The Increase in Household Transmission of Pertussis Associated With Diagnostic Delays.

BACKGROUND: Pertussis is a highly contagious respiratory illness that can be especially dangerous to young children. Transmission of pertussis often occurs in household settings and is impacted by the timing of treatment and postexposure chemoprophylaxis. This study analyzes the risk for secondary household transmission and if delays in diagnosing pertussis increased the risk for household transmission. METHODS: We conducted 2 population-based studies using a large nationally representative administrative claims database. The first study utilized a stratified monthly incidence model to compare the incidence of pertussis among enrollees exposed to a family member with pertussis versus those not exposed. The second study was conducted at a household level following the index case of pertussis in each household. We identified diagnostic delays in the initial household case and used a logistic regression model to evaluate if such delays were associated with a greater risk for transmission. RESULTS: The incidence rate ratio of pertussis was 938.99 [95% confidence interval (CI): 880.19-1001.73] among enrollees exposed to a family member with pertussis relative to those not exposed. The odds of secondary household transmission in households where the index case experienced a diagnostic delay was 5.10 (CI: 4.44-5.85) times the odds of transmission when the index case was not delayed. We found that longer delays were associated with a greater risk for secondary household transmission (P < 0.0001). CONCLUSIONS: There is a high rate of secondary transmission of pertussis in household settings. Diagnostic delays increase the likelihood that pertussis will transmit in the household.

Comparing multiple infection control measures in a nursing home setting: a simulation study.

OBJECTIVE: Compare the effectiveness of multiple mitigation measures designed to protect nursing home residents from infectious disease outbreaks. DESIGN: Agent-based simulation study. SETTING: Simulation environment of a small nursing home. METHODS: We collected temporally detailed and spatially fine-grained location information from nursing home healthcare workers (HCWs) using sensor motes. We used these data to power an agent-based simulation of a COVID-19 outbreak using realistic time-varying estimates of infectivity and diagnostic sensitivity. Under varying community prevalence and transmissibility, we compared the mitigating effects of (i) regular screening and isolation, (ii) inter-resident contact restrictions, (iii) reduced HCW presenteeism, and (iv) modified HCW scheduling. RESULTS: Across all configurations tested, screening every other day and isolating positive cases decreased the attack rate by an average of 27% to 0.501 on average, while contact restrictions decreased the attack rate by an average of 35%, resulting in an attack rate of only 0.240, approximately half that of screening/isolation. Combining both interventions impressively produced an attack rate of only 0.029. Halving the observed presenteeism rate led to an 18% decrease in the attack rate, but if combined with screening every 6 days, the effect of reducing presenteeism was negligible. Altering work schedules had negligible effects on the attack rate. CONCLUSIONS: Universal contact restrictions are highly effective for protecting vulnerable nursing home residents, yet adversely affect physical and mental health. In high transmission and/or high community prevalence situations, restricting inter-resident contact to groups of 4 was effective and made highly effective when paired with weekly testing.

Cystic Fibrosis Carrier States Are Associated With More Severe Cases of Bronchiectasis.

Background: People with cystic fibrosis (CF) are at increased risk for bronchiectasis, and several reports suggest that CF carriers may also be at higher risk for developing bronchiectasis. The purpose of this study was to determine if CF carriers are at risk for more severe courses or complications of bronchiectasis. Methods: Using MarketScan data (2001-2021), we built a cohort consisting of 105 CF carriers with bronchiectasis and 300 083 controls with bronchiectasis but without a CF carrier diagnosis. We evaluated if CF carriers were more likely to be hospitalized for bronchiectasis. In addition, we examined if CF carriers were more likely to be infected with Pseudomonas aeruginosa or nontuberculous mycobacteria (NTM) or to have filled more antibiotic prescriptions. We considered regression models for incident and rate outcomes that controlled for age, sex, smoking status, and comorbidities. Results: The odds of hospitalization were almost 2.4 times higher (95% CI, 1.116-5.255) for CF carriers with bronchiectasis when compared with non-CF carriers with bronchiectasis. The estimated odds of being diagnosed with a Pseudomonas infection for CF carriers vs noncarriers was about 4.2 times higher (95% CI, 2.417-7.551) and 5.4 times higher (95% CI, 3.398-8.804) for being diagnosed with NTM. The rate of distinct antibiotic fill dates was estimated to be 2 times higher for carriers as compared with controls (95% CI, 1.735-2.333), and the rate ratio for the total number of days of antibiotics supplied was estimated as 2.8 (95% CI, 2.290-3.442). Conclusions: CF carriers with bronchiectasis required more hospitalizations and more frequent administration of antibiotics as compared with noncarriers. Given that CF carriers were also more likely to be diagnosed with Pseudomonas and NTM infections, CF carriers with bronchiectasis may have a phenotype more resembling CF-related bronchiectasis than non-CF bronchiectasis.

Diurnal temperature variation and the implications for diagnosis and infectious disease screening: a population-based study.

OBJECTIVES: Fevers have been used as a marker of disease for hundreds of years and are frequently used for disease screening. However, body temperature varies over the course of a day and across individual characteristics; such variation may limit the detection of febrile episodes complicating the diagnostic process. Our objective was to describe individual variation in diurnal temperature patterns during episodes of febrile activity using millions of recorded temperatures and evaluate the probability of recording a fever by sex and for different age groups. METHODS: We use timestamped deidentified temperature readings from thermometers across the US to construct illness episodes where continuous periods of activity in a single user included a febrile reading. We model the mean temperature recorded and probability of registering a fever across the course of a day using sinusoidal regression models while accounting for user age and sex. We then estimate the probability of recording a fever by time of day for children, working-age adults, and older adults. RESULTS: We find wide variation in body temperatures over the course of a day and across individual characteristics. The diurnal temperature pattern differed between men and women, and average temperatures declined for older age groups. The likelihood of detecting a fever varied widely by the time of day and by an individual's age or sex. CONCLUSIONS: Time of day and demographics should be considered when using body temperatures for diagnostic or screening purposes. Our results demonstrate the importance of follow-up thermometry readings if infectious diseases are suspected.

Association between household opioid prescriptions and risk for overdose among family members not prescribed opioids.

BACKGROUND: Prescription opioids have contributed to the rise in opioid-related overdoses and deaths. The presence of opioids within households may increase the risk of overdose among family members who were not prescribed an opioid themselves. Larger quantities of opioids may further increase risk. OBJECTIVES: To determine the risk of opioid overdose among individuals who were not prescribed an opioid but were exposed to opioids prescribed to other family members in the household, and evaluate the risk in relation to the total morphine milligram equivalents (MMEs) present in the household. METHODS: We conducted a cohort study using a large database of commercial insurance claims from 2001 to 2021. For inclusion in the cohort, we identified individuals not prescribed an opioid in the prior 90 days from households with two or more family members, and determined the total MMEs prescribed to other family members. Individuals were stratified into monthly enrollment strata defined by household opioid exposure and other confounders. A generalized linear model was used to estimate incidence rate ratios (IRRs) for overdose. RESULTS: Overall, the incidence of overdose among enrollees in households where a family member was prescribed an opioid was 1.73 (95% confidence interval [CI]: 1.67-1.78) times greater than households without opioid prescriptions. The risk of overdose increased continuously with the level of potential MMEs in the household from an IRR of 1.23 (95% CI: 1.16-1.32) for 1-100 MMEs to 4.67 (95% CI: 4.18-5.22) for >12,000 MMEs. The risk of overdose associated with household opioid exposure was greatest for ages 1-2 years (IRR: 3.46 [95% CI: 2.98-4.01]) and 3-5 years (IRR: 3.31 [95% CI: 2.75-3.99]). CONCLUSIONS: The presence of opioids in a household significantly increases the risk of overdose among other family members who were not prescribed an opioid. Higher levels of MMEs, either in terms of opioid strength or quantity, were associated with increased levels of risk. Risk estimates may reflect accidental poisonings among younger family members.

Comparison of Different Antibiotics and the Risk for Community-Associated Clostridioides difficile Infection: A Case-Control Study.

Background: Antibiotics are the greatest risk factor for Clostridioides difficile infection (CDI). Risk for CDI varies across antibiotic types and classes. Optimal prescribing and stewardship recommendations require comparisons of risk across antibiotics. However, many prior studies rely on aggregated antibiotic categories or are underpowered to detect significant differences across antibiotic types. Using a large database of real-world data, we evaluate community-associated CDI risk across individual antibiotic types. Methods: We conducted a matched case-control study using a large database of insurance claims capturing longitudinal health care encounters and medications. Case patients with community-associated CDI were matched to 5 control patients by age, sex, and enrollment period. Antibiotics prescribed within 30 days before the CDI diagnosis along with other risk factors, including comorbidities, health care exposures, and gastric acid suppression were considered. Conditional logistic regression and a Bayesian analysis were used to compare risk across individual antibiotics. A sensitivity analysis of antibiotic exposure windows between 30 and 180 days was conducted. Results: We identified 159 404 cases and 797 020 controls. Antibiotics with the greatest risk for CDI included clindamycin and later-generation cephalosporins, and those with the lowest risk included minocycline and doxycycline. We were able to differentiate and order individual antibiotics in terms of their relative level of associated risk for CDI. Risk estimates varied considerably with different exposure windows considered. Conclusions: We found wide variation in CDI risk within and between classes of antibiotics. These findings ordering the level of associated risk across antibiotics can help inform tradeoffs in antibiotic prescribing decisions and stewardship efforts.

Using Thermal Imaging to Track Cellulitis.

Background: Cellulitis is a common soft tissue infection and a major cause of morbidity. The diagnosis is based almost exclusively on clinical history and physical exam. To improve the diagnosis of cellulitis, we used a thermal camera to track how skin temperature of the affected area changed during a hospital stay for patients with cellulitis. Methods: We recruited 120 patients admitted with a diagnosis of cellulitis. Daily thermal images of the affected limb were taken. Temperature intensity and area were analyzed from the images. Highest daily body temperature and antibiotics administered were also collected.We estimated a longitudinal linear mixed-effects model with a random intercept for the affected body area. All observations on a given day were included, and we used an integer time indicator indexed to the initial day (ie, t = 1 for the first day the patient was observed, etc.). We then analyzed the effect of this time trend on both severity (ie, normalized temperature) and scale (ie, area of skin with elevated temperature). Results: We analyzed thermal images from the 41 patients with a confirmed case of cellulitis who had at least 3 days of photos. For each day that the patient was observed, the severity decreased by 1.63 (95% CI, -13.45 to 10.32) units on average, and the scale decreased by 0.63 (95% CI, -1.08 to -0.17) points on average. Also, patients' body temperatures decreased by 0.28°F each day (95% CI, -0.40 to -0.17). Conclusions: Thermal imaging could be used to help diagnose cellulitis and track clinical progress.

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

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.

The incidence, duration, risk factors, and age-based variation of missed opportunities to diagnose pertussis: A population-based cohort study.

OBJECTIVE: To estimate the incidence, duration and risk factors for diagnostic delays associated with pertussis. DESIGN: We used longitudinal retrospective insurance claims from the Marketscan Commercial Claims and Encounters, Medicare Supplemental (2001-2020), and Multi-State Medicaid (2014-2018) databases. SETTING: Inpatient, emergency department, and outpatient visits. PATIENTS: The study included patients diagnosed with pertussis (International Classification of Diseases [ICD] codes) and receipt of macrolide antibiotic treatment. METHODS: We estimated the number of visits with pertussis-related symptoms before diagnosis beyond that expected in the absence of diagnostic delays. Using a bootstrapping approach, we estimated the number of visits representing a delay, the number of missed diagnostic opportunities per patient, and the duration of delays. Results were stratified by age groups. We also used a logistic regression model to evaluate potential factors associated with delay. RESULTS: We identified 20,828 patients meeting inclusion criteria. On average, patients had almost 2 missed opportunities prior to diagnosis, and delay duration was 12 days. Across age groups, the percentage of patients experiencing a delay ranged from 29.7% to 37.6%. The duration of delays increased considerably with age from an average of 5.6 days for patients aged <2 years to 13.8 days for patients aged ≥18 years. Factors associated with increased risk of delays included emergency department visits, telehealth visits, and recent prescriptions for antibiotics not effective against pertussis. CONCLUSIONS: Diagnostic delays for pertussis are frequent. More work is needed to decrease diagnostic delays, especially among adults. Earlier case identification may play an important role in the response to outbreaks by facilitating treatment, isolation, and improved contact tracing.

A clinically-guided unsupervised clustering approach to recommend symptoms of disease associated with diagnostic opportunities.

OBJECTIVES: A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis. METHODS: We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-means clustering to recommend conditions that exhibit similar trends to the initial symptoms provided. We evaluate the clinical plausibility of the recommended conditions and the corresponding number of possible diagnostic delays based on these diseases. RESULTS: For both diseases of interest, the clustering approach suggested a large number of clinically-plausible conditions to consider (e.g., fever, hemoptysis, and pneumonia before tuberculosis). The recommended conditions had a high degree of precision in terms of clinical plausibility: >70% for tuberculosis and >90% for appendicitis. Including these additional clinically-plausible conditions resulted in more than twice the number of possible diagnostic delays identified. CONCLUSIONS: Our approach can mine administrative datasets to detect patterns of diagnostic delay and help investigators avoid under-identifying potential missed diagnostic opportunities. In addition, the methods we describe can be used to discover less-common presentations of diseases that are frequently misdiagnosed.

Evidence of within-facility patient-patient Clostridiodes difficile infection spread across diverse settings.

Previous studies have suggested that a hospital patient's risk of developing healthcare facility-onset (HCFO) Clostridioides difficile infections (CDIs) increases with the number of concurrent spatially proximate patients with CDI, termed CDI pressure. However, these studies were performed either in a single institution or in a single state with a very coarse measure of concurrence. We conducted a retrospective case-control study involving over 17.5 million inpatient visits across 700 hospitals in eight US states. We built a weighted, directed network connecting overlapping inpatient visits to measure facility-level CDI pressure. We then matched HCFO-CDIs with non-CDI controls on facility, comorbidities and demographics and performed a conditional logistic regression to determine the odds of developing HCFO-CDI given the number of coincident patient visits with CDI. On average, cases' visits coincided with 9.2 CDI cases, which for an individual with an average length of stay corresponded to an estimated 17.7% (95% CI 12.9-22.7%) increase in the odds of acquiring HCFO-CDI compared to an inpatient visit without concurrent CDI cases or fully isolated from both direct and indirect risks from concurrent CDI cases. These results suggest that, either directly or indirectly, hospital patients with CDI lead to CDIs in non-infected patients with temporally overlapping visits.

Understanding the Prodromal Period of Necrotizing Soft Tissue Infections of the Genitalia (Fournier's Gangrene) and the Incidence, Duration, and Risk Factors Associated With Potential Missed Opportunities for an Earlier Diagnosis: A Population-based Longitudinal Study.

PURPOSE: The purpose of this paper was to investigate patterns of health care utilization leading up to diagnosis of necrotizing soft tissue infections of the genitalia and to identify risk factors associated with potential diagnostic delay. MATERIALS AND METHODS: IBM MarketScan Research Databases (2001-2020) were used to identify index cases of necrotizing soft tissue infections of the genitalia. We identified health care visits for symptomatically similar diagnoses (eg, penile swelling, cellulitis) that occurred prior to necrotizing soft tissue infections of the genitalia diagnosis. A change-point analysis identified the window before diagnosis where diagnostic opportunities first appeared. A simulation model estimated the likelihood symptomatically similar diagnosis visits represented a missed opportunity for earlier diagnosis. Patient and provider characteristics were evaluated for their associations with delay. RESULTS: We identified 8,098 patients with necrotizing soft tissue infections of the genitalia, in which 4,032 (50%) had a symptomatically similar diagnosis visit in the 21-day diagnostic window, most commonly for "non-infectious urologic abnormalities" (eg, genital swelling; 64%): 46% received antibiotics; 16% saw a urologist. Models estimated that 5,096 of the symptomatically similar diagnosis visits (63%) represented diagnostic delay (mean duration 6.2 days; mean missed opportunities 1.8). Risk factors for delay included urinary tract infection history (OR 2.1) and morbid obesity (OR 1.6). Visits to more than 1 health care provider/location in a 24-hour period significantly decreased delay risk. CONCLUSIONS: Nearly 50% of insured patients who undergo debridement for, or die from, necrotizing soft tissue infections of the genitalia will present to a medical provider with a symptomatically similar diagnosis suggestive of early disease development. Many of these visits likely represent diagnostic delay. Efforts to minimize logistic and cognitive biases in this rare condition may lead to improved outcomes if they lead to earlier interventions.

Frequency and Duration of, and Risk Factors for, Diagnostic Delays Associated with Histoplasmosis.

Histoplasmosis is often confused with other diseases leading to diagnostic delays. We estimated the incidence, length of, and risk factors for, diagnostic delays associated with histoplasmosis. Using data from IBM Marketscan, 2001-2017, we found all patients with a histoplasmosis diagnosis. We calculated the number of visits that occurred prior to the histoplasmosis diagnosis and the number of visits with symptomatically similar diagnoses (SSDs). Next, we estimated the number of visits that represented a delay using a simulation-based approach. We also computed the number of potential opportunities for diagnosis that were missed for each patient and the length of time between the first opportunity and the diagnosis. Finally, we identified risk factors for diagnostic delays using a logistic regression model. The number of SSD-related visits increased significantly in the 97 days prior to the histoplasmosis diagnosis. During this period, 97.4% of patients had a visit, and 90.1% had at least one SSD visit. We estimate that 82.9% of patients with histoplasmosis experienced at least one missed diagnostic opportunity. The average delay was 39.5 days with an average of 4.0 missed opportunities. Risk factors for diagnostic delays included prior antibiotic use, history of other pulmonary diseases, and emergency department and outpatient visits, especially during weekends. New diagnostic approaches for histoplasmosis are needed.

Risk for Asymptomatic Household Transmission of Clostridioides difficile Infection Associated with Recently Hospitalized Family Members.

We evaluated whether hospitalized patients without diagnosed Clostridioides difficile infection (CDI) increased the risk for CDI among their family members after discharge. We used 2001-2017 US insurance claims data to compare monthly CDI incidence between persons in households with and without a family member hospitalized in the previous 60 days. CDI incidence among insurance enrollees exposed to a recently hospitalized family member was 73% greater than enrollees not exposed, and incidence increased with length of hospitalization among family members. We identified a dose-response relationship between total days of within-household hospitalization and CDI incidence rate ratio. Compared with persons whose family members were hospitalized <1 day, the incidence rate ratio increased from 1.30 (95% CI 1.19-1.41) for 1-3 days of hospitalization to 2.45 (95% CI 1.66-3.60) for >30 days of hospitalization. Asymptomatic C. difficile carriers discharged from hospitals could be a major source of community-associated CDI cases.

The Rapid Reduction of Infection-Related Visits and Antibiotic Use Among People With Cystic Fibrosis After Starting Elexacaftor-Tezacaftor-Ivacaftor.

BACKGROUND: People with cystic fibrosis (CF) routinely suffer from recurrent sinopulmonary infections. Such infections require frequent courses of antimicrobials and often involve multidrug-resistant organisms. The goal of this study was to identify real-world evidence for the effectiveness of elexacaftor-tezacaftor-ivacaftor (ELX/TEZ/IVA) in decreasing infection-related visits and antimicrobial use in people with CF. METHODS: Using IBM MarketScan data, we identified 389 enrollees with CF who began taking ELX/TEZ/IVA before 1 December 2019 and were enrolled from 1 July 2019 to 14 March 2020. We also identified a comparison population who did not begin ELX/TEZ/IVA during the study period. We compared the following outcomes in the 15 weeks before and after medication initiation: total healthcare visits, inpatient visits, infection-related visits, and antimicrobial prescriptions. We analyzed outcomes using both a case-crossover analysis and a difference-in-differences analysis, to control for underlying trends. RESULTS: For the case-crossover analysis, ELX/TEZ/IVA initiation was associated with the following changes over a 15-week period: change in overall healthcare visit dates, -2.5 (95% confidence interval, -3.31 to -1.7); change in inpatient admissions, -0.16 (-.22 to -.10); change in infection-related visit dates, -0.62 (-.93 to -.31); and change in antibiotic prescriptions, -0.78 (-1.03 to -.54). Results from the difference-in-differences approach were similar. CONCLUSIONS: We show a rapid reduction in infection-related visits and antimicrobial use among people with CF after starting a therapy that was not explicitly designed to treat infections. Currently, there are >30 000 people living with CF in the United States alone. Given that this therapy is effective for approximately 90% of people with CF, the impact on respiratory infections and antimicrobial use may be substantial.

Diagnostic delays in infectious diseases.

OBJECTIVES: Diagnostic delays are a major source of morbidity and mortality. Despite the adverse outcomes associated with diagnostic delays, few studies have examined the incidence and factors that influence diagnostic delays for different infectious diseases. The objective of this study was to understand the relative frequency of diagnostic delays for six infectious diseases commonly seen by infectious diseases (ID) consultants and to examine contributing factors for these delays. METHODS: A 25-item survey to examine diagnostic delays in six infectious diseases was sent to all infectious diseases physicians in the Emerging Infections Network (EIN) who provide care to adult patients. Diseases included (1) tuberculosis, (2) non-tuberculous mycobacterial infections, (3) syphilis, (4) epidural abscess, (5) infective endocarditis, and (6) endemic fungal infections (e.g., histoplasmosis, blastomycosis). RESULTS: A total of 533 of 1,323 (40%) EIN members responded to the survey. Respondents perceived the diagnosis not being considered initially and the appropriate test not being ordered as the two most important contributors to diagnostic delays. Unusual clinical presentations and not consulting ID physicians early enough were also reported as a contributing factor to delays. Responses recorded in open-text fields also indicated errors related to testing as a likely cause of delays; specifically, test-related errors included ordering the wrong laboratory test, laboratory delays (specialized labs not available at the facility), and lab processing delays. CONCLUSIONS: Diagnostic delays commonly occur for the infectious diseases we considered. The contributing factors we identified are potential targets for future interventions to decrease diagnostic delays.

Incidence, Duration, and Risk Factors Associated With Missed Opportunities to Diagnose Herpes Simplex Encephalitis: A Population-Based Longitudinal Study.

BACKGROUND: Delays in diagnosing herpes simplex encephalitis (HSE) are associated with increased morbidity and mortality. The purpose of this paper is to determine the frequency and duration of diagnostic delays for HSE and risk factors for diagnostic delays. METHODS: Using data from the IBM Marketscan Databases, 2001-2017, we performed a retrospective cohort study of patients with HSE. We estimated the number of visits with HSE-related symptoms before diagnosis that would be expected to occur in the absence of delays and compared this estimate to the observed pattern of visits. Next, we used a simulation-based approach to compute the number of visits representing a delay, the number of missed diagnostic opportunities per case patient, and the duration of delays. We also investigated potential risk factors for delays. RESULTS: We identified 2667 patients diagnosed with HSE. We estimated 45.9% (95% confidence interval [CI], 43.6%-48.1%) of patients experienced at least 1 missed opportunity; 21.9% (95% CI, 17.3%-26.3%) of these patients had delays lasting >7 days. Risk factors for delays included being seen only in the emergency department, age <65, or a history of sinusitis or schizophrenia. CONCLUSIONS: Many patients with HSE experience multiple missed diagnostic opportunities before diagnosis.

Incidence, duration and risk factors associated with delayed and missed diagnostic opportunities related to tuberculosis: a population-based longitudinal study.

OBJECTIVES: Missed opportunities to diagnose tuberculosis are costly to patients and society. In this study, we (1) estimate the frequency and duration of diagnostic delays among patients with active pulmonary tuberculosis and (2) determine the risk factors for experiencing a diagnostic delay. DESIGN: A retrospective cohort study of patients with tuberculosis using longitudinal healthcare encounters prior to diagnosis. SETTING: Commercially insured enrollees from the Commercial Claims and Encounters or Medicare Supplemental IBM Marketscan Research Databases, 2001-2017. PARTICIPANTS: All patients diagnosed with, and receiving treatment for, pulmonary tuberculosis, enrolled at least 365 days prior to diagnosis. PRIMARY AND SECONDARY OUTCOME MEASURES: We estimated the number of visits with tuberculosis-related symptoms prior to diagnosis that would be expected to occur in the absence of delays and compared this estimate to the observed pattern. We computed the number of visits representing a delay and used a simulation-based approach to estimate the number of patients experiencing a delay, number of missed opportunities per patient and duration of delays (ie, time between diagnosis and earliest missed opportunity). We also explored risk factors for missed opportunities. RESULTS: We identified 3371 patients diagnosed and treated for active tuberculosis that could be followed up for 1 year prior to diagnosis. We estimated 77.2% (95% CI 75.6% to 78.7%) of patients experienced at least one missed opportunity; of these patients, an average of 3.89 (95% CI 3.65 to 4.14) visits represented a missed opportunity, and the mean duration of delay was 31.66 days (95% CI 28.51 to 35.11). Risk factors for delays included outpatient or emergency department settings, weekend visits, patient age, influenza season presentation, history of chronic respiratory symptoms and prior fluoroquinolone use. CONCLUSIONS: Many patients with tuberculosis experience multiple missed diagnostic opportunities prior to diagnosis. Missed opportunities occur most commonly in outpatient settings and numerous patient-specific, environment-specific and setting-specific factors increase risk for delays.

Opioid abuse surveillance in patients with endocarditis.

BACKGROUND: Opioid abuse is a significant cause of morbidity and mortality in the United States, and injection drug use (IDU) is a common form of opioid abuse. IDU is a major risk factor for infections including infective endocarditis (IE). OBJECTIVES: To determine the prevalence of opioid abuse among patients with IE in both patient problem lists and diagnostic codes and describe underlying patient characteristics. METHODS: A retrospective chart review from 1-1-2010 to 11-19-2018 of a large academic medical center's patients with documented IE was performed. Demographic, comorbidity, opioid prescription data and records of drug abuse in both the patient's problem list and ICD9/10 codes were recorded. RESULTS: Of the 796 patients with documented IE, 105 patients (13.2%) had opioid abuse or related IDU in their problem list, but only 22 received an ICD-9/10 code associated with drug abuse. IE patients with opioid abuse were generally younger (43.6 vs 61.7 years [P < 0.001]), had fewer chronic comorbidities, and were prescribed opioids more often (86.7% vs 53.8% [P < 0.001]). CONCLUSIONS: Opioid abuse and IDU are commonly recorded in the problem list of patients with IE, but opioid abuse is frequently not listed as a diagnosis in administrative billing codes.