Bayesian frailty modeling of correlated survival data with application to under-five mortality

Alotaibi, Refah; Rezk, Hoda; Guure, Chris

doi:10.1186/s12889-020-09328-7

Cited by 10 publications

(8 citation statements)

References 34 publications

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“…Step Ten: Model comparison was carried out using the deviance information criteria (DIC), which is the Bayesian version of the frequentist Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), and the Bayes factors (BF). A model with the smallest DIC value and BF>1 was selected as the best-fitted model ( Table 3 ) [ 58 ].…”

Section: Methodsmentioning

confidence: 99%

Cause and risk factors of early neonatal death in Ethiopia

et al. 2022

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Background Globally, three fourth of neonatal deaths occur during the early neonatal period, this makes it a critical time to reduce the burden of neonatal death. The survival status of a newborn is determined by the individual (neonatal and maternal), and facility-level factors. Several studies were conducted in Ethiopia to assess early neonatal death; however, most of the studies had limited participants and did not well address the two main determinant factors covered in this study. In response to this gap, this study attempted to examine factors related to early neonatal death based on perinatal death surveillance data in consideration of all the possible determinants of early neonatal death. Methods The national perinatal death surveillance data were used for this study. A total of 3814 reviewed perinatal deaths were included in the study. Bayesian multilevel parametric survival analysis was employed to identify factors affecting the survival of newborns during the early neonatal period. Adjusted time ratio (ATR) with 95% Bayesian credible intervals (CrI) was reported and log-likelihood was used for model comparison. Statistical significance was declared based on the non-inclusion of 1.0 in the 95% CrI. Result More than half (52.4%) of early neonatal deaths occurred within the first two days of birth. Per the final model, as gestational age increases by a week the risk of dying during the early neonatal period is reduced by 6% [ATR = 0.94,95%CrI:(0.93–0.96)]. There was an increased risk of death during the early neonatal period among neonates deceased due to birth injury as compared to neonates who died due to infection [ATR = 2.05,95%CrI:(1.30–3.32)]; however, perinates who died due to complication of an intrapartum event had a lower risk of death than perinates who died due to infection [ATR = 0.87,95%CrI:(0.83–0.90)]. As the score of delay one and delay three increases by one unit, the newborn’s likelihood of surviving during the early neonatal period is reduced by 4% [ATR = 1.04,95%CrI:(1.01–1.07)] and 21% [ATR = 1.21,95%CrI:(1.15–1.27)] respectively. Neonates born from mothers living in a rural area had a higher risk of dying during the early neonatal period than their counterparts living in an urban area [ATR = 3.53,95%CrI:(3.34–3.69)]. As compared to neonates treated in a primary health facility, being treated in secondary [ATR = 1.14,95%CrI:(1.02–1.27)] and tertiary level of care [ATR = 1.15,95%CrI:(1.04–1.25)] results in a higher risk of death during the early neonatal period. Conclusion The survival of a newborn during the early neonatal period is determined by both individual (gestational age, cause of death, and delay one) and facility (residence, type of health facility and delay three) level factors. Thus, to have a positive early neonatal outcome, a tailored intervention is needed for the three major causes of death (i.e Infection, birth injury, and complications of the intrapartum period). Furthermore, promoting maternal health, improving the health-seeking behaviour of mothers, strengthening facility readiness, and narrowing down inequalities in service provision are recommended to improve the newborn’s outcomes during the early neonatal period.

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Section: Methodsmentioning

confidence: 99%

Cause and risk factors of early neonatal death in Ethiopia

et al. 2022

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“…In addition, let the random variable be the frailty effect of the cluster with known distribution function . Conditional on the frailty variable and covariates , the survival function at time of the observation of cluster , is given by (18,20,21); [7] where, is the cumulative baseline hazard function which needs to be specified for generating survival data, and represents the unknown regression parameters. For invertible ,the failure times can be computed by [8] where, and is the failure time of member of cluster .…”

Section: Shared Frailty Modelmentioning

confidence: 99%

“…Like the Gamma frailty model, simple closed-form expressions exist for the unconditional survival and hazard functions, which makes the model more plausible. The probability density function of an inverse Gaussian distributed frailty random variable with parameters µ > 0 and is given by (18)(19)(20)(21)(22), [13] where, the Laplace transform of this function has a form [14] Reporting estimates from statistical analysis presented…”

Section: A Shared Gamma Frailty Modelmentioning

confidence: 99%

Parametric Bayesian Modelling of Tuberculosis Mortality determinants and Facility level heterogeneity effect using Gamma and Gaussian shared frailty techniques

Fwemba

Ngah

Rangoanana

et al. 2022

Preprint

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Background: In a normal regression analysis for determinants of TB outcomes, assumptions that the sample is homogenous is made. This model does not account for the overall effect of unobserved or unmeasured covariates. This study aims to quantify the amount of heterogeneity that exists at community level, and to ascertain the determinants of TB mortality across all the catchment areas in Lesotho. Methods: This was a retrospective record review of patients on TB treatment registered between January 2015 to December 2020 at 12 health care facilities in the district of Butha Buthe, Lesotho. Data collected from patient medical and statistical analysis was performed using R and INLA statistical software. Descriptive statistics were presented using frequency tables. Differences between binary outcomes were analysed using Person's X2 test. Mixed effect model with five Bayesian regression models of varying distributions were used to assess heterogeneity at facility level. Kaplan-Meier curves were used to demonstrate time-to-death events. Results: The total number of patients included in the analysis were 1729 of which 70% were males. And half of them were employed (54.2%). Being over 60 years (HR: 0.02, Cl: 0.01-0.04) and having a community health worker as a treatment contact person (HR: 0.36, Cl: 0.19-0.71) decreased the risk of dying. Miners had 1.73 times increased risk of dying (HR: 1.73, Cl: 1.07-2.78). The frailty variance was observed to be very minimal (<0.001), but significant indicating heterogeneity between catchment areas. Although similar hazard ratios and confidence intervals of covariates are seen between Gamma and Gaussian frailty log-logistic models, the credibility intervals for the Gamma model are consistently narrower. Conclusion: The results from both Gamma and Gaussian demonstrate that heterogeneity affected significance of the determinants for TB mortality. The results showed community level to significantly affect the risk of dying indicating differences between catchment areas.

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“…Notably, there is a dearth of research on the determinants of multimorbidity of infectious diseases among Bangladeshi children. Additionally, the role of the community behind the occurrence of multimorbidity needs to be explored as children residing in the same community often share similar characteristics [ 22 , 23 ]. This necessitates understanding the relationship of community-level contextual factors with the multimorbidity of infectious diseases in children.…”

Section: Introductionmentioning

confidence: 99%

Determinants of multimorbidity of infectious diseases among under-five children in Bangladesh: role of community context

Rashmi

Paul

2022

BMC Pediatr

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Background The presence of more than one morbid condition among children has become a global public health concern. Studies carried out in Bangladesh have primarily focused on diarrhoea and acute respiratory tract infections independently without considering their co-occurrence effect. The present study examines the multimorbid conditions of infectious diseases in under-five Bangladeshi children. It explores multimorbidity determinants and the role of community context, which are often overlooked in previous literature. Methods Utilizing the most recent Demographic and Health Survey of Bangladesh (2017–18), we used mixed-effects random-intercept Poisson regression models to understand the determinants of multimorbidity of infectious diseases in under-five Bangladeshi children considering the community-level characteristics. Results The present study found that 28% of the children experienced multimorbidity two weeks prior to the survey. Community-level variability across all the statistical models was statistically significant at the 5% level. On average, the incidence rate of multimorbidity was 1.34 times higher among children from high-risk communities than children from low-risk communities. Moreover, children residing in rural areas and other urban areas involved 1.29 [CI: 1.11, 1.51] and 1.28 [CI: 1.11, 1.47] times greater risk of multimorbidity respectively compared to children from city corporations. Additionally, the multimorbidity incidence was 1.16 times [CI: 1.03, 1.30] higher among children from high-altitude communities than children living in low-altitude communities. Conclusion The significant effect of public handwashing places suggests community-based interventions among individuals to learn hygiene habits among themselves, thus, the severity of coexistence nature of infectious diseases. A higher incidence of coexistence of such infectious diseases in the poor and semi-urban populace further recommends a targeted awareness of a clean environment and primary healthcare programmes.

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Bayesian frailty modeling of correlated survival data with application to under-five mortality

Cited by 10 publications

References 34 publications

Cause and risk factors of early neonatal death in Ethiopia

Cause and risk factors of early neonatal death in Ethiopia

Parametric Bayesian Modelling of Tuberculosis Mortality determinants and Facility level heterogeneity effect using Gamma and Gaussian shared frailty techniques

Determinants of multimorbidity of infectious diseases among under-five children in Bangladesh: role of community context

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