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Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study

机译:预测患者和私人提供者行为对印度肺结核患者诊断延迟的影响:仿真建模研究

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Background Tuberculosis (TB) incidence in India continues to be high due, in large part, to long delays experienced by patients before successful diagnosis and treatment initiation, especially in the private sector. This diagnostic delay is driven by patients’ inclination to switch between different types of providers and providers’ inclination to delay ordering of accurate diagnostic tests relevant to TB. Our objective is to quantify the impact of changes in these behavioral characteristics of providers and patients on diagnostic delay experienced by pulmonary TB patients. Methods and findings We developed a discrete event simulation model of patients’ diagnostic pathways that captures key behavioral characteristics of providers (time to order a test) and patients (time to switch to another provider). We used an expectation-maximization algorithm to estimate the parameters underlying these behavioral characteristics, with quantitative data encoded from detailed interviews of 76 and 64 pulmonary TB patients in the 2 Indian cities of Mumbai and Patna, respectively, which were conducted between April and August 2014. We employed the estimated model to simulate different counterfactual scenarios of diagnostic pathways under altered behavioral characteristics of providers and patients to predict their potential impact on the diagnostic delay. Private healthcare providers including chemists were the first point of contact for the majority of TB patients in Mumbai (70%) and Patna (94%). In Mumbai, 45% of TB patients first approached less-than-fully-qualified providers (LTFQs), who take 28.71 days on average for diagnosis. About 61% of these patients switched to other providers without a diagnosis. Our model estimates that immediate testing for TB by LTFQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 35.53 days (95% CI: 34.60, 36.46) to 18.72 days (95% CI: 18.01, 19.43). In Patna, 61% of TB patients first approached fully qualified providers (FQs), who take 9.74 days on average for diagnosis. Similarly, immediate testing by FQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 23.39 days (95% CI: 22.77, 24.02) to 11.16 days (95% CI: 10.52, 11.81). Improving the diagnostic accuracy of providers per se, without reducing the time to testing, was not predicted to lead to any reduction in diagnostic delay. Our study was limited because of its restricted geographic scope, small sample size, and possible recall bias, which are typically associated with studies of patient pathways using patient interviews. Conclusions In this study, we found that encouraging private providers to order definitive TB diagnostic tests earlier during patient consultation may have substantial impact on reducing diagnostic delay in these urban Indian settings. These results should be combined with disease transmission models to predict the impact of changes in provider behavior on TB incidence.
机译:背景技术印度的结核病(TB)发病率在很大程度上,患者在成功诊断和治疗开始之前经历的长期延迟,特别是在私营部门。这种诊断延迟由患者倾向于在不同类型的提供商和提供商之间切换,以延迟与TB相关的准确诊断测试的排序。我们的目标是量化提供者和患者对肺结核患者诊断延迟的这些行为特征的影响。方法和结果我们开发了一个离散事件仿真模型的患者诊断途径,捕获提供者的关键行为特征(订购测试的时间)和患者(时间切换到另一个提供者)。我们利用期望最大化算法来估计这些行为特征的基础的参数,分别从孟买和巴特纳的2个印度城市的详细访谈中编码了76个肺结核患者的定量数据,这是在2014年4月和8月之间进行的。我们雇用了估计的模型来模拟提供者和患者的行为特征的改变行为特征下的诊断途径的不同反应场景,以预测其对诊断延迟的潜在影响。包括化学家在内的私人医疗保健提供者是孟买(70%)和髌骨(94%)的大多数TB患者的第一个接触点。在孟买,45%的TB患者首先接近较少合格的提供者(LTFQ),平均每年服用28.71天进行诊断。约有61%的这些患者切换到其他提供商,没有诊断。我们的模型估计,首次访问(目前的诊断准确率下)对TB的直接测试可能会降低35.53天的平均诊断延迟(95%CI:34.60,36.46)至18.72天(95%CI:18.01 ,19.43)。在Patna中,61%的TB患者首先接近完全合格的提供者(FQS),平均需要9.74天进行诊断。同样,在第一次访问的FQS(目前的诊断精度水平)立即测试可能会降低23.39天(95%CI:22.77,24.02)至11.16天(95%CI:10.52,11.81)的平均诊断延迟。不预测,提高提供者本身的诊断准确性,而不需要减少测试时间,导致诊断延迟的任何降低。我们的研究由于其受限的地理范围,小样本大小和可能的召回偏差而受到限制,这通常与使用患者访谈的患者途径的研究相关。结论在本研究中,我们发现,鼓励私人提供商在患者咨询期间更早地命令明确的结核病诊断测试可能会对降低这些城市印度环境中的诊断延误产生大量影响。这些结果应与疾病传输模型相结合,以预测TB发病率对提供者行为变化的影响。

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