Generalized Linear Mixed Model Model Research Articles

Intra-patient analysis of individual weight gain or loss between IVF cycles: cycle now and transfer later.

What is the impact of clinically significant weight change on outcomes related to IVF cycle performance? While individual weight loss did not significantly impact ovarian response to stimulation or other cycle outcome parameters in our study, some positive associations were found for individual weight gain. The role of weight-change in patients undergoing IVF has been largely studied by comparing weight loss in different cohorts of patients stratified by a static BMI. Specifically, obesity has been extensively studied in relation to its negative effects on assisted or unassisted conception outcomes and ovulatory function. Previous research has shown conflicting results, while BMI, which is commonly used as a marker of obesity, may not accurately reflect the underlying factors affecting fertility in obese patients. This study utilized a retrospective within-patient repeated measurement analysis design to assess the impact of weight change on IVF outcomes in cycles where all embryos were cryopreserved at the blastocyst stage for transfer at a later date. The study was conducted at an academically affiliated fertility center. The data included 961 women who underwent at least two IVF cycles between December 2014 and June 2020, with documented short-term weight gain (n = 607) or weight loss (n = 354) within 1year from their initial IVF cycle. Multivariable generalized estimating equations (GEE) and generalized linear mixed models (GLMM) were employed to assess associations between weight change and outcomes across cycles. The multivariable models indicated that weight loss did not show any significant associations with the numbers of oocytes retrieved, or mature oocytes, the fertilization rate or the blastulation rate. However, weight gain demonstrated a minor positive association with the number of oocytes retrieved in both GEE models (coefficient: 0.01, 95% CI: 0.00-0.01) and GLMM models (0.01, 95% CI: 0.01-0.00). There was also a potential increase in the fertilization rate with weight gain, as indicated by a positive coefficient in both GEE models (coefficient: 0.01, 95% CI: 0.00-0.02) and GLMM models (coefficient: 0.01, 95% CI: 0.00-0.01). However, the association between weight gain and the embryo blastulation rate was not statistically significant in any model. This study focused on cycle performance parameters instead of reproductive outcomes, which restricted our ability to evaluate the impact of weight change on cumulative live birth rates. Additionally, the study did not account for variables such as stimulation protocols, potentially introducing confounding factors and limiting the generalizability of the results. Although obesity is associated with adverse obstetrical risks, there is less evidence of adverse reproductive outcomes in IVF cycles. We therefore recommend that an IVF cycle should not be delayed due to weight, so that the patient is not adversely affected by increasing age. The IVF cycle should aim to freeze all embryos, so that embryo transfer can then occur after weight loss, so as to limit the recognized obstetrical risks. The study was not funded and there were no competing interests. N/A.

Construction of CPUE standardization model and its simulation testing for chub mackerel (Scomber japonicus) in the Northwest Pacific Ocean

Standardized catch per unit effort (CPUE) data not only yield precise and biologically meaningful abundance indices but also provide crucial insights into the spatio-temporal distribution of fisheries resources, critical for their sustainable utilization and management. In this study, we integrated chub mackerel fishery statistics from the Northwest Pacific Ocean (NPO) and marine remote sensing environmental data, and constructed CPUE standardization models for chub mackerel based on the generalized linear mixed model (GLMM) and spatio-temporal GLMM (Vector Autoregressive Spatio-Temporal, VAST) and evaluated their performance. GLMM is a statistical technique extending GLM with random effects for complex datasets with non-independent observations and correlations or hierarchies. The VAST model is a statistical model for multivariate time series data with observations at different spatial locations, displaying both temporal autocorrelation and spatial dependence. Additionally, influence analysis and simulation testing were employed to quantify the effects of explanatory variables on the differences between nominal CPUE and standardized CPUE, and to assess the estimation accuracy of different models in CPUE standardization, respectively. Finally, the distribution pattern of chub mackerel in the NPO was analyzed by estimating centers of gravity (COGs). The results indicated that: 1) The GLMM model containing all explanatory variables was considered the best model with the smallest conditional Akaike Information Criterion (CAIC), while the VAST model, with only the covariate SST, performed the best. 2) Interactions of year and spatial effects in GLMM and the spatio-temporal variable in VAST, exerted a notable influence on annual standardized CPUE and should be accounted for in the model. 3) Based on simulation testing results, the VAST model exhibited lower model error (root mean square error, RMSE) and model bias, outperforming the GLMM model in CPUE standardization. 4) From 2014 to 2018, chub mackerel exhibited high biomass density widely distributed throughout almost the entire NPO, which shifted to the central-north region from 2019 to 2021. There was no significant northing and easting shift in COGs for the population (P > 0.05). The findings of this research enhance our understanding of the variations in resource abundance indices and spatio-temporal distribution patterns of chub mackerel in the NPO.

Heartwood Relationship with Stem Diameter in Pinus canariensis Plantations of Gran Canaria, Spain

The development of stem heartwood and the factors that control it play an important role in tree physiology, thereby impacting demographic and ecological processes of woody species. We investigated the relationships of stem heartwood with site- and tree-level variables in Pinus canariensis plantations. A total of 30 plots were sampled in the island of Gran Canaria, Spain, over a large elevation range (995–1875 m) and on terrain with different slopes (4%–70%) and exposures. The 15 pines closest to each plot center were measured and cored to quantify growth rates and the size of heartwood, also known as “tea”. We used generalized linear mixed models (GLMMs) to account for both fixed and random effects while evaluating the best predictors of heartwood presence. Stem diameter was the variable most correlated with heartwood radius, and allowing for a random slope and intercept of this relationship accounted for spatially related variability. Furthermore, the GLMM model became more effective when the relationship between stem diameter and heartwood was modeled using the presence/absence of the “tea” rather than its measured size. Other site- and tree-level variables either were not statistically significant or improved the model relatively little. Because stem heartwood affects both wood quality and the amount of carbon that trees can store, our findings have implications for forest management and carbon-conscious policies.

The association between decreased choriocapillary flow and electroretinogram impairments in patients with diabetes

PurposeTo evaluate the association between choriocapillary flow (CCF) and electroretinogram (ERG) in patients with diabetes. MethodThis was a cross-sectional study. Patients with type 2 diabetes and healthy controls who had undergone both flicker ERG and optical coherence tomography angiography (OCTA) were included, while patients with severe diabetic retinopathy (DR) and macular edema were excluded. Correlations among OCTA and ERG parameters were conducted by generalized linear mixed models (GLMM). ResultOne hundred ninety-four eyes of 102 patients with diabetes and fifty-six eyes of 28 controls were included. The implicit time of 30-Hz flicker ERG successively increased, while the amplitudes, inner-retina vessel density and CCF were decreased from the control to the nondiabetic retinopathy (NDR) to DR group. In patients with diabetes, all GLMM models of ERG parameters had statistically significance (P<0.05), and CCF was correlated with ERG parameters (coefficient index=-0.601, P< 0.001 with 16 Td-s implicit time; coefficient index=-0.687, P< 0.001 with 32 Td-s implicit time; coefficient index=0.933, P=0.035 with 32 Td-s amplitude) and the thickness of retinal pigment epithelium, while in the GLMM model of CCF, it was correlated with the thickness of retinal pigment epithelium and the level of glycosylated hemoglobin(both P= 0.001). ConclusionCCF decreased in patients with diabetes, and it was related with ERG. Choroidopathy and its functional impairment in the retina may occur very early in patients with diabetes by influencing the outer retina.

Assessing chronic stress in wild mammals using claw-derived cortisol: a validation using European badgers (Meles meles).

Measuring stress experienced by wild mammals is increasingly important in the context of human-induced rapid environmental change and initiatives to mitigate human-wildlife conflicts. Glucocorticoids (GC), such as cortisol, mediate responses by promoting physiological adjustments during environmental perturbations. Measuring cortisol is a popular technique; however, this often reveals only recent short-term stress such as that incurred by restraining the animal to sample blood, corrupting the veracity of this approach. Here we present a protocol using claw cortisol, compared with hair cortisol, as a long-term stress bio-indicator, which circumvents this constraint, where claw tissue archives the individual's GC concentration over preceding weeks. We then correlate our findings against detailed knowledge of European badger life history stressors. Based on a solid-phase extraction method, we assessed how claw cortisol concentrations related to season and badger sex, age and body-condition using a combination of generalized linear mixed models (GLMM) (n= 668 samples from 273 unique individuals) followed by finer scale mixed models for repeated measures (MMRM) (n= 152 re-captured individuals). Claw and hair cortisol assays achieved high accuracy, precision and repeatability, with similar sensitivity. The top GLMM model for claw cortisol included age, sex, season and the sex*season interaction. Overall, claw cortisol levels were significantly higher among males than females, but strongly influenced by season, where females had higher levels than males in autumn. The top fine scale MMRM model included sex, age and body condition, with claw cortisol significantly higher in males, older and thinner individuals. Hair cortisol was more variable than claw; nevertheless, there was a positive correlation after removing 34 outliers. We discuss strong support for these stress-related claw cortisol patterns from previous studies of badger biology. Given the potential of this technique, we conclude that it has broad application in conservation biology.

Improving assessments of coastal ecosystems – Adjusting coastal fish indicators to variation in ambient environmental factors

The application of ecological indictors for assessing the environmental status of ecosystems play an important role for effective management. However, natural variability may limit the indicators’ ability to provide relevant information about anthropogenic pressures and guide management action. Coastal fish species are not only a resource for commercial and recreational fisheries but also key ecosystem components in the Baltic Sea, and is therefore used as management objectives within the EU Marine Strategy Framework Directive and the HELCOM Baltic Sea Action Plan. A challenge, however, is that the distribution and abundance of coastal fish populations in Baltic Sea is also influenced by spatial and temporal variation in ambient environmental factors. Here, using 16 years of monitoring data, over a latitudinal range of 56 – 66°N along the Swedish Baltic Sea coast, we evaluated the effect of variability in water temperature and depth, and wave exposure for three indicators of environmental status assessment in the Baltic Sea: Abundance of perch, Abundance of Cyprinids, and Abundance of Piscivores. Generalized linear mixed models (GLMM) revealed an overall positive linear relationship between water temperature for all indicators, and overall negative linear relationships to depth and wave exposure. When adjusting indicator values using the parameter estimates from the GLMM models, the variability and 95 % confidence interval for all three indicators were reduced. The adjustment, however, did not have a strong impact on the assessment of the ecological state of the indicator. Our results suggest that adjusting coastal fish indicators to variation in local ambient environmental factors will increase their precision, and hence, the confidence in the assessment of environmental status.

Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model

We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons (‘exuviae’) and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples.

Magnitudes of Anemia and Its Determinant Factors Among Lactating Mothers in East African Countries: Using the Generalized Mixed-Effect Model.

Background: The number of studies on the magnitude of anemia and its determinant factors among lactating mothers is limited in East African countries regardless of its multivariate consequences. Even though few studies were conducted on the magnitude of anemia and its determinants, most of them focused on the country level and different parts of countries. Therefore, the current study is aimed to determine the magnitude of anemia and determinant factors among lactating mothers in East African countries.Methods: From nine East African countries, a total weighted sample of 25,425 lactating mothers was included in the study. Determinate factors of anemia were identified using generalized linear mixed models (GLMM). Variables with a p < 0.05 in the final GLMM model were stated to confirm significant association with anemia.Result: The magnitude of anemia in East African countries was found to be 36.5% [95% confidence interval (CI): 35.55%, 36.75%]. Besides, as for the generalized linear mixed-effect model, age, educational status, working status, country of residence, wealth index, antenatal care service, place of delivery, history of using family planning in a health facility, current pregnancy, and visited by fieldworker in the last 12 months were factors that have a significant association with anemia in lactating mothers.Conclusion: In East Africa, more than one-third of lactating mothers have anemia. The odds of anemia were significantly low among young mothers (15–34), who had primary education, were working, country of residence, and higher wealth index (middle and high). In addition, the likelihood of anemia was also low among lactating mothers who had antenatal care, used family planning, delivered at a health facility, were pregnant during the survey, and visited by fieldworkers. Therefore, promoting maternal care services (family planning, Antenatal Care (ANC), and delivery at health facilities) and a field visit by health extension workers are strongly recommended.

Prey Specialization by Cougars on Feral Horses in a Desert Environment

ABSTRACTNatural controls on the distribution, abundance, or growth rates of exotic species are a desirable mode of intervention because of lower costs compared to anthropogenic controls and greater social acceptance. In the Great Basin, cougars (Puma concolor) are the most widely distributed carnivore capable of killing large ungulate prey. Populations of feral horses (Equus ferus) are widely distributed throughout the Great Basin and can grow at rates up to 20%/year. Although cougars exhibit distributional overlap with horses, it has been assumed that predation is minimal because of differences in habitat use and body‐size limitations. To evaluate this hypothesis, we monitored the diets of 21 global positioning system (GPS)‐collared cougars in the western Great Basin (5 males, 8 females) and eastern Sierra Nevada (2 males, 6 females) from 2009–2012. We investigated 1,310 potential kill sites and located prey remains of 820 predation events. We compared prey composition and kill rates of cougars inhabiting the Sierra Nevada and Great Basin, and among male and female cougars across seasons. We used generalized linear mixed models (GLMMs) to examine the effects of prey availability and habitat characteristics on the probability of predation on horses by cougars. Mule deer (Odocoileus hemionus) comprised 91% of prey items killed on the Sierra Nevada reference site but only comprised 29% of prey items in the Great Basin study area. Average annual kill rates for deer differed between the Sierra Nevada ( = 0.85 deer/week, range = 0.44–1.3) and Great Basin ( = 0.21 deer/week, range = 0.00–0.43). Diets of cougars in the Great Basin were composed predominantly of horses (59.6%, n = 460 prey items; 13 individuals). Ten cougars regularly consumed horses, and horses were the most abundant prey in the diet of 8 additional individuals in the Great Basin. Cougars on average killed 0.38 horses/week in the Great Basin (range=0.00–0.94 horses/week). Differences in predation on horses between the sexes of cougars were striking; Great Basin females incorporated more horses across all age classes year‐round, whereas male cougars tended to exploit neonatal young during spring and summer before switching to deer during winter. Within GLMM models, the probability of predation on horses compared to other prey species increased with elevation, horse density, and decreasing density of mule deer on the landscape, and was more likely to occur in sagebrush (Artemesia spp.) than in pinyon (Pinus monophylla)–juniper (Juniperus osteosperma) forests. Behavior of individual cougars accounted for more than a third of the variation explained by our top models predicting predation on horses in the Great Basin. At landscape scales, cougar predation is unlikely to limit the growth of feral horse populations. In the Great Basin ecosystem, however, cougars of both sexes successfully preyed on horses of all age classes. Moreover, some reproductive, female cougars were almost entirely dependent on feral horses year‐round. Taken together, our data suggest that cougars may be an effective predator of feral horses, and that some of our previous assumptions about this relationship should be reevaluated and integrated into management and planning. © 2021 The Wildlife Society.

A generalized linear mixed model for understanding determinant factors of student's interest in pursuing bachelor's degree at Universitas Syiah Kuala

Generalized Linear Mixed Model (GLMM) is a framework that has a response variable, fixed effects, and random effects. The response variable comes from an exponential family, whereas random effects have a normal distribution. Estimating parameters can be calculated using the maximum likelihood method using the Laplace approach or the Gauss-Hermite Quadrature (GHQ) approach. The purpose of this study was to identify factors that trigger student's interest to continue studying at Universitas Syiah Kuala (USK) using both techniques. The GLMM is suitable for the data because the variable response has a Bernoulli distribution, and the random effects are assumed to be having a normal distribution. Also, the model helps identify the relationship between the dependent variable and the predictors. This study utilizes data from six high schools in Banda Aceh city drawn using a two-stage sampling technique. Stage 1, we randomly chose six out of sixteen public senior high schools in Banda Aceh. Stage 2, we selected students from each school from four different major classes. The GLMM model includes one binary response variable, five numerical fixed-effects, and two random effects. The response variable is the interest of high school students to continue study at USK (yes or no). The five fixed effects in the model including scores of collaboration (C), Action (A), Emotion (E), Purposes (P), and Hope (H). Finally, the random effects are schools (S) and majors (M). In this study, both Laplace and GHQ techniques produce identical results. The predictors that can explain student interest are A, E, and H. These predictors have a positive effect. The random effects of schools and majors are not significantly different from zero. The model with three significant predictors is better than the complete predictor model.

Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease

The purpose of this paper is to investigate the determinants influencing the costs of cardiovascular disease in the regional health service in Italy’s Apulia region from 2014 to 2016. Data for patients with acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) were collected from the hospital discharge registry. Generalized linear models (GLM), and generalized linear mixed models (GLMM) were used to identify the role of random effects in improving the model performance. The study was based on socio-demographic variables and disease-specific variables (diagnosis-related group, hospitalization type, hospital stay, surgery, and economic burden of the hospital discharge form). Firstly, both models indicated an increase in health costs in 2016, and lower spending values for women (p < 0.001) were shown. GLMM indicates a significant increase in health expenditure with increasing age (p < 0.001). Day-hospital has the lowest cost, surgery increases the cost, and AMI is the most expensive pathology, contrary to AF (p < 0.001). Secondly, AIC and BIC assume the lowest values for the GLMM model, indicating the random effects’ relevance in improving the model performance. This study is the first that considers real data to estimate the economic burden of CVD from the regional health service’s perspective. It appears significant for its ability to provide a large set of estimates of the economic burden of CVD, providing information to managers for health management and planning.

Effects of mechanical ventilation on indoor air quality and occupant health status in energy-efficient homes: A longitudinal field study

Despite the growing interest in energy-efficient homes (EEHs), there is still a lack of evidence regarding whether the mechanical ventilation system of an EEH positively or negatively impacts indoor air quality (IAQ) and the health and wellbeing of occupants. This study aimed to evaluate the IAQ level and daily health symptoms of adults and children living in EEHs compared to conventional buildings over the course of one year. A two-way mixed analysis of variance was conducted to compare the level of IAQ between the two housing types. A binomial generalized linear mixed model (GLMM) and generalized additive mixed model was developed to investigate the association between IAQ and daily risks of symptoms. Differences in the daily prevalence of symptoms between the two housing types were assessed using a Poisson GLMM model. Overall, the indoor concentrations of particulate matter (PM10 and PM2.5), carbon dioxide (CO2), and volatile organic compounds (VOCs) were lower in EEH after controlling for seasonality. The indoor temperature and relative humidity level were relatively constant in the EEH. We also found that an increased level of indoor air quality parameters, particularly CO2, which is closely related to the indoor ventilation rate, was associated with the daily risk of eye fatigue, allergic rhinitis, and atopic dermatitis symptoms. Considering that EEH effectively reduced indoor air pollutants and IAQ improvement was associated with a reduction in the risk of individual symptoms, the IAQ improvement of EEH may have positively impacted occupants' health. Symptoms such as eye fatigue and skin dryness, which have been reported in previous studies as potential side effects of mechanical ventilation, were reported in this study; however, they were not found to be statistically significantly different from those reported in the conventional building.

Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling

Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

Generalized Linear Mixed Models by penalized Lasso in modelling the scores of Indonesian students

The Generalized linear mixed model (GLMM) is an extension of the generalized linear model by adding random effects to linear predictors to accommodate clustered or over dispersion. Severe computational problems in the GLMM modelling cause its use restricted for only a few predictors. When many predictors are available, the estimators become very unstable. Therefore, the procedure for selecting relevant variables is essential in modelling. The use of penalty techniques for selecting variables in mixed models is still rarely applied. In this article, the penalized Lasso approach proposed to handle these kinds of problems. The proposed methods select variables and estimate coefficients simultaneously in GLMM. Based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and standard error criteria, it was found that glmmLasso has a better performance than GLMM. For the factors affecting Indonesian’s student scores, where glmmLasso produces three significant covariates for the GLMM model while GLMM without penalized Lasso has five covariates, which means that the GLMM model is more complicated than glmmLasso. Gender, school quality based on National Examination (UN) scores and the opportunity for students to investigate to test their ideas are essential covariates as factors that influence the rating of Indonesian students.

KAJIAN PENGARUH PENAMBAHAN INFORMASI GEROMBOL TERHADAP PREDIKSI AREA NIRCONTOH PADA DATA BINOMIAL

Employment data is one of the important indicators related to the development progress of a country. Labor conditions in the territory of Indonesia can only be compared between times through the Survei Angkatan Kerja Nasional (Sakernas) data. Data generated from Sakernas and published by BPS is the number of employed and unemployed. The obstacle in estimating the semester unemployment rate at the regency/municipality level is the lack of a number of examples. One of the indirect estimates currently developing is small area estimation (SAE). This study developed the generalized linear mixed model (GLMM) by adding cluster information and examines the development of modifications with several model scenarios. The purpose of this study was to develop a prediction model for basic GLMM on a small area approach by adding cluster information as a fixed effect or random effect. The simulation results show that Model-2, a model that adds a fixed effect k-cluster and also adds a mean from the estimated effect of random areas in the sample area, is the best model with the smallest relative bias (RB) and Relative root mean squares error (RRMSE). This model is better than the basic GLMM model (Model-0) and Model-1 (a model which only adds a mean from the estimated random effect area in the sample area). Model-2 is applied to estimate the proportion of unemployed sub-district level in Southeast Sulawesi Province. Estimating the proportion of unemployed with calibration Model-2 produced an estimated aggregation of the unemployment proportion of Southeast Sulawesi Province at 0.0272. These results are similar to BPS (0.0272). Thus, the results of the estimated proportion of unemployment at the sub-district level with a calibration Model-2 can be said to be feasible to use.

The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa

IntroductionBacterial meningitis still constitutes an important threat in Africa. In the meningitis belt, a clear seasonal pattern in the incidence of meningococcal disease during the dry season has been previously correlated with several environmental parameters like dust and sand particles as well as the Harmattan winds. In parallel, the evidence of seasonality in meningitis dynamics and its environmental variables remain poorly studied outside the meningitis belt. This study explores several environmental factors associated with meningitis cases in the Democratic Republic of Congo (DRC), central Africa, outside the meningitis belt area.MethodsNon-parametric Kruskal-Wallis’ tests were used to establish the difference between the different health zones, climate and vegetation types in relation to both the number of cases and attack rates for the period 2000–2018. The relationships between the number of meningitis cases for the different health zones and environmental and socio-economical parameters collected were modeled using different generalized linear (GLMs) and generalized linear mixed models (GLMMs), and different error structure in the different models, i.e., Poisson, binomial negative, zero-inflated binomial negative and more elaborated multi-hierarchical zero-inflated binomial negative models, with randomization of certain parameters or factors (health zones, vegetation and climate types). Comparing the different statistical models, the model with the smallest Akaike’s information criterion (AIC) were selected as the best ones. 515 different health zones from 26 distinct provinces were considered for the construction of the different GLM and GLMM models.ResultsNon-parametric bivariate statistics showed that there were more meningitis cases in urban health zones than in rural conditions (χ2 = 6.910, p-value = 0.009), in areas dominated by savannah landscape than in areas with dense forest or forest in mountainous areas (χ2 = 15.185, p-value = 0.001), and with no significant difference between climate types (χ2 = 1.211, p-value = 0,449). Additionally, no significant difference was observed for attack rate between the two types of heath zones (χ2 = 0.982, p-value = 0.322). Conversely, strong differences in attack rate values were obtained for vegetation types (χ2 = 13.627, p-value = 0,001) and climate types (χ2 = 13.627, p-value = 0,001). This work demonstrates that, all other parameters kept constant, an urban health zone located at high latitude and longitude eastwards, located at low-altitude like in valley ecosystems predominantly covered by savannah biome, with a humid tropical climate are at higher risk for the development of meningitis. In addition, the regions with mean range temperature and a population with a low index of economic well-being (IEW) constitute the perfect conditions for the development of meningitis in DRC.ConclusionIn a context of global environmental change, particularly climate change, our findings tend to show that an interplay of different environmental and socio-economic drivers are important to consider in the epidemiology of bacterial meningitis epidemics in DRC. This information is important to help improving meningitis control strategies in a large country located outside of the so-called meningitis belt.

Terpenoid Accumulation Links Plant Health and Flammability in the Cypress-Bark Canker Pathosystem

To explore the possible relationship between diseased trees and wildfires, we assessed the flammability of canker-resistant and susceptible common cypress clones that were artificially infected with Seiridium cardinale compared to healthy trees. This study explored the effect of terpenoids produced by the host plant in response to infection and the presence of dead plant portions on flammability. Terpenoids were extracted and quantified in foliage and bark samples by gas chromatography–mass spectrometry (GC–MS). A Mass Loss Calorimeter was used to determine the main flammability descriptors. The concentration of terpenoids in bark and leaf samples and the flammability parameters were compared using a generalized linear mixed models (GLMM) model. A partial least square (PLS) model was generated to predict flammability based on the content of terpenoid, clone response to bark canker and the disease status of the plants. The total terpenoid content drastically increased in the bark of both cypress clones after infection, with a greater (7-fold) increase observed in the resistant clone. On the contrary, levels of terpenoids in leaves did not alter after infection. The GLMM model showed that after infection, plants of the susceptible clone appeared to be much more flammable in comparison to those of resistant clones, showing higher ignitability, combustibility, sustainability and consumability. This was mainly due to the presence of dried crown parts in the susceptible clone. The resistant clone showed a slightly higher ignitability after infection, while the other flammability parameters did not change. The PLS model (R2Y = 56%) supported these findings, indicating that dead crown parts and fuel moisture content accounted for most of the variation in flammability parameters and greatly prevailed on terpenoid accumulation after infection. The results of this study suggest that a disease can increase the flammability of trees. The deployment of canker-resistant cypress clones can reduce the flammability of cypress plantations in Mediterranean areas affected by bark canker. Epidemiological data of diseased tree distribution can be an important factor in the prediction of fire risk.

Extinction Dynamics of the Foot-and-Mouth Disease Virus Carrier State Under Natural Conditions.

Foot-and-mouth disease (FMD) is one of the most economically important livestock diseases worldwide. Following the clinical phase of FMD, a large proportion of ruminants remain persistently infected for extended periods. Although extinction of this carrier state occurs continuously at the animal and population levels, studies vary widely in their estimates of the duration of persistent infection. There is a need for robust statistical models to capture the dynamics of persistent infection for the sake of guiding FMD control and trade policies. The goal of the current study was to develop and assess statistical models to describe the extinction of FMD virus (FMDV) persistent infection using data from primary longitudinal studies of naturally infected cattle and Asian buffalo in Vietnam and India. Specifically, accelerated failure time (AFT) models and generalized linear mixed models (GLMM) were developed to predict the probability of persistent infection in seropositive animals and identified carriers at the individual animal level at sequential time points after outbreaks. The primary studies were analyzed by country and combined using an individual-participant data meta-analysis approach. The models estimated similar trends in the duration of persistent infection for the study/species groups included in the analyses, however the significance of the trends differed between the models. The overall probabilities of persistent infection were similar as predicted by the AFT and GLMM models: 6 months: 99% (AFT) /80% (GLMM), 12 months: 51% (AFT) /32% (GLMM), 18 months: 6% (AFT) /5% (GLMM), 24 months: 0.8% (AFT) /0.6% (GLMM). These models utilizing diverse and robust data sets predict higher probabilities of persistence than previously published, suggesting greater endurance of carriers subsequent to an outbreak. This study demonstrates the utility of statistical models to investigate the dynamics of persistent infection and the importance of large datasets, which can be achieved by combining data from several smaller studies in meta-analyses. Results of this study enhance current knowledge of the FMDV carrier state and may inform policy decisions regarding FMDV persistent infection.

A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis.

Introduction. Estimating costs of medical care attributable to treatments over time is difficult due to costs that cannot be explained solely by observed risk factors. Unobserved risk factors cannot be accounted for using standard econometric techniques, potentially leading to imprecise prediction. The goal of this work is to describe methodology to account for latent variables in the prediction of longitudinal costs. Methods. Latent class growth mixture models (LCGMMs) predict class membership using observed risk factors and class-specific distributions of costs over time. Our motivating example models cost of care for children with cystic fibrosis from birth to age 17. We compare a generalized linear mixed model (GLMM) with LCGMMs. Both models use the same covariates and distribution to predict average costs by combinations of observed risk factors. We adopt a Bayesian estimation approach to both models and compare results using the deviance information criterion (DIC). Results. The 3-class LCGMM model has a lower DIC than the GLMM. The LCGMM latent classes include a low-cost group where costs increase slowly over time, a medium-cost group with initial higher costs than the low-cost group and with more rapidly increasing costs at older ages, and a high-cost group with a U-shaped trajectory. The risk profile-specific mixtures of classes are used to predict costs over time. The LCGMM model shows more delineation of costs by age by risk profile and with less uncertainty than the GLMM model. Conclusions. The LCGMM approach creates flexible prediction models when using longitudinal cost data. The Bayesian estimation approach to LCGMM presented fits well into cost-effectiveness modeling where the estimated trajectories and class membership can be used for prediction.

A Copula-Based GLMM Model for Multivariate Longitudinal Data with Mixed-Types of Responses

We propose a copula-based generalized linear mixed model (GLMM) to jointly analyze multivariate longitudinal data with mixed types, including continuous, count and binary responses. The association of repeated measurements is modelled through the GLMM model, meanwhile a pair-copula construction (D-vine) is adopted to measure the dependency structure between different responses. By combining mixed models and D-vine copulas, our proposed approach could not only deal with unbalanced data with arbitrary margins but also handle moderate dimensional problems due to the efficiency and flexibility of D-vines. Based on D-vine copulas, algorithms for sampling mixed data and computing likelihood are also developed. Leaving the random effects distribution unspecified, we use nonparametric maximum likelihood for model fitting. Then an E-M algorithm is used to obtain the maximum likelihood estimates of parameters. Both simulations and real data analysis show that the nonparametric models are more efficient and flexible than the parametric models.