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Through the electronic hospital registration system, the records office administrator at HUSM provided the following demographic and administrative variables in electronic format: a) sex, b) age, c) race, d) marital status, e) date of admission, f) date of discharge, g) discharge diagnosis and h) survival outcome on discharge (dead or alive).

The neurology team gave the final diagnosis of stroke (discharge diagnosis) and the coder at the records office coded it based on the ICD-10 criteria. This type of diagnosis is known as administrative diagnosis.

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KIM extracted abstracted the biophysical, medical history and biochemistry data from the case notes. The biophysical data contained the variables a) systolic blood pressure (SBP, mmHg), b) diastolic blood pressure (DBP, mmHg), c) Glasgow Coma Scale (GCS) score and d) capillary blood sugar (mmol/l). The medical history data provided the variables a) history of high blood pressure (yes or no), b) diabetes mellitus (yes or no) and c) abnormal lipid profile (yes or no). The biochemistry data contained the variables a) total white cell (TWC) count, b) haemoglobin (Hb) level (mg/dl), c) platelet count, d) sodium level (mmol/l), e) potassium level (mmol/l) and f) urea level (mmol/l). These variables, especially the biophysical and biochemistry data, are taken as standard diagnostic tests for all patients suspected with acute stroke. Their predictive role in stroke should be assessed to indicate their usability in early stroke care in HUSM setting.

The GCS is used in the acute setting to measure the level of consciousness and is predictive of stroke outcome (Chen et al., 2011, Stroke Unit Trialists, 2007). The GCS score consisted of the best eye, motor and verbal responses, and ranged from a minimum of 3 (worst) to a maximum of 15 (best).

The outcome variable was the time (in days) until an event (death due to stroke during admission). Other outcomes were considered censored observations.

3.3.4

Statistical analysis

We used EpiData Entry (Lauritsen, 2000) for data entry and Stata version 11.2 (StataCorp., 2010) for data cleaning and analyses.

We described the variables on admission using mean (SD) and frequency (%) where appropriate based on the overall patients’ data and then based on survival status at discharge (alive or dead). For comparisons, we used the independent t-test and Pearson chi-square test.

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In assessing survival, the outcome variable was time-to-stroke fatality (event = death) after admission to HUSM. The time was calculated in days (the difference of days between the date of admission and the date of discharge). The event was defined as either failure (death due to stroke, coded as 1) or censored (alive at discharge or death from causes other than stroke, coded as 0).

We used Cox proportional hazard regression—a widely used semi-parametric survival analysis method in medicine—to explore the important prognostic factors for in-hospital stroke case fatality (Hosmer et al., 2013, Hosmer et al., 2011). During model building, we performed the following: a) univariable Cox proportional hazard regression, b) manual selection of variables, c) checking of the functional form of numerical variables, d) checking of interaction between prognostic factors and lastly, e) checking of the assumptions for the hazard proportionality of the chosen model.

Based on univariable analysis (crude), blood pressure, blood count, blood urea, serum electrolytes, capillary blood sugar and marital status were selected for multivariable selection. In multivariable analysis, each candidate variable was added to the model individually. At each step of adding or removing a variable, we performed the likelihood ratio (LR) test, retaining variables with a significance level of less than 2-tailed 0.05. Simultaneously, we examined any change in the coefficient, as a change of 20% or more indicates important confounding effects (Hosmer et al., 2013).

We used fractional polynomials to estimate the relationship between numerical covariates (age and population density) and the outcome (case or control). We did not categorize the numerical covariates because to do so would have reduced the power of analysis and provide less information on the relationship between the covariates and the outcome (Royston and Sauerbrei, 2004, Royston and Sauerbrei, 2005). Fractional polynomials also improve model fit and provide more realistic non-linear relationship if the model (Wong

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et al., 2011, Royston and Sauerbrei, 2013, Royston and Sauerbrei, 2005, Royston and Sauerbrei, 2004, Royston and Altman, 1994). We checked the functional form of two numerical variables: a) age and b) GCS score, using fractional polynomial (FP) analysis (Wong et al., 2011, Hosmer et al., 2011, Hosmer et al., 2013, Royston and Altman, 1994, Sauerbrei et al., 2007, Royston et al., 2006). FP determines whether the variables age and GCS score should enter the model in their linear or transformed form. In Stata, the mfp function executes FP analysis. Three models were analysed: 1) the null model, 2) the untransformed model (age and GCS score in linear form) and 3) transformed model (age and GCS score transformed by mfp). In our model, both GCS score and age were best presented in their untransformed (linear) form.

We generated a 2-way interaction term between age and GCS score but the product term was not statistically significant (p = 0.294), hence it was excluded from the model.

We tested the proportionality assumption—the estimated hazard does not depend on time—for Cox hazard regression using Schoenfeld residuals. Using these residuals, we performed two tests: a) the ‘global test’ (the overall model test), and b) the ‘detail test’ (test for each numerical covariate in the model). Our chosen model passed both tests.

When performing survival analysis for our data, we assumed our data fulfilled these three assumptions about censoring (for examples to those censored because they were discharged well) : a) independent censoring, b) random censoring and, c) non-informative censoring (Resche-Rigon et al., 2006, Kleinbaum and Klein, 2012). The common analyses of survival data using the Kaplan-Meier method and the Cox regression model will provide bias results when these assumptions are violated. Specifically, in the case of informative censoring, censored observations provide important relationships between censoring and the outcome of interest (the remaining survival time). When informative censoring is suspected, the imputation method for missing observations and sensitivity analysis to estimate the models in

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various scenarios can be performed. Further details of imputation and sensitivity analysis are provided in Appendix H. The presence of shared dependencies between the covariates and the outcomes is one of the ways to support the presence of non-informative censoring. The methods to assess it and the results obtained from the assessments are shown in Appendix H.