Research Article
Spatial Modeling of Mental Health on Outpatient Morbidity in Kenya
Issue:
Volume 13, Issue 3, June 2025
Pages:
45-55
Received:
20 May 2025
Accepted:
3 June 2025
Published:
25 June 2025
Abstract: The cognitive, emotional, and behavioral functioning of humans is greatly impacted by mental health issues. An increasing public health concern in Kenya is outpatient mental health morbidity. The geographic distribution of these symptoms and their correlation with infectious diseases have not, however, been thoroughly investigated. The objective of this research was to investigate the spatial distribution of mental health conditions in Kenya and their correlation with infectious diseases, including HIV, TB, and STIs. To evaluate the regional distribution of outpatient mental health cases, a spatial modeling approach was used. In order to find high-prevalence regions and possible links, the study used geostatistical approaches to integrate epidemiological data on infectious diseases and mental health issues. The results showed that mental health issues were not evenly distributed, with a higher emphasis in Nairobi and the Western areas. Infectious diseases and mental health disorders were shown to be strongly correlated, indicating possible connections between these health costs. High accuracy and validity were displayed by the spatial model, which provided insightful information for planning interventions and allocating resources. The distribution of mental health disorders and its relationship to infectious diseases in Kenya are better understood thanks to this study. The results emphasize the necessity of locally focused mental health treatments, especially in high-risk areas. These insights can be used by policymakers to enhance mental health services accessibility, optimize healthcare methods, and create integrated treatment plans for people with co-occurring disorders.
Abstract: The cognitive, emotional, and behavioral functioning of humans is greatly impacted by mental health issues. An increasing public health concern in Kenya is outpatient mental health morbidity. The geographic distribution of these symptoms and their correlation with infectious diseases have not, however, been thoroughly investigated. The objective of...
Show More
Research Article
Comparing Two-parameter Burr Type (X) and Gamma-Weibull Distributions Using Information Criteria for the Heights of Akwa Ibom State University Students
Michael Itoro Tim*,
Iseh Matthew Joshua,
Usoro Anthony Effiong
Issue:
Volume 13, Issue 3, June 2025
Pages:
56-62
Received:
28 May 2025
Accepted:
21 June 2025
Published:
15 July 2025
DOI:
10.11648/j.sjams.20251303.12
Downloads:
Views:
Abstract: Overtime, many researchers have introduced different probability distribution functions and fitted them to a given datasets using Information Criteria. Among the distribution introduced are the four-parameter Gamma-Weibull, the beta-Weibull distribution, four-parameter beta-normal distribution provides flexibility in modelling not only symmetric heavy-tailed distributions, but also skewed and bimodal distributions, gamma-Lagrange distribution, the Kumaraswamy-Weibull Geometric distribution, beta-Laplace distribution, Kumaraswamy-generalized Exponential Pareto distribution. The AIC and BIC for the Kumaraswamy-generalized Exponential Pareto distribution are smaller than the Pareto and Exponential Pareto distribution. Thus, making Kumaraswamy-generalized Exponential Pareto distribution very competitive for the fitting an uncensored data set corresponding to 100 observations on breaking stress of carbon fibers (in Gba) using the model selection criteria. This paper compares two-parameter Burr type (X), a special case of the Beta-Weibull distribution and four-parameter Gamma-Weibull distribution using log-likelihood function, Bayesian and Akaike’s Information Criteria for fitting heights of Akwa Ibom State University Students. The heights of 617 students were obtained from the medical Centre of the Akwa Ibom State University main Campus. It was observed that the log-likelihood function, Akaike information criterion (AIC) and the Bayesian information criterion (BIC) values of the Gamma-Weibull distribution are less than that of the two-parameter Burr type (X). The Gamma-Weibull distribution has a smaller AIC and BIC than that of the two-parameter Burr Type (X) distribution. Hence, the Gamma-Weibull distribution fits the data better than the two-parameter Burr Type (X) distribution. The graphs of the Gamma-Weibull distribution and the two-parameter distributions are also presented.
Abstract: Overtime, many researchers have introduced different probability distribution functions and fitted them to a given datasets using Information Criteria. Among the distribution introduced are the four-parameter Gamma-Weibull, the beta-Weibull distribution, four-parameter beta-normal distribution provides flexibility in modelling not only symmetric he...
Show More
,
Mwalili Samuel
