A Joint modelling approach of monthly anthropometry and time to death among hospitalized severe malnourished children in Kenya
Maronga, Christopher Sianyo
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Background: In follow up studies, interest often lies in understanding the association between biomarkers measured over time and a time-to-event outcome. For this, a two-stage separate analysis or the use of time-dependent Cox models are often used. The former approach does not account for shared features between the two processes while the latter ignores the indigeneity in the biomarker, resulting in inefficient and biased estimates. The objective of this project was to _x joint models on longitudinal anthropometry and time to death among children hospitalized with complicated SAM in four hospitals in Kenya. Methods: Data from a randomised placebo-controlled trial for 1,778 children aged 2 to 59 months admitted to hospital with complicated Severe Acute Malnutrition (SAM) but without HIV was analysed. We used Linear mixed effects models to model longitudinal anthropometry and Cox proportional hazards model to assess the effect of a priori selected baseline covariates on mortality. The two models were linked through current value and slope association to create a joint model used to study the effect of longitudinal anthropometry on risk of death. Results: The joint model results showed that a unit centimetre gain in monthly midupper arm circumference (MUAC) was associated with 46.8% reduction in hazard of death, 0.532(95% CI: 0.476-0.596), while a unit gain in standard deviation (SD) for weight-forheight WHZ) was associated with 37.1% reduction in the risk of death, 0.629(95% CI:0.579- 0.683). A unit gain in SD for monthly weight-for-age (WAZ) and height-for-age (HAZ) was associated with 21.2%, 0.788(95% CI: 0.742-0.837) and 2.5%, 0.227(95% C.I: 0.008 - 6.556) reduction in risk of mortality respectively. Conclusion: In studying the relationship between survival outcome and covariates, researchers often use baseline values of the covariates which fails to account for the interdependencies. Using joint modelling framework, we quantified the association between four longitudinal anthropometry and risk of death. Through current value and slope association, MUAC and WHZ have the strongest association with risk of death respectively hence are better metrics and can be used to screen and identify high-risk children.