Longitudinal Data Research Articles

Rural households' livelihood transitions in China: processes, drivers and outcomes

PurposeThis study aims to comprehensively examine the transitions in household livelihood strategies within rural China, including the underlying processes, drivers and outcomes.Design/methodology/approachThis study uses two waves (2010 and 2018) of longitudinal data from the China Family Panel Studies (CFPS), employing latent cluster analysis, regression models and cumulative distribution function within a dynamic household livelihood strategy framework.FindingsThe results show that (1) households’ livelihood strategies can be categorized into four distinct types, i.e. agricultural dominated, agricultural dominated with non-agricultural supplementation, non-agricultural dominated with agricultural supplementation and employment oriented. (2) During 2010–2018, approximately 60% of households underwent transitions in their livelihoods, encompassing both upward and downward trajectories, with a prevalence of upward transitions. (3) Various forms of livelihood capital significantly contribute to upward transitions, while the availability of land resources and exposure to shocks impede the potential for upward mobility. (4) The transition towards non-agricultural livelihood strategies by households leads to a notable enhancement in their livelihood welfare.Research limitations/implicationsIn the context of urbanization, industrialization and globalization, rural areas in China are undergoing a gradual socioeconomic transformation, which has also led to changes in rural households’ livelihood strategies. Nevertheless, a dearth of empirical investigation exists regarding the dynamics of rural households’ livelihood strategies, the determinants behind such transitions and the resulting outcomes on their livelihoods. A comprehensive understanding of livelihood transitions can provide valuable insights for policymakers in their endeavors to promote rural revitalization in China.Originality/valueBased on the nationwide representative datasets in China, it examines the micro-level livelihood transitions of rural households within the broader context of socioeconomic transformation that presents both opportunities and challenges, as well as vulnerable contexts, shaped by various government policies. This exploration would offer valuable theoretical and empirical evidence to advance our understanding of the process, driver and outcome of rural households’ livelihood transition in developing countries.

Dupilumab Improves Patient-Reported Outcomes as Early as 1 Month among Adults with Prurigo Nodularis in Clinical Practice: Initial Results from the RELIEVE-PN Study

Introduction: Dupilumab is a monoclonal antibody approved in the United States (US) for patients aged ≥18 years with prurigo nodularis (PN). This analysis evaluated the real-world initial impact of dupilumab therapy on symptoms and treatment satisfaction among US adults with PN. Methods: RELIEVE-PN is an ongoing longitudinal prospective patient survey study assessing the real-world effectiveness of dupilumab in the treatment of adult patients with PN. Adults with PN were recruited through the US dupilumab patient support program and surveyed before (baseline) and 1 month after dupilumab initiation. PN symptoms over the last 7 days were assessed using the worst-itch numeric rating scale (WI-NRS), average-itch NRS, skin pain NRS, and skin burning or stinging/tingling NRS (range 0 [no symptom] to 10 [most severe symptom] for all). Treatment satisfaction with current therapy was evaluated using a 7-point Likert scale from “extremely satisfied” to “extremely dissatisfied”. Results: Of 84 patients (mean age, 57.5 years; 79.8% female; 83.3% White) completing the baseline survey and initiating dupilumab, 62 (mean age, 56.4 years; 77.4% female; 83.9% White) completed the Month 1 survey. At baseline, mean (SD) WI-NRS, average itch NRS, skin pain NRS, and skin burning/tingling NRS were 7.70 (2.24), 6.45 (2.25), 5.06 (2.87), and 5.51 (3.02), respectively. By month 1, mean (SD) WI-NRS, average itch NRS, skin pain NRS, and skin burning or stinging/tingling NRS improved to 5.56 (2.89), 4.35 (2.23), 3.40 (2.46), and 3.76 (2.60), respectively (p<0.001 for all). Additionally, significantly more patients reported they were satisfied with current PN treatment(s) at 1 month after dupilumab treatment initiation vs prior to starting dupilumab (77.4% vs 13.1%; p<0.001). Conclusion: Early results from the RELIEVE-PN study demonstrate improvement in symptoms and patient satisfaction with therapy as early as 1-month from treatment initiation of dupilumab. Further comparative research is needed to confirm these findings. Acknowledgment: Research was funded by Sanofi and Regeneron Pharmaceuticals Inc.

Identifying predictors of stroke in young adults: a machine learning analysis of sex-specific risk factors

IntroductionStroke among Americans under age 49 is increasing. While the risk factors for stroke among older adults are well-established, evidence on stroke causes in young adults remains limited. This study used machine learning techniques to explore the predictors of stroke in young men and women.MethodsThe least absolute shrinkage and selection operator algorithm (LASSO) was applied to data from Wave V of the National Longitudinal Survey of Adolescent to Adult Health (N = 12,300)—nationally representative, longitudinal panel containing demographic, lifestyle, and clinical information for individuals aged 33–43—to identify the key factors associated with stroke in men and women. The resulting LASSO model was tested and validated on an independent sample and model performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration. For robustness, synthetic minority over sampling technique (SMOTE) was applied to address data imbalance and analyses were repeated on the balanced sample.ResultsApproximately 1.1% (N = 59) and 1.3% (N = 90) of the 5,318 and 6,970 men and women in the sample reported having a stroke. LASSO was used to predict stroke using demographic, lifestyle, and clinical predictors on both balanced and imbalanced data sets. LASSO performed slightly better on the balanced data set for women compared to the unbalanced set (Female AUC: 0.835 vs. 0.842), but performance for men was nearly identical (Male AUC: 0.820 vs. 0.822). Predictor identification was similar across both sets. For females, marijuana use, receipt of health services, education, self-rated health status, kidney disease, migraines, diabetes, depression, and PTSD were predictors. Among males, income, kidney disease, heart disease, diabetes, PTSD, and anxiety were risk factors.ConclusionsThis study showed similar clinical risk factors among men and women. However, variations in the behavioral and lifestyle determinants between sexes highlight the need for tailored interventions and public health strategies to address sex-specific stroke risk factors among young adults.

Bridging theory and data: A computational workflow for cultural evolution

Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

Impact of Therapeutic Inertia on Patient-Reported Outcomes in Moderate-to-Severe Atopic Dermatitis: A 12-Month Longitudinal Study from the TARGET-DERM AD Registry

Background: Therapeutic inertia, the delay or reluctance to modify treatment when goals are unmet, is a significant challenge in managing chronic diseases, including atopic dermatitis (AD). This inertia can lead to suboptimal disease control and affect patient outcomes. Objectives: This study evaluates the effect of therapeutic inertia on patient-reported outcomes (PROs) in moderate-to-severe AD patients undergoing systemic treatment over 3 to 12 months. Methods: We analyzed longitudinal data from the TARGET-DERM AD registry, which includes 3,457 patients with moderate-to-severe AD from 39 centers across the U.S. and Canada. Eligible patients had documented patient-reported outcomes (PROs) at the initiation of systemic therapy and at subsequent 3-month intervals up to 12 months of follow-up. We assessed the proportion of patients not meeting treatment targets based on expert consensus. Patients had a validated Investigator Global Assessment (vIGA-AD) score of 3 or more at initiation of either an advanced systemic therapy (AST) such as biologics or JAK inhibitors or a conventional systemic therapy (CST) such as cyclosporine, methotrexate, or prednisone. PROs were evaluated at 3-month intervals up to 12 months, assessing achievement against the predefined treatment targets. PRO measures included Worst-Itch (PROMIS Itch-Severity), POEM, PO-SCORAD, NRS-sleep, and NRS-pain with specific moderate and optimal target levels established for each. Itch-Severity (range: 0–10; moderate target: ≥4-point reduction, optimal target: score ≤1), POEM (range: 0-28; moderate target: ≥4-point reduction, optimal target: score ≤2), PO-SCORAD (range: 0-103; moderate target: score ≤24; optimal target: score ≤10), NRS-sleep and NRS-pain (range: 0-10; moderate target: reduction ≥3-points; optimal target: score ≤1). Results: Out of 2107 patients with moderate-to-severe AD, 445 qualifying participants were included (63.8% adult, 62.0% female, 45.4% Non-Hispanic White, mean age of 31 years). Most patients (88.8%) initiated AST, with dupilumab being the most common (86.5%). At 6 months, significant proportions of AST-treated patients failed to reach moderate and optimal targets for itch (67% and 79%, respectively), POEM (46% and 69%), and NRS-sleep (59% and 47%). By 12 months, these figures were similar, with 66% and 88% failing to meet itch targets, 53% and 73% failing to meet POEM targets, and 62% and 45% failing to meet NRS-sleep targets, respectively. A similar pattern was observed for other PROs. CST-treated patients exhibited similar trends. Conclusions: The study highlights the profound impact of therapeutic inertia on the quality of life of patients with moderate-to-severe AD. Despite systemic therapy, a considerable proportion failed to meet treatment targets over a 12-month period, underscoring the need for more proactive and responsive treatment strategies in AD management.

Computational fluid dynamics and shape analysis enhance aneurysm rupture risk stratification.

Accurately quantifying the rupture risk of unruptured intracranial aneurysms (UIAs) is crucial for guiding treatment decisions and remains an unmet clinical challenge. Computational Flow Dynamics and morphological measurements have been shown to differ between ruptured and unruptured aneurysms. It is not clear if these provide any additional information above routinely available clinical observations or not. Therefore, this study investigates whether incorporating image-derived features into the established PHASES score can improve the classification of aneurysm rupture status. A cross-sectional dataset of 170 patients (78 with ruptured aneurysm) was used. Computational fluid dynamics (CFD) and shape analysis were performed on patients' images to extract additional features. These derived features were combined with PHASES variables to develop five ridge constrained logistic regression models for classifying the aneurysm rupture status. Correlation analysis and principal component analysis were employed for image-derived feature reduction. The dataset was split into training and validation subsets, and a ten-fold cross validation strategy with grid search optimisation and bootstrap resampling was adopted for determining the models' coefficients. Models' performances were evaluated using the area under the receiver operating characteristic curve (AUC). The logistic regression model based solely on PHASES achieved AUC of 0.63. All models incorporating derived features from CFD and shape analysis demonstrated improved performance, reaching an AUC of 0.71. Non-sphericity index (shape variable) and maximum oscillatory shear index (CFD variable) were the strongest predictors of a ruptured status. This study demonstrates the benefits of integrating image-based fluid dynamics and shape analysis with clinical data for improving the classification accuracy of aneurysm rupture status. Further evaluation using longitudinal data is needed to assess the potential for clinical integration.

The Influence of Artificial Light Exposure on Indigenous Populations: Exploring Its Impact on Menarcheal Age and Reproductive Function

The impact of artificial light exposure on human health has garnered significant attention in recent years. In particular, its effects on reproductive health have raised concerns. Given that the onset of menarche serves as a crucial indicator of reproductive maturity, understanding the implications of artificial light exposure becomes paramount. Age of menarche onset occurs relatively earlier in urban females than females raised in rural areas. Besides the decline in age of menarche onset, exposure to artificial light may contribute to impairment in reproductive hormones, particularly gonadotropins, by disrupting rhythms of reproductive hormones, modulating stress hormones and kisspeptin productions, and causing body weight changes. This drastic environmentally induced change may increase the proportion of teenage pregnancies, unfulfilled childhood dreams, depression, and ill-prepared marriages, thus creating a potential need for public health intervention. Due to limited studies and often lack of longitudinal data, a significant knowledge gap exists in unraveling the potential mechanism involved in alteration of these physiologic processes. The purpose of the current review was to elucidate the intricate interplay between environmental factors, cultural practices, and biological processes within indigenous communities. By meticulously examining the multifaceted influences of artificial light, including its prevalence and varying intensity based on geographical locations and light pollution levels, this study aimed to provide scholarly insights in-to the pathophysiologic mechanisms underlying the observed changes. The findings of this inquiry will also inform evidence-based strategies and interventions aimed at safeguarding the reproductive well-being of indigenous populations amidst the escalating challenges posed by artificial light exposure.

Circulating collagen breakdown products as a biomarker for presence and instability of human intracranial aneurysms.

There is an unmet need for improved detection of intracranial aneurysms (IAs) and distinction between stable and unstable (high rupture risk) IAs. Within the IA wall, synthesis and degradation of type I collagen as the main molecular constituent balance each other to maintain IA stability. We hypothesized that collagen breakdown products could serve as molecular markers for IA presence and instability. This prospective, cross-sectional, single-center study included patients with unstable (growing/symptomatic/ruptured) and stable IAs and controls. We determined C-telopeptide (CTx) and c-terminal telopeptide (ICTP) as breakdown products of type I collagen in arterial and venous blood. We included 107 participants with IAs (52 stable/44 unstable) and 41 controls. The correlation between intra-aneurysmal and venous levels was r = 0.63 (p < 0.001) for ICTP, r = 0.55 (p = 0.001) for CTx. The odds of harboring an IA were five times higher for participants with high compared to low venous levels of collagen breakdown products (ICTP: odds ratio (OR) 4.9 (95% CI 1.1-22.7); CTx: OR 5.3 (95% CI 1.4-20.0)). The OR for having an unstable IA was 9.3 (95% CI 2.1-41.5) for patients with high compared to low venous ICTP levels. The area under the curve for ICTP levels as a marker for IA instability was 0.75. Increased levels of venous collagen breakdown products, especially ICTP levels, could serve as a biomarker for IA presence and instability and complement current data for management of unruptured IAs on an individual patient level. Future studies with longitudinal data are needed to validate ICTP as a biomarker for high risk IAs.

Impact of Select Variables on Tangible Book Value Multiple of U.S. Regional Banks

Aims: This study explores the relationship between regional banks' Price-to-Tangible Book Value (P/TBV) multiple and metrics such as profitability, liquidity, capital adequacy, asset quality, leverage, and operational efficiency. Study Design: A longitudinal panel data analysis of 101 regional U.S. banks, covering the financial years from 2003 to 2023. It employs fixed effects, random effects, and pooled OLS regression models to determine the drivers of P/TBV multiple. The analysis controls for time-specific factors such as pre – and post – financial crisis periods, enabling a more comprehensive evaluation of valuation trends over different economic cycles. Results: All three models were statistically significant in identifying the determinants of P/TBV. However, the fixed effects model was deemed the most appropriate, as confirmed by the Hausman test (p &lt; 0.001), which demonstrated its consistency over the random effects model. Additionally, the F-test (p &lt; 0.001) indicated the relevance of the fixed effects model over the pooled OLS model by highlighting the significance of individual effects. The fixed effects model and the fixed effects model with robust standard errors revealed that return on equity and bank efficiency positively influenced P/TBV, while asset size, non-performing loans, leverage, and dividend yield had significant negative effect. The model also showed higher valuations for regional banks in the pre-2008 financial crisis period compared to the post-crisis era. Additionally, upon accounting for the impact of heteroscedasticity, the statistical significance of loan-to-deposit ratio variable noted in the fixed effects model turns out to be insignificant. Conclusion: Financial stability, profitability, capital, and asset quality are crucial in determining regional bank valuations. The insights from this study will help bank managers identify key focus areas to optimize valuation and pricing strategies, ensuring that banks can align internal financial health with broader market dynamics for sustainable growth. Additionally, the findings will benefit investors by providing a deeper understanding of the financial factors driving bank valuations, enabling more informed investment decisions. While this research does not directly address the impact of macroeconomic variables on regional banks' valuations, it highlights an area that merits further investigation in future research.

Use of potentially inappropriate psychotropic medicines among older adults in 23 residential aged care facilities in Australia: a retrospective cohort study.

Psychotropic medications are frequently utilised in residential aged care facilities (RACFs). Longitudinal medication administration data can offer crucial insights into the potential inappropriate use of psychotropic medicines (PIPMs), guiding future quality improvement initiatives. This study aimed to determine the prevalence and predictors of PIPMs use and assess variation in PIPMs use by facility for residents of RACFs. We conducted a retrospective longitudinal cohort study using routinely collected electronic health data (2020-2021) relating to 3064 residents from 23 RACFs in New South Wales, Australia. The study included permanent residents aged ≥ 65 years and median length of stay was 483 days. The prevalence of PIPMs use was estimated using updated Beers criteria 2023. The extent of exposure to PIPMs was measured using two metrics i.e., number of days residents were exposed to PIPMs and the proportion of days covered by PIPMs. We used logistic regression model to determine factors associated with PIPM use. Funnel plots to visualised variation in PIPMs use across facilities. In total 40% (n = 1224) residents used at least one PIPM and 10% (n = 302) used ≥ 2. The most frequently used PIPMs categories were benzodiazepines and Z-drugs (27.4%), followed by first and second generation antipsychotics (17.2%). Certain diagnoses (dementia, pain, depression, anxiety, and endocrine disorders) were associated with the increased use of PIPMs. For example, residents with dementia were 1.94 times more likely to use ≥ 2 PIPMs (OR 1.94; 95% CI 1.50-2.51). The prevalence of at least one PIPM by residents in each facility ranged from 23.3 to 57.0% across facilities. The overall median number of days residents were exposed to PIPMs were 91 days (IQR 6-320) while the median proportion of days covered by at least one PIPM was 39.3% (IQR 2.6-86.6%). Residents in aged care facilities showed a high rate of PIPMs use with substantial variation across facilities. Quality improvement initiatives which target inappropriate psychotropic medication use are necessary, particularly considering the link between psychotropic drug use and adverse events such as falls.

An empirical study on smart city construction to enhance residents' participation in physical activity.

Smart city construction, as a new model of urban development, has become a crucial means to encourage residents' participation in physical exercise. However, there is still a lack of systematic exploration regarding the mechanisms by which smart city construction specifically affects residents' engagement in physical activity. This paper analyzes the impact of smart city construction on residents' participation in physical activity using a Difference-in-Differences (DID) model, based on data from the 2010 to 2020 China Family Panel Studies (CFPS). Data processing involved removing missing values, addressing outliers, and introducing control variables at the individual, household, and city levels. The study found that smart city construction significantly promotes residents' participation in physical activity. Smart city policies have a notable positive effect on this participation, and there are significant differences in the responses of various resident groups to physical activity engagement. The results of the mediation analysis indicate that the built environment, information access channels, and enjoyment of consumption play significant mediating roles between smart city policies and residents' participation in physical activity. Smart city construction influences residents' participation in physical activity both directly or indirectly by enhancing urban infrastructure, expanding access to information, and fostering enjoyable consumption experiences. In the future, smart city initiatives should prioritize the equitable distribution of resources, strengthen infrastructure development in rural and western regions, and further enhance the national fitness level.

Does Experience of Vaccination Improve Vaccine Confidence and Trust? Policy Feedback Effects of Mass COVID-19 Vaccination in the United States.

Policy feedback research has demonstrated that a highly tangible policy that shapes public attitudes through direct and day-to-day experiences often reshapes public opinion, with the effect of generating supportive or skeptical constituencies that determine the sustainability of future programs. This paper examines the impact of mass vaccination efforts on attitudes towards vaccines in a context of high vaccine hesitancy in the U.S. We analyzed 73,092 survey responses from 9,229 participants in the longitudinal data from the Understanding America Study project, covering December 2020 to July 2023. Using two-way fixed-effects ordinary least squares regression and ordinal logistic regression, we estimated the changes in attitudes towards vaccines, including trust in vaccine manufacturing and approval processes, following COVID-19 vaccinations. COVID-19 vaccination was associated with improved perceptions of vaccine effectiveness and social benefits and reduced mistrust in vaccine-related processes. However, it did not significantly alleviate concerns regarding vaccine side effects and illness. The strongest effects were observed among respondents initially hesitant but who eventually vaccinated. The experience of COVID-19 vaccination generally improved attitudes and confidence in COVID-19 vaccines among the U.S. public, particularly among vaccine-hesitant people. These effects could have positive impacts on future immunization programs by mitigating vaccine hesitancy.

Progression of Motor Speech Function in Speakers With Primary Progressive Apraxia of Speech.

Speakers with primary progressive apraxia of speech (PPAOS) have an insidious onset of motor speech planning/programming difficulties. As the disease progresses, the apraxia of speech (AOS) becomes more severe and a co-occurring dysarthria often emerges. Here, longitudinal data from speakers with phonetic- and prosodic-predominant PPAOS are used to characterize the progression of their motor speech impairment, including the development of dysarthria and mutism. Data are presented from 52 speakers who had PPAOS at enrollment (i.e., progressive AOS in the absence of aphasia, cognitive, or other neurologic symptoms). Twenty-one had predominantly phonetic features, whereas 31 had primarily prosodic features. All participants underwent a comprehensive motor speech evaluation at their enrollment visit and each annual return visit, with a median of three visits per participant. Almost 25% of the speakers with PPAOS presented with dysarthria at their enrollment visit (median disease duration of 3.65 years), whereas more than 70% of them had developed dysarthria by their last visit (median disease duration of 6.85 years). Neither the likelihood to develop dysarthria nor the disease duration at which it was detected differed significantly between the phonetic and prosodic groups. However, muteness emerged sooner for speakers with phonetic-predominant PPAOS; the median disease duration at which they became mute was 1.5 years shorter than for their prosodic counterparts. Clinically, these results facilitate more accurate prognostication of motor speech symptoms in speakers with PPAOS, allowing for timely introduction of alternative means of communication. The results also support the differentiation between progressive AOS and dysarthria as distinct motor speech disorders that often co-occur in these individuals.

Does the presence of a sustainability committee strengthen the impact of ESG disclosure on tax aggressiveness? Insights from North America

AbstractWe investigate the influence of ESG disclosure on tax aggressiveness within the North American Travel and Leisure (T&amp;L) sectors, specifically examining the role of sustainability committees in this relationship. Our analysis utilizes longitudinal panel data from the USA B3000 and Canadian S&amp;P/TSX indices over the period from 2010 to 2020. Employing fixed-effects panel quantile regression with two distinct measures of tax aggressiveness, our findings indicate that firms with a focus on ESG tend to display higher levels of tax aggressiveness. This suggests that some companies might use strong ESG performance as a facade to obscure aggressive tax strategies. Moreover, our research introduces new evidence that the existence of sustainability committees can both hinder corporate tax aggressiveness and foster an ethical corporate culture, which aligns higher ESG engagement with lower tax aggressiveness. Our study underscores the importance of fostering tax compliance in T&amp;L companies, emphasizing that individuals and corporations, which often seek direct state benefits, regard robust public services as essential for encouraging adherence to tax regulations. Furthermore, sustainability committees play a crucial role in enabling firms to address broader social issues, including tackling tax aggressiveness, thus shaping their sustainability agendas.

The Moderating Role of Self-Esteem in the Relationship Between Social Media Use and Life Satisfaction Among Older Adults

This study examines the relationship between social media use and life satisfaction among older adults, with a focus on the moderating role of self-esteem. Cross-sectional data from the 2021 Korea Media Panel Survey were analyzed, focusing on responses from 192 older adults aged 65 and older who had experience using social media platforms. The findings reveal that among older adults with low self-esteem, social media use is negatively associated with life satisfaction, whereas for those with high self-esteem, this association reverses and becomes positive. However, for older adults with medium self-esteem, the relationship between social media use and life satisfaction is not significant. This study contributes to the scholarly understanding of the structural relationship between social media use, self-esteem, and life satisfaction among older adults and offers insights for tailored interventions aimed at enhancing well-being in this demographic.

Multistate Markov chain modeling for child undernutrition transitions in Ethiopia: a longitudinal data analysis, 2002-2016.

The use of the multistate Markov chain model is a valuable tool for studying child undernutrition. This allows us to examine the trends of children's transitions from one state to multiple states of undernutrition. In this study, our objective was to estimate the median duration for a child to first transition from one state of undernutrition to another as well as their first recurrence of undernutrition and also to analyze the typical duration of undernourishment. This involves understanding the central tendency of these transitions and durations in the context of longitudinal data. We used a longitudinal dataset from the Young Lives cohort study (YLCS), which included approximately 1997 Ethiopian children aged 1-15years. These children were selected from five regions and followed through five survey rounds between 2002 and 2016. The surveys provide comprehensive health and nutrition data and are designed to assess childhood poverty. To analyze this dataset, we employed a Markov chain regression model. The dataset constitutes a cohort with repeated measurements, allowing us to track the transitions of individual children across different states of undernutrition over time. The findings of our study indicate that 46% of children experienced concurrent underweight, stunting, and wasting (referred to as USW). The prevalence of underweight and stunted concurrent condition (US) was 18.7% at baseline, higher among males. The incidence density of undernutrition was calculated at 22.5% per year. On average, it took 3.02months for a child in a wasting state to transition back to a normal state for the first time, followed by approximately 3.05months for stunting and 3.89months for underweight. It is noteworthy that the median duration of undernourishment among children in the US (underweight and stunted concurrently) state was 48.8months, whereas those concurrently underweight and wasting experienced a median of 45.4months in this state. Additionally, rural children (HR = 1.75; 95% CI: 1.53-1.97), those with illiterate fathers (HR = 1.50; 95% CI: 1.38-1.62) and mothers (HR = 1.45; 95% CI: 1.02-3.29), and those in households lacking safe drinking water (HR = 1.70; 95% CI: 1.26-2.14) or access to cooking fuel (HR = 1.95; 95% CI: 1.75-2.17) exhibited a higher risk of undernutrition and a slower recovery rate. This study revealed that rural children, especially those with illiterate parents and households lacking safe drinking water but cooking fuels, face an increased risk of undernutrition and slower recovery.

Remote work and psychological distress: evidence from the understanding America study dataset

PurposeThe purpose of this paper is to examine how working remotely relates to psychological distress and also how this relationship varies by gender, age and employment sector.Design/methodology/approachWe examined 30 waves of longitudinal data (observations = 76,845 and respondents = 4,542) drawn from the Understanding America Study (UAS), a nationally representative panel collected during the period March 2020–October 2021, and analyzed the data using a negative binomial panel model.FindingsThe results show that both female and male workers who worked remotely during this time period experienced psychological distress. For workers aged 30–54 and those 55 years of age or above, and those who worked for nonprofit and private organizations, remote work was strongly related to psychological distress. Alternatively, remote work had no significant effect on psychological distress among younger and government workers.Originality/valueThe paper contributes to both research and practice related to flexible work arrangements by delving into the influence of remote work on a key employee well-being outcome, psychological distress, which has been understudied in research on human resource practices.

Natural assets, changes, and variations of the socioeconomic-environmental systems along the Asian drylands belt

Abstract The amount of and changes in a country's natural assets are crucial for developing national strategic plans and policies due to their foundational role in determining the resilience of social-environmental systems (SES), especially under a changing climate. Many integrated metrics on natural assets have been proposed based on individual measures to provide insights into the state of national natural resources. This is particularly true for countries experiencing extreme environmental stresses. Drawing on longitudinal data spanning from 1980 to 2020, the objective of this study is to analyze nuanced distinctions across 23 political entities (PEs) in the dryland regions of mid-latitudinal Asia, investigating their interannual variabilities over the four-decade study period and discerning potential driving forces. We examined three key integrated measures of the SES: ecosystem water use efficiency (WUE), human appropriation of net primary production (HANPP), and human appropriation of water resources (HAWR). We found increased variability as well as spikes in extreme values in each of these three measures of SES function among the 23 PEs over the study period. Water stress played an increasing and more important role than temperature in influencing the magnitude and variations of the three measures. Our results also indicate that human interventions may help increase the efficiency of water use in this dryland region of the world.

Understanding anthropomorphic voice-AI chatbot continuance from a human-AI interaction perspective

ABSTRACT Although anthropomorphic AI technologies nowadays are significantly changing the human-AI interaction, the mechanism of how voice-AI chatbots’ anthropomorphism affects users’ continuance behaviour is unclear. To explore the role of human-AI interaction and privacy concerns in the continuance intention of anthropomorphic voice-AI chatbots, a research model, based on parasocial relationship theory, is developed. The research model was then empirically tested against a cross-sectional survey from 473 voice-AI chatbot users and two-wave longitudinal data collected from 271 voice-AI chatbot users. The Structural Equation Modelling (SEM) results indicate that voice-AI chatbots’ anthropomorphism affects continuance intention via both human-AI interaction fluency and human-AI rapport building. In particular, the impact of voice-AI chatbots’ anthropomorphism on voice-AI chatbots’ continuance intention is fully mediated by human-AI interaction fluency and human-AI rapport building. Moreover, the influence of human-AI interaction fluency on voice-AI chatbots’ continuance intention will decrease and the impact of human-AI rapport building on voice-AI chatbots’ continuance intention will increase when users’ privacy concerns are high. The findings provide new insights into AI chatbot research from a human-AI interaction perspective. Developers of voice AI chatbots could focus on anthropomorphism, interaction and user information collection strategies to increase user continuance intention.

A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.

Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.