A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods

Abstract

Background: The incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different diseases or settings. Administrative and other real-world data sources may offer the ability to better identify and study diagnostic delays for a range of diseases.

Methods: We propose a comprehensive framework to estimate the frequency of missed diagnostic opportunities for a given disease using real-world longitudinal data sources. We provide a conceptual model of the disease-diagnostic, data-generating process. We then propose a bootstrapping method to estimate measures of the frequency of missed diagnostic opportunities and duration of delays. This approach identifies diagnostic opportunities based on signs and symptoms occurring prior to an initial diagnosis, while accounting for expected patterns of healthcare that may appear as coincidental symptoms. Three different bootstrapping algorithms are described along with estimation procedures to implement the resampling. Finally, we apply our approach to the diseases of tuberculosis, acute myocardial infarction, and stroke to estimate the frequency and duration of diagnostic delays for these diseases.

Results: Using the IBM MarketScan Research databases from 2001 to 2017, we identified 2,073 cases of tuberculosis, 359,625 cases of AMI, and 367,768 cases of stroke. Depending on the simulation approach that was used, we estimated that 6.9-8.3% of patients with stroke, 16.0-21.3% of patients with AMI and 63.9-82.3% of patients with tuberculosis experienced a missed diagnostic opportunity. Similarly, we estimated that, on average, diagnostic delays lasted 6.7-7.6 days for stroke, 6.7-8.2 days for AMI, and 34.3-44.5 days for tuberculosis. Estimates for each of these measures was consistent with prior literature; however, specific estimates varied across the different simulation algorithms considered.

Conclusions: Our approach can be easily applied to study diagnostic delays using longitudinal administrative data sources. Moreover, this general approach can be customized to fit a range of diseases to account for specific clinical characteristics of a given disease. We summarize how the choice of simulation algorithm may impact the resulting estimates and provide guidance on the statistical considerations for applying our approach to future studies.

Keywords: Acute myocardial infarction; Delayed diagnosis; Diagnosis; Diagnostic errors; Epidemiologic methods; Stroke; Tuberculosis.

Fig. 1

Count of SSD-related visits prior to index tuberculosis diagnosis aggregated across all patients with tuberculosis diagnosis

Fig. 2

Diagram of conceptual framework representing the number of missed diagnostic opportunities. The diagnostic opportunity window represents the period of time where diagnostic opportunities may occur. The red line depicts the trend in the number of SSD visits that would be expected to occur in absence of diagnostic delays. The blue curve represents the observed trend in SSD visits during the diagnostic opportunity window. The shaded blue region corresponds to the number of missed diagnostic opportunities. The shaded red region corresponds to the number of expected SSD visits

Fig. 3

Simple algorithm to simulated missed opportunities using uncorrelated draws

Fig. 4

An algorithm to draw patients with preference given to patients previously drawn

Fig. 5

A generalized algorithm to draw patients with preference to previously drawn patients and those with multiple symptoms

Fig. 6

Estimating expected number of visits using linear (left) or exponential (right) curves to represent the expected number of SSD visits

Fig. 7

Counts of SSD visits each day prior to the index diagnosis. For each disease of interest there is an upward spike in the occurrence of healthcare visits with SSDs in the period just preceding the index diagnosis. The black vertical line represents the estimated change-point separating the diagnostic opportunity window from the prior crossover period. The red line represents the expected level of healthcare utilization (i.e., estimated to occur in absence of diagnostic delays)