Application of Binomial Models on the Determinants of Hypertension Patients in Haramaya Woreda, Eastern Hararghe, Ethiopia

Introduction: Hypertension is along with a medical condition, in which the blood pressure in the arteries is high, which is a major health problem in the community. The main aim of the study was to assess the determinants associated with patients who experienced hypertension in Haramaya Woreda using a binomial model. Methods: A cross-sectional study design was conducted to assess the associated factors of hypertension patients’ complications in the Haramaya Hospital, Eastern Hararghe zone from December 1st to 30th, 2020. Data were statistically analyzed using the statistical package for social sciences (SPSS) version-23 (SPSS Inc., USA). A sample of 1417 respondents having hypertension chronic disease was included. Statistical tools such as descriptive statistics, chi-square test of association, and binomial regression were used to summarize and interpret the hypertension patients’ dataset and a 5% level of significance was also used as a baseline comparison. Results: Both chi-square test of association and binomial model revealed that age group, gender, residence, presence of diabetes mellitus and congestive heart failure were significantly associated with hypertension patients at a 5% level of significance. Multivariable binomial regression model indicated that an estimated odd ratio(OR) with 95% confidence interval were age group of 21-30year3.502(95%CI=1.310-9.361),31-40year6.108(95%CI=2.364-15.782),41-50year 11.070(95%CI=4.276-28.661), 51-60 year 12.530(95% CI=4.890-32.107) and greater than 60 years 12.713(95%CI=4.827-33.482), being male (OR=1.859; 95%CI=1.320-2.617), living in rural (OR=0.545. 95%CI=0.381-0.778).The presence of diabetes mellitus (OR=0.028, 95%CI=0.019-0.040), and congestive heart failure (OR=0.017, 95%CI=0.009-0.032) are associated risk factors of hypertension patients. Conclusions: The main hypertension risk factors were age category, gender, residence, having diabetes mellitus, and congestive heart failure (p=0.0001). Those were significantly associated with hypertension in both the chi-square test of association and binomial model. To predict the participants being a hypertensive binomial model with logit link function best fit the dataset.

Three-Way Interaction Effect Model: Moderating Effect of Resource Between Business Age and Host Performance

In the field of accommodation sharing, little attention has been paid to micro-entrepreneurship of hosts. Based on the signaling theory and the resource-based theory, we proposed a three-way interaction effect model to investigate the moderating effect of resource configuration (business size and host reputation) on the relationship between business age and host performance. A statistical analysis of the secondary panel data crawled from Airbnb.com was tested through the negative binomial model. The results shown that: (1) Business age is positively related to host performance; (2) the positive impact of business age on host performance is stronger for smaller size; host reputation has no significant moderating effect on the relationship between business age and host performance; (3) the joint consideration of business age, size, and host reputation has a three-way interaction effect on host performance. The positive impact of business age on host performance is strongest for hosts with smaller size and higher host reputation. These results are helpful to understand the micro-entrepreneurship performance of hosts in the field of accommodation sharing.

Modelling Claim Frequency in Insurance Using Count Models

Background: In modelling claim frequency in actuary science, a major challenge is the number of zero claims associated with datasets. Aim: This study compares six count regression models on motorcycle insurance data. Methodology: The Akaike Information Criteria (AIC) and the Bayesian Information Criterion (BIC) were used for selecting best models. Results: Result of analysis showed that the Zero-Inflated Poisson (ZIP) with no regressors for the zero component gives the best predictive ability for the data with the least BIC while the classical Negative Binomial model gives the best result for explanatory purpose with the least AIC.

Allele expression biases in mixed-ploid sugarcane accessions

Allele-specific expression (ASE) represents differences in the magnitude of expression between alleles of the same gene. This is not straightforward for polyploids, especially autopolyploids, as knowledge about the dose of each allele is required for accurate estimation of ASE. This is the case for the genomically complex Saccharum species, characterized by high levels of ploidy and aneuploidy. We used a Beta-Binomial model to test for allelic imbalance in Saccharum, with adaptations for mixed-ploid organisms. The hierarchical Beta-Binomial model was used to test if allele expression followed the expectation based on genomic allele dosage. The highest frequencies of ASE occurred in sugarcane hybrids, suggesting a possible influence of interspecific hybridization in these genotypes. For all accessions, ASEGs were less frequent than those with balanced allelic expression. These genes were related to a broad range of processes, mostly associated with general metabolism, organelles, responses to stress and responses to stimuli. In addition, the frequency of ASEGs in high-level functional terms was similar among the genotypes, with a few genes associated with more specific biological processes. We hypothesize that ASE in Saccharum is largely a genotype-specific phenomenon, as a large number of ASEGs were exclusive to individual accessions.

Performance of the Beta-Binomial Model for Clustered Binary Responses: Comparison with Generalized Estimating Equations

This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

Influence of weekday of admission and level of distress on length of hospital stay in patients with low back pain: a retrospective cohort study

Background Low back pain (LBP) is often a complex problem requiring interdisciplinary management to address patients’ multidimensional needs. Providing inpatient care for patients with LBP in primary care hospitals is a challenge. In this setting, interdisciplinary LBP management is often unavailable during weekends. Delays in therapeutic procedures may result in a prolonged length of hospital stay (LoS). The impact of delays on LoS might be strongest in patients reporting high levels of psychological distress. Therefore, this study investigates the influence of weekday of admission and distress on LoS of inpatients with LBP. Methods This retrospective cohort study was conducted between 1 February 2019 and 31 January 2020. In part 1, a negative binomial model was fitted to LoS with weekday of admission as a predictor. In part 2, the same model included weekday of admission, distress level, and their interaction as covariates. Planned contrast was used in part 1 to estimate the difference in log-expected LoS between group 1 (admissions Friday/Saturday) and the reference group (admissions Sunday-Thursday). In part 2, the same contrast was used to estimate the corresponding difference in (per-unit) distress trends. Results We identified 173 patients with LBP. The mean LoS was 7.8 days (SD = 5.59). Patients admitted on Friday (mean LoS = 10.3) and Saturday (LoS = 10.6) had longer stays, but not those admitted on Sunday (LoS = 7.1). Analysis of the weekday effect and planned contrast showed that admission on Friday or Saturday was associated with a significant increase in LoS (log ratio = 0.42, 95% CI = 0.21 to 0.63). A total of 101 patients (58%) returned questionnaires, and complete data on distress were available from 86 patients (49%). According to the negative binomial model for LoS and the planned contrast, the distress effect on LoS was significantly influenced (difference in slopes = 0.816, 95% CI = 0.03 to 1.60) by dichotomic weekdays of admission (Friday/Saturday vs. Sunday-Thursday). Conclusions Delays in interdisciplinary LBP management over the weekend may prolong LoS. This may particularly affect patients reporting high levels of distress. Our study provides a platform to further explore whether interdisciplinary LBP management addressing patients’ multidimensional needs reduces LoS in primary care hospitals.