Multinomial Model Research Articles

Preferences in treating polyomavirus infection in kidney transplant recipients: A discrete choice experiment with patients, caregivers, and clinicians.

Treatment strategies for BK polyomavirus (BKPyV) infection in kidney transplant recipients are heterogeneous among clinicians. We aimed to identify the treatment preferences of key stakeholders for BKPyV infection and measure the trade-offs between treatment outcomes. Adult kidney transplant recipients, caregivers, and clinicians were eligible to participate in a discrete choice experiment between February 2021 and June 2022. The five treatment-related attributes were achieving viral clearance and optimal graft function, as well as reducing the risk of graft loss, acute rejection, and complications. Results were analyzed using multinomial logistic models. In total, 109 participants (57 kidney transplant recipients, 10 caregivers, and 42 health professionals) were included. The most important attribute was the risk of graft loss, followed by side effects and acute rejection. As the risk of graft loss increased, all participants were less inclined to accept an assigned treatment strategy. For instance, if graft loss risk was increased from 1% to 50%, the probability of uptake of a treatment strategy for BKPyV infection was reduced from 87% to 3%. Graft loss is the predominant concern for patients, caregivers, and health professionals when deciding on the treatment for BKPyV infection, and should be included in intervention trials of BKPyV infection.

Revisiting the Briggs Ancient DNA Damage Model: A Fast Maximum Likelihood Method to Estimate Post-Mortem Damage.

One essential initial step in the analysis of ancient DNA is to authenticate that the DNA sequencing reads are actually from ancient DNA. This is done by assessing if the reads exhibit typical characteristics of post-mortem damage (PMD), including cytosine deamination and nicks. We present a novel statistical method implemented in a fast multithreaded programme, ngsBriggs that enables rapid quantification of PMD by estimation of the Briggs ancient damage model parameters (Briggs parameters). Using a multinomial model with maximum likelihood fit, ngsBriggs accurately estimates the parameters of the Briggs model, quantifying the PMD signal from single and double-stranded DNA regions. We extend the original Briggs model to capture PMD signals for contemporary sequencing platforms and show that ngsBriggs accurately estimates the Briggs parameters across a variety of contamination levels. Classification of reads into ancient or modern reads, for the purpose of decontamination, is significantly more accurate using ngsBriggs than using other methods available. Furthermore, ngsBriggs is substantially faster than other state-of-the-art methods. ngsBriggs offers a practical and accurate method for researchers seeking to authenticate ancient DNA and improve the quality of their data.

Effects of Altitude on Socio-economic Conditions and Environmental Sustainability of Farm Households in North Pakistan (A Case Study of Namli Maira, Hazara Division)

This research study was based on the effect of altitude on farmers’ socio-economic conditions in northern Pakistan. Based on the high altitude, the air is thinner and has less ability to retain heat, which makes these mountainous areas more vulnerable to environmental sustainability and climate change as a result of increased natural hazards in the region. Thus, by employing a multinomial regression model, we analyzed the data by taking categorical observations for transect1, transect2 and transect3. The findings of the study show that for both commercial and subsistence purposes, agriculture was the most prominent source of income in all transects, particularly in transect1. It also contributed the largest level of income in transect1; however, in the other two transects, private employment was reported to contribute the largest levels of income, respectively. Furthermore, transect1, based on the education of the households, has a significant and negative effect. Income from off-farm and income from agricultural practices also have significant effects. Moreover, transect2 and transect3 have significant effects on land cultivated and income from agricultural practices. This study recommends that the government should subsidize the agricultural inputs for those remote households that have been engaged in agricultural productivity since the beginning of the country’s existence. It will make those households much more food secure and provide sufficient earnings from these farms.

Judgments of learning (JOLs) impact item memory but not source memory: Insights into JOL reactivity using a multinomial model.

Past research has evaluated participants' understanding of their memory by soliciting judgments of learning (JOLs). Importantly, JOLs sometimes change memory for the judged material, leading to JOL reactivity. The cue-strengthening account (Soderstrom et al., 2015) and changed-goal account (Mitchum et al., 2016) propose different mechanisms that lead to JOL reactivity. In the present study, we collected measures that can provide further insight into these mechanisms. Specifically, participants studied related and unrelated word pairs in different colored fonts for a source recognition test. Across three experiments, data were analyzed using a hierarchical Bayesian model of multidimensional source memory to determine how JOLs impact item memory as well as relatedness and color source memory. In Experiment 2, we also compared the effects of making JOLs to making judgments of relatedness (JORs), and Experiment 3 examined how JOLs impact study time allocation. The results of our experiments failed to fully follow predictions of either account. Making JOLs (Experiments 1-3) and JORs (Experiment 2) strengthened item memory for related as well as unrelated pairs, the latter finding not predicted by either account. In addition, JOLs and JORs did not specifically strengthen source memory for relatedness, as the cue-strengthening account predicts, nor did JOLs change study time (Experiment 3), as suggested by the changed-goal account. In all, our results provide novel insight that enhanced item memory may be largely responsible for JOL reactivity, thus adjudicating between candidate explanations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Relationship Between Low Education and Poor Periodontal Status among Mexican Adults Aged ≥50 Years.

To examine the association between educational level and the presence of periodontal disease in adults ages ≥ 50 years in Mexico. A cross-sectional study was conducted on 2098 Mexican adults, using data from the annual reports of the Epidemiological Monitoring System for Oral Pathologies from 2019-2022. Data were collected on sociodemographic characteristics such as gender, age, educational level, oral hygiene, and diabetes. Periodontal status was evaluated using the Community Periodontal Index (CPI) and was classified into: CPI = 0 (healthy); CPI = 1 (bleeding on probing); CPI = 2 (calculus); and CPI = 3 or 4 (pocket depth ≥ 4 mm). A multinomial regression model was used to estimate the odds ratio (OR) and the 95% confidence intervals (CI), using periodontal status as the result. 39.9% of subjects presented periodontal pockets of ≥ 4 mm, 20.8% presented calculus, and 12.8% presented bleeding, while only 26.4% were classified as healthy. A low level of education (≤ 9 years) (OR = 4.84; p 0.001), age ≥ 65 years (OR = 1.33; p = 0.025), poor oral hygiene (OR = 6.86; p 0.001), smoking (OR = 1.51; p = 0.025), and diabetes (OR = 1.73; p 0.001) were statistically significantly associated with the presence of periodontal pockets ≥ 4 mm. A low level of education is associated with worse periodontal status in adults aged 50 years or more. These findings reiterate the importance of implementing effective strategies and the incorporation of interventions for improving the access to and quality of services targeted at aging communities.

On aggregation invariance of multinomial processing tree models

Multinomial processing tree (MPT) models are prominent and frequently used tools to model and measure cognitive processes underlying responses in many experimental paradigms. Although MPT models typically refer to cognitive processes within single individuals, they have often been applied to group data aggregated across individuals. We investigate the conditions under which MPT analyses of aggregate data make sense. After introducing the notions of structural and empirical aggregation invariance of MPT models, we show that any MPT model that holds at the level of single individuals must also hold at the aggregate level when it is both structurally and empirically aggregation invariant. Moreover, group-level parameters of aggregation-invariant MPT models are equivalent to the expected values (i.e., means) of the corresponding individual parameters. To investigate the robustness of MPT results for aggregate data when one or both invariance conditions are violated, we additionally performed a series of simulation studies, systematically manipulating (1) the sample sizes in different trees of the model, (2) model parameterization, (3) means and variances of crucial model parameters, and (4) their correlations with other parameters of the respective MPT model. Overall, our results show that MPT parameter estimates based on aggregate data are trustworthy under rather general conditions, provided that a few preconditions are met.

Probabilistic bias analysis for exposure misclassification of household income by neighbourhood in a cohort of individuals with colorectal cancer.

Despite poor agreement, neighbourhood income is used as a proxy for household income, due to a lack of data availability. We quantified misclassification between household and neighbourhood income and demonstrate quantitative bias analysis (QBA) in scenarios where only neighbourhood income is available in assessing income inequalities on colorectal cancer mortality. This was a retrospective study of adults with colorectal cancer diagnosed 2006-14 from Statistics Canada's Canadian Census Health and Environment Cohort. Neighbourhood income quintiles from Statistics Canada were used. Census household income quintiles were used to determine bias parameters and confirm results of the QBA. We calculated positive and negative predictive values using multinomial models, adjusting for age, sex and rural residence. Probabilistic QBA was conducted to explore the implication of exposure misclassification when estimating the effect of income on 5-year mortality. We found poor agreement between neighbourhood and household income: positive predictive values ranged from 21% to 37%. The bias-adjusted risk of neighbourhood income on 5-year mortality was similar to the risk of mortality by household income. The bias-adjusted relative risk of the lowest income quintile compared with the highest was 1.42 [95% simulation interval (SI) 1.32-1.53] compared with 1.46 [95% confidence interval (CI) 1.39-1.54] for household income and 1.18 (95% CI 1.12-1.24) for neighbourhood income. QBA can be used to estimate adjusted effects of neighbourhood income on mortality which represent household income. The predictive values from our study can be applied to similar cohorts with only neighbourhood income to estimate the effects of household income on cancer mortality.

Psychopathy checklist-youth version scores and recurring victimisation by an intimate partner in a justice-involved sample: A comparison of young men and women using longitudinal data.

People involved in the criminal justice system are at increased risk of recurring intimate partner violent (IPV) victimisation. Experience of trauma is linked to a variety of negative outcomes, including repeated experiences, so it is important to identify factors that may distinguish non-victims, single-event victims and recurring victims at an early stage as this could hold potential for intervention efforts. Research studies have identified individual-level risk factors for IPV victimisation but have not investigated psychopathy traits or sex differences. To examine sex differences in psychopathy scores and later IPV victimisation. Using the Pathways to Desistance study, a longitudinal study of 1354 adjudicated adolescent offenders, relationships between psychopathy checklist-youth version (PCL-YV) scores measured during the baseline wave, independent/control variables in wave 6 and self-reported IPV victimisation in wave 7 were explored using multinomial regression models. Among justice-involved young adults (average age 19), PCL-YV scores differentiated between non-victims, single-event victims and recurring victims. Higher total scores and higher ratings on the antisocial behaviours facet of psychopathy significantly increased the relative risk of recurring victimisation by an intimate partner overall, and specifically of emotional or physical intimate partner violence. For the young women but not the young men, a higher scoring on the interpersonal manipulation facet of psychopathy specifically increased the relative risk of being a recurring victim of any intimate partner violence. Sex-specific differences in relationships between high PVL-YV scores and recurring IPV victimisation can be used to screen for risk of repeated trauma for justice-involved young adults, which may change a trajectory of continued involvement in the criminal justice system to a trajectory of resilience and recovery.

A Comparative Analysis of the Factors Influencing Job Expectations among Unemployed Men and Women in Sri Lanka

In Sri Lanka, persistent gender differences in unemployment rates exist, with women enduring greater rates of unemployment de-spite having a higher average educational attainment than men. Gender differences in job search intensity vary with attitudes to-ward employment or unemployment as attitudes play a significant role in employment decision-making. These differences are intimately linked to people's expectations and mindsets regarding their work, which are strongly influenced by societal and cultural gender roles. This study looks at how job expectations differ between unemployed men and women in Sri Lanka. This study relies on data from the Department of Census and Statistics Sri Lanka Labour Force Survey (2021) and a multinomial logistic model to analyse the total unemployed, male unemployed, and female unemployed separately while taking two dependent variables; job sector expectations and occupational expectations. The study's findings emphasise the pivotal role of gender in sharpening job expectations among the unemployed. Regarding job sector expectations, unemployed women are more inclined towards jobs in the public sector than those in the private sector because of the government's reputation for offering standardized working hours and a work-life balance conducive environment. Furthermore, there are noteworthy differences in occupational expectations as unemployed women are more likely to expect professional and managerial level jobs with the increase in higher education enrolment. This study offers a groundbreaking contribution to the literature by unveiling unique, gender-specific determinants of job expectations among Sri Lanka's unemployed, highlighting the influence of unemployment duration, and employing distinct analytical models to bridge significant theoretical, empirical and methodological gaps. Finally, this study puts forth the broader discourse on gender, unemployment, and labour market dynamics in Sri Lanka, offering gender-specific policy recommendations aimed at bridging the expectation gap between unemployed men and women in Sri Lanka.

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

ABSTRACT Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

A systematic review of individual, social, and societal resilience factors in response to societal challenges and crises

Societal challenges put public mental health at risk and result in a growing interest in resilience as trajectories of good mental health during stressor exposure. Resilience factors represent multilevel psychosocial resources that increase the likelihood of resilient responses. This preregistered systematic review aims at summarizing evidence on the predictive value of individual, social and societal resilience factors for resilient responses to societal challenges and crises. Eligible studies examined the predictive value of resilience factors in stressor-exposed populations in high-income countries by means of multinomial regression models based on growth mixture modeling. Five databases were searched until August 2, 2023. Data synthesis employed a rating scheme to assess the incremental predictive value of resilience factors beyond sociodemographic variables and other resilience factors. An adapted version of the Newcastle-Ottawa Scale was used for risk of bias assessment. Fifty studies (sample sizes: 360–65,818 participants) with moderate study quality reported on various stressors (e.g., pandemics, natural disasters, terrorist attacks). Higher income, socioeconomic status and perceived social support, better emotion regulation and psychological flexibility were related to more resilient responses. The association between resilience factors and resilient responses was stronger in samples with younger mean age and a larger proportion of women. Most studies used non-representative convenience samples and effects were smaller when accounting for sociodemographic variables and other resilience factors. For many factors, findings were mixed, supporting the importance of the fit between resilience factors and situational demands. Research into social and societal resilience factors and multilevel resilience interventions is needed. Preregistration-ID: 10.17605/OSF.IO/GWJVA. Funding source: Robert Koch Institute (ID: LIR_2023_01).

Prevalence and correlates of e-cigarette source and use of e-cigarettes with nicotine: A case study of Mexico, where e-cigarettes are banned

BackgroundLow- and middle-income countries have increasingly banned e-cigarettes, as in Mexico. In these countries, little is known about where adults obtain e-cigarettes or who uses e-cigarettes with nicotine. MethodsData were analyzed from eight online surveys of Mexican adults who both smoked and used e-cigarettes (November 2018-March 2021; n = 2,060). For the e-cigarette they used most often, participants reported how they acquired it (social sources=reference; online purchase; vape shop purchase; other retail purchase) and if it contained nicotine (no=reference group; yes; don't know). Multinomial models regressed each of these outcomes on smoking- and e-cigarette-related factors, as well as sociodemographics. ResultsAlmost half the sample (45.9 %) reported obtaining their e-cigarettes from social sources, with online purchase being the second most common source (28.7 %). Being male, having recently attempted to quit smoking, and more frequent e-cigarette use were positively associated with purchasing e-cigarettes (vs social sources) across all venues. Most reported that their e-cigarettes contained nicotine (58.2 %), a third reported using e-cigarettes without nicotine (35.9 %), and some did not know (5.8 %). More frequent smoking and e-cigarette use, using closed e-cigarette devices and purchasing e-cigarettes online were positively associated with using e-cigarettes with nicotine. ConclusionsDespite Mexico's e-cigarette ban, adults who smoke access e-cigarettes through multiple sources, including online and vape shop purchases. Most participants reported using e-cigarettes with nicotine, though many did not or did not know.

Differential impact of financial hardship on food security between lesbian, gay, and bisexual and heterosexual cancer survivors in the United States.

137 Background: Lesbian, gay, and bisexual (LGB) cancer survivors experience more food insecurity as well as financial hardship than heterosexual cancer survivors. However, how financial hardship impacts food security differently between LGB and heterosexual survivors has not been explored. Methods: National Health Interview Survey data from 2019-2022 were pooled and weighted. Outcomes included a food insecurity measure (food secure, low food security, very low food security) and the manifestations of food insecurity (e.g., cutting size of meals, losing weight due to lack of food). Financial hardship was captured as a binary measure, dichotomized from an 11-item scale which included ability to pay medical bills, worry about paying bills, and delayed or foregone care due to costs. Bivariate analyses were conducted using Chi-Squared tests. Multinomial logistic regression models with an interaction between LGB status and financial hardship status were used to generate relative risk ratios controlling for age, time since last cancer diagnosis, sex, race, region, and year. Results: Of N=13,131 cancer survivors, 2.8% (n=374) identified as LGB. Low/very low food security was reported by 15.6% of LGB survivors in comparison to 6.1% of heterosexual survivors (p<0.001). When stratified by financial hardship, LGB survivors were more likely to report food insecurity in the presence of (22.3% vs. 11.2%, p<0.001) and absence of (6.8% vs. 2.2%, P<0.001) financial hardship. LGB survivors with very low food security often reported cutting the size/cutting meals (93.4%), eating less than they should (94.7%), and being hungry because they could not afford food (80.7%). In the multinomial model, LGB survivors with financial hardship had 5.6 (95%CI:2.9-10.7) times the relative risk of low food security, while heterosexual survivors with financial hardship had 3.9 the relative risk of low food security (95%CI: 2.9-5.2) in comparison to heterosexual survivors with no financial hardship. Furthermore, LGB survivors with financial hardship had 11.8 (95%CI:6.2-22.5) the relative risk of very low food security, LGB survivors with no financial hardship had 6.4 (95%CI: 2.0-20.2) the relative risk of very low food security, and heterosexual survivors with financial hardship had 5.3 the relative risk of low food security (95%CI: 3.6-7.7) in comparison to heterosexual survivors with no financial hardship. Conclusions: Experiencing financial hardship was associated with an increased risk of food insecurity among LGB and heterosexual cancer survivors. However, LGB survivors were more likely to experience food insecurity than heterosexual survivors even when controlling for financial hardship.

Primary care payment models and avoidable hospitalizations in Ontario, Canada: A multivalued treatment effects analysis.

Improving access to primary care physicians' services may help reduce hospitalizations due to Ambulatory Care Sensitive Conditions (ACSCs). Ontario, Canada's most populous province, introduced blended payment models for primary care physicians in the early- to mid-2000s to increase access to primary care, preventive care, and better chronic disease management. We study the impact of payment models on avoidable hospitalizations due to two incentivized ACSCs (diabetes and congestive heart failure) and two non-incentivized ACSCs (angina and asthma). The data for our study came from health administrative data on practicing primary care physicians in Ontario between 2006 and 2015. We employ a two-stage estimation strategy on a balanced panel of 3710 primary care physicians (1158 blended-fee-for-service (FFS), 1388 blended-capitation models, and 1164 interprofessional team-based practices). First, we account for the differences in physician practices using a generalized propensity score based on a multinomial logit regression model, corresponding to three primary care payment models. Second, we use fractional regression models to estimate the average treatment effects on the treated outcome (i.e., avoidable hospitalizations). The capitation-based model sometimes increases avoidable hospitalizations due to angina (by 7 per 100,000 patients) and congestive heart failure (40 per 100,000) relative to the blended-FFS-based model. Switching capitation physicians into interprofessional teams mitigates this effect, reducing avoidable hospitalizations from congestive heart failure by 30 per 100,000 patients and suggesting better access to primary care and chronic disease management in team-based practices.

Preferences for Delivery of HIV Prevention Services Among Healthcare Users in South Africa: A Discrete Choice Experiment.

Progress has been made to scale oral pre-exposure prophylaxis (PrEP) as part of combination HIV prevention, with the WHO recommending differentiated, simplified and demedicalized approaches. This study explored user preferences for components of a PrEP service delivery package, through a discrete choice experiment (DCE) among 307 people accessing primary healthcare services in South Africa between November 2022 and February 2023. Attributes included were: Source of information about HIV prevention and PrEP; Site for PrEP initiation and follow-up; Frequency of follow-up; PrEP pick-up point; HIV testing whilst using PrEP; Contact between appointments. A D-efficient DCE design was created with 16 choice sets in 2 blocks. DCE data were analysed using generalised multinomial logistic models. Compared to printed materials, participants preferred getting information about PrEP online (aOR = 7.73, 95% CI = 5.13-11.66) and through WhatsApp (aOR = 2.23, 95% CI = 0.98-5.55). PrEP initiation at a pharmacy or mobile clinic was valued equally to initiating PrEP at a clinic, but a community pop-up site was less preferred (aOR = 0.46, 95% CI = 0.33-0.64). There was a preference for 6-monthly over 3-monthly follow-up (aOR = 11.88, 95% CI = 5.44-25.94). Participants preferred collecting PrEP from a pharmacy (aOR = 5.02, 95% CI = 3.45-7.31), through home delivery (aOR = 2.18, 95% CI = 1.26-3.78) and from a vending machine (aOR = 1.43, 95% CI = 1.02-1.99) relative to where they initiated PrEP. Participants also preferred HIV self-testing over a healthcare provider test (aOR = 5.57, 95% CI = 3.72, 8.36). WhatsApp or Facebook groups (aOR = 4.12, 95% CI = 3.00-5.67), monthly phone calls (aOR = 2.84, 95% CI = 1.73-4.67) and weekly messages (aOR = 1.47, 95% CI = 1.10-1.97) were preferred contact between appointments, relative to no contact. To meet users' preferences, there is a need to expand decentralised and self-led HIV prevention services.

Synthetic promoter design in Escherichia coli based on multinomial diffusion model

Synthetic promoter design in Escherichia coli based on multinomial diffusion model

Effects of Social Vulnerability and Environmental Burden on Care Fragmentation and Social Needs Among Individuals With Rheumatic Conditions.

Environmental hazards and heightened neighborhood social vulnerability coexist and disproportionately affect minoritized populations. We investigated associations between exposure to adverse environmental burden concentrated in areas with high social vulnerability and care fragmentation (missed appointments, emergency department visits, and hospitalizations) and social needs (eg, food and housing insecurity) among individuals with rheumatic conditions. We identified adults receiving care in a Massachusetts multihospital system with at least two rheumatic disease codes and complete street addresses. Geocoded addresses were linked to the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry Social-Environmental Ranking (SER), which combines census-tract social vulnerability variables (eg, socioeconomic status) with environmental hazards (eg, air and water pollution). Social needs were obtained from self-reported surveys. Multilevel, multinomial regression models estimated associations between SER quartiles and care fragmentation and social need burden, accounting for demographics and comorbidities. Among 16,856 individuals with rheumatic conditions, 70% were female, 6% were Black, 82% were White, and 7% resided in the highest combined social vulnerability and environmental burden (SER quartile 4) areas. Among 7,083 with social needs data, 19% experienced more than one challenge. Individuals in SER quartile 4 areas (vs quartile 1) had 2.02 (95% confidence interval [CI] 1.67-2.46) times greater odds of at least four care fragmentation occurrences (vs 0) and 2.37 (95% CI 1.73-3.25) times greater odds of at least two social needs (vs 0). Residence in areas of high combined adverse environmental burden and social vulnerability was associated with significantly greater odds of care fragmentation and social needs. Addressing structural factors and emerging environmental threats contributing to these adverse exposures is essential to reduce rheumatic disease care inequities.

Factors affecting farmers' choice to adopt risk management strategies: The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers' adoption of risk management strategies (RMS) in Pakistan during times of uncertainty. The study examines farmers' RMS adoption choices using both multinomial probit (MNP) and multivariate probit (MVP). Data were collected from 382 sampled farmers from four districts in Khyber-Pakhtunkhwa (KP) province of Pakistan via a multistage sampling technique. This study utilizes the MNP model, considering the assumption of Independence of Irrelevant Alternatives (IIA) and incorporating correlated error terms. The objective is to understand farmers' behavior in risky situations and determine if there is heterogeneity. Results are compared with the MVP model to assess robustness and gain insights into farmers' decision-making processes.The research findings reveal that our results are robust, and farmers behave homogeneously in different settings of the provision of risk management strategies. Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood. The risk-averse farmers, who perceive weather-related risks as a threat, access credits and information, and have farms close to a river are more likely to adopt RMS, irrespective of the format of the strategies available. Moreover, the predicted probabilities and correlation of the RMS and RM categories further strengthened our model estimation. These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

Association between child marriage and high blood glucose level in women: A birth cohort analysis

ObjectivesChild marriage prematurely forces girls (<18 years of age) to perform adult roles prior to physical and psychological maturity. Such precocious transitions to young adulthood can have consequences on their long-term health, however, limited work has examined such relationships to date. As such, this study examines whether child marriage is associated with the risk of having hyperglycemia, or high blood glucose, in adulthood. Study designObservational study using repeated cross-sectional data. MethodsUsing data from the 2015-16 and 2019-21 waves of the India National Family Health Survey, we matched 432,080 and 418,409 women, aged 20 to 49 years, by birth year and month to create birth cohorts. Fitting multivariable binomial and multinomial logistic models, we compared the odds of having hyperglycemia across groups by marriage age (i.e., before or after age 18 years) within respective birth cohorts. ResultsWe found that the adjusted odds of having high blood glucose among women married as children were 1.12 (95% CI: 1.07 – 1.16) times that of their peers married as adults in the full-sample. The adjusted relative risks of having blood glucose levels higher than normal but lower than diabetic and diabetic ranges were 1.09 (95% CI: 1.04 – 1.14) and 1.23 (95% CI: 1.15 – 1.31), respectively, in comparison to blood glucose within normal range. These results were persistent across sub-groups of different birth cohorts. ConclusionOur findings suggest that child marriage was associated with higher risk of having high blood glucose in women, later in life.

Predictors Of Loneliness In Older Irish Adults And Its Relationship To Depression

Abstract Background Loneliness can affect up to 30% of older adults and is associated with increased morbidity and all cause mortality. Loneliness is also a major risk factor for depression. We aimed to explore the predictors of loneliness in older Irish adults and its relationship to depression. Methods Study participants were Irish adults aged &amp;gt;60 years in the Trinity Ulster University Department of Agriculture (TUDA) study. Participants with an MMSE &amp;lt;24 were excluded, to prevent the inclusion of individuals with dementia. Loneliness in the last week was identified from the 'loneliness' question in the Center for Epidemiological Studies Depression Scale (CES-D) and considered present when experienced ≥1-2 days in the last week and absent when experienced rarely or none of the time. Depression was defined as a CES-D ≥16. Predictors of loneliness were explored in multinomial regression models as well as its association with depression. Results There were 4561 participants, 70% female, mean age 70.1 years and 21.6% experienced loneliness. Positive independent predictors of loneliness were living alone (OR 5.2, CI 4.41-6.14, p&amp;lt;0.001), female sex (OR 1.29, CI 1.07-1.56, p=0.009), timed up and go &amp;gt;12 seconds (OR 1.46-2.32, p&amp;lt;0.001) non-driving status (OR 1.37, CI 1.13-1.67, P=0.001) and lower MMSE (OR 0.57, CI 0.41-0.79, p=0.0006). Those who reported loneliness had a higher prevalence of depression (32.1 vs 3.3%, p&amp;lt;0.0001). Conclusion Living alone was, as expected, the greatest predictor of loneliness followed by physical frailty, non-driving status, female sex, and lower cognition. A high proportion (one third) who felt lonely had depression, though low mood may increase the perception of loneliness independent of social engagement. A limitation is that we did not have a specific validated tool to assess loneliness or to distinguish between `emotional' or `social' loneliness.