Department of Statistics
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Item Determinants of under-fve mortality in informal settlements in Nairobi, Kenya from 2002 to 2018(BMC Public Health, 2024) Iddi, S.; Akeyo, D.; Sanya, R.E.; Wamukoya, M.; Asiki, G.Background Childhood mortality persists as a significant public health challenge in low and middle-income countries and is uneven within countries, with poor communities such as urban informal settlements bearing the highest burden. There is limited literature from urban informal settlements on the risk factors of mortality. We assessed under-five mortality and associated risk factors from the period 2002 to 2018 in Nairobi urban informal settlements. Methods We used secondary data from the Nairobi Urban Health and Demographic Surveillance System (NUH DSS), a longitudinal surveillance platform that routinely collects individual and household-level data in two informal settlements (Viwandani and Korogocho) in Nairobi, Kenya. We used Kaplan-Meier curves to estimate overall survival and the Cox proportional hazard model with a frailty term to evaluate the impact of risk factors on survival time. Results Overall under-five survival rate was 96.8% and this improved from 82.6% (2002-2006) to 95% (2007-2012) and 98.4% (2012-2018). There was a reduced risk of mortality among children who had BCG vaccination, those born to a married mother or a mother not engaging in any income-generating activity (both from 2007 to 2011), children from singleton pregnancy, children born in Viwandani slum and ethnicity of the child. Conclusion Under-five mortality is still high in urban informal settlements. Targeted public health interventions such as vaccinations and interventions empowering women such as single mothers, those with multiple pregnancies, and more impoverished slums are needed to further reduce under-five mortality in urban informal settlements.Item Assessment of Neonatal Mortality and Associated Hospital-Related Factors in Healthcare Facilities Within Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana(Health Services Insights, 2024) Tawiah, K.; Asosega, K.A.; Iddi, S.; et al.Objectives: Ghana’s quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana. Methods: The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors that contribute to this menace at the facility level. Cause-specific deaths were obtained to determine the leading causes of neonatal deaths within health facilities in the two municipal assemblies. Results: The study established that the leading causes of neonatal mortality in these districts are birth asphyxia (46%), premature birth (33%), neonatal sepsis (11%) and neonatal jaundice (7%). The NBH showed that neonatal mortality in hospital facilities depend on the num ber of incubators, monitoring equipment, hand washing facilities, CPAPb machines, radiant warmers, physiotherapy machines, midwives, paediatric doctors and paediatric nurses in the hospital facility. Conclusions: Early management of neonatal sepsis, birth asphyxia, premature birth and neonatal infections is required to reduce neonatal deaths. The government and all stakeholders in the health sector should provide all hospital facilities with the essential equipment and the medical human resources necessary to eradicate the menace. This will make the realization of Sustainable Development Goal three, which calls for healthy lives and well-being for all, a reality.Item Characterization of Healthy Housing in Africa: Method, Profiles, and Determinants(Journal of Urban Health, 2022) Iddi, S.; Muindi, K.; Gitau, H.; Mberu, B.Housing is a key social determinant of health, with implications for both physical and mental health. The measurement of healthy housing and studies characterizing the same in sub-Saharan Africa (SSA) are uncommon. This study described a methodological approach employed in the assessment and characterization of healthy housing in SSA using the Demographic and Health Survey (DHS) data for 15 countries and explored healthy housing determinants using a multiple survey-weighted logistic regression analysis. For all countries, we demonstrated that the healthy housing index developed using factor analysis reasonably satisfied with both reliability and validity tests and can therefore be used to describe the distribution of healthy housing across different groups and in understanding the linkage with individual health outcomes. We infer from the results that unhealthy housing remains quite high in most SSA countries. Having a male head of household was associated with decreased odds of healthy housing in Burkina Faso (OR = 0.80, CI = 0.68–0.95), Cameroon (OR=0.65, CI=0.57, 0.76), Malawi (OR=0.70, CI=0.64–0.78), and Senegal (OR=0.62, CI=0.51– 0.74). Further, increasing household size was associated with reducing odds of healthy housing in Kenya (OR=0.53, CI=0.44–0.65), Namibia (OR=0.34, CI=0.24–0.48), Nigeria (OR=0.57, CI=0.46–0.71), and Uganda (OR=0.79, CI=0.67–0.94). Across In all countries, household wealth was a strong deterrent of healthy housing, with middle and rich households having higher odds of residing in healthy homes compared to poor households. Odds ratios ranged from 3.63 (CI = 2.96–4.44) for households in the middle wealth group in the DRC to 2812.2 (CI = 1634.8–4837.7) in Namibia’s wealthiest households. For other factors, the analysis also showed variation across countries. Our findings provide timely insights for the implementation of housing policies across SSA countries, drawing attention to aspects of housing that would promote occupant health and wellbeing. Beyond the contribution to the measurement of healthy housing in SSA, our paper highlights key policy and program issues that need further investigation in the search for pathways to addressing the healthy housing demand across most SSA countries. This has become critical amid the COVID-19 pandemic, where access to healthy housing is pivotal in its control.Item Utilizing A Multi-Stage Transition Model For Analysing Child Stunting In Two Urban Slum Settlements Of Nairobi: A Longitudinal Analysis, 2011-2014(PLOS ONE, 2024) Oduro, M.S.; Iddi, S.; Asiedu, L.; et al.Introduction Stunting is common among children in many low- and middle-income countries, particularly in rural and urban slum settings. Few studies have described child stunting transitions and the associated factors in urban slum settlements. We describe transitions between stunting and states and associated factors among children living in Nairobi slum settlements. Methods This study used data collected between 2010 and 2014 from the Nairobi Urban and Demographic Surveillance System (NUHDSS) and a vaccination study conducted within the surveillance system. A subset of 692 children aged 0 to 3 years, with complete anthropometric data, and household socio-demographic data was used for the analysis. Height-for-age Z-scores (HAZ) was used to define stunting: normal (HAZ 1), marginally stunted (-2 HAZ). < -1), moderately stunted (-3; HAZ < -2), and severely stunted (HAZ < -3). Transitions from one stunting level to another and in the reverse direction were computed. The associations between explanatory factors and the transitions between four child stunting states were modeled using a continuous-time multi-state model. Results We observed that 48%, 39%, 41%, and 52% of children remained in the normal, marginally stunted, moderately stunted, and severely stunted states, respectively. About 29% transitioned from normal to marginally stunted state, 15% to a moderately stunted state, and 8% to the severely stunted state. Also, 8%, 12%, and 29% back transitioned from severely stunted, moderately stunted, and marginally stunted states to the normal state,respectively. The shared common factors associated with all transitions to a more severe state include: male gender, ethnicity (only for mild and severe transition states), child’s age, and household food insecurity. In Korogocho, children whose parents were married and those whose mothers had attained primary or post-primary education were associated with a transition from a mild state into a moderately stunted state. Children who were breastfed exclusively were less likely to transition from moderate to severe stunting state. Conclusion These findings reveal a high burden of stunting and transitions in urban slums. Context-specific interventions targeting the groups of children identified by the socio-demographic factors are needed. Improving food security and exclusive breastfeeding could potentially reduce stunting in the slums.Item SARS‑CoV‑2 incidence monitoring and statistical estimation of the basic and time‑varying reproduction number at the early onset of the pandemic in 45 sub‑Saharan African countries(BMC Public Health, 2024) Oduro, M.S.; Arhin‑Donkor, S.; Asiedu, L.; Kadengye, D.T.; Iddi, S.The world battled to defeat a novel coronavirus 2019 (SARS-CoV-2 or COVID-19), a respiratory illness that is transmitted from person to person through contacts with droplets from infected persons. Despite efforts to disseminate preventable messages and adoption of mitigation strategies by governments and the World Health Organization (WHO), transmission spread globally. An accurate assessment of the transmissibility of the coronavirus remained a public health priority for many countries across the world to fight this pandemic, especially at the early onset. In this paper, we estimated the transmission potential of COVID-19 across 45 countries in sub-Saharan Africa using three approaches, namely, R0 based on (i) an exponential growth model (ii) maximum likelihood (ML) estimation and (iii) a time-varying basic reproduction number at the early onset of the pandemic. Using data from March 14, 2020, to May 10, 2020, sub-Saharan African countries were still grappling with COVID-19 at that point in the pandemic. The region’s basic reproduction number ( R0 ) was 1.89 (95% CI: 1.767 to 2.026) using the growth model and 1.513 (95% CI: 1.491 to 1.535) with the maximum likelihood method, indicating that, on average, infected individuals transmitted the virus to less than two secondary persons. Several countries, including Sudan ( R0 : 2.03), Ghana ( R0 : 1.87), and Somalia ( R0 : 1.85), exhibited high transmission rates. These findings highlighted the need for continued vigilance and the implementation of effective control measures to combat the pandemic in the region. It is anticipated that the findings in this study would not only function as a historical record of reproduction numbers during the COVID-19 pandemic in African countries, but can serve as a blueprint for addressing future pandemics of a similar nature.Item Housing and health outcomes: evidence on child morbidities from six Sub-Saharan African countries(BMC Pediatrics, 2023) Muindi, K.; Iddi, S.; Gitau, H.; Mberu, B.Background The connection between healthy housing status and health is well established. The quality of housing plays a significant role in infectious and non-communicable as well as vector-borne diseases. The global burden of disease attributable to housing is considerable with millions of deaths arising from diarrheal and respiratory diseases annually. In sub-Saharan Africa (SSA), the quality of housing remains poor although improvements have been documented. There is a general dearth of comparative analysis across several countries in the sub-region. We assess in this study, the association between healthy housing and child morbidity across six countries in SSA. Methods We use the Demographic and Health Survey (DHS) data for six countries where the most recent survey collected health outcome data on child diarrhoea, acute respiratory illness, and fever. The total sample size of 91,096 is used in the analysis (representing 15, 044 for Burkina Faso, 11, 732 for Cameroon, 5, 884 for Ghana, 20, 964 for Kenya, 33, 924 for Nigeria, and 3,548 for South Africa). The key exposure variable is healthy housing status. We control for various factors associated with the three childhood health outcomes. These include quality housing status, residency (rural/urban), age of the head of the household, mother’s education, mother’s BMI status, marital status, mother’s age, and religious status. Others include the child’s gender, age, whether the child is from multiple or single births, and breastfeeding status. Inferential analysis using survey-weighted logistic regression is employed. Results Our findings indicate that housing is an important determinant of the three outcomes investigated. Compared to unhealthier housing, healthy housing status was found to be associated with reduced odds of diarrhoea in Cameroon [Healthiest: aOR=0.48, 95% CI, (0.32,0.71), healthier: aOR=0.50, 95% CI,(0.35,0.70), Healthy: aOR=0.60, 95% CI, (0.44,0.83), Unhealthy: aOR=0.60, 95% CI, (0.44,0.81)], Kenya [Healthiest: aOR=0.68, 95% CI, (0.52,0.87), Healtheir: aOR=0.79, 95% CI, (0.63,0.98), Healthy: aOR=0.76, 95% CI, (0.62,0.91)], South Africa[Healthy: aOR=0.41, 95% CI, (0.18, 0.97)], and Nigeria [Healthiest: aOR=0.48, 95% CI,(0.37,0.62), Healthier: aOR=0.61, 95% CI,(0.50,0.74), Healthy: aOR=0.71, 95%CI, (0.59,0.86), Unhealthy: aOR=0.78, 95% CI, (0.67,0.91)], and reduced odds of Acute Respiratory Infection in Cameroon [Healthy: aOR=0.72, 95% CI,(0.54,0.96)], Kenya [Healthiest: aOR=0.66, 95% CI, (0.54,0.81), Healthier: aOR=0.81, 95% CI, (0.69,0.95)], and Nigeria [Healthiest: aOR=0.69, 95% CI, (0.56,0.85), Healthier: aOR=0.72,95% CI, (0.60,0.87), Healthy: aOR=0.78, 95% CI, (0.66,0.92), Unhealthy: aOR=0.80, 95% CI, (0.69,0.93)] while it was associated with increased odds in Burkina Faso [Healthiest: aOR=2.45, 95% CI, (1.39,4.34), Healthy: aOR=1.55, 95% CI, (1.09,2.20)] and South Africa [Healthy: aOR=2.36 95% CI, (1.31, 4.25)]. In addition, healthy housing was significantly associated with reduced odds of fever among children in all countries except South Africa [Healthiest: aOR=2.09, 95% CI, (1.02, 4.29)] where children living in the healthiest homes had more than double the odds of having fever. In addition, household-level factors such as the age of the household head, and place of residence were associated with the outcomes. Child-level factors such as breastfeeding status, age, and sex, and maternal-level factors such as education, age, marital status, body mass index (BMI), and religion were also associated with the outcomes. Conclusions The dissimilarity of findings across similar covariates and the multiple relations between healthy housing and under 5 morbidity patterns show unequivocally the heterogeneity that exists across African countries and the need to account for different contexts in efforts to seek an understanding of the role of healthy housing in child morbidity and general health outcomesItem Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression(Hindawi, 2021) Nortey, E.N.N.; Pometsey, R.; Asiedu, L.; Iddi, S.; Mettle, F.O.Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. +e model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. +e overall accuracy of the fitted model was assessed to be 92%. +rough the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.Item Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images(Hindawi, 2021) Asiedu, L.; Essah, B.O.; Iddi, S.; Doku-Amponsah, K.; Mettle, F.O.The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.Item Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints(Hindawi, 2021) Mensah, J.A.; Asiedu, L.; Mettle, F.O.; Iddi, S.Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.Item Coping strategies adapted by Ghanaians during the COVID-19 crisis and lockdown: A population-based study(PLOS, 2021) Iddi, S.; Obiri-Yeboah, D.; Aboh, I.K.; Quansah, R.; Owusu, S.A.; Enyan, N.I.E.; Kodom, R.V.; Nsabimana, E.; Jansen, S.; Ekumah, B.; Boamah, S.A.; Boateng, G.O.; Doku, D.T.; Armah, F.A.The COVID-19 pandemic and control measures adopted by countries globally can lead to stress and anxiety. Investigating the coping strategies to this unprecedented crisis is essential to guide mental health intervention and public health policy. This study examined how people are coping with the COVID-19 crisis in Ghana and identify factors influencing it. This study was part of a multinational online cross-sectional survey on Personal and Family Coping with COVID-19 in the Global South. The study population included adults, �18 years and residents in Ghana. Respondents were recruited through different platforms, including social media and phone calls. The questionnaire was composed of different psychometrically validated instruments with coping as the outcome variable measured on the ordinal scale with 3 levels, namely, Not well or worse, Neutral, and Well or better. An ordinal logistic regression model using proportional odds assumption was then applied. A total of 811 responses were included in the analysis with 45.2% describing their coping level as well/better, 42.4% as neither worse nor better and 12.4% as worse/not well. Manyrespondents (46.9%) were between 25–34 years, 50.1% were males while 79.2% lived in urban Ghana. Having pre-existing conditions increased the chances of not coping well (aOR = 1.86, 95%CI: 1.15–3.01). Not being concerned about supporting the family financially (aOR = 1.67, 95%CI: 1.06–2.68) or having the feeling that life is better during the pandemic (aOR = 2.37, 95%CI: 1.26–4.62) increased chances of coping well. Praying (aOR: 0.62, 95%CI: 0.43–0.90) or sleeping (aOR: 0.55, 95%CI: 0.34–0.89) more during the pandemic than before reduces coping. In Ghana, during the COVID-19 pandemic, financial security and optimism about the disease increase one’s chances of coping well while having pre-existing medical conditions, praying and sleeping more during the pandemic than before reduces one’s chances of coping well. These findings should be considered in planning mental health and public health intervention/policy.