Exact confidence intervals for population growth rate, longevity and generation time.

By quantifying key life history parameters in populations, such as growth rate, longevity, and generation time, researchers and administrators can obtain valuable insights into its dynamics. Although point estimates of demographic parameters have been available since the inception of demography as a scientific discipline, the construction of confidence intervals has typically relied on approximations through series expansions or computationally intensive techniques. This study introduces the first mathematical expression for calculating confidence intervals for the aforementioned life history traits when individuals are unidentifiable and data are presented as a life table. The key finding is the accurate estimation of the confidence interval for r, the instantaneous growth rate, which is tested using Monte Carlo simulations with four arbitrary discrete distributions. In comparison to the bootstrap method, the proposed interval construction method proves more efficient, particularly for experiments with a total offspring size below 400. We discuss handling cases where data are organized in extended life tables or as a matrix of vital rates. We have developed and provided accompanying code to facilitate these computations.

A novel method for genomic-enabled prediction of cultivars in new environments.

Introduction: Genomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs. Objectives of the research: In this research we proposed a method to improve the prediction accuracy of tested lines in new (untested) environments. Method-1: The new method trained the model with a modified response variable (a difference of response variables) that decreases the lack of a non-stationary distribution between the training and testing and improved the prediction accuracy. Comparing new and conventional method: We compared the prediction accuracy of the conventional genomic best linear unbiased prediction (GBLUP) model (M1) including (or not) genotype × environment interaction (GE) (M1_GE; M1_NO_GE) versus the proposed method (M2) on several data sets. Results and discussion: The gain in prediction accuracy of M2, versus M1_GE, M1_NO_GE in terms of Pearson´s correlation was of at least 4.3%, while in terms of percentage of top-yielding lines captured when was selected the 10% (Best10) and 20% (Best20) of lines was at least of 19.5%, while in terms of Normalized Root Mean Squared Error (NRMSE) was of at least of 42.29%.

Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective.

Diabetes mellitus is a disease with no cure that can cause complications and even death. Moreover, over time, it will lead to chronic complications. Predictive models have been used to identify people with a tendency to develop diabetes mellitus. At the same time, there is limited information regarding the chronic complications of patients with diabetes. Our study is aimed at creating a machine-learning model that will be able to identify the risk factors of a diabetic patient developing chronic complications such as amputations, myocardial infarction, stroke, nephropathy, and retinopathy. The design is a national nested case-control study with 63,776 patients and 215 predictors with four years of data. Using an XGBoost model, the prediction of chronic complications has an AUC of 84%, and the model has identified the risk factors for chronic complications in patients with diabetes. According to the analysis, the most crucial risk factors based on SHAP values (Shapley additive explanations) are continued management, metformin treatment, age between 68 and 104 years, nutrition consultation, and treatment adherence. But we highlight two exciting findings. The first is a reaffirmation that high blood pressure figures across patients with diabetes without hypertension become a significant risk factor at diastolic > 70 mmHg (OR: 1.095, 95% CI: 1.078-1.113) or systolic > 120 mmHg (OR: 1.147, 95% CI: 1.124-1.171). Furthermore, people with diabetes with a BMI > 32 (overall obesity) (OR: 0.816, 95% CI: 0.8-0.833) have a statistically significant protective factor, which the paradox of obesity may explain. In conclusion, the results we have obtained show that artificial intelligence is a powerful and feasible tool to use for this type of study. However, we suggest that more studies be conducted to verify and elaborate upon our findings.

Waning immunity to SARS-CoV-2 following vaccination or infection.

We use survival analysis to analyze the decay in the protection induced by eight SARS-CoV-2 vaccines using data from 33,418 fully anonymized patients from the IMSS public health system in Mexico, including only previously vaccinated, confirmed SARS-CoV-2 positive with a PCR test. We analyze the waning effect in those with complete vs. incomplete dose fitting a Weibull distribution. We compare these results with an estimate of the waning effect due to active infection. In two-dose vaccines, we found that the average protection time of a complete dose increases 2.6 times compared to that of an incomplete dose. All analyzed vaccines provided a protection that lasted longer than the protection due to active infection, except in those patients that did not fulfilled the complete dose. The average protection of a full dose is 2.2 times larger than that provided by active infection. The average protection of active infection is about the same as the average protection of an incomplete dose. All evaluated vaccines had lost most of their protective effect between 8 and 11 months of application of first shot. Our results highly correlate with NT50 and other estimates of vaccine efficacy. We found that on average, vaccination increases Age50, the age at which there is a 50% probability of severe disease if infected, in 15 years. We also found that Age50 increases with mean protection time.

[Obesity, CKD and gender on the 30-day in-hospital mortality risk in adults with COVID-19]. / Obesidad, ERC y género en el riesgo de mortalidad hospitalaria a 30 días en adultos con COVID-19.

Background: The coronavirus disease 2019 (COVID-19) pandemic is a serious health problem. The Mexican adult population has a high prevalence of obesity and chronic diseases that increase the risk of dying from this disease. Objective: To identify comorbidities predicting the risk of mortality at 30 days in hospitalized adult subjects with positive laboratory COVID-19 test and to evaluate the interaction between chronic diseases and gender. Material and methods: A retrospective cohort study was conducted in 2020, in a western region of the Mexican Pacific. Data from 51,135 hospitalized patients with positive COVID-19 test were analyzed and were retrieved from a normative system for the epidemiological surveillance of viral respiratory diseases (SINOLAVE, according to its initials in Spanish). Death within the first 30 days from hospital admission was the main outcome and risk ratios (RR) with 95% confidence intervals (95% CI) were calculated. Results: The overall mortality rate was 49.6% and most of the comorbidities analyzed were associated with a higher risk of death. There were significant interactions between gender and obesity (p = 0.003) and chronic kidney disease (p = 0.019). The effect of obesity on the risk of a fatal outcome varied by gender: female, RR = 1.04 (95% CI 1.03-1.07); male, RR = 1.07 (95% CI: 1.06-1.09). Conclusions: A high mortality was observed among the hospitalized patients analyzed and statistically significant factors associated with their risk were identified (gender, obesity, and kidney disease).

Introducción: la pandemia de la enfermedad por coronavirus 2019 (COVID-19) es un problema serio de salud. La población adulta mexicana tiene una alta prevalencia de obesidad y de enfermedades crónicas que incrementan el riesgo de morir por esta enfermedad. Objetivo: identificar comorbilidades predictoras del riesgo de mortalidad a 30 días en sujetos adultos hospitalizados con COVID-19 demostrado por laboratorio y evaluar la interacción entre enfermedades crónicas y el género del paciente. Material y métodos: se hizo un estudio de cohorte retrospectivo en el 2020, en una región del occidente del pacífico mexicano. Se analizaron los datos de 51,135 pacientes hospitalizados con COVID-19, los cuales fueron extraídos de un sistema normativo para la vigilancia epidemiológica de enfermedades respiratorias virales (SINOLAVE). La muerte dentro de los primeros 30 días desde la admisión hospitalaria fue el evento principal y fueron estimadas razones de riesgo (RR) con intervalos de confianza del 95% (IC 95%). Resultados: la mortalidad global fue del 49.6% y la mayoría de las comorbilidades analizadas se asociaron con un mayor riesgo de muerte. Hubo interacciones significativas entre el género y la obesidad (p = 0.003) y la enfermedad renal crónica (p = 0.019). El efecto de la obesidad sobre el riesgo de un desenlace fatal varió en función del género: mujeres, RR = 1.04 (IC 95% 1.03-1.07); hombres, RR = 1.07 (IC 95% 1.06-1.09). Conclusiones: se observó una alta mortalidad entre los pacientes hospitalizados analizados y se identificaron factores asociados a su riesgo (género, obesidad y enfermedad renal).

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype × environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection.

Deep-learning power and perspectives for genomic selection.

Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but nowadays, artificial machines have surpassed the ability of humans in this specific task. This is just one example of how the application of these models has surpassed human abilities and the performance of other machine-learning algorithms. For this reason, DL models have been adopted for genomic selection (GS). In this article we provide insight about the power of DL in solving complex prediction tasks and how combining GS and DL models can accelerate the revolution provoked by GS methodology in plant breeding. Furthermore, we will mention some trends of DL methods, emphasizing some areas of opportunity to really exploit the DL methodology in GS; however, we are aware that considerable research is required to be able not only to use the existing DL in conjunction with GS, but to adapt and develop DL methods that take the peculiarities of breeding inputs and GS into consideration.

Effect of an ecosystem-centered community participation programme on the incidence of dengue. A field randomized, controlled trial.

OBJECTIVES: The purpose of this study is to analyse the effect of a community participation programme based on the ecosystem model on the incidence of dengue in urban communities. METHODS: A randomized controlled field trial was conducted in the state of Colima, Mexico. The intervention consisted of a community participation programme focused on the ecosystem; simultaneously, the control groups were communities that only received the usual official prevention programs. The incidence of dengue was estimated in people of both groups due to the appearance of de novo IgM antibodies during the follow-up period. RESULTS: The incidence of dengue in the intervened group was 2.58%/month (n = 818) and in control group 2.26%/month (n = 994), with a risk ratio of 1.14 (95% CI 0.89-1.45) and a PAF of 0.06 (95% CI - 0.056 to 0.16). The A. aegypti larval density (Breteau Index) was reduced in the treated group. CONCLUSIONS: The implementation of a community participation programme in the cities of Colima, Mexico, showed a slightly counterproductive effect on the incidence of dengue. This happened even with a reduction in the A. aegypti index.

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype × environment (G×E) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS.

Building matrix population models when individuals are non-identifiable.

Matrix Population Models (MPM) are among the most widely used tools in ecology and evolution. These models consider the life cycle of an individual as composed by states to construct a matrix containing the likelihood of transitions between these states as well as sexual and/or asexual per-capita offspring contributions. When individuals are identifiable one can parametrize an MPM based on survival and fertility data and average development times for every state, but some of this information is absent or incomplete for non-cohort data, or for cohort data when individuals are not identifiable. Here we introduce a simple procedure for the parameterization of an MPM that can be used with cohort data when individuals are non-identifiable; among other aspects our procedure is a novelty in that it does not require information on stage development (or stage residence) times, which current procedures require to be estimated externally, and it is a frequent source of error. We exemplify the procedure with a laboratory cohort dataset from Eratyrus mucronatus (Reduviidae, Triatominae). We also show that even if individuals are identifiable and the duration of each stage is externally estimated with no error, our procedure is simpler to use and yields the same MPM parameter estimates.

A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture.

Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.

Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.

Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance of MTDL models was compared to the performance of the Bayesian multi-trait and multi-environment (BMTME) model proposed by Montesinos-López et al. (2016), which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype×environment interaction term. The prediction performance of both models was evaluated in terms of Pearson's correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype×environment interaction, the MTDL model was the best, while among models with genotype×environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model.

Cost-Effectiveness of the Strategies to Reduce the Incidence of Dengue in Colima, México.

Dengue fever is considered to be one of the most important arboviral diseases globally. Unsuccessful vector-control strategies might be due to the lack of sustainable community participation. The state of Colima, located in the Western region of Mexico, is a dengue-endemic area despite vector-control activities implemented, which may be due to an insufficient health economic analysis of these interventions. A randomized controlled community trial took place in five urban municipalities where 24 clusters were included. The study groups (n = 4) included an intervention to improve the community participation in vector control (A), ultra-low volume (ULV) spraying (B), both interventions (AB), and a control group. The main outcomes investigated were dengue cumulative incidence, disability-adjusted life years (DALYs), and the direct costs per intervention. The cumulative incidence of dengue was 17.4%, A; 14.3%, B; 14.4%, AB; and 30.2% in the control group. The highest efficiency and effectiveness were observed in group B (0.526 and 6.97, respectively) and intervention A was more likely to be cost-effective ($3952.84 per DALY avoided) followed by intervention B ($4472.09 per DALY avoided). Our findings suggest that efforts to improve community participation in vector control and ULV-spraying alone are cost-effective and may be useful to reduce the vector density and dengue incidence.

Aggregation is the key to succeed in random walks.

In a random walk (RW) in Z an individual starts at 0 and moves at discrete unitary steps to the right or left with respective probabilities p and 1-p. Assuming p > 1/2 and finite a, a > 1, the probability that state a will be reached before -a is Q(a, p) where Q(a, p) > p. Here we introduce the cooperative random walk (CRW) involving two individuals that move independently according to a RW each but dedicate a fraction of time θ to approach the other one unit. This simple strategy seems to be effective in increasing the expected number of individuals arriving to a first. We conjecture that this is a possible underlying mechanism for efficient animal migration under noisy conditions.

Empirical evidence of the effect of school gathering on the dynamics of dengue epidemics.

INTRODUCTION: Dengue fever is an important vector-transmitted disease that affects more than 100 countries worldwide. Locations where individuals tend to gather may play an important role in disease transmission in the presence of the vector. By controlling mosquitoes' breeding places, this study aims to analyze the effect of reducing transmission in elementary schools (grades 1-9) on the dynamics of the epidemic at a regional level. MATERIALS AND METHODS: In 2007, we implemented a massive campaign in a region of México (Colima state, 5,191 km(2), population 568,000) focused on training janitors to locate and avoid mosquitoes' breeding places, the objective being to maintain elementary schools free of mosquitoes. RESULTS: We observed 45% reduction in dengue incidence compared to the previous year. In contrast, the rest of Mexico observed an 81% increase in incidence on average. DISCUSSION: Costs associated with campaigns focusing on cleaning schools are very low and results seem to be promising. Nevertheless, more controlled studies are needed.

Biological Control of the Chagas Disease Vector Triatoma infestans with the Entomopathogenic Fungus Beauveria bassiana Combined with an Aggregation Cue: Field, Laboratory and Mathematical Modeling Assessment.

BACKGROUND: Current Chagas disease vector control strategies, based on chemical insecticide spraying, are growingly threatened by the emergence of pyrethroid-resistant Triatoma infestans populations in the Gran Chaco region of South America. METHODOLOGY AND FINDINGS: We have already shown that the entomopathogenic fungus Beauveria bassiana has the ability to breach the insect cuticle and is effective both against pyrethroid-susceptible and pyrethroid-resistant T. infestans, in laboratory as well as field assays. It is also known that T. infestans cuticle lipids play a major role as contact aggregation pheromones. We estimated the effectiveness of pheromone-based infection boxes containing B. bassiana spores to kill indoor bugs, and its effect on the vector population dynamics. Laboratory assays were performed to estimate the effect of fungal infection on female reproductive parameters. The effect of insect exuviae as an aggregation signal in the performance of the infection boxes was estimated both in the laboratory and in the field. We developed a stage-specific matrix model of T. infestans to describe the fungal infection effects on insect population dynamics, and to analyze the performance of the biopesticide device in vector biological control. CONCLUSIONS: The pheromone-containing infective box is a promising new tool against indoor populations of this Chagas disease vector, with the number of boxes per house being the main driver of the reduction of the total domestic bug population. This ecologically safe approach is the first proven alternative to chemical insecticides in the control of T. infestans. The advantageous reduction in vector population by delayed-action fungal biopesticides in a contained environment is here shown supported by mathematical modeling.

The long-run distribution of births across environments under environmental stochasticity and its use in the calculation of unconditional life-history parameters.

Matrix population models assume individuals develop in time along different stages that may include age, size or degree of maturity to name a few. Once in a given stage, an individual's ability to survive, reproduce or move to another stage are fixed for that stage. Some demographic models consider that environmental conditions may change, and thus the chances of reproducing, dying or developing to another stage depend on the current stage and environmental conditions. That is, models have evolved from a single transition matrix to a set of several transition matrices, each accounting for the properties of a given environment. These models require information on the transition between environments, which is in general assumed to be Markovian. Although great progress has been made in the analysis of these models, they present new challenges and some new parameters need to be calculated, mainly the ones related to how births are distributed among environments. These parameters may help in population management and to calculate unconditional life history parameters. We derive for the first time an expression for the long-run distribution of births across environments, and show that it does not depend only on the long-range frequency of different environments, but also on the set of all transition and fertility matrices. We also derive the long-run distribution of deaths across environments. We provide an example using a real data set of the dynamics of Saiga antelope. Theoretical values closely match the observed values obtained in a large set of stochastic simulations.

An application of queuing theory to SIS and SEIS epidemic models.

In this work we consider every individual of a population to be a server whose state can be either busy (infected) or idle (susceptible). This server approach allows to consider a general distribution for the duration of the infectious state, instead of being restricted to exponential distributions. In order to achieve this we first derive new approximations to quasistationary distribution (QSD) of SIS (Susceptible- Infected- Susceptible) and SEIS (Susceptible- Latent- Infected- Susceptible) stochastic epidemic models. We give an expression that relates the basic reproductive number, R0 and the server utilization,p.

Violencia doméstica y riesgo de conducta suicida en universitarios adolescentes / Domestic violence and risk of suicidal behavior among university students

OBJETIVO: Estimar el grado de asociación entre violencia doméstica física, verbal y sexual con la conducta suicida en adolescentes universitarios. MATERIAL Y MÉTODOS: Estudio de casos y controles pareado en estudiantes universitarios de Colima. Los casos fueron 235 adolescentes que presentaron tanto ideación suicida como intento suicida; los controles fueron 470 individuos de la misma edad y sexo. RESULTADOS: El abuso sexual mostró el mayor grado de asociación con conducta suicida (RM= 27.4), seguido de violencia verbal (RM= 9.28), uso de drogas (RM= 8.6), violencia física (RM= 5.5) y tabaquismo (RM= 3.6). La regresión logística multivariada mostró que la violencia verbal se asoció con conducta suicida en forma independiente, mientras que violencia física, abuso sexual, tabaquismo y uso de drogas parecen depender de aquella. CONCLUSIONES: La violencia intrafamiliar, particularmente la verbal, está fuertemente asociada con la conducta suicida en adolescentes y debe ser considerada dentro de programas preventivos contra suicidio.

OBJECTIVE: To estimate the degree of association between domestic violence -physical, verbal or sexual- with suicidal behavior among university students. MATERIAL AND METHODS: A matched case-control study was done with students attending the University of Colima, Mexico. The cases were 235 teenagers who presented both suicidal ideation and suicide attempt; the controls were 470 individuals of the same age and sex. RESULTS: Sexual abuse showed the highest degree of association with suicidal behavior (OR= 27.4), followed by verbal violence (OR= 9.28), drug use (OR= 8.6), physical violence (OR= 5.5) and smoking (OR= 3.6). Multivariate logistic regression showed that verbal violence was associated with suicidal behavior independently of the other variables, while physical violence, sexual abuse, smoking and drug use seem to depend on verbal violence. CONCLUSIONS: Domestic violence, particularly verbal or sexual, is strongly associated with suicidal behavior in adolescents and should be considered in suicide prevention programs.