Determining length of stay patterns in psychiatric Care: a survival analysis approach at Kisumu County Referral hospital

Abstract

A key indicator in healthcare management is length of stay (LOS), which measures how long a patient stays in the hospital from the time of admission until they are discharged. Statistics from Kenya National Commission of Human Rights shows approximately 40% of inpatients and 25% of outpatients are affected by disorders in relation to psychological well-being. Kisumu County Referral Hospital (KCRH) is a key provider of mental healthcare services in western Kenya. Nonetheless, there is insufficient data analysis in regard to patient flow and length of stay within the psychiatric unit. This hinders effective resource allocation, capacity planning, and eventually the care standards administered to the victims. The purpose of this study was to ascertain how long mental patients stayed at the Kisumu County Referral Hospital using both survival and regression analysis modellings. Specifically, the research was aimed at fitting Kaplan-Meier model of survival to the data pertaining length of stay for psychiatric patients at Kisumu County Referral Hospital; analyze the impact of covariates on the survival function related to psychiatric patients and compare the Cox regression hazard model with a linear regression model of length of stay for psychiatric patients at Kisumu County Referral Hospital, incorporating covariates to assess differences in predictive performance. The study used monthly secondary data spanning over 60 months between 2018 and 2022, sourced from psychiatric patients’ records from Kisumu County Referral Hospital using a secondary data capture form. On fitting the Kaplan-Meir survival model, the study found that the overall median survival time (time to discharge) was 8 days. No difference was attained in duration taken to discharge males and females (p=0.66). Additionally, the study found that the effect of several plausible interactions such as age and gender, diagnosis and treatment, age and diagnosis but their inclusion was not justified as none of them was significant, the concordance index and Akaike Information Criterion (AIC) only improved by a negligible margin. On comparing the survival model to linear model, the study established that the findings from cox regression and linear regression led to the same conclusions. However, the association with the form of treatment was slightly different. The study concluded that survival analysis models are superior for data sets where survival time is the primary outcome, requiring careful consideration of censoring and time effects. The study therefore suggested that policymakers should promote the use of survival analysis to determine patients’ hospital length of stay.