Supporting cognitive catch-up: The effects of cluster-randomized psychosocial stimulation interventions on preterm low birthweight children in rural China.

Improved survival of preterm low birthweight (LBW) infants due to advances in neonatal care has brought issues such as postnatal development trajectories to the foreground. This study pools evidence from three cluster-randomized experiments evaluating community-based psychosocial stimulation programs conducted from 2014 to 2017 that included 3571 rural Chinese children aged 6-24 months (51.1% male, 96.2% Han Chinese). The risk of severe cognitive delay was found to be 26.5 percentage points higher for preterm LBW children than for their peers at age 2.5, with a prevalence rate of 48.3%. Results show that psychosocial stimulation interventions can improve child cognitive development at scale, with beneficial impacts on child cognition disproportionately larger for preterm LBW children, helping them to catch up developmentally.

Reducing wait times and avoiding unnecessary use of high-cost mental health services through a Rapid Access and Stabilization Program: protocol for a program evaluation study.

BACKGROUND: Emergency psychiatric care, unplanned hospital admissions, and inpatient health care are the costliest forms of mental health care. According to Statistics Canada (2018), almost 18% (5.3 million) of Canadians reported needing mental health support. However, just above half of this figure (56.2%) have reported their needs were fully met. In light of this evidence there is a pressing need to provide accessible mental health services in flexible yet cost-effective ways. To further expand capacity and access to mental health care in the province, Nova Scotia Health has launched a novel mental health initiative for people in need of mental health care without requiring emergency department visits or hospitalization. This new service is referred to as the Rapid Access and Stabilization Program (RASP). This study evaluates the effectiveness and impact of the RASP on high-cost health services utilization (e.g. ED visits, mobile crisis visits, and inpatient treatments) and related costs. It also assesses healthcare partners' (e.g. healthcare providers, policymakers, community leaders) perceptions and patient experiences and satisfaction with the program and identifies sociodemographic characteristics, psychological conditions, recovery, well-being, and risk measures in the assisted population. METHOD: This is a hypothesis-driven program evaluation study that employs a mixed methods approach. A within-subject comparison (pre- and post-evaluation study) will examine health services utilization data from patients attending RASP, one year before and one year after their psychiatry assessment at the program. A controlled between-subject comparison (cohort study) will use historical data from a control population will examine whether possible changes in high-cost health services utilization are associated with the intervention (RASP). The primary analysis involves extracting secondary data from provincial information systems, electronic medical records, and regular self-reported clinical assessments. Additionally, a qualitative sub-study will examine patient experience and satisfaction, and health care partners' impressions. DISCUSSION: We expect that RASP evaluation findings will demonstrate a minimum 10% reduction in high-cost health services utilization and corresponding 10% cost savings, and also a reduction in the wait times for patient consultations with psychiatrists to less than 30 calendar days, in both within-subject and between-subject comparisons. In addition, we anticipate that patients, healthcare providers and healthcare partners would express high levels of satisfaction with the new service. CONCLUSION: This study will demonstrate the results of the Mental Health and Addictions Program (MHAP) efforts to provide stepped-care, particularly community-based support, to individuals with mental illnesses. Results will provide new insights into a novel community-based approach to mental health service delivery and contribute to knowledge on how to implement mental health programs across varying contexts.

Advancements in predicting and modeling rare event outcomes for enhanced decision-making.

Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy of rare event prediction and modeling.

Improving social justice in observational studies: protocol for the development of a global and Indigenous STROBE-equity reporting guideline.

BACKGROUND: Addressing persistent and pervasive health inequities is a global moral imperative, which has been highlighted and magnified by the societal and health impacts of the COVID-19 pandemic. Observational studies can aid our understanding of the impact of health and structural oppression based on the intersection of gender, race, ethnicity, age and other factors, as they frequently collect this data. However, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline, does not provide guidance related to reporting of health equity. The goal of this project is to develop a STROBE-Equity reporting guideline extension. METHODS: We assembled a diverse team across multiple domains, including gender, age, ethnicity, Indigenous background, disciplines, geographies, lived experience of health inequity and decision-making organizations. Using an inclusive, integrated knowledge translation approach, we will implement a five-phase plan which will include: (1) assessing the reporting of health equity in published observational studies, (2) seeking wide international feedback on items to improve reporting of health equity, (3) establishing consensus amongst knowledge users and researchers, (4) evaluating in partnership with Indigenous contributors the relevance to Indigenous peoples who have globally experienced the oppressive legacy of colonization, and (5) widely disseminating and seeking endorsement from relevant knowledge users. We will seek input from external collaborators using social media, mailing lists and other communication channels. DISCUSSION: Achieving global imperatives such as the Sustainable Development Goals (e.g., SDG 10 Reduced inequalities, SDG 3 Good health and wellbeing) requires advancing health equity in research. The implementation of the STROBE-Equity guidelines will enable a better awareness and understanding of health inequities through better reporting. We will broadly disseminate the reporting guideline with tools to enable adoption and use by journal editors, authors, and funding agencies, using diverse strategies tailored to specific audiences.

Canine hypoadrenocorticism: Insights into the Addisonian crisis.

Objective: Primary objectives of this study were to determine presenting complaints, physical examination, clinicopathologic findings, and hospitalization time of dogs with spontaneous hypoadrenocorticism presenting with critical disease; and to compare those end points to dogs with a more stable presentation. Secondary objectives were to evaluate the shock index and to identify precipitating stressors. Animals: Eighty-four dogs at the Western College of Veterinary Medicine between 1998 and 2018 were included. Procedure: Data were retrieved from the medical records. Results: Collapse and depression were more common among critically ill dogs. Hyperlactatemia was rare despite a diagnosis of hypovolemic shock, and a shock index was ineffective in this patient subset. Isosthenuria, total hypocalcemia, and more severe acidosis were more common (P < 0.05) in critical dogs. Owner separation was the most common precipitating stressor. Conclusion and clinical relevance: We concluded that the critical Addisonian dog has unique characteristics that may aid in early disease identification.

Hypoadrénocorticisme canin : aper ç u de la crise Addisonienne. Objectif: Les principaux objectifs de cette étude étaient de déterminer les motifs de présentation, l'examen physique, les résultats clinico-pathologiques et la durée d'hospitalisation des chiens atteints d'hypoadrénocorticisme spontané présentant une maladie critique; et de comparer ces paramètres aux chiens avec une présentation plus stable. Les objectifs secondaires étaient d'évaluer l'indice de choc et d'identifier les facteurs de stress déclencheurs. Animaux: Quatre-vingt-quatre chiens du Western College of Veterinary Medicine entre 1998 et 2018 ont été inclus. Procédure: Les données ont été extraites des dossiers médicaux. Résultats: L'effondrement et la dépression étaient plus fréquents chez les chiens gravement malades. L'hyperlactatémie était rare malgré un diagnostic de choc hypovolémique, et un indice de choc était inefficace dans ce sous-groupe de patients. L'isosthénurie, l'hypocalcémie totale et l'acidose plus grave étaient plus fréquentes (P < 0,05) chez les chiens critiques. La séparation du propriétaire était le facteur de stress déclencheur le plus courant. Conclusion et pertinence clinique: Nous avons conclu que le chien addisonien critique a des caractéristiques uniques qui peuvent aider à l'identification précoce de la maladie.(Traduit par Dr Serge Messier).

Suicidal ideation among Canadian adults during the COVID-19 pandemic: the role of psychosocial factors and substance use behaviours.

BACKGROUND: Suicide is one of the most important and increasing public health agenda around the world. Since the COVID-19 pandemic, concerns have been raised about the potential adverse impacts of the pandemic on suicide-related outcomes. The main objective of this study was to examine the association of psychosocial risk factors (mental health illnesses and social isolation) and substance use behaviors (cannabis and alcohol consumption) with suicidal ideation during the COVID-19 pandemic among Canadian adults. METHODS: The study was conducted based on a total of 4005 persons 18 years of age or older, living in Canada's ten provinces. The data used in this study were collected during April 20-28, 2021, by Mental Health Research Canada. Multivariable logistic regression was used to determine the association of mental health conditions (anxiety, depression, and other mood disorder) before and since COVID-19 outbreaks, social isolation and living arrangement, as well as cannabis and alcohol consumption with suicidal ideation during COVID-19. RESULTS: The results of adjusted logistic regression showed that the odds of suicidal ideation were 1.526 times higher (95% CI:1.082-2.152) among those who reported continued negative impacts of social isolation. The odds of suicidal ideation were also higher for those who were diagnosed as having depression before (OR = 3.136, 95% CI: 2.376-4.138) and since the COVID-19 pandemic (OR = 3.019, 95% CI:1.929-4.726) and 1.627 times higher (95% CI: 1.225-2.163) for those who were diagnosed as having anxiety before the COVID-19 pandemic. Those who reported having increased and those who were consuming cannabis during the pandemic were 1.970 (95% CI: 1.463-2.653) and 1.509 times (95% CI: 1.158-1.966) more likely to have thought of suicide than non-takers, respectively. CONCLUSION: Given the significant associations of psychosocial factors (mental health illnesses and social isolation) and cannabis use with suicidal ideation, more attention and support need to be given to adults who had mental health conditions before and since COVID-19, those who were negatively impacted by social isolation, and those are exposed to substance use (cannabis).

Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning.

BACKGROUND: Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk. METHODS: We used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data. RESULTS: In the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age. CONCLUSION: Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets.

Breast-feeding, rapid growth in the first year of life and excess weight at the age of 2 years: the 3D Cohort Study.

OBJECTIVE: To assess relationships between breast-feeding, rapid growth in the first year of life and overweight/obesity status at the age of 2 years. DESIGN: As part of an observational, longitudinal study beginning in early pregnancy, multivariable logistic regressions were used to assess associations between breast-feeding duration (total and exclusive) and rapid weight gain (RWG) between birth and 1 year of age, and to determine predictors of overweight/obesity status at the age of 2 years. SETTING: Nine hospitals located in the province of Quebec, Canada. PARTICIPANTS: A sample of 1599 term infants who participated in the 3D Cohort Study. RESULTS: Children having RWG in the first year and those having excess weight at the age of 2 years accounted for 28 % and < 10 %, respectively. In multivariable models, children breastfed < 6 months and from 6 months to < 1 year were, respectively, 2·5 times (OR 2·45; 95 % CI 1·76, 3·41) and 1·8 times (OR 1·78; 95 % CI 1·29, 2·45) more likely to show RWG up to 1 year of age compared to children breastfed ≥ 1 year. Children exclusively breastfed < 3 months had significantly greater odds of RWG in the first year (OR 1·94; 95 % CI 1·25, 3·04) compared to children exclusively breastfed for ≥ 6 months. Associations between breast-feeding duration (total or exclusive) and excess weight at the age of 2 years were not detected. RWG in the first year was found to be the main predictor of excess weight at the age of 2 years (OR 6·98; 95 % CI 4·35, 11·47). CONCLUSIONS: The potential beneficial effects of breast-feeding on rate of growth in the first year of life suggest that interventions promoting breast-feeding are relevant for obesity prevention early in life.

Perceived mental health, work, and life stress in association with the amount of weekly alcohol consumption among Canadian adults who have ever drank.

BACKGROUND: Excess alcohol consumption has multifaceted adverse impacts at individual, household, and community levels. The study primarily aims at assessing the role of perceived health and stress in alcohol consumption among adults in Canada who have ever drank. METHODS: The study was conducted based on a total of 35,928 Canadian adults aged 18 and above who have ever drank, extracted from the 2017-2018 Canadian Community Health Survey (CCHS) data. A mixed-effect Negative Binomial (NB) regression model was used to determine the effects of three key risk factors (perceived mental health, life stress, and work stress) in association with the self-reported number of weekly alcohol consumption, controlling for other variables in the model. RESULTS: The study found that regular alcohol consumption among ever drank Canadian adults is high, with the self-reported number of weekly alcohol consumption ranging from 0 to 210. The results of adjusted mixed-effect NB regression showed that the expected mean of alcohol consumption was significantly higher among those with a poorer perception of mental health, higher perceived work, and life stress. Nonsmokers have a much lower mean score of alcohol consumption compared to those who smoke daily. There was a significant interaction between racial background and the three key predictors (perceived mental health, life stress, and work stress). CONCLUSION: Given the reported perceived health and stress significantly impacts alcohol consumption, the findings suggested improving individual/group counseling, and health education focusing on home and work environment to prevent and manage life stressors and drivers to make significant program impacts.

Diet quality during pregnancy and its association with social factors: 3D Cohort Study (Design, Develop, Discover).

Good diet quality during pregnancy provides adequate nutrition to support both the mothers and the fetus. The objective of this study is to describe the distribution of diet quality during pregnancy and to study the association between social factors and diet quality during pregnancy in a Canadian population. This study was based on 1535 pregnant women who provided dietary information in the 3D Cohort Study in Quebec, Canada. A 3-day food record was used to collect dietary intake in the second trimester of pregnancy. A Canadian adaption of the Healthy Eating Index (HEI-C) 2010 was used to quantify diet quality. Univariate and multiple linear regression models were used to calculate unadjusted and adjusted effect estimates and confidence intervals for the association between social factors and HEI-C. The mean HEI-C 2010 score in this study was 62.9 (SD: 11.2). Only 4.5% and 8.3% of the pregnant women consumed the recommended amounts of whole grains and 'greens and beans', respectively. Diet quality was lower in some subgroups of pregnant women. After multivariable adjustment, lower diet quality was observed in participants who were less educated, younger, overweight or obese before pregnancy, or parous. There was an interaction between ethnicity and immigration status on diet quality in pregnancy. These findings could be useful for health practitioners and policymakers in developing strategies to improve the diet quality of pregnant women.

Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data.

BACKGROUND: For differential abundance analysis, zero-inflated generalized linear models, typically zero-inflated NB models, have been increasingly used to model microbiome and other sequencing count data. A common assumption in estimating the false discovery rate is that the p values are uniformly distributed under the null hypothesis, which demands that the postulated model fit the count data adequately. Mis-specification of the distribution of the count data may lead to excess false discoveries. Therefore, model checking is critical to control the FDR at a nominal level in differential abundance analysis. Increasing studies show that the method of randomized quantile residual (RQR) performs well in diagnosing count regression models. However, the performance of RQR in diagnosing zero-inflated GLMMs for sequencing count data has not been extensively investigated in the literature. RESULTS: We conduct large-scale simulation studies to investigate the performance of the RQRs for zero-inflated GLMMs. The simulation studies show that the type I error rates of the GOF tests with RQRs are very close to the nominal level; in addition, the scatter-plots and Q-Q plots of RQRs are useful in discerning the good and bad models. We also apply the RQRs to diagnose six GLMMs to a real microbiome dataset. The results show that the OTU counts at the genus level of this dataset (after a truncation treatment) can be modelled well by zero-inflated and zero-modified NB models. CONCLUSION: RQR is an excellent tool for diagnosing GLMMs for zero-inflated count data, particularly the sequencing count data arising in microbiome studies. In the supplementary materials, we provided two generic R functions, called rqr.glmmtmb and rqr.hurdle.glmmtmb, for calculating the RQRs given fitting outputs of the R package glmmTMB.

Model diagnostics for censored regression via randomized survival probabilities.

Residuals in normal regression are used to assess a model's goodness-of-fit (GOF) and discover directions for improving the model. However, there is a lack of residuals with a characterized reference distribution for censored regression. In this article, we propose to diagnose censored regression with normalized randomized survival probabilities (RSP). The key idea of RSP is to replace the survival probability (SP) of a censored failure time with a uniform random number between 0 and the SP of the censored time. We prove that RSPs always have the uniform distribution on (0, 1) under the true model with the true generating parameters. Therefore, we can transform RSPs into normally distributed residuals with the normal quantile function. We call such residuals by normalized RSP (NRSP residuals). We conduct simulation studies to investigate the sizes and powers of statistical tests based on NRSP residuals in detecting the incorrect choice of distribution family and nonlinear effect in covariates. Our simulation studies show that, although the GOF tests with NRSP residuals are not as powerful as a traditional GOF test method, a nonlinear test based on NRSP residuals has significantly higher power in detecting nonlinearity. We also compared these model diagnostics methods with a breast-cancer recurrent-free time dataset. The results show that the NRSP residual diagnostics successfully captures a subtle nonlinear relationship in the dataset, which is not detected by the graphical diagnostics with CS residuals and existing GOF tests.

Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods.

BACKGROUND: Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system's burden. The present study aimed to assess the predictive accuracy of machine learning methods to predict the COVID-19 mortality risk. METHODS: We compared the performance of classification tree, random forest (RF), extreme gradient boosting (XGBoost), logistic regression, generalized additive model (GAM) and linear discriminant analysis (LDA) to predict the mortality risk among 49,216 COVID-19 positive cases in Toronto, Canada, reported from March 1 to December 10, 2020. We used repeated split-sample validation and k-steps-ahead forecasting validation. Predictive models were estimated using training samples, and predictive accuracy of the methods for the testing samples was assessed using the area under the receiver operating characteristic curve, Brier's score, calibration intercept and calibration slope. RESULTS: We found XGBoost is highly discriminative, with an AUC of 0.9669 and has superior performance over conventional tree-based methods, i.e., classification tree or RF methods for predicting COVID-19 mortality risk. Regression-based methods (logistic, GAM and LASSO) had comparable performance to the XGBoost with slightly lower AUCs and higher Brier's scores. CONCLUSIONS: XGBoost offers superior performance over conventional tree-based methods and minor improvement over regression-based methods for predicting COVID-19 mortality risk in the study population.

Socio-demographic and environmental risk factors associated with multiple under-five child loss among mothers in Bangladesh.

BACKGROUND: Despite the substantial decline in child mortality globally over the last decade, reducing neonatal and under-five mortality in Bangladesh remains a challenge. Mothers who experienced multiple child losses could have substantial adverse personal and public health consequences. Hence, prevention of child loss would be extremely desirable during women's reproductive years. The main objective of this study was to determine the risk factors associated with multiple under-five child loss from the same mother in Bangladesh. METHODS: In this study, a total of 15,877 eligible women who had given birth at least once were identified from the 2014 Bangladesh Demographic and Health Survey. A variety of count regression models were considered for identifying socio-demographic and environmental factors associated with multiple child loss measured as the number of lifetime under-five child mortality (U5M) experienced per woman. RESULTS: Of the total sample, approximately one-fifth (18.9%, n = 3003) of mothers experienced at least one child's death during their reproductive period. The regression analysis results revealed that women in non-Muslim families, with smaller household sizes, with lower education, who were more advanced in their childbearing years, and from an unhygienic environment were at significantly higher risk of experiencing offspring mortality. This study also identified the J-shaped effect of age at first birth on the risk of U5M. CONCLUSIONS: This study documented that low education, poor socio-economic status, extremely young or old age at first birth, and an unhygienic environment significantly contributed to U5M per mother. Therefore, improving women's educational attainment and socio-economic status, prompting appropriate timing of pregnancy during reproductive life span, and increasing access to healthy sanitation are recommended as possible interventions for reducing under-five child mortality from a mother. Our findings point to the need for health policy decision-makers to target interventions for socio-economically vulnerable women in Bangladesh.

Multiple anthropometric and nutritional deficiencies in young children in Ethiopia: a multi-level analysis based on a nationally representative data.

BACKGROUND: In Ethiopia, child undernutrition and anemia are major public health concerns, resulting in increased childhood morbidity and mortality. Despite progress made to reduce the prevalence of malnutrition (especially stunting) from 50% in 2000 to 38% in 2016, little is known about the magnitude and risk factors for concurrent nutritional deficiencies in Ethiopia. METHODS: Analysis for this study was based on a total sample of 9218 children aged 6-59 months drawn from the Ethiopian Demographic and Health Survey (EDHS) conducted in the year 2016. The study used two outcome variables: Multiple nutrition deficit index formed by combining stunting, underweight, wasting and anemia status; and a concurrent stunting and anemia (CAS) index. Two mixed effect regression models, Poisson and Logistic, were used to identify the key risk factors of the two outcome variables, respectively. RESULTS: The proportion of children with stunting (length-for-age), underweight (weight-for-age) and wasting children (weight-for-length) was 38%, 25.2% and 9.4%, respectively. About 58% of the children had anemia. The prevalence of children with concurrent stunting and anemia children was 24.8%. Our results showed that the risks of multiple nutritional problems were determined by a range of individual, household and behavioral factors including: sex of the child, age of the child, birth order, parity, parental education, religion, household wealth index and type of family structure. The proximate variables (hygiene and sanitation score, feeding practice, and child health service utilization score) were also found to exert a strong influence on the risk of multiple nutritional deficiencies. The likelihood of co-occurrence of stunting and anemia was determined by certain individual and household factors, including sex of the child, age of the child, maternal education, household asset based wealth, religion and household hygiene and sanitation. CONCLUSIONS: This study underscores the importance of improving parental education, household wealth, hygiene and sanitation conditions, promoting feeding practice and child health service utilization. Also, any nutrition sensitive and specific intervention should consider a child's characteristics such as his/her age, gender and birth order.

Assessing the contextual effect of community in the utilization of postnatal care services in Ghana.

BACKGROUND: Inequalities in the use of postnatal care services (PNC) in Ghana have been linked to poor maternal and neonatal health outcomes. This has ignited a genuine concern that PNC interventions with a focus on influencing solely individual-level risk factors do not achieve the desired results. This study aimed to examine the community-level effect on the utilization of postnatal care services. Specifically, the research explored clusters of non-utilization of PNC services as well as the effect of community-level factors on the utilization of PNC services, with the aim of informing equity-oriented policies and initiatives. METHODS: The 2014 Ghana Demographic and Health Survey GDHS dataset was used in this study. Two statistical methods were used to analyze the data; spatial scan statistics were used to identify hotspots of non-use of PNC services and second two-level mixed logistic regression modeling was used to determine community-level factors associated with PNC services usage. RESULTS: This study found non-use of PNC services to be especially concentrated among communities in the Northern region of Ghana. Also, the analyses revealed that community poverty level, as well as community secondary or higher education level, were significantly associated with the utilization of PNC services, independent of individual-level factors. In fact, this study identified that a woman dwelling in a community with a higher concentration of poor women is less likely to utilize of PNC services than those living in communities with a lower concentration of poor women (Adjusted odds ratio (AOR) = 0.60, 95%CI: 0.44-0.81). Finally, 24.0% of the heterogeneity in PNC services utilization was attributable to unobserved community variability. CONCLUSION: The findings of this study indicate that community-level factors have an influence on women's health-seeking behavior. Community-level factors should be taken into consideration for planning and resource allocation purposes to reduce maternal health inequities. Also, high-risk communities of non-use of obstetric services were identified in this study which highlights the need to formulate community-specific strategies that can substantially shift post-natal use in a direction leading to universal coverage.

Disparities in mothers' healthcare seeking behavior for common childhood morbidities in Ethiopia: based on nationally representative data.

BACKGROUND: Childhood morbidities such as diarrhea and pneumonia are the leading causes of death in Ethiopia. Appropriate healthcare-seeking behavior of mothers for common childhood illnesses could prevent a significant number of these early deaths; however, little nation-wide research has been conducted in Ethiopia to assess mothers' healthcare-seeking behavior for their under five children. METHODS: The study used the Ethiopian Demographic and Health Surveys (EDHS) data. The EDHS is a cross sectional survey conducted in 2016 on a nationally representative sample of 10,641 respondents. The main determinants of care-seeking during diarrhea and acute respiratory infection (ARI) episodes were assessed using multiple logistic regression analyses while adjusting for complex survey design. RESULTS: Only 43% and 35% of households sought medical attention for their children in episodes of diarrhea and ARI, respectively, during a reference period of 2 weeks before the survey. The odds of seeking care for diarrhea are lower for non-working mothers versus working mothers. The likelihood of seeking care for diarrhea or ARI is higher for literate fathers compared to those with no education. The place of delivery for the child, receiving postnatal checkup and getting at least one immunization in the past determined the likelihood of seeking care for ARI, but not for diarrhea. The odds of seeking care are higher for both diarrhea and ARI among households that are headed by females and where mothers experienced Intimate Partner Violence (IPV) violence. Religion and types of family structure are also significant factors of seeking care for diarrhea episodes, but not for ARI. CONCLUSIONS: The findings call for more coordinated efforts to ensure equitable access to health care services focusing on mothers living in deprived household environment. Strengthening partnerships with public facilities, private health care practitioners, and community-based organizations in rural areas would help further improve access to the services.

Influence of maternal, anesthetic, and surgical factors on neonatal survival after emergency cesarean section in 78 dogs: A retrospective study (2002 to 2020).

Neonatal mortality rate (NMR) may be affected by maternal physical condition, anesthesia, and uterine incision or en-bloc neonate removal. The association of selected factors with number of dogs with dead puppies at hospital discharge was evaluated using 78 records. Data obtained at admittance for emergency cesarean section included: age, small or large body size, rectal temperature, packed cell volume, serum total protein, blood urea nitrogen, glucose, puppy in pelvic canal, and heart rate. Administration of opioids, propofol, alfaxalone, isoflurane, and sevoflurane, and anesthesia/surgical times and surgical technique were evaluated using Fisher's exact tests. There were 238 live puppies and 38 dogs had 58 dead puppies; the NMR was 19.6%. Mortality was associated with puppy in the pelvic canal (P = 0.003) and duration of anesthesia > 80 minutes (P = 0.029). Age > 8 years (P = 0.054) and induction time to start of surgery > 30 minutes (P = 0.17) may be associated with mortality. Expedient cesarean section with obstructive dystocia and an induction time to start of surgery < 30 minutes are important for puppy survival.

Influence des facteurs maternels, anesthésiques et chirurgicaux sur la survie néonatale après césarienne d'urgence chez 78 chiens : Une étude rétrospective (2002 à 2020). Le taux de mortalité néonatale (TMN) peut être affecté par l'état physique de la mère, l'anesthésie et l'incision utérine ou l'ablation en bloc du nouveau-né. L'association de facteurs sélectionnés avec le nombre de chiens avec des chiots morts à la sortie de l'hôpital a été évaluée à l'aide de 78 dossiers. Les données obtenues à l'admission pour une césarienne d'urgence comprenaient : l'âge, la taille corporelle petite ou grande, la température rectale, l'hématocrite, les protéines sériques totales, l'azotémie, le glucose, la présence de chiot dans le canal pelvien et la fréquence cardiaque. L'administration d'opioïdes, de propofol, d'alfaxalone, d'isoflurane et de sévoflurane, ainsi que les temps d'anesthésie/de chirurgie et la technique chirurgicale ont été évalués à l'aide des tests exacts de Fisher. Il y avait 238 chiots vivants et 38 chiens avaient 58 chiots morts; le TMN était de 19,6 %. La mortalité était associée la présence de chiot dans le canal pelvien (P = 0,003) et à la durée de l'anesthésie > 80 minutes (P = 0,029). Un âge > 8 ans (P = 0,054) et un délai d'induction avant le début de la chirurgie > 30 minutes (P = 0,17) peuvent être associés à la mortalité. Une césarienne opportune lors de dystocie obstructive et un temps d'induction avant le début de la chirurgie < 30 minutes sont importants pour la survie du chiot.(Traduit par Dr Serge Messier).

A comparison of residual diagnosis tools for diagnosing regression models for count data.

BACKGROUND: Examining residuals is a crucial step in statistical analysis to identify the discrepancies between models and data, and assess the overall model goodness-of-fit. In diagnosing normal linear regression models, both Pearson and deviance residuals are often used, which are equivalently and approximately standard normally distributed when the model fits the data adequately. However, when the response vari*able is discrete, these residuals are distributed far from normality and have nearly parallel curves according to the distinct discrete response values, imposing great challenges for visual inspection. METHODS: Randomized quantile residuals (RQRs) were proposed in the literature by Dunn and Smyth (1996) to circumvent the problems in traditional residuals. However, this approach has not gained popularity partly due to the lack of investigation of its performance for count regression including zero-inflated models through simulation studies. Therefore, we assessed the normality of the RQRs and compared their performance with traditional residuals for diagnosing count regression models through a series of simulation studies. A real data analysis in health care utilization study for modeling the number of repeated emergency department visits was also presented. RESULTS: Our results of the simulation studies demonstrated that RQRs have low type I error and great statistical power in comparisons to other residuals for detecting many forms of model misspecification for count regression models (non-linearity in covariate effect, over-dispersion, and zero inflation). Our real data analysis also showed that RQRs are effective in detecting misspecified distributional assumptions for count regression models. CONCLUSIONS: Our results for evaluating RQRs in comparison with traditional residuals provide further evidence on its advantages for diagnosing count regression models.

Modeling socio-demographic and clinical factors influencing psychiatric inpatient service use: a comparison of models for zero-Inflated and overdispersed count data.

BACKGROUND: Psychiatric disorders may occur as a single episode or be persistent and relapsing, sometimes leading to suicidal behaviours. The exact causes of psychiatric disorders are hard to determine but easy access to health care services can help to reduce their severity. The aim of this study was to investigate the factors associated with repeated hospitalizations among the patients with psychiatric illness, which may help the policy makers to target the high-risk groups in a more focused manner. METHODS: A large linked administrative database consisting of 200,537 patients with psychiatric diagnosis in the years of 2008-2012 was used in this analysis. Various counts regression models including zero-inflated and hurdle models were considered for analyzing the hospitalization rate among patients with psychiatric disorders within three months follow-up since their index visit dates. The covariates for this study consisted of socio-demographic and clinical characteristics of the patients. RESULTS: The results show that the odds of hospitalization are significantly higher among registered Indians, male patients and younger patients. Hospitalization rate depends on the patients' disease types. Having previously visited a general physician served a protective role for psychiatric hospitalization during the study period. Patients who had seen an outpatient psychiatrist were more likely to have a higher number of psychiatric hospitalizations. This may indicate that psychiatrists tend to see patients with more severe illnesses, who require hospital-based care for managing their illness. CONCLUSIONS: Providing easier access to registered Indian people and youth may reduce the need for hospital-based care. Patients with mental health conditions may benefit from greater and more timely access to primary care.