Exploring functional abilities and competing risks among stroke patients: a longitudinal and survival analysis study at Felege Hiwot Referral Hospital, Ethiopia.

OBJECTIVES: This study aimed to evaluate competing risks and functional ability measures among patients who had a stroke. DESIGN: A joint model comprising two related submodels was applied: a cause-specific hazard submodel for competing drop-out and stroke-related death risks, and a partial proportional odd submodel for longitudinal functional ability. SETTING: Felege Hiwot Referral Hospital, Ethiopia. PARTICIPANTS: The study included 400 patients who had a stroke from the medical ward outpatient stroke unit at Felege Hiwot Referral Hospital, who were treated from September 2018 to August 2021. RESULTS: Among the 400 patients who had a stroke, 146 (36.5%) died and 88 (22%) dropped out. At baseline, 14% of patients had no symptoms and/or disability while 24% had slight disability, and 25% had severe disability. Most patients (37.04%) exhibited moderate functional ability. The presence of diabetes increased the cause-specific hazard of death by 3.95 times (95% CI 2.16 to 7.24) but decreased the cause-specific hazard of drop-out by 95% (aHR 0.05; 95% CI 0.01 to 0.46) compared with non-diabetic patients who had a stroke. CONCLUSION: A substantial proportion of patients who had a stroke experienced mortality and drop-out during the study period, highlighting the importance of considering competing risks in stroke research. Age, diabetes, white cell count and stroke complications were significant covariates affecting both longitudinal and survival submodels. Compared with stand-alone models, the joint competing risk modelling technique offers comprehensive insights into the disease's transition pattern.

Post-surgery survival and associated factors for cardiac patients in Ethiopia: applications of machine learning, semi-parametric and parametric modelling.

INTRODUCTION: Living in poverty, especially in low-income countries, are more affected by cardiovascular disease. Unlike the developed countries, it remains a significant cause of preventable heart disease in the Sub-Saharan region, including Ethiopia. According to the Ethiopian Ministry of Health statement, around 40,000 cardiac patients have been waiting for surgery in Ethiopia since September 2020. There is insufficient information about long-term cardiac patients' post-survival after cardiac surgery in Ethiopia. Therefore, the main objective of the current study was to determine the long-term post-cardiac surgery patients' survival status in Ethiopia. METHODS: All patients attended from 2012 to 2023 throughout the country were included in the current study. The total number of participants was 1520 heart disease patients. The data collection procedure was conducted from February 2022- January 2023. Machine learning algorithms were applied. Gompertz regression was used also for the multivariable analysis report. RESULTS: From possible machine learning models, random survival forest were preferred. It emphasizes, the most important variable for clinical prediction was SPO2, Age, time to surgery waiting time, and creatinine value and it accounts, 42.55%, 25.17%,11.82%, and 12.19% respectively. From the Gompertz regression, lower saturated oxygen, higher age, lower ejection fraction, short period of cardiac center stays after surgery, prolonged waiting time to surgery, and creating value were statistically significant predictors of death outcome for post-cardiac surgery patients' survival in Ethiopia. CONCLUSION: Some of the risk factors for the death of post-cardiac surgery patients are identified in the current investigation. Particular attention should be given to patients with prolonged waiting times and aged patients. Since there were only two fully active cardiac centers in Ethiopia it is far from an adequate number of centers for more than 120 million population, therefore, the study highly recommended to increase the number of cardiac centers that serve as cardiac surgery in Ethiopia.

The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model.

Background: Food insecurity and vulnerability in Ethiopia are historical problems due to natural- and human-made disasters, which affect a wide range of areas at a higher magnitude with adverse effects on the overall health of households. In Ethiopia, the problem is wider with higher magnitude. Moreover, this geographical distribution of this challenge remains unexplored regarding the effects of cultures and shocks, despite previous case studies suggesting the effects of shocks and other factors. Hence, this study aims to assess the geographic distribution of corrected-food insecurity levels (FCSL) across zones and explore the comprehensive effects of diverse factors on each level of a household's food insecurity. Method: This study analyzes three-term household-based panel data for years 2012, 2014, and 2016 with a total sample size of 11505 covering the all regional states of the country. An extended additive model, with empirical Bayes estimation by modeling both structured spatial effects using Markov random field or tensor product and unstructured effects using Gaussian, was adopted to assess the spatial distribution of FCSL across zones and to further explore the comprehensive effect of geographic, environmental, and socioeconomic factors on the locally adjusted measure. Result: Despite a chronological decline, a substantial portion of Ethiopian households remains food insecure (25%) and vulnerable (27.08%). The Markov random field (MRF) model is the best fit based on GVC, revealing that 90.04% of the total variation is explained by the spatial effects. Most of the northern and south-western areas and south-east and north-west areas are hot spot zones of food insecurity and vulnerability in the country. Moreover, factors such as education, urbanization, having a job, fertilizer usage in cropping, sanitation, and farming livestock and crops have a significant influence on reducing a household's probability of being at higher food insecurity levels (insecurity and vulnerability), whereas shocks occurrence and small land size ownership have worsened it. Conclusion: Chronically food insecure zones showed a strong cluster in the northern and south-western areas of the country, even though higher levels of household food insecurity in Ethiopia have shown a declining trend over the years. Therefore, in these areas, interventions addressing spatial structure factors, particularly urbanization, education, early marriage control, and job creation, along with controlling conflict and drought effect by food aid and selected coping strategies, and performing integrated farming by conserving land and the environment of zones can help to reduce a household's probability of being at higher food insecurity levels.

Lung cancer mortality and associated predictors: systematic review using 32 scientific research findings.

Background: Cancer is a chronic disease brought on by mutations to the genes that control our cells' functions and become the most common cause of mortality and comorbidities. Thus, this study aimed to assess the comprehensive and common mortality-related risk factors of lung cancer using more than thirty scientific research papers. Methods: Possible risk factors contributing to lung cancer mortality were assessed across 201 studies sourced from electronic databases, including Google Scholar, Cochrane Library, Web of Science (WOS), EMBASE, Medline/PubMed, the Lung Cancer Open Research Dataset Challenge, and Scopus. Out of these, 32 studies meeting the eligibility criteria for meta-analysis were included. Due to the heterogeneous nature of the studies, a random-effects model was applied to estimate the pooled effects of covariates. Results: The overall prevalence of mortality rate was 10% with a 95% confidence interval of 6 and 16%. Twenty studies (62.50%) studies included in this study considered the ages of lung cancer patients as the risk factors for mortality. Whereas, eighteen (56.25%) and thirteen (40.63%) studies incorporated the gender and smoking status of patients respectively. The comorbidities of lung cancer mortality such as cardiovascular disease, hypertension, diabetes, and pneumonia were also involved in 7 (21.90%), 6 (18.75%), 5 (15.63%), and 2 (6.25%) studies, respectively. Patients of older age are more likely to die as compared to patients of younger age. Similarly, lung patients who had smoking practice were more likely to die as compared to patients who hadn't practiced smoking. Conclusion: The mortality rate of lung cancer patients is considerably high. Older age, gender, stage, and comorbidities such as cardiovascular, hypertension, and diabetes have a significant positive effect on lung cancer mortality. The study results will contribute to future research, management, and prevention strategies for lung cancer.

Household food insecurity levels in Ethiopia: quantile regression approach.

Introduction: Numerous natural and man-made factors have afflicted Ethiopia, and millions of people have experienced food insecurity. The current cut-points of the WFP food consumption score (FCS) have limitations in measuring the food insecurity level of different feeding patterns due to the diversified culture of the society. The aim of this study is to adapt the WFP food security score cut-points corrected for the different feeding cultures of the society using effect-driven quantile clustering. Method: The 2012, 2014, and 2016 Ethiopian socio-economic household-based panel data set with a sample size of 3,835 households and 42 variables were used. Longitudinal quantile regression with fixed individual-specific location-shift intercept of the free distribution covariance structure was adopted to identify major indicators that can cluster and level quantiles of the FCS. Result: Household food insecurity is reduced through time across the quintiles of food security score distribution, mainly in the upper quantiles. The leveling based on effect-driven quantile clustering brings 35.5 and 49 as the FCS cut-points corrected for cultural diversity. This corrected FCS brings wider interval for food insecure households with the same interval range for vulnerable households, where the WFP FCS cut-points under estimate it by 7 score. Education level, employment, fertilizer usage, farming type, agricultural package, infrastructure-related factors, and environmental factors are found to be the significant contributing factors to food security. On the other hand, the age of the head of the household, dependency ratio, shock, and no irrigation in households make significant contributions to food insecurity. Moreover, households living in rural areas and farming crops on small lands are comparatively vulnerable and food insecure. Conclusion: Measuring food insecurity in Ethiopia using the WFP FCS cut-off points underestimates households' food insecurity levels. Since the WFP FCS cut-points have universality and comparability limitations, there is a need for a universally accepted local threshold, corrected for local factors those resulted in different consumption patterns in the standardization of food security score. Accordingly, the quantile regression approach adjusts the WFP-FCS cut points by adjusting for local situations. Applying WFP cut-points will wrongly assign households on each level, so the proportion of households will be inflated for the security level and underestimated for the insecure level, and the influence of factors can also be wrongly recommended the food security score for the levels. The quantile clustering approach showed that cropping on a small land size would not bring about food security in Ethiopia. This favors the Ethiopian government initiative called integrated farming "ኩታ ገጠም እርሻ" which Ethiopia needs to develop and implement a system that fits and responds to this technology and infrastructure.

Spatial distribution and multilevel analysis of the ideal number of children among Ethiopian women.

BACKGROUND: Ideal number of children (INC) is the number of children that a woman or man would have if they could go back to the time when they did not have any children and could choose accurately the number of children to have in their total life. Despite numerous studies on the prevalence and associated factors of the ideal number of children, there is a lack of studies that incorporated spatial and multilevel analysis. Thus, this study was aimed at the spatial and multilevel analysis of an ideal number of children and associated factors. METHODS: The study design was a cross-sectional study in which the data was obtained from Ethiopian Demographic and Health Survey (EDHS) in 2016. About 13,961 women ages 15-49 who fulfill the inclusion criterion were considered. A negative binomial regression model that incorporates spatial and multilevel analysis was employed. RESULTS: About 33 and 12.8% of the women had four and six ideal numbers of children respectively. The highest INC per woman was recorded in Oromia region 5055 (36.1%) and the lowest in Harare 35(0.2%). The INC per woman is high in rural 10,726 (76.6%) areas as compared to urban areas 3277(23.4%). The ideal number of children was spatially clustered (Global Moran's I = 0.1439, p < .00043). Significant hotspot clusters were found in the Somali region such as in Afder, Shabelle, Korahe, and Doolo zone. CONCLUSION: The spatial analysis revealed a significant clustering of the ideal number of children in the Ethiopia zone. Specifically, higher INC was observed in the Somali region, specifically in the Afder, Shabelle, Korahe, and Doolo zones. Among the various factors considered, women's age, region, place of residence, women's education level, contraception use, religion, marital status, family size, and age at first birth year were identified as significant predictors of the ideal number of children. These findings indicate that these factors play a crucial role in shaping reproductive preferences and decisions among women in the study population. Based on these findings, responsible bodies should prioritize targeted interventions and policies in high-risk regions to address women's specific reproductive needs.

Joint modeling of longitudinal changes of blood pressure and time to remission of hypertensive patients receiving treatment: Bayesian approach.

INTRODUCTION: Hypertension is a widespread condition when the blood's force on the artery walls is extremely high to develop adverse health effects. This paper aimed to jointly model the longitudinal change of blood pressures (systolic and diastolic) and time to the first remission of hypertensive outpatients receiving treatment. METHODS: A retrospective study design was used to collect appropriate data on longitudinal changes in blood pressure and time-to-event from the medical charts of 301 hypertensive outpatients under follow-up at Felege Hiwot referral hospital, Ethiopia. The data exploration was done using summary statistics measures, individual profile plots, Kaplan-Meier plots, and log-rank tests. To get wide-ranging information about the progression, joint multivariate models were employed. RESULTS: A total of 301 hypertensive patients who take treatment was taken from Felege Hiwot referral hospital recorded between Sep. 2018 to Feb. 2021. Of this 153 (50.8%) were male, and 124 (49.2%) were residents from rural areas. About 83(27.6%), 58 (19.3%), 82 (27.2%), and 25 (8.3%) have a history of diabetes mellitus, cardiovascular disease, stroke, and HIV respectively. The median time of hypertensive patients to have first remission time was 11 months. The hazard of the patient's first remission time for males was 0.63 times less likely than the hazard for females. The time to attain the first remission for patients who had a history of diabetes mellitus was 46% lower than for those who had no history of diabetes mellitus. CONCLUSION: Blood pressure dynamics significantly affect the time to the first remission of hypertensive outpatients receiving treatment. The patients who had a good follow-up, lower BUN, lower serum calcium, lower serum sodium, lower hemoglobin, and take the treatment enalapril showed an opportunity in decreasing their blood pressure. This compels patients to experience the first remission early. Besides, age, patient's history of diabetes, patient's history of cardiovascular disease, and treatment type were the joint determinant factors for the longitudinal change of BP and the first remission time. The Bayesian joint model approach provides specific dynamic predictions, wide-ranging information about the disease transitions, and better knowledge of disease etiology.

Discrepancies of recurrent birth intervals using longitudinal data analysis in Ethiopia: interim EDHS 2019.

OBJECTIVE: This study aimed to determine whether the birth interval changes differently over time among women in Ethiopia and whether the change depends on women, children and household characteristics measured at the last visit. METHODS: Longitudinal study design was implemented based on the data obtained from the 2019 Ethiopia Mini Demographic and Health Survey consisting of a total of 3630 mothers. Generalised estimating equation and generalised linear mixed model were employed to estimate the effect of the determinants given the correlation between birth intervals within a mother is under consideration. RESULTS: The majority of women were Muslims (48.1%) and come from rural areas (82.2%). About 77.2% of women at first birth were below 20 years old. A significant correlation (p value <0.0001) between the first and second birth intervals of mothers was observed. The estimated birth interval of women from the poorest household was 0.877 (e-0.1317) times the estimated birth intervals of women from the richest household. This indicates richest households were likely to have higher birth intervals as compared with the poorest households (95% CI e-0.1754=0.839 to e-0.088=0.916). CONCLUSION: The birth intervals of over one-fifth of mothers were 1 year, less than the birth interval recommended by the WHO standard. It was also perceived that successive birth intervals are correlated. Mothers who have delivered female children had lower birth intervals than mothers who have delivered male children. As compared with the birth intervals of mothers from a household with higher economic status, the birth intervals of mothers from a household with lower economic status had lower birth intervals. In this study, significant effects of religion, contraceptive use, region, mothers' current age, education level and mothers' current marital status on birth intervals were also noted.

The spatial modification effect of predictors on household level food insecurity in Ethiopia.

Household food insecurity remains highly prevalent in developing countries (including in Ethiopia) and it has been recognized as a serious public health problem. Several factors such as demographic, economic, social, and clinical factors influence household food insecurity, and these vary geographically. In this work, we investigate the geographical modification of the effect of several factors on chronic food insecurity. The data is from the Ethiopia socioeconomic survey conducted by the Ethiopia Central Statistics Agency (ECSA) in collaboration with the World Bank. Ethiopia socioeconomic survey is a long-term project to collect nationally representative panel survey of over 6500 households. A geo-additive model which accounts the structured and unstructured special effect was adopted to estimate household food insecurity risk factors. The study also revealed significant spatial variations on household food insecurity among administrative zones. Mainly, household living in the Sidama, Gamo Gofa, Shinille, Basketo, Wolyita, Wag Hemira, Liben, Awi, Eastern Tigray and West Harerghe zones, having higher food insecurity than the other zones in Ethiopia. Moreover, the analysis also showed that availability of credit services, proximity to service centers, average years of schooling of members of the household, and household assets are negatively associated with household food insecurity, whereas shocks, age, and dependency ratio increase the odds of a household to be food insecured. The generalized geo-additive mixed-effects model enables simultaneous modeling of spatial correlation, heterogeneity and possible nonlinear effects of covariates. Our study investigated the spatial heterogeneity of household level food insecurity, and its association with shocks, age, dependency ratio, availability of credit services, average years of schooling, and household assets. Our findings have also an important implication for planning as well as in the search for the variables that might account for the residual spatial patterns.

Clinical management of COVID-19 in hospitals and the community: A snapshot from a medical insurance database in South Africa.

BACKGROUND: Little is known about the clinical care, use of medicines, and risk factors associated with mortality among the population with private health insurance with COVID-19 in South Africa. METHODS: This was a retrospective cross-sectional study using claims data of patients with confirmed COVID-19. Sociodemographics, comorbidities, severity, concurrent/progressive comorbidity, drug treatment, and outcomes were extracted from administrative data. Univariate and multivariate logistic regression models were used to explore the risk factors associated with in-hospital death. RESULTS: This study included 154,519 patients with COVID-19; only 24% were categorized as severe because they received in-hospital care. Antibiotic (42.8%) and steroid (30%) use was high in this population. After adjusting for known comorbidities, concurrent/progressive diagnosis of the following conditions were associated with higher in-hospital death odds: acute respiratory distress syndrome (aOR = 1.55; 95% CI = 1.44-1.68), septic shock (aOR = 1.55; 95% CI = 2.00-4.12), pneumonia (aOR = 1.35; 95% CI = 1.24-1.47), acute renal failure (aOR = 2.30; 95% CI = 2.09-2.5), and stroke (aOR = 2.09; 95% CI = 1.75-2.49). The use of antivirals (aOR = 0.47; 95% CI= 0.40-0.54), and/or steroids (aOR = 0.46; 95% CI = 0.43-0.50) were associated with decreased death odds. The use of antibiotics in-hospital was not associated with increased survival (aOR = 0.97; 95% CI = 0.91-1.04). CONCLUSIONS: Comorbidities remain significant risk factors for death mediated by organ failure. The use of antibiotics did not change the odds of death, suggesting inappropriate use.

Multilevel multivariate analysis on the anthropometric indicators of under-five children in Ethiopia: EMDHS 2019.

BACKGROUND: Undernutrition is the main cause of morbidity and mortality of children aged under five and it is an important indicator of countries' economic and health status. Limited attention is given to research papers conducted in Ethiopia that identified and estimates the determinants of under-five anthropometric indicators by considering their association and clustering effect. Therefore, this study aimed to identify and estimate the effects of important determinants of anthropometric indicators by taking into account their association and cluster effects. METHODS: In this study, a cross-sectional study design was implemented based on the data obtained from the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) consists a total of 5027 under-five children. A multilevel multivariate logistic regression model was employed to estimate the effect of the determinants given their association of anthropometric indicators and clustering effect. RESULTS: Among 5027 children considered in the study 36.0, 23.3, and 9.1% of them were stunted, underweight, and wasted, respectively. Whereas the total number of undernourished (stunting, underweight and/or wasting) children was 42.9%. More than half of the children (51.2%) were males and 77.0% lived in rural area. The estimated odds of children from households with secondary and above education levels being stunted was 0.496 (OR = 0.496) times the estimated odds of children from households with no education. Whereas children from the richest households were less likely to be stunted as compared to children from the poorest households (OR = 0.485). The estimated odds of children from urban areas being underweight and wasting were lower by 24.9 and 33.7% of estimated odds of children from rural areas respectively. CONCLUSION: The prevalence of anthropometric indicators of stunting, underweight, and wasting in Ethiopia was increased. The children underweight has significant dependency with both stunting and wasting. The sex of the child, wealth index, and education level of a household are the common important determinants of stunting, underweight and wasting. The undernourished status of children was more alike within the region and differences between regions.

Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients.

BACKGROUND: Mortality rates of coronavirus disease-2019 (COVID-19) continue to rise across the world. The impact of several risk factors on coronavirus mortality has been previously reported in several meta-analyses limited by small sample sizes. In this systematic review, we aimed to summarize available findings on the association between comorbidities, complications, smoking status, obesity, gender, age and D-dimer, and risk of mortality from COVID-19 using a large dataset from a number of studies. METHOD: Electronic databases including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till 31 August 2020. We included all human studies regardless of language, publication date or region. Forty-two studies with a total of 423,117 patients met the inclusion criteria. To pool the estimate, a mixed-effect model was used. Moreover, publication bias and sensitivity analysis were evaluated. RESULTS: The findings of the included studies were consistent in stating the contribution of comorbidities, gender, age, smoking status, obesity, acute kidney injury, and D-dimer as a risk factor to increase the requirement for advanced medical care. The analysis results showed that the pooled prevalence of mortality among hospitalized patients with COVID-19 was 17.62% (95% CI 14.26-21.57%, 42 studies and 423,117 patients). Older age has shown increased risk of mortality due to coronavirus and the pooled odds ratio (pOR) and hazard ratio (pHR) were 2.61 (95% CI 1.75-3.47) and 1.31 (95% CI 1.11-1.51), respectively. A significant association were found between COVID-19 mortality and male (pOR = 1.45; 95% CI 1.41-1.51; pHR = 1.24; 95% CI 1.07-1.41), and current smoker (pOR = 1.42; 95% CI 1.01-1.83). Furthermore, risk of mortality among hospitalized COVID-19 patients is highly influenced by patients with Chronic Obstructive Pulmonary Disease (COPD), Cardiovascular Disease (CVD), diabetes, hypertension, obese, cancer, acute kidney injury and increase D-dimer. CONCLUSION: Chronic comorbidities, complications, and demographic variables including acute kidney injury, COPD, diabetes, hypertension, CVD, cancer, increased D-dimer, male gender, older age, current smoker, and obesity are clinical risk factors for a fatal outcome associated with coronavirus. The findings could be used for disease's future research, control and prevention.

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

BACKGROUND: Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements. METHODS: The data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected patients in Kwazulu-Natal, South Africa. Participants were enrolled into the acute infection, then into early infection subsequently into established infection and afterward on cART. Generalized linear multilevel models were applied. RESULTS: Multilevel ordinal non-proportional and proportional-odds growth models were presented and compared. We observed that the effects of covariates can't be assumed identical across the three cumulative logits. Our analyses also revealed that the rate of change of immune recovery of patients increased as the follow-up time increases. Patients with stable sexual partners, middle-aged, cART initiation, and higher educational levels were more likely to have better immunological stages with time. Similarly, patients having high electrolytes component scores, higher red blood cell indices scores, higher physical health scores, higher psychological well-being scores, a higher level of independence scores, and lower viral load more likely to have better immunological stages through the follow-up time. CONCLUSION: It can be concluded that the multilevel non-proportional-odds method provides a flexible modeling alternative when the proportional-odds assumption of equal effects of the predictor variables at every stage of the response variable is violated. Having higher clinical parameter scores, higher QoL scores, higher educational levels, and stable sexual partners were found to be the significant factors for trends of CD4 count recovery.

Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model.

BACKGROUND: HIV infected patients may experience many intermediate events including between-event transition throughout their follow up. Through modelling these transitions, we can gain a deeper understanding of HIV disease process and progression and of factors that influence the disease process and progression pathway. In this work, we present transition-specific parametric multi-state models to describe HIV disease process and progression. METHODS: The data is from an ongoing prospective cohort study conducted amongst adult women who were HIV-infected in KwaZulu-Natal, South Africa. Participants were enrolled during the acute HIV infection phase and then followed up during chronic infection, up to ART initiation. RESULTS: Transition specific distributions for multi-state models, including a variety of accelerated failure time (AFT) models and proportional hazards (PH) models, were presented and compared in this study. The analysis revealed that women enrolling with a CD4 count less than 350 cells/mm3 (severe and advanced disease stages) had a far lower chance of immune recovery, and a considerably higher chance of immune deterioration, compared to women enrolling with a CD4 count of 350 cells/mm3 or more (normal and mild disease stages). Our analyses also showed that older age, higher educational levels, higher scores for red blood cell counts, higher mononuclear scores, higher granulocytes scores, and higher physical health scores, all had a significant effect on a shortened time to immunological recovery, while women with many sex partners, higher viral load and larger family size had a significant effect on accelerating time to immune deterioration. CONCLUSION: Multi-state modelling of transition-specific distributions offers a flexible tool for the study of demographic and clinical characteristics' effects on the entire disease progression pathway. It is hoped that the article will help applied researchers to familiarize themselves with the models, including interpretation of results.

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach.

INTRODUCTION: Combination antiretroviral therapy has become the standard care of human immunodeficiency virus (HIV)-infected patients and has further led to a dramatically decreased progression probability to acquired immune deficiency syndrome (AIDS) for patients under such a therapy. However, responses of the patients to this therapy have recorded heterogeneous complexity and high dynamism. In this paper, we simultaneously model long-term viral suppression, viral rebound, and state-specific duration of HIV-infected patients. METHODS: Full-parametric and semi-parametric Markov multistate models were applied to assess the effects of covariates namely TB co-infection, educational status, marital status, age, quality of life (QoL) scores, white and red blood cell parameters, and liver enzyme abnormality on long-term viral suppression, viral rebound and state-specific duration for HIV-infected individuals before and after treatment. Furthermore, two models, one including and another excluding the effect of the frailty, were presented and compared in this study. RESULTS: Results from the diagnostic plots, Akaike information criterion (AIC) and likelihood ratio test showed that the Weibull multistate frailty model fitted significantly better than the exponential and semi-parametric multistate models. Viral rebound was found to be significantly associated with many sex partners, higher eosinophils count, younger age, lower educational level, higher monocyte counts, having abnormal neutrophils count, and higher liver enzyme abnormality. Furthermore, viral suppression was also found to be significantly associated with higher QoL scores, and having a stable sex partner. The analysis result also showed that patients with a stable sex partner, higher educational levels, higher QoL scores, lower eosinophils count, lower monocyte counts, and higher RBC indices were more likely to spend more time in undetectable viral load state. CONCLUSIONS: To achieve and maintain the UNAIDS 90% suppression targets, additional interventions are required to optimize antiretroviral therapy outcomes, specifically targeting those with poor clinical characteristics, lower education, younger age, and those with many sex partners. From a methodological perspective, the parametric multistate approach with frailty is a flexible approach for modeling time-varying variables, allowing for dealing with heterogeneity between the sequence of transitions, as well as allowing for a reasonable degree of flexibility with a few additional parameters, which then aids in gaining a better insight into how factors change over time.

Systematic Review of Important Viral Diseases in Africa in Light of the 'One Health' Concept.

Emerging and re-emerging viral diseases are of great public health concern. The recent emergence of Severe Acute Respiratory Syndrome (SARS) related coronavirus (SARS-CoV-2) in December 2019 in China, which causes COVID-19 disease in humans, and its current spread to several countries, leading to the first pandemic in history to be caused by a coronavirus, highlights the significance of zoonotic viral diseases. Rift Valley fever, rabies, West Nile, chikungunya, dengue, yellow fever, Crimean-Congo hemorrhagic fever, Ebola, and influenza viruses among many other viruses have been reported from different African countries. The paucity of information, lack of knowledge, limited resources, and climate change, coupled with cultural traditions make the African continent a hotspot for vector-borne and zoonotic viral diseases, which may spread globally. Currently, there is no information available on the status of virus diseases in Africa. This systematic review highlights the available information about viral diseases, including zoonotic and vector-borne diseases, reported in Africa. The findings will help us understand the trend of emerging and re-emerging virus diseases within the African continent. The findings recommend active surveillance of viral diseases and strict implementation of One Health measures in Africa to improve human public health and reduce the possibility of potential pandemics due to zoonotic viruses.

Modelling immune deterioration, immune recovery and state-specific duration of HIV-infected women with viral load adjustment: using parametric multistate model.

BACKGROUND: CD4 cell and viral load count are highly correlated surrogate markers of human immunodeficiency virus (HIV) disease progression. In modelling the progression of HIV, previous studies mostly dealt with either CD4 cell counts or viral load alone. In this work, both biomarkers are in included one model, in order to study possible factors that affect the intensities of immune deterioration, immune recovery and state-specific duration of HIV-infected women. METHODS: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. Participants were enrolled in the acute HIV infection phase, then followed-up during chronic infection up to ART initiation. Full-parametric and semi-parametric Markov models were applied. Furthermore, the effect of the inclusion and exclusion viral load in the model was assessed. RESULTS: Inclusion of a viral load component improves the efficiency of the model. The analysis results showed that patients who reported a stable sexual partner, having a higher educational level, higher physical health score and having a high mononuclear component score are more likely to spend more time in a good HIV state (particularly normal disease state). Patients with TB co-infection, with anemia, having a high liver abnormality score and patients who reported many sexual partners, had a significant increase in the intensities of immunological deterioration transitions. On the other hand, having high weight, higher education level, higher quality of life score, having high RBC parameters, high granulocyte component scores and high mononuclear component scores, significantly increased the intensities of immunological recovery transitions. CONCLUSION: Inclusion of both CD4 cell count based disease progression states and viral load, in the time-homogeneous Markov model, assisted in modeling the complete disease progression of HIV/AIDS. Higher quality of life (QoL) domain scores, good clinical characteristics, stable sexual partner and higher educational level were found to be predictive factors for transition and length of stay in sequential adversity of HIV/AIDS.

Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model.

BACKGROUND: Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers. METHODS: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. We presented a joint model that consists of two related submodels: a Markov multistate model for CD4 cell count transitions and a linear mixed effect model for longitudinal viral load dynamics. RESULTS: Viral load dynamics significantly affect the transition intensities of HIV/AIDS disease progression. The analysis also showed that patients with relatively high educational levels (ß = - 0.004; 95% confidence interval [CI]:-0.207, - 0.064), high RBC indices scores (ß = - 0.01; 95%CI:-0.017, - 0.002) and high physical health scores (ß = - 0.001; 95%CI:-0.026, - 0.003) were significantly were associated with a lower rate of viral load increase over time. Patients with TB co-infection (ß = 0.002; 95%CI:0.001, 0.004), having many sex partners (ß = 0.007; 95%CI:0.003, 0.011), being younger age (ß = 0.008; 95%CI:0.003, 0.012) and high liver abnormality scores (ß = 0.004; 95%CI:0.001, 0.01) were associated with a higher rate of viral load increase over time. Moreover, patients with many sex partners (ß = - 0.61; 95%CI:-0.94, - 0.28) and with a high liver abnormality score (ß = - 0.17; 95%CI:-0.30, - 0.05) showed significantly reduced intensities of immunological recovery transitions. Furthermore, a high weight, high education levels, high QoL scores, high RBC parameters and being of middle age significantly increased the intensities of immunological recovery transitions. CONCLUSION: Overall, from a clinical perspective, QoL measurement items, being of a younger age, clinical attributes, marital status, and educational status are associated with the current state of the patient, and are an important contributing factor to extend survival of the patients and guide clinical interventions. From a methodological perspective, it can be concluded that a joint multistate model approach provides wide-ranging information about the progression and assists to provide specific dynamic predictions and increasingly precise knowledge of diseases.

Multivariate multilevel modeling of quality of life dynamics of HIV infected patients.

BACKGROUND: Longitudinal quality of life (QoL) is an important outcome in many chronic illness studies aiming to evaluate the efficiency of care both at the patient and health system level. Although many QoL studies involve multiple correlated hierarchical outcome measures, very few of them use multivariate modeling. In this work, we modeled the long-term dynamics of QoL scores accounting for the correlation between the QoL scores in a multilevel multivariate framework and to compare the effects of covariates across the outcomes. METHODS: The data is from an ongoing prospective cohort study conducted amongst adult women who were HIV-infected and on the treatment in Kwazulu-Natal, South Africa. Independent and related QoL outcome multivariate multilevel models were presented and compared. RESULTS: The analysis showed that related outcome multivariate multilevel models fit better for our data used. Our analyses also revealed that higher educational levels, middle age, stable sex partners and higher weights had a significant effect on better improvements in the rate of change of QoL scores of HIV infected patients. Similarly, patients without TB co-infection, without thrombocytopenia, with lower viral load, with higher CD4 cell count levels, with higher electrolytes component score, with higher red blood cell (RBC) component score and with lower liver abnormality component score, were associated with significantly improved the rate of change of QoL, amongst HIV infected patients. CONCLUSION: It is hoped that the article will help applied researchers to familiarize themselves with the models and including interpretation of results. Furthermore, three issues are highlighted: model building of multivariate multilevel outcomes, how this model can be used to assess multivariate assumptions, involving fixed effects (for example, to examine the size of the covariate effect varying across QoL domain scores) and random effects (for example, to examine the rate of change in one response variable associated to changes in the other).