 °C increase in PET, and 1.2% per 1 °C increase in UTCI). Males in cold periods and the elderly (older than 64 years) were more vulnerable. Awareness about the effects of the thermal environment on mortality, including those during the cold months of the year, should be enhanced.

ESG and dividend distribution decisions: evidence of moderation by shareholder activism

Purpose Environment, social and governance (ESG) practices and shareholder activism are making significant strides in the decision-making policies and processes for all firms. This study aims to assess the impact of ESG on the dividend payout decisions of firms in India. In addition, it also aims to determine how shareholder activism influences the impact of ESG on dividend distribution decisions. Design/methodology/approach The authors gather relevant data from 78 non-financial listed Indian firms from 2016 to 2020. This study undertakes longitudinal data analysis, with fixed effects and calculation of robust standard errors. In addition, the slope test is used to examine the effects of the interaction between ESG and shareholder activism. Findings It is found in the study that not only does ESG positively impact the dividends but also shareholder activism positively impacts the dividend distribution decisions. Surprisingly, the authors see a significant but negative interaction impact of shareholder activism on the positive association of ESG with dividend distribution decisions. In other words, ESG impacts dividend distribution decisions differently at levels of shareholder activism. When shareholder activism is low, ESG positively influences dividend distribution decisions. However, when shareholder activism is high, ESG negatively influences dividend distribution decisions. Practical implications This result has significant implications for all the stakeholders, including shareholders. A shareholder expecting a dividend could decide correctly through the current study’s findings. In cases of high shareholder activism, investors may skip picking a stock if investors expect high ESG to influence the dividend distribution decisions favourably. On the contrary, investors may choose a stock if shareholder activism is low and all else remains the same. Originality/value Literature has some evidence of the influence of shareholder activism and ESG (in silos) on the dividend distribution decisions in the firms. This study attempts to contribute by bringing forth the interaction effects of shareholder activism and ESG on dividend distribution decisions.