Veterinary drug therapies used for undesirable behaviours in UK dogs under primary veterinary care

Undesirable behaviours (UBs) in dogs are common and important issues with serious potential welfare consequences for both the dogs and their owners. This study aimed to investigate the usage of drug therapy for UBs in dogs and assess demographic risk factors for drug-prescribed UBs within the dog population under primary-care veterinary care in the UK in 2013. Dogs receiving drug therapy for UB were identified through the retrospective analysis of anonymised electronic patient records in VetCompass™. Risk factor analysis used multivariable logistic regression modelling. The study population comprised 103,597 dogs under veterinary care in the UK during 2013. There were 413 drug-prescribed UBs recorded among 404 dogs. The prevalence of dogs with at least one UB event treated with a drug in 2013 was 0.4%. Multivariable modelling identified 3 breeds with increased odds of drug-prescribed UB compared with crossbred dogs: Toy Poodle (OR 2.75), Tibetan Terrier (OR 2.68) and Shih-tzu (OR 1.95). Increasing age was associated with increased odds of drug-prescribed UB, with dogs ≥ 12 years showing 3.1 times the odds compared with dogs < 3 years. Neutered males (OR 1.82) and entire males (OR 1.50) had increased odds compared with entire females. The relatively low prevalence of dogs with at least one UB event that was treated with a drug in 2013 could suggest that opportunities for useful psychopharmaceutical intervention in UBs may be being missed in first opinion veterinary practice. While bodyweight was not a significant factor, the 3 individual breeds at higher odds of an UB treated with a behaviour modifying drug all have a relatively low average bodyweight. The current results also support previous research of a male predisposition to UBs and it is possible that this higher risk resulted in the increased likelihood of being prescribed a behaviour modifying drug, regardless of neuter status.

Survival analysis of neonatal mortality in Ghana using three population-based surveys

Background Neonatal mortality in many low-and middle-income countries (LMICs) remains high despite global efforts at addressing this challenge. Tackling neonatal death in LMICs is further complicated by lack of reliable data from individual countries in the region to inform effective context specific interventions. This study investigates the probability of neonatal survival and socio-demographic risk factors of neonatal mortality in Ghana. Methods Pooled data from three population-based surveys (N = 12,148) were analysed using multivariable Cox Proportional Hazards regression models. Results The risk of dying within the first 28 days of life was highest in the first week of life (early neonatal period), it then decreases sharply around the middle of the second week of life and remains low over the late neonatal period. Adjusted hazard ratios (HRs) showed that: rural residency (HR = 1.80, 95% CI: 1.15-2.75); birth order 2-3 (HR = 1.63, 95% CI: 1.10-2.42); birth order ≥7 (HR = 1.89, 95% CI: 1.07-3.33) increased the risk of neonatal death. Additionally, children born to women who were obese had higher risk of neonatal death (HR = 1.69, CI: 1.12-2.56) compared with those of women with optimal weight. Disparities in the risk of neonatal death by geographical regions were also found. Conclusion The risk of neonatal mortality is highest during the first week of life and it is socio-demographically patterned. The findings emphasise the need to tackle socio-demographic risk factors of neonatal mortality in order to achieve the Sustainable Development Goal 3, which is aimed at reducing neonatal mortality to 12 per 1000 live births by the year 2030.

Behavioral, health- related and demographic risk factors of death in floods: A case-control study

During the first half of 2019, many provinces of Iran were affected by floods, which claimed the lives of 82 people. The present study aimed to investigate the behavioral, health related and demographic risk factors associated with deaths due to floods. We measured the odds ratio and investigated the contribution and significance of the factors in relation to mortality. This case-control study was conducted in the cities affected by flood in Iran. Data were collected on the flood victims using a questionnaire. Survivors, a member of the flood victim’s family, were interviewed. In total, 77 subjects completed the survey in the case group, and 310 subjects completed the survey in the control group. The findings indicated that factors such as the age of less than 18 years, low literacy, being trapped in buildings/cars, and risky behaviors increased the risk of flood deaths. Regarding the behavioral factors, perceived/real swimming skills increased the risk of flood deaths although it may seem paradoxical. This increment is due to increased self confidence in time of flood. On the other hand, skills and abilities such as evacuation, requesting help, and escape decreased the risk of flood deaths. According to the results, the adoption of support strategies, protecting vulnerable groups, and improving the socioeconomic status of flood-prone areas could prevent and reduce the risk of flood deaths.

The Risk of Readmission after Early Postpartum Discharge during the COVID-19 Pandemic

Objective To determine whether early postpartum discharge during the coronavirus disease 2019 (COVID-19) pandemic was associated with a change in the odds of maternal postpartum readmissions. Study Design This is a retrospective analysis of uncomplicated postpartum low-risk women in seven obstetrical units within a large New York health system. We compared the rate of postpartum readmissions within 6 weeks of delivery between two groups: low-risk women who had early postpartum discharge as part of our protocol during the COVID-19 pandemic (April 1–June 15, 2020) and similar low-risk patients with routine postpartum discharge from the same study centers 1 year prior. Statistical analysis included the use of Wilcoxon's rank-sum and chi-squared tests, Nelson–Aalen cumulative hazard curves, and multivariate logistic regression. Results Of the 8,206 patients included, 4,038 (49.2%) were patients who had early postpartum discharge during the COVID-19 pandemic and 4,168 (50.8%) were patients with routine postpartum discharge prior to the COVID-19 pandemic. The rates of postpartum readmissions after vaginal delivery (1.0 vs. 0.9%; adjusted odds ratio [OR]: 0.75, 95% confidence interval [CI]: 0.39–1.45) and cesarean delivery (1.5 vs. 1.9%; adjusted OR: 0.65, 95% CI: 0.29–1.45) were similar between the two groups. Demographic risk factors for postpartum readmission included Medicaid insurance and obesity. Conclusion Early postpartum discharge during the COVID-19 pandemic was associated with no change in the odds of maternal postpartum readmissions after low-risk vaginal or cesarean deliveries. Early postpartum discharge for low-risk patients to shorten hospital length of stay should be considered in the face of public health crises. Key Points

Association of multidimensional poverty and tuberculosis in India

Background Reduction of multidimensional poverty and tuberculosis are priority development agenda worldwide. The SDGs aims to eradicate poverty in all forms (SDG 1.2) and to end tuberculosis (SDG 3.3.2) by 2030. While poverty is increasingly being measured across multiple domains, reduction of tuberculosis has been an integral part of public health programmes. Though literature suggests a higher prevalence of tuberculosis among the economically poor, no attempt has been made to understand the association between multidimensional poverty and tuberculosis in India. The objective of this paper is to examine the association of multidimensional poverty and tuberculosis in India. Methods The unit data from the National Family Health Survey-4, conducted in 2015–16 covering 628,900 households and 2,869,043 individuals across 36 states and union territories of India was used in the analysis. The survey collected information on the self-reported tuberculosis infection of each member of a sample household at the time of the survey. Multidimensional poverty was measured in the domains of education, health, and standard of living, with a set of 10 indicators. The prevalence of tuberculosis was estimated among the multidimensional poor and non-poor populations across the states of India. A binary logistic regression model was used to understand the association of tuberculosis and multidimensional poverty. Results Results suggest that about 29.3% population of India was multidimensional poor and that the multidimensional poverty index was 0.128. The prevalence of tuberculosis among the multidimensional poor was 480 (95% CI: 464–496) per 100,000 population compared to 250 (95% CI: 238–262) among the multidimensional non-poor. The prevalence of tuberculosis among the multidimensional poor was the highest in the state of Kerala (1590) and the lowest in the state of Himachal Pradesh (220). Our findings suggest a significantly higher prevalence of tuberculosis among the multidimensional poor compared to the multidimensional non-poor in most of the states in India. The odds of having tuberculosis among the multidimensional poor were 1.82 times higher (95% CI, 1.73–1.90) compared to the non-poor. Age, sex, smoking, crowded living conditions, caste, religion, and place of residence are significant socio-demographic risk factors of tuberculosis. Conclusion The prevalence of tuberculosis is significantly higher among the multidimensional poor compared to the multidimensional non-poor in India.

Prevalence and Demographic Risk Factors of Mycobacterium tuberculosis Infections in Captive Asian Elephants (Elephas maximus) Based on Serological Assays

To address putative TB statuses of elephants and to identify and quantify potential demographic risk factors for TB, three ELISAs specific for different mycobacterial antigens (ESAT6, CFP10, MPB83) and the TB Stat-Pak assay were used as surrogate serological markers for TB infection in elephants. In view of the low number of animals of which the infected status could be confirmed (4 out of 708) Latent Class Analyses of TB serology test outcomes was used to predict the putative TB status of each of 708 elephants as positive (17.3%), inconclusive (48.7%), or negative (34%) when assessed on a population basis. Correlation between test performance of the individual assays was high between the ELISAs, but low with that of the TB Stat-Pak assay. Risk factors, assessed based on cut off values for each of the ELISAs determined by ROC analysis, included sex, BCS, age, working time, feed type, management system, camp size and region. Old age elephants were more likely to show a positive TB serology test outcome, than younger ones. Elephants working 7 h per day and the ones in good condition BCS (7–11) were less likely to be positive in TB serology testing. In addition, fewer animals in the large camp size (31–50 elephants) were found to be positive in ELISA tests, compared to elephants in the other camp sizes. In this study, the North region had the lowest percentages of elephants with positive TB test outcome, the West region and to a lesser extend the other regions showed clearly higher percentages of positive animals. Even though assays used in the present study have not been validated yet, results obtained showed promise as diagnostic or screening tests. For the diagnosis of animals suspected to be infected, the ELISA tests, once further optimized for the individual antigens, can be used in parallel. For screening of complete camps for presence or absence of infection, a single optimized ELISA test can be utilized.

Effects of environmental polycyclic aromatic hydrocarbon exposure and pro-Inflammatory activity on Type 2 Diabetes Mellitus

ABSTRACTBackgroundPolycyclic aromatic hydrocarbons (PAHs) are formed due to incomplete combustion and known for their potential impact and persistence in the environment. PAHs exposure have been linked to cause adverse health effect including cancer and genetic mutations. The understanding of metabolic effects of PAH exposure are still less clear especially in the presence of pro-inflammatory stress like alcoholism or diabetes.ObjectiveThe aim of this article is to understand the metabolic effects of PAH exposure by analyzing the clinical biomarkers. This study has also accessed the interactive impact of PAH and other proinflammatory factors, like alcohol intake on the metabolic syndrome, especially Type 2 Diabetes Mellitus (T2DM).MethodsAll the data in this study are retrieved from CDC NHANES (2015-16). We investigated urinary levels of hydroxylated PAH metabolites (OH-PAHs) along with demographic, clinical and laboratory data. Questionnaire data for alcohol use and diabetes status were also included along with laboratory data. Laboratory measures included in the study were levels of PAHs, glycohemoglobin, glucose, cholesterol, lipids, triglyceride, complete blood count, lymphocytes, and monocytes. Generalize linear model Univariate factorial ANOVA was used to evaluate the group differences (both between the groups; as well as across all the groups) in the demographics, PAH exposure, drinking patterns, clinical data, and biomarker levels. Linear regression model was used to analyze the association of biomarkers, PAH exposure and drinking data. Multivariable regression model was used for multi-independent model to assess comorbidity association and their effect sizes on the clinical outcomes.ResultsBMI (p=0.002), and age (≤0.001) are independent demographic risk factors for T2DM in high PAH exposure. Acute proinflammatory activity characterized by CRP, is augmented by elevated monocyte levels (p≤0.001) and stepwise addition of 1-HN (p=0.005), and 2-HN (p=0.001) independently. Prevalence of highest average drinks over time is observed in the high PAH exposure; with males drinking almost twice compared to females in Gr.3. Pathway response of T2DM shows sexual dimorphism; with males showing association with triglycerides (p≤0.001), and females with CRP (p=0.015) independently with HbA1C. The arrangement of CRP, absolute monocyte levels, serum triglycerides and average drinks over time predict the HbA1C levels (adjusted R2=0.226, p≤0.001) in individuals with high PAH exposure.DiscussionIn this large dataset investigation on humans, the adverse effects of high exposure of PAHs identified candidate demographic risk factors. Preclinical experimental studies on mice have suggested that PAHs exposure induces lipid metabolic disorders in a time-dependent manner, which we found in humans too. Sexual dimorphism is observed in alcohol drinking with males drinking more in the high PAH exposure group. Alcohol drinking as an independent factor associated with the DMT2 indicator, HbA1C in individuals with high PAH exposure.HighlightsBMI and Age are demographic risk factors for Diabetes Mellitus Type 2 (DMT2) in high PAH exposureAcute proinflammatory activity characterized by CRP, is augmented by elevated monocyte levels and 1-HN and 2-HN independentlyPrevalence of higher average drinks over time is observed with high PAH exposurePathway of DMT2 shows sexual dimorphism, with males showing association with triglycerides, and females with CRP independently with HbA1CThe arrangement of CRP, absolute monocyte levels, serum triglycerides and average drinks over time predict the HbA1C levels in individuals with high PAH exposure.

Canine hyperactivity, impulsivity, and inattention share similar demographic risk factors and behavioural comorbidities with human ADHD

Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder impairing the quality of life of the affected individuals. The domestic dog can spontaneously manifest high hyperactivity/impulsivity and inattention which are components of human ADHD. Therefore, a better understanding of demographic, environmental and behavioural factors influencing canine hyperactivity/impulsivity and inattention could benefit both humans and dogs. We collected comprehensive behavioural survey data from over 11,000 Finnish pet dogs and quantified their level of hyperactivity/impulsivity and inattention. We performed generalised linear model analyses to identify factors associated with these behavioural traits. Our results indicated that high levels of hyperactivity/impulsivity and inattention were more common in dogs that are young, male and spend more time alone at home. Additionally, we showed several breed differences suggesting a substantial genetic basis for these traits. Furthermore, hyperactivity/impulsivity and inattention had strong comorbidities with compulsive behaviour, aggressiveness and fearfulness. Multiple of these associations have also been identified in humans, strengthening the role of the dog as an animal model for ADHD.

Identifying socio-demographic risk factors for suicide using data on an individual level

Background Suicide is a complex issue. Due to the relative rarity of the event, studies into risk factors are regularly limited by sample size or biased samples. The aims of the study were to find risk factors for suicide that are robust to intercorrelation, and which were based on a large and unbiased sample. Methods Using a training set of 5854 suicides and 596,416 control cases, we fit a logistic regression model and then evaluate the performance on a test set of 1425 suicides and 594,893 control cases. The data used was micro-data of Statistics Netherlands (CBS) with data on each inhabitant of the Netherlands. Results Taking the effect of possible correlating risk factors into account, those with a higher risk for suicide are men, middle-aged people, people with low income, those living alone, the unemployed, and those with mental or physical health problems. People with a lower risk are the highly educated, those with a non-western immigration background, and those living with a partner. Conclusion We confirmed previously known risk factors such as male gender, middle-age, and low income and found that they are risk factors that are robust to intercorrelation. We found that debt and urbanicity were mostly insignificant and found that the regional differences found in raw frequencies are mostly explained away after correction of correlating risk factors, indicating that these differences were primarily caused due to the differences in the demographic makeup of the regions. We found an AUC of 0.77, which is high for a model predicting suicide death and comparable to the performance of deep learning models but with the benefit of remaining explainable.