A novel approach for estimating fertility rates in finite populations using count regression models

Abstract Demographic health surveys (DHS) contain in-depth information about the demographic characteristics and the factors affecting them. However, fertility rates which are the important indicators of population growth have been estimated by utilizing the design-based approaches. Model-based approach, on the other hand, facilitates efficient predictive estimates for these rates by utilizing the demographic and other family planning related characters. In this article, we first attempt to observe the effect of various socio-demographic and family planing related factors on births counts by fitting different regression models to Pakistan Demographic Health Survey 2017–2018 data under classical as well as Bayesian frameworks. The births occurred during the time periods of 1-year, 3-years and 5-years are taken as the responses and modeled using different non-linear models. The model-based approach is then used for estimation of the fertility measures including age-specific fertility rates, total fertility rate, general fertility rate, and gross reproduction rate for ever-married women in Pakistan. The performance of the model-based estimators is examined using a bootstrapped sampling algorithm. While the age-specific fertility rates are over-estimated for some age groups and under-estimated for others. The model-based fertility estimates are recommended for estimating the demographic indicators at national and sub-national levels when survey data contains incomplete or missing responses

Multi-state statistical modelling to investigate the association between serious mental illness and offending behaviour in longitudinally linked data

Background:Mental illness and offending behaviour are complex issues for both the community and the justice health system. Thedemand for special treatment and for mental health programs and services for reducing the reoffending rate amongindividuals with mental illness has been a critical focus for those working in the criminal justice system.Aims: This thesis aims to apply various statistical methods—survival analysis, competing risks and multi-state models—for analysing complex time-to-event data to examine the relationship between psychosis, mental health treatment andoffending behaviour.Methods:First, the factors associated with reoffending (violent or non-violent) were identified using Cox regression models.Second, competing risks models were used to identify the predictors of reoffending after categorising the first offence aseither violent or non-violent. Third, multi-state models were employed to analyse the time to reoffending amongindividuals who had a serious mental health diagnosis (psychosis). In this subpopulation, the factors associated with timeto reoffending were investigated using a three-state modelling scenario. The transition probabilities for reoffence werederived for those who continued mental health treatment and those who did not. Lastly, the variables used in this thesiswere evaluated for their ability to predict reoffence in a risk prediction model.Results:Individuals diverted into a treatment order were less likely to reoffend than those who received a punitive sanction fortheir first offence. The probability of non-violent reoffence was more than that of violent reoffence, and there was a higherprobability observed for the risk of reoffending for those who disengaged from treatment compared with those whoremained in treatment. The lack of treatment for a mental health illness indicated a higher contribution to the risk ofreoffence.Conclusion:The thesis demonstrates that using the survival analysis, the competing risks, the risk prediction and the multi-statestatistical models provides novel insights and increases the understanding of the role of mental health treatment inpreventing reoffending. In particular, receiving treatment following an offence reduces reoffending, and diversion intomental health treatment by the courts is important in managing those with serious mental illness to prevent reoffending.The thesis outcomes can facilitate developing policies around treatment and interventions for reoffences associated withthose diagnosed with severe mental illness. Further, these findings may assist with developing guidelines to support areduction in reoffending as well improvement in treatment strategies, counseling and discussion for those diagnosed withpsychosis. Indeed, when this thesis was being written, the NSW Attorney General presented draft legislation to the NSWParliament, parts of which are based on this research

Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (R2), adjusted R2 ( Radj2), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R2 = 99.88%, Radj2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic

Parametric Frailty Analysis in Presence of Collinearity: An Application to Assessment of Infant Mortality

This paper analyzes the time to event data in the presence of collinearity. To address collinearity, the ridge regression estimator was applied in multiple and logistic regression as an alternative to the maximum likelihood estimator (MLE), among others. It has a smaller mean square error (MSE) and is therefore more precise. This paper generalizes the approach to address collinearity in the frailty model, which is a random effect model for the time variable. A simulation study is conducted to evaluate its performance. Furthermore, the proposed method is applied on real life data taken from the largest sample survey of India, i.e., national family health survey (2005–2006 ) data to evaluate the association of different determinants on infant mortality in India

Clinical characteristics and predictors of mortality among COVID-19 patients in Saudi Arabia

Background: The new coronavirus disease (COVID-19) has caused more than 1.8 million deaths, with a fatality rate of 2.5% in more than 200 countries as of January 4, 2021. Analysis of COVID-19 clinical features can help predict disease severity and risk of mortality, early identification of high-risk patients, and provide knowledge to inform clinical interventions. Objective: The purpose of this study is to investigate the clinical characteristics and possible predictors associated with mortality in patients with COVID-19 admitted to King Fahad (KFH), Ohood, and Miqat hospitals in Madina, Saudi Arabia. Methods: This retrospective observational study to investigate the clinical characteristic and possible predictors associated with mortality for those 119 mild, moderate, or critically ill patients confirmed by laboratory results to have COVID-19 who were admitted to three hospitals in Madina, Saudi Arabia, from March 25, 2020, to July 30, 2020. Data were collected from December 1, 2020, to December 14, 2020. Results: Of the 119 patients included in the study, the mean age was 54.2 (±15.7) years, with 78.2% survivors and 21.8% non-survivors. The demographic analysis indicated that the likelihood of mortality for patients in the older age group (i.e., ≥65 years) was five times higher than those in the younger age group (OR = 5.34, 95% CI 1.71–16.68, p = 0.004). The results also indicated those patients who admitted to the intensive care unit (ICU) was approximately seven times higher odds of mortality compare with those who were not admitted (OR = 6.48, 95% CI 2.52–16.63, p < 0.001). In addition, six laboratory parameters were positively associated with the odds of mortality: white blood cell count (OR = 1.11, 95% CI 1.02–1.21, p = 0.018), neutrophil (OR = 1.11, 95% CI 1.02–1.22, p = 0.020), creatine kinase myocardial band (OR = 1.02, 95% CI 1.00–1.03, p = 0.030), C-reactive protein (OR = 1.01, 95% CI 1.00–1.01, p = 0.002), urea (OR = 1.06, 95% CI 1.01–1.11, p = 0.026), and lactate dehydrogenase (OR = 1.00, 95% CI 1.00–1.01, p = 0.020). Conclusions: In this cohort, COVID-19 patients within the older age group (≥65 years) admitted to the ICU with increased C-reactive protein levels in particular, were associated with increased odds of mortality. Further clinical observations are warranted to support these findings and enhance the mapping and control of this pandemic