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Analysis of Health System Delays Including Spatial Factors
for Tuberculosis Cases in Kaohsiung, Taiwan
tuberculosis,spatial analysis,health system delays,
|Publication Year :||2021|
Tuberculosis (TB) remains a major source of morbidity and mortality both globally and in Taiwan. Delays in identify, diagnosing, and treating cases is a challenge to effective tuberculosis control and slows the goal of eventual elimination. Previous studies in Taiwan and internationally have analyzed the issue of delays in the context of patient delays and health system delays, but few previous studies have subdivided health system delays into the period of time between when the medical and public health system first begins suspecting TB as the causative agent and the patient starting treatment. This study aims to analyze the distribution and causes of this type of suspicion to action health system delay. This study contributes to the literature in important way because it is one of the few studies on TB treatment delays to use explicitly spatial methods to investigate if delays are heterogeneously distributed.
The study population was all culture confirmed TB cases in Kaohsiung (a major city in southern Taiwan) who were notified between Jan 1-Dec 31 2019 (n=1193). The data was collected by the Kaohsiung Health department and consisted of all reported cases who had a home address in Kaohsiung City Taiwan. Additional medical and epidemiological data including age, sex, dates of diagnostic tests and test results and patient home address were collected by the Kaohsiung City Health Department.
The main exposure of interest was the patient’s home address to see if where the patient resided was predictive of the length of delay between suspicion and treatment. The outcome of interest was defined as the delay between when a TB case was suspected by the health system as a suspected TB cases as defined by a patient: 1. Being reported as a suspected TB cases 2. starting TB treatment 3. enrolling in DOTS or 4. having a chest X-ray, TB smear, or TB culturing ordered and when the patient started treatment.
Spatial predictors of delay were analyzed using the patients’ home addresses. Global and local Moran’s I calculations were performed to see if the median delay within administrative units (Taiwanese districts and lis) were spatially clustered with nearby administrative units having similar values or if delay values were distributed in a spatially random way. P-values were adjusted using the Bonferroni method and the method described in Benjamini (1995). Spatial autorgression using a simultaneous autoregressive model (SAR) was done to see if a patient’s home address was associated with an increased risk of delay. A model was construced using simple linear regression to look at the association between factors such as the patien’ts age, sex, the first type of TB suspicion, and distance from home to hospital, district level factors where they lived such as urbanization level and hospital bed density. This model was then analyzed using analysis of variance (ANOVA) to identify variables that were significant at a level of less than or equal to 0.05 and that had an F-statistic value equal to or greater than three. These variables were then used to construct a second simple linear model and calculate odds ratios.
There did not appear to be spatial heterogeneity in delays beyond what might be expected by random processes. Global Moran’s I did not reveal any statistically significant spatial heterogeneity for median delay among districts (I= -0.035, observed rank = 4805, p-value=0.5200) nor any significant spatial heterogeneity for median delay among lis (I=-0.006, observed rank = 4138, p-value= 0.5862) . Spatial autoregression did not identify a significant spatial component as a predictor of average delay for districts (lambda= 0.02, CI= -0.44 to 0.49) nor for lis (lambda=0.04, CI:-0.06 to 0.14) There was significant variation among cases in terms of how long delays lasted even without considering chest x-rays. Patients with delays under 20 days were considerably more likely to be smear positive than (67.6%) than patients with delays equal to or over 20 days (20.5%) (p-value <0.001). For the linear regression model, the first suspicion action being an x-ray (OR= 4.65, CI: 3.36 to 6.44, p-value=0.004) and distance from home to treatment hospital (OR=1.13, CI: 1.05 to 1.21, p-value=0.0006) were associated with a higher risk of delay compared to the reference variables with while smear positivity (OR <0.0001, CI: <0.0001 to 0.0008, p-value<0.0001) was associated with a lower likelihood of delay. The urbanization level and the doctor and hospital density of the district where the patient lived were not associated with an increased risk.
Spatial factors did not appear to have a statically significant association with increased delay for this study population at the level of the individual patient’s home address nor at the level of district of Li. Delays appear to be most extreme among culture negative cases who are either waiting on culture results or follow up on an abnormal chest X-ray. New methods such as PCR screening or administrative to change X-ray follow up procedures may reduce the delays that culture positive/smear negative TB cases experience and reduce the amount of time that they are out in the community untreated and infectious.
|Appears in Collections:||全球衛生碩士/博士學位學程|
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