The paper studies the dynamics of a full delay-logistic population model incorporated with a proportionate harvesting function. The study discusses the stability of the model in comparison with the well-known Hutchinson logistic growth equation with harvesting function using the rate of harvesting as a bifurcation parameter to determine sustainable harvesting rate even at a bigger time delay of τ = 3.00 . In all cases, the Hutchinson equation with harvesting was forced to converge to equilibrium using an additional and a different time delay parameter, a deficiency previous researchers have failed to address when the Hutchinson model is used for this purpose. The population fluctuations are catered for with this model making the estimated maximum sustainable growth and harvest reflect realities as this model drastically reduces time-delay associated oscillations compared to the well-known Hutchinson delayed logistic models. The numerical simulations were be done using the MatLab Software.

Background: Generalized Linear models are mostly fitted to data that are not correlated. However, very often data that are collected from health and epidemiological studies are correlated either as a result of the sampling methods or the randomness associated with the collection of such data. Therefore, fitting generalized linear models to such data that produce only fixed effects could lead to over dispersion in the model estimates. Objectives: The objective of this study is to fit both generalized linear and generalized linear mixed models to a correlated data and compare the results of the two models. Methods: Logistic regression is employed in fitting the generalized linear model since the dependent variable in the study is bivariate whilst the GLIMMIX model in SAS is used to fit the generalized linear mixed model. Results: The generalized linear model produces over dispersion with higher errors among the parameter estimates than the generalized linear mixed model. Conclusion: In dealing with a more correlated data, generalized linear mixed model, which can handle both fixed and random effects, is preferable to generalized linear model.

Background Breast lumps or lumpiness are a prevalent issue among women seeking guidance, with 40–70% reporting lumps or lumpiness. Any woman, regardless of age, who discovers a breast lump by self-examination, screening, or medical intervention begins to worry about developing breast cancer. Late stage of reporting suspected lumps is on the rise and this was hitted by the pandemic. The study examined factors that are associated breast lump and the risk on women who ever had breast lump. Method An institutional-based cross-sectional study was conducted on women who visited the facility for breast screening or other medical consultation. Closed-ended questionnaire was used to solicit information from 301 women within a period of six weeks. Chi-square and binary logistic regression model was used to determine the association and the risk respectively. Results Breast lump was dominant in women between 41–50 years and in those who do not have family history of breast cancer. The findings reveal that educational level [χ2 = 11.170; p = 0.011] and the practice of breast self-examination [χ2 = 7.998; p = 0.005] were significantly associated with breast lump. Married women were 0.764 less likely to have breast lump than those who are singles. Women between 31–40 years were 2 times more likely [AOR = 2.061, CI = 0.876–4.846] and those between 41–50 years 1 time more likely [AOR = 1.131,CI = 0.451–2.837] to have breast lump than women between 18–30 years. Conclusion Breast lump is predominant in women between 31–50 years. Factors associated with a woman having breast lump are educational background and the practice of breast self-examination. Surgeon managing a breast lump in women over 30 years old are encouraged to be extremely suspicious and cautious in order to detect and treat malignant lumps early.

Background: Low birth weight incidence is quite high in the sub region, which has a public health concern. The weight of a baby at birth has dire consequences on the child as an infant, in childhood and as an adult. Methods: The aim of this study was to explore and examine the spread and gravity of incidence of low birth weight by using a multi-state model to understand low birth weight progression. This study utilised data by Ghana Statistical Service from Multiple Indicators Cluster Survey conducted in 2011 to monitor progress of children and women. Results: The multi-state Markov model dealt into the low birth weight transitions and severity under three treatments where transition intensities, transition probabilities and the mean sojourn times were estimated which show that low birth weight children tend to spend less time in bad states than in good states. Conclusion: Generally, the survival of a low birth weight child in future time decreases from state 1 to state 4, hence treatment must be applied on time.

Lethal opportunistic diseases like Tuberculosis and Hepatitis C are deeply ingrained complications for patients diagnosed with human immunodeficiency virus (HIV).  The effect of Highly Active Antiretroviral Therapy (HAART) on Hepatitis C and Tuberculosis in HIV patients in Ghana continues to be unpredictable, especially in younger patients. This study aimed to describe the patient survival time distribution on antiretroviral treatment using Statistical Growth model. A retrospective cohort of 634 patients aged between 22 to 73 years were selected from the District Health Information Management System 2 (DHIMS 2), a secondary source, using a random sampling approach. These patients were diagnosed with HIV and started antiretroviral therapy between 2000 and 2019 at St. Martins Catholic Hospital in Amansie South District of the Ashanti Region. The probability of survival for almost all of the risk factors decreases gradually at different clinical states, i.e., from state 1 through to state 4. Hepatitis C or Tuberculosis can also be diagnosed chronically in approximately one in ten patients. Age, sex and the CD4 cell count of patients substantially (p- value =0.001 in log-rank tests) contributed to the prevalence of human immunodeficiency virus. Survival of infants, aged <1year, after treatment was of negative effect. The statistical growth analytical approach offers a good estimate of survival rate ( 79.82%) among major risk factors for infants, aged <1yearon ART with proportion of survival growth of 0.95, hence the survival time of infants, aged <1yearon HAART is negatively affected irrespective of the treatment initiation period.