Survival analysis of time-to-death for under-five children in Somalia: Application of AFT modeling approach.

IntroductionThe preceding birth interval is the time between a child's birth and the conception of the next child, is essential for maternal and child well-being. Short birth intervals can lead to maternal nutritional depletion, inadequate prenatal care, and increased risks of preterm birth and low birth weight, all of which can negatively impact child survival. Approximately 5 million children under the age of five died in 2019, with the majority of these deaths occurring in low- and middle-income countries. The leading causes of mortality include neonatal conditions, pneumonia, diarrhea, and malaria, which are often exacerbated by inadequate maternal health practices. The existing challenges and barriers to promoting optimal birth spacing practices in Ethiopia to improve child survival outcomes are multifaceted. The aim of this study was to investigate how the length of the preceding birth interval influences child survival in Ethiopia, by applying parametric and parametric shared frailty models.MethodThis study was conducted based on secondary data that obtained from the Ethiopian Demographic and Health Survey. The Cox proportional hazard model, accelerated failure time (AFT) model, and parametric shared frailty models were employed for data analysis. Parametric shared frailty models were employed to analyze the relationship between preceding birth intervals and child survival. A shared frailty model accounts for unobserved heterogeneity across individuals or households by including a random effect term that captures this heterogeneity. This approach allows us to control for unmeasured factors that may influence both preceding birth intervals and child survival outcomes.ResultAmong the covariates, mother of a child living in an urban area (ϕ = 0.96; 95% CI 0.94, 0.97; p-value = 0.001), non-educated women (ϕ = 1.03; 95% CI (1.00, 1.06); p-value = 0.039), primary educated women (ϕ = 1.13; 95% CI (1.11, 1.15); p-value 0.001), age of a child (ϕ = 0.99; 95% CI (0.76.0.99; p-value = 1.02; 95% CI (1.09, 1.25; p-value = 0.027), birth interval between 2–3 years (ϕ = 1.02; 95% CI 1.09, 1.25; p-value = 0.027), birth interval > = 4 years (ϕ = 1.28; 95% p-value 0.01 significantly affected the survival of the child. The relevant variable was significantly impacted by clustering (region).ConclusionFactors such as age of the child at subsequent births, Current Breastfeeding, vaccination, Mother's Age at First Birth, birth type, place of delivery at health facility, mother's education level, and interval between births positively affect child survival rates. Conversely, residing rural areas, maternal smoking habits, and mother's wealth status were associated with negative impacts on child survival. These findings underscore the need for targeted interventions and educational programs aimed at promoting optimal birth spacing practices and increasing awareness among women with short preceding birth intervals, rural residents, and those with low education to improve child survival outcome.

BackgroundBoth short and long birth intervals are associated with many risk factors and about 29% of births are short birth intervals in Ethiopia. The purpose of this study is to model the birth intervals of adult women aged 15–49 years using accelerated failure time and shared frailty models in order to analyze the birth intervals of Ethiopian women.MethodsThe data was obtained from the 2016 Ethiopian Demographic and Health Survey (EDHS). Accelerated failure time with different baseline and shared frailty models are used for the analysis to identify important demographic and socio-economic factors affecting the length of birth intervals and correlates of the birth intervals respectively.ResultsThe data consists of 9147 women, of which about 7842 (85.5%) are closed interval and the rest of 1323(14.5%) are open interval. Accelerated failure time (AFT) result revealed that women education level, husbands education level, age at first birth, marital status, religion and family wealth index are significant factors affecting birth interval of women in Ethiopia.ConclusionWomen with closely spaced births tend to have larger family sizes when compared with women with longer inter-birth interval. Longer successive birth interval tends to reduce the total fertility rate of women. Furthermore, improvements in socio-economic status and level of education of women associate with reduced fertility, improved maternal and child wellbeing, and longer birth interval.

The importance of the length of preceding birth intervals for the survival chances of young children has been established, but the debate concerning the causal biomedical or behavioral mechanisms continues. This article uses data from 17 Demographic and Health Surveys to investigate the effect of birth intervals on child mortality: Anthropometry of children, recent morbidity of children, and use of health services are examined in addition to child survival data for children born in the five years before the survey. Various methodological approaches are used to investigate the relative importance of the postulated mechanisms linking birth intervals and child survival. Short preceding birth intervals are associated with increased mortality risks in the neonatal period and at 1-6 months of age, and, to a much lesser extent, at 7-23 months of age. The effects of short birth intervals on nutritional status are rather moderate, and there is a weak relationship with lower attendance at prenatal care services. No consistent relationship exists between the length of birth intervals and other health status or health-service utilization variables. The results indicate that prenatal mechanisms are more important than postnatal factors, such as sibling competition, in explaining the causal nature of the birth interval effect.

The accelerated failure time model is presented as an alternative to the proportional hazard model in the analysis of survival data. We investigate the effect of covariates omission in the case of applying a Weibull accelerated failure time model. In an uncensored setting, the asymptotic bias of the treatment effect is theoretically zero when important covariates are omitted; however, the asymptotic variance estimator of the treatment effect could be biased and then the size of the Wald test for the treatment effect is likely to exceed the nominal level. In some cases, the test size could be more than twice the nominal level. In a simulation study, in both censored and uncensored settings, Type I error for the test of the treatment effect was likely inflated when the prognostic covariates are omitted. This work remarks the careless use of the accelerated failure time model. We recommend the use of the robust sandwich variance estimator in order to avoid the inflation of the Type I error in the accelerated failure time model, although the robust variance is not commonly used in the survival data analyses.

An accelerated failure time (AFT) model has been widely used for the analysis of censored survival or failure time data. However, the AFT imposes the restrictive log-linear relation between the survival time and the explanatory variables. In this paper, we introduce a relevance vector machine survival (RVMS) model based on Weibull AFT model that enables the use of kernel framework to automatically learn the possible nonlinear effects of the input explanatory variables on target survival times. We take advantage of the Bayesian inference technique in order to estimate the model parameters. We also introduce two approaches to accelerate the RVMS training. In the first approach, an efficient smooth prior is employed that improves the degree of sparsity. In the second approach, a fast marginal likelihood maximization procedure is used for obtaining a sparse solution of survival analysis task by sequential addition and deletion of candidate basis functions. These two approaches, denoted by smooth RVMS and fast RVMS, typically use fewer basis functions than RVMS and improve the RVMS training time; however, they cause a slight degradation in the RVMS performance. We compare the RVMS and the two accelerated approaches with the previous sparse kernel survival analysis method on a synthetic data set as well as six real-world data sets. The proposed kernel survival analysis models have been discovered to be more accurate in prediction, although they benefit from extra sparsity. The main advantages of our proposed models are: 1) extra sparsity that leads to a better generalization and avoids overfitting; 2) automatic relevance sample determination based on data that provide more accuracy, in particular for highly censored survival data; and 3) flexibility to utilize arbitrary number and types of kernel functions (e.g., non-Mercer kernels and multikernel learning).

There is considerable literature on the associations of short birth intervals with adverse perinatal outcomes. However, less is known about the associations with child growth and development. In this study, we investigated the associations between birth intervals and child growth and development and examined child illness, child diet, and maternal stimulation as potential mechanisms. We pooled Demographic and Health Survey data on 8300 children aged 36–59 months from 13 countries (Benin, Burundi, Cambodia, Cameroon, Chad, Congo, Haiti, Honduras, Rwanda, Senegal, Timor-Leste, Togo, and Uganda). Longer birth interval was defined as a preceding birth interval ≥33 months. Child growth was assessed using height-for-age Z-score (HAZ). Child cognitive and socio-emotional development were measured using the Early Childhood Development Index. Child morbidity was defined as any illness in the past two weeks. Child diet was assessed using dietary diversity score and maternal stimulation by the number of stimulation activities. We used generalised linear models to estimate associations between longer birth intervals and child growth and development. Structural equation modelling was used to assess direct and indirect effects. In our sample, 44% of children had a preceding birth interval ≥33 months, 42% were stunted, 25% were cognitively off-track, and 33% socio-emotionally off-track. Longer birth intervals were associated with higher HAZ (mean difference 0.23 (95% CI 0.14, 0.32)) and socio-emotional development (relative risk (RR) 1.04 (95% CI 1.00, 1.09), but not cognitive development (RR 1.02 (95% CI 0.98, 1.06). We observed no significant indirect effects via child illness, child dietary diversity, or maternal stimulation. Although longer birth intervals were beneficial for child growth and socio-emotional development, we found no empirical support for the biological and behavioural mechanisms we explored. Additional research is needed to investigate alternative mechanisms to elucidate underlying processes and inform future interventions.

Background: Thalassemia major (TM) is a severe disease and the most common anemia worldwide. The survival time of the disease and its risk factors are of importance for physicians. The present study was conducted to apply the semi-parametric Cox PH model and use parametric proportional hazards (PH) and accelerated failure time (AFT) models to identify the risk factors related to survival of TM patients. Methods: The data of this historical cohort study (296 patients with TM) were collected during 1994 and 2013 in Zafar Clinic in Tehran. Gompertz PH and Weibull AFT models were used for survival analysis (SA) of these patients. Data analysis was performed using R3.2.2 software. Results: 153 (51.7%) of patients were female; the mean (±SD) age of the patients was 29.11 (±0.47) years. One-year survival rate for males and females was 0.963±0.007 and 0.973±0.013, respectively; and 3-year survival rate for males and females was 0.711±0.057 and 0.733±0.114, respectively. In the Gompertz model, birthplace and age at onset of the disease were significant factors (p= 0.035, and p= 0.005) in survival time. Also, in the Weibull model, birth place and age at onset of the disease were significant factors (p= 0.013, and p= 0.008) in survival time. The Akaike Information Criterion (AIC) for Weibull model was 158.51, which was lower than other parametric models. Conclusion: According to the results, the Weibull AFT model was found to be a better model for identifying the risk factors related to survival of patients with TM disease. Informing parents, especially mothers and paying attention to blood screening for early diagnosis may increase the survival rate of patients.

A primary focus of Survival analysis in medicine is modeling time to surviving of a particular disease. In this paper, survival analysis was carried out on the cardiovascular disease data modeling time to surviving the disease. The data was gotten from Barau-Dikko teaching hospital Kaduna, Nigeria. Accelerated Failure Time (AFT) models like Weibull AFT model, Logistic AFT model, Log-normal AFT model,Log-logistic AFT model and Exponential AFT model are considered to be used for modeling the time to surviving cardiovascular diseases . Models selection criteria were used as a guide to unravel the best model for modeling cardiovascular diseases. The test for assumption of proportionality was conducted; the result revealed that the data violated the assumption of proportionality. Hence guarantee the use of accelerated failure time models. Based on the result from accelerated failure time models, the lognormal AFT model out-performed the other models since it has the lowest AIC and the highest log-likelihood value with 1022.23 and -47.82 respectively.

Stroke is a life-threatening condition that occurs due to impaired blood flow to brain tissues. Every year, about 15 million people worldwide suffer from a stroke, with five million of them suffering from some form of permanent physical disability. Globally, stroke is the second-leading cause of death following ischemic heart disease. It is a public health burden for both developed and developing nations, including Ethiopia. This study is aimed at estimating the time to death among stroke patients at Jimma University Medical Center, Southwest Ethiopia. A facility-based retrospective cohort study was conducted among 432 patients. The data were collected from stroke patients under follow-up at Jimma University Medical Center from January 1, 2016, to January 30, 2019. A log-rank test was used to compare the survival experiences of different categories of patients. The Cox proportional hazard model and the accelerated failure time model were used to analyze the survival analysis of stroke patients using R software. An Akaike's information criterion was used to compare the fitted models. Of the 432 stroke patients followed, 223 (51.6%) experienced the event of death. The median time to death among the patients was 15 days. According to the results of the Weibull accelerated failure time model, the age of patients, atrial fibrillation, alcohol consumption, types of stroke diagnosed, hypertension, and diabetes mellitus were found to be the significant prognostic factors that contribute to shorter survival times among stroke patients. The Weibull accelerated failure time model better described the time to death of the stroke patients' data set than other distributions used in this study. Patients' age, atrial fibrillation, alcohol consumption, being diagnosed with hemorrhagic types of stroke, having hypertension, and having diabetes mellitus were found to be factors shortening survival time to death for stroke patients. Hence, healthcare professionals need to thoroughly follow the patients who pass risk factors. Moreover, patients need to be educated about lifestyle modifications.

The under-five child mortality in sub-Saharan African (sSA) countries is a persistent problem with limited effort being made to explore the determinants of disparities across countries and their lower administrative districts. A child's survival may depend on several known and unknown covariates and vary across the study areas. The main objective of this study is to assess the time to death of under-five children and its associated risk factors by comparing the performance of semiparametric and parametric frailty models across sSA regions. We used a dataset from the Demographic and Health Survey (DHS) across 33 sSA countries. The semiparametric and parametric models with different frailty distributions were used to model the under-five survival time of children across the administrative districts of 33 sSA countries. A total of 330,373 under-five children were included in the study, of whom 19,893 (6.02%) died before reaching their 5th birthday. Unobserved country-level variance [Formula: see text] and district-level variance (0.183) effects considerably impacted the survival time of under-five children in sSA countries. Under-five children born to mothers aged 25-29 and 30-49 were 16% and 20% less likely to die compared to children born to mothers younger than 24 years. Moreover, children born in rural areas were 8.3% more likely to die than those who were born in urban areas. Children who were born from mothers with better access to improved water sources and clean fuel were 9% and 11% less likely to die than their counterparts, respectively. The exponential shared frailty hazard model with lognormal frailty distribution demonstrated better performance compared to the Cox semiparametric model for identifying risk factors for under-five children across sSA countries. Place of residence, wealth index, media exposure, birth order, birth interval, access to improved water, and use of clean fuels for cooking were the significant risk factors on time to death of under-five children in sSA.

Development of spatial statistical methods for modelling point-referenced spatial data in malaria epidemiology

Introduction: Colorectal cancer (CRC) represents the second leading cause of cancer-related mortality. This study focused on the development of a robust conceptual causal model designed to predict early recurrence and mortality following curative surgery in colorectal cancer patients. Methods: In this retrospective cohort study, we included 284 patients with colorectal cancer (CRC) who underwent surgery at the Imam Khomeini (RA) Clinic in Hamadan, Iran, between 2001 and 2017. Demographic characteristics, treatment modalities, and other relevant data were extracted from patient records. Predictors were analyzed using Generalized Structural Equation Modeling (GSEM) for survival analysis, employing an accelerated failure time (AFT) approach. Both unadjusted and adjusted time ratios (TRs) were calculated using STATA software. Results: The results of our developed causal model indicated that receiving chemotherapy was significantly associated with a shorter survival time ratio (TR = 0.415, 95% CI: 0.290-0.593), and recurrence time (TR = 0.363, 95% CI: 0.190-0.696). Conversely, patients who underwent multiple chemotherapy sessions exhibited a longer survival time (TR = 2.130, 95% CI: 1.790-2.534) and recurrence time (TR = 2.206, 95% CI: 1.609- 3.023). Age had a direct impact on the recurrence time (TR = 0.758, 95% CI: 0.602-0.955). Additionally, age had a significant direct effect on the receipt of chemotherapy, the cancer site, and the receipt of radiotherapy. Conclusion: In summary, our study's causal model reveals that chemotherapy shortens survival time but multiple sessions can extend both survival and recurrence times. Age significantly affects recurrence time and chemotherapy receipt. These findings highlight the importance of personalized treatment strategies in colorectal cancer management

Breast cancer is the most common cause of cancer death and is a frequently diagnosed cancer among women worldwide. It is becoming a challenging health condition in Ethiopia with a high rate of morbidity and mortality. The main aim of this study was to model the time to death in breast cancer patients at Hiwot Fana Specialized University Hospital. A retrospective cohort study was carried out from April 1st, 2020, to April 1st, 2023, and 296 women were included in the study. We used nonparametric methods and Bayesian accelerated failure time models (with Laplace approximation) to identify risk factors and choose a model fitting breast cancer patient data. Model comparison was performed using the marginal likelihood, deviance information criterion and Watanabe Akaike information criterion. From the total of 296 patients in the study, 56 (18.9%) died. The estimated median survival time was 33 months. The log-rank test showed that age group, stage, alcohol consumption, smoking habit, and comorbidity were potential risk factors associated with the time to death in breast cancer patients at the 5% level of significance. The Bayesian Weibull accelerated failure time model was found to be the best fitted model for predicting the survival time of patients with minimum DIC (520.39) and WAIC (521.59) values. The final Bayesian Weibull AFT model with the integrated nested Laplace approximation estimation technique revealed that age group, stage, alcohol consumption, smoking habit, and comorbidity were significantly associated with the time to death in breast cancer patients. Individuals older than 65 years, with stage IV disease, drinking alcohol, smoking cigarettes and having comorbidities had shortened survival times in patients with breast cancer. Hence, Hiwot Fana Specialized University Hospital and related bodies should work on awareness creation to reduce smoking habits and alcohol use as well as give due attention to elderly and stage IV breast cancer patients during intervention.

This paper analyzes the effect of socioeconomic and demographic factors on infant mortality in India. The Paper also examines reasonable comparison of the three National Family Health Survey (NFHS), the largest sample survey of India from the 1993–1994, 1998–1999 and 2005–06 with respect to different determinants of infant mortality. Accelerated Failure Time (AFT) model is used to measure the direct effect of the above factors on the survival of infants. On the basis of Akaikes Information Criterion (AIC), the Weibull AFT model fits better to other selected models. In all the three NFHS, Weibull AFT model shows the significant effect of the factors on infant survival.

The objective of this study is to determine the significant predictors of endometrial cancer using accelerated failure time models (AFTM). We have demonstrated the applications of AFTM viz. Exponential, Weibull, Log-normal, Log-logistic, Gompertz, Gamma and Generalized Gamma AFTM, as an alternative of Cox proportional hazard model. Data for the analysis was collected from Acharya Harihar Post Graduate Institute of Cancer (AHPGIC), Cuttack, Odisha during the period 2016–20. Based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) value, the Weibull AFTM has been chosen as the best fitted AFT model. The predictors such as age, comorbidity, tumor size, isolated para-aortic and adnexa have been found as significant predictors (p-value < 0.05) to explain the survival of endometrial cancer patients. Hence, by optimizing different treatments, based on such prognostic factors plays an important role in managing endometrial cancer at an early stage.