Competing risk model for dengue epidemiology in Sri Lanka: modeling length of stay in hospital

Karunarathna, G. H. S.; Sooriyarachchi, M. R.

doi:10.4038/tar.v28i2.8197

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In a recent study, factors related to the length of stay (LOS) in the hospital for dengue patients in Sri Lanka using a proportional hazards survival model was analysed by Karunarathna and Sooriyarachchi [10]. These factors were age, sex, ethnicity, place treated initially (private, government hospital, etc.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Aswi

Cramb

Duncan

et al. 2020

IJERPH

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Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay.

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Section: Introductionmentioning

confidence: 99%

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Aswi

Cramb

Duncan

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…This table shows that standard deviation of the random effect (district clustering effect (RI.sd)), residual (Res.Sd) and scale parameter (λ). The equation 12- (14) with equation (15), emphasizes that month, sex and place treated is only associated with the log of platelet count. Age is negatively associated with the discharge patients while positively associated with transfer patients.…”

Section: Analysis Results and Discussionmentioning

confidence: 99%

“…The relation between LOS and platelet count of a dengue patient is motivatated by the study in past literature in order to evaluate the dengue patients (Pooransingh, Teeluksingh, and Dialsingh, 2016;Karunarathna and Sooriyarachchi, 2017). The joint association was built through the competing risk event response for the LOS of dengue patients and continuous response for platelet count after taking the logorithm of that variable.…”

Section: Introductionmentioning

confidence: 99%

Joint Multilevel Model for Analyzing Length of Stay through Competing Endpoints in Dengue Epidemiology

Karunarathna

Sooriyarachchi

2019

Sri Lankan J App Stats

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Dengue is a common mosquito-borne tropical disease caused by a virus. It is a life threatening disease since it sometimes leads to death within a short period of time. Multilevel modeling is a form of statistical modeling when data is at different levels. Due to dengue seriousness and risk being more similar for patients within districts than between districts, there is correlation between patients within districts. Thus district has to be taken as a cluster variable. A frequently encountered response in epidemiological studies is the length of Stay (LOS) of a patient, which measures the time until the event of interest occurs. Complexity arises with the different states/destinations of the time event and competing risk modelling is a more appropriate method for handling such states. The association of platelet count and length of stay of a dengue patient leads to the joint modelling approach for analyzing the dengue patients. Formulation criteria for the joint model with clustered data is to link these models through two sub models that is by using the multilevel multinomial logistic model for the LOS of dengue patients with different destinations and multilevel continuous model for the log platelet count. The linkage between two responses was derived by sharing a common random effect. Factors that have an effect on different destinations of LOS are, time indicators, year, age, classification, rainfall, temperature and humidity, while age, sex, classification, year place treated, rainfall, temp and humidity are associated factors for the log platelet count of dengue patients. Moreover, supremacy of joint modelling was proved by the AIC and BIC values over two separate univariate models.

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Multilevel joint competing risk models

Karunarathna

Sooriyarachchi

2017

J. Phys.: Conf. Ser.

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Competing risk model for dengue epidemiology in Sri Lanka: modeling length of stay in hospital

Abstract: ABSTRACT. This paper focuses on exploring methods

Cited by 3 publications

References 14 publications

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Joint Multilevel Model for Analyzing Length of Stay through Competing Endpoints in Dengue Epidemiology

Multilevel joint competing risk models

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