multiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

The sequence kernel association test for multicategorical outcomes.

Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data ( N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC.

Avoidance of care: how health-care affordability influenced COVID-19 disease severity and outcomes.

In this study we examined the association between payor type, a proxy for health-care affordability, and presenting COVID-19 disease severity among 2108 polymerase chain reaction-positive nonelderly patients admitted to an acute-care hospital between March 1 and June 30, 2020. The adjacent-category logit model was used to fit pairwise odds of individuals' having (1) an asymptomatic-to-mild modified sequential organ failure assessment (mSOFA) score (0-3) versus a moderate-to-severe mSOFA score (4-7) and (2) a moderate-to-severe mSOFA score (4-7) versus a critical mSOFA score (>7). Despite representing the smallest population, Medicare recipients experienced the highest in-hospital death rate (19%), a rate twice that of the privately insured. The uninsured had the highest rate of critical mSOFA score on admission and had twice the odds of presenting with a critical illness when compared with the privately insured (odds ratio = 2.08, P =.03). Because payor type was statistically related to the most severe presentations of COVID-19, we question whether policy changes affecting health-care affordability might have prevented deaths and rationing of scarce resources, such as intensive care unit beds and ventilators.

Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.

Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practical applications.

The effect of fine particulate matter (PM2.5) pollution on health inequality: an intergenerational perspective.

Air pollution poses a serious challenge to public health and simultaneously exacerbating regional & intergenerational health inequality. This research introduces PM2.5 pollution into the intergenerational health transmission model, and estimates its impact on health inequality in China using Ordered Logit Regression (OLR) and Multi-scale Geographically Weighted Regression (MGWR) model. The results indicate that PM2.5 pollution exacerbate the intergenerational health inequality, and its impacts show inconsistency across family income levels, parental health insurance status, and area of residence. Specifically, it is more difficult for offspring in low-income families to escape from the influence of unhealthy family to become upwardly mobile. Additionally, this health inequality is more significant in households in which at least one parent does not have health insurance. Moreover, the intergenerational solidification caused by PM2.5 pollution is higher in the east and lower in the west. Both the PM2.5 level and solidification effect are high in Beijing-Tianjin-Hebei region, Yangtze River Delta region and central areas of China, which is the focus of air pollution management. These findings suggest that more emphasis should be placed on family-based health promotion. In areas with high PM2.5 pollution levels, resources, subsidies and air pollution protection should be provided for less healthy families with lower incomes and no health insurance.

Flock level socio-economic and other associated risk factors for Peste des petits ruminants (PPR) exposure in sheep and goats in Madhya Pradesh state, India.

To effectively control and eradicate PPR, the comprehensive understanding of risk factors associated with PPR exposure is vital. Hence, this study investigated socioeconomic and other associated risk determinants for PPR exposure at flock level in sheep and goats in a non-vaccination programme implemented Madhya Pradesh state India. A total of 410 sheep and goat flocks, comprised mostly of goats but also some mixed flocks, were surveyed during 2016 using a multistage random sampling procedure. Further, 230 blood samples were also collected from the farmers-reported PPR affected flocks and sera were tested using c-ELISA to confirm PPR exposure. The primary data on socioeconomic factors, farm management factors, health status, vaccination details and other epidemiological risk factors were collected from flock owners and descriptive statistics, chi-square analysis and logistic regression models were fitted to identify the significant risk factors for PPR incidence. The farmer's education, flock size, rearing pattern, and awareness of PPR vaccination were found to be significant pre-disposing risk factors for PPR exposure in the flocks. Hence, the control and eradication strategy need to be designed comprehensively considering the key social factors like education and vaccination awareness along with other flock level risk factors to eradicate PPR by 2030 in consonance with the global plan.

Boosting multivariate structured additive distributional regression models.

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

Simulation study comparing analytical methods for single-item longitudinal patient-reported outcomes data.

PURPOSE: Efficient analytical methods are necessary to make reproducible inferences on single-item longitudinal ordinal patient-reported outcome (PRO) data. A thorough simulation study was performed to compare the performance of the semiparametric probabilistic index models (PIM) with a longitudinal analysis using parametric cumulative logit mixed models (CLMM). METHODS: In the setting of a control and intervention arm, we compared the power of the PIM and CLMM to detect differences in PRO adverse event (AE) between these groups using several existing and novel summary scores of PROs. For each scenario, PRO data were simulated using copula multinomial models. Comparisons were also exemplified using clinical trial data. RESULTS: On average, CLMM provided substantially greater power than the PIM to detect differences in PRO-AEs between the groups when the baseline-adjusted method was used, and a small advantage in power when using the baseline symptom as a covariate. CONCLUSION: Although the CLMM showed the best performance among analytical methods, it relies on assumptions difficult to verify and that might not be fulfilled in the real world, therefore our recommendation is the use of PIM models with baseline symptom as a covariate.

Polygenic Prediction of Education and Its Role in the Intergenerational Transmission of Education: Cohort Changes Among Finnish Men and Women Born in 1925-1989.

Major changes in the educational distribution of the population and in institutions over the past century have affected the societal barriers to educational attainment. These changes can possibly result in stronger genetic associations. Using genetically informed, population-representative Finnish surveys linked to administrative registers, we investigated the polygenic associations and intergenerational transmission of education for those born between 1925 and 1989. First, we found that a polygenic index (PGI) designed to capture genetic predisposition to education strongly increased the predictiveness of educational attainment in pre-1950s cohorts, particularly among women. When decomposing the total contribution of PGI across different educational transitions, the transition between the basic and academic secondary tracks was the most important. This transition accounted for 60-80% of the total PGI-education association among most cohorts. The transition between academic secondary and higher tertiary levels increased its contribution across cohorts. Second, for cohorts born between 1955 and 1984, we observed that one eighth of the association between parental and one's own education is explained by the PGI. There was also an increase in the intergenerational correlation of education among these cohorts, which was partly explained by an increasing association between family education of origin and the PGI.

The crowding-out effect of sugar-sweetened beverages (SSBs) on household expenditure patterns in Bangladesh.

BACKGROUND: Consumption of sugar-sweetened beverages (SSBs) or sugary drinks may reduce or even eliminate the household income allocation for other essential commodities. Reducing expenditure for consumption of other household commodities is known as the crowding-out effect of SSB. We aimed to determine the crowding-out effect of SSB expenditure on other household commodities. In addition, we also identified the factors influencing the household's decision to purchase of SSBs. METHODS: We used the logistic regression (logit and multinomial logit models) and the Seemingly Unrelated Regression (SUR) models. In order to find the probability of a given change in the socio-demographic variables, we also estimated the average marginal effects from the logistic regression. In addition, we regressed the SUR model by gender differences. We used Household Income and Expenditure Survey (HIES) 2016 data to estimate our chosen econometric models. HIES is nationally representative data on the household level across the country and is conducted using a multistage random sampling method by covering 46,075 households. RESULTS: The findings from the logit model describe that the greater proportion of male members, larger household size, household heads with higher education, profession, having a refrigerator, members living outside of the house, and households with higher income positively affect the decision of purchasing SSB. However, the determinants vary with the various types of SSB. The unadjusted crowding out effect shows that expenditure on SSB or sugar-added drinks crowds out the household expenditure on food, clothing, housing, and energy items. On the other hand, the adjusted crowding out effect crowds out the spending on housing, education, transportation, and social and state responsibilities. CONCLUSION: Although the household expenditure on beverages and sugar-added drinks is still moderate (around 2% of monthly household expenditure), the increased spending on beverages and sugar-added drinks is a concern due to the displacement of household expenditure for basic commodities such as food, clothing, housing, education, and energy. Therefore, evidence-based policies to regulate the sale and consumption of SSB are required for a healthy nation.

The impact of COVID-19 on the intention of third-child in China: an empirical analysis based on survey data.

BACKGROUND: Against the grim background of declining intention to have children, the ravages of COVID-19 have pushed China and the world into a more complex social environment. To adapt to the new situation, the Chinese government implemented the three-child policy in 2021. OBJECTIVE: COVID-19 pandemic indirectly affects the country's internal economic development, employment, fertility plans or intention, and other major issues related to the people's livelihood, while undermining the stable operation of society. This paper explores the question that will COVID-19 pandemic affect Chinese people's intention to have a third child. And What are the relevant factors inside? METHOD: The data in this paper are from the Survey released by the Population Policy and Development Research Center of Chongqing Technology and Business University (PDPR-CTBU), including 10,323 samples from mainland China. This paper uses the logit regression model and KHB mediated effect model (a binary response model given by Karlson, Holm, and Breen) to investigate the impact of the COVID-19 pandemic and other factors on Chinese residents' intention to have a third child. RESULTS: The results suggest that the COVID-19 pandemic has a negative effect on Chinese residents' intention to have a third child. In-depth research on the mediating effect of KHB shows that COVID-19 pandemic will further inhibit residents' intention to have a third child by affecting their childcare arrangements, increasing their childcare costs, and increasing their exposure to occupational hazards. CONTRIBUTION: This paper is more pioneering in focusing on the impact of the COVID-19 epidemic on the intention to have three children in China. The study provides empirical evidence for understanding the impact of COVID-19 epidemic on fertility intentions, albeit in the context of policy support.

Marginal and Conditional Confounding Using Logits.

This article presents two ways of quantifying confounding using logistic response models for binary outcomes. Drawing on the distinction between marginal and conditional odds ratios in statistics, we define two corresponding measures of confounding (marginal and conditional) that can be recovered from a simple standardization approach. We investigate when marginal and conditional confounding may differ, outline why the method by Karlson, Holm, and Breen recovers conditional confounding under a "no interaction"-assumption, and suggest that researchers may measure marginal confounding by using inverse probability weighting. We provide two empirical examples that illustrate our standardization approach.

Ordered Regression Models: a Tutorial.

Ordinal outcomes are common in the social, behavioral, and health sciences, but there is no commonly accepted approach to analyzing them. Researchers make a number of different seemingly arbitrary recoding decisions implying different levels of measurement and theoretical assumptions. As a result, a wide array of models are used to analyze ordinal outcomes, including the linear regression model, binary response model, ordered models, and count models. In this tutorial, we present a diverse set of ordered models (most of which are under-utilized in applied research) and argue that researchers should approach the analysis of ordinal outcomes in a more systematic fashion by taking into consideration both theoretical and empirical concerns, and prioritizing ordered models given the flexibility they provide. Additionally, we consider the challenges that ordinal independent variables pose for analysts that often go unnoticed in the literature and offer simple ways to decide how to include ordinal independent variables in ordered regression models in ways that are easier to justify on conceptual and empirical grounds. We illustrate several ordered regression models with an empirical example, general self-rated health, and conclude with recommendations for building a sounder approach to ordinal data analysis.

Factors associated with knowledge about pharmacological management of pregnant women in Peruvian dental students: a logistic regression analysis.

BACKGROUND: Clinical management to maintain or restore oral health through the use of drugs during pregnancy is crucial, since at this stage physiological changes significantly influence the absorption, distribution and elimination of the drug, considering also that excessive administration of drugs during this period may have adverse effects on the mother and/or fetus. Therefore, the aim of the present study was to evaluate the factors associated with knowledge of pharmacological management of pregnant women in dental students of a Peruvian university located in the capital and province. METHODS: This analytical, cross-sectional, prospective and observational study assessed 312 Peruvian dental students from third to fifth year of study between February and April 2022. A validated questionnaire of 10 closed questions was used to measure knowledge about pharmacological management in pregnant women. A logit model was used to assess the influence of the variables: gender, age, year of study, marital status, place of origin and area of residence. A significance of p < 0.05 was considered. RESULTS: The 25.96, 55.13 and 18.91% of the dental students showed poor, fair and good knowledge about pharmacological management in pregnant women; respectively. In addition, it was observed that students under 24 years of age and those from the capital were significantly (p < 0.05) 44% less likely to have poor knowledge of pharmacological management in pregnant women compared to those aged 24 years or older (OR = 0.56; CI: 0.34-0.92) and those from the province (OR = 0.56; CI: 0.32-0.98); respectively. Finally, those in their third and fourth year of study were significantly three times more likely to have poor knowledge (OR = 3.17; CI: 1.68-5.97 and OR = 3.88; CI: 2.07-7.31; respectively) compared to fifth year dental students. CONCLUSION: The knowledge of dental students about pharmacological management in pregnant women was predominantly of fair level. In addition, it was observed that being under 24 years of age and being from the capital city were protective factors against poor knowledge, while being a third- and fourth-year student was a risk factor. Finally, gender, marital status and area of residence were not influential factors in the level of knowledge.

Agent-Based Approach for (Peri-)Urban Inter-Modality Policies: Application to Real Data from the Lille Metropolis.

Transportation authorities have adopted more and more incentive measures (fare-free public transport, construction of park-and-ride facilities, etc.) to reduce the use of private cars by combining them with public transit. However, such measures remain difficult to assess with traditional transport models. This article proposes a different approach: an agent-oriented model. To reproduce realistic applications in an urban context (a metropolis), we investigate the preferences and choices of different agents based on utilities and focus on a modal choice performed through a multinomial logit model. Moreover, we propose some methodological elements to identify the individuals' profiles using public data (census and travel surveys). We also show that this model, applied in a real case study (Lille, France), is able to reproduce travel behaviors when combining private cars and public transport. Moreover, we focus on the role played by park-and-ride facilities in this context. Thus, the simulation framework makes it possible to better understand individuals' intermodal travel behavior and assess its development policies.

Households' willingness to pay for water for the environment in an urban setting.

Continued population growth, and climate change are placing stress on many of the world's water sources and this often manifests in environmental damage to rivers and wetlands. Most of the published literature around allocating more water to the environment considers trade-offs with agriculture. In contrast this study focusses on scenarios for different potable water supplies in cities and thus adds a novel perspective on the value of riverine restoration. This study sheds light on urban households' willingness to pay for more water to be allocated to the environment where it directly competes with their own water demands. The study uses two stated preference techniques (choice modelling and best-worst scaling) to establish the value of environmental water and the motivations for households paying for an increase in environmental water reserves. The study is set in Australia's fastest growing city, Melbourne, although the approach and method have implications for other developed-world settings. The paper also offers practical advice on the management of water allocated for different uses. Overall, the results indicate a positive and significant willingness to pay by households for additional water entitlements. Importantly, this provides a benchmark for contemplating the costs and benefits of activating alternative water supplies, such as desalination, to free up rainwater for environmental purposes.

Assessing the preference and spatial dependence of a solid waste management system in Nepal: A choice experiment approach.

This study deploys a choice experiment method to estimate the preference and willingness to pay for a better solid waste management system in Siddharthanagar municipality in Nepal. A primary survey of 611 households was conducted, and the results from the Generalized Multinomial Logit Model (GMNL) indicate a public preference for a better waste management service. Significant heterogeneity in household preferences is evident after accommodating each choice selection's preference certainty in the GMNL model. On average, households prefer to pay the highest amount for constructing and maintaining a sanitary landfill, which is Nepalese Rupee (NPR) 158/month (USD 1.43). The geographic distribution of the marginal willingness to pay (MWTP) by hot spot analysis from the geocoded location also indicates spatial heterogeneity across the study area. The MWTP for each waste management attribute is spatially autocorrelated, and household awareness and attitude significantly impact this spatial dependence. Overall, both the choice models result and spatial analyses indicate the policy should be targeted at a localized level to increase awareness concerning the proper management of solid waste.

The Influence of Ambient Weather Conditions on Stated Preferences for Ecosystem Services Management.

One of the assumptions in stated preference studies is the stability of respondents' preferences. This assumption might be violated in situations of context dependence, i.e., when the contingent situation influences respondents' choices. Ambient weather conditions (AWCs) are one element of the context that may influence stated preferences. The literature suggests that AWCs affect people's emotions, behaviors, and decision-making processes; however, the potential AWCs impact in environmental preference studies has not yet been investigated. This aspect is of high importance because context-dependent choices return biased willingness to pay estimates and affect the subsequent welfare analysis that informs public policy. To shed light on this important aspect of non-market valuation studies, we explore the effect of AWCs on preferences elicited with a Discrete Choice Experiment for ecosystem services management of a Nature Park. Results of a generalized mixed logit model evidenced a significant effect of AWCs on respondents' choices, with good weather conditions leading to higher preferences and willingness to pay for ecosystem services management. This result, which is consistent with previous psychological studies, raises the issue of sampling design and reveals the importance of a sensitivity analysis of WTP. As this issue is still unexplored in stated preference studies, we also encourage undertaking similar studies to add a priori knowledge for more accurate ex-post calibration of WTP estimates.