Cystic fibrosis carriers are at increased risk for a wide range of cystic fibrosis-related conditions.

Autosomal recessive diseases, such as cystic fibrosis (CF), require inheritance of 2 mutated genes. However, some studies indicate that CF carriers are at increased risk for some conditions associated with CF. These investigations focused on single conditions and included small numbers of subjects. Our goal was to determine whether CF carriers are at increased risk for a range of CF-related conditions. Using the Truven Health MarketScan Commercial Claims database (2001-2017), we performed a population-based retrospective matched-cohort study. We identified 19,802 CF carriers and matched each carrier with 5 controls. The prevalence of 59 CF-related diagnostic conditions was evaluated in each cohort. Odds ratios for each condition were computed for CF carriers relative to controls. All 59 CF-related conditions were more prevalent among carriers compared with controls, with significantly increased risk (P < 0.05) for 57 conditions. Risk was increased for some conditions previously linked to CF carriers (e.g., pancreatitis, male infertility, bronchiectasis), as well as some conditions not previously reported (e.g., diabetes, constipation, cholelithiasis, short stature, failure to thrive). We compared our results with 23,557 subjects with CF, who were also matched with controls; as the relative odds of a given condition increased among subjects with CF, so did the corresponding relative odds for carriers (P < 0.001). Although individual-level risk remained low for most conditions, because there are more than 10 million carriers in the US, population-level morbidity attributable to the CF carrier state is likely substantial. Genetic testing may inform prevention, diagnosis, and treatment for a broad range of CF carrier-related conditions.

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 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.

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.

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.

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.

Risk for Clostridioides difficile Infection Among Hospitalized Patients Associated With Multiple Healthcare Exposures Prior to Admission.

BACKGROUND: Clostridioides difficile infection (CDI) is a common healthcare-associated infection and is often used as an indicator of hospital safety or quality. However, healthcare exposures occurring prior to hospitalization may increase risk for CDI. We conducted a case-control study comparing hospitalized patients with and without CDI to determine if healthcare exposures prior to hospitalization (ie, clinic visits, antibiotics, family members with CDI) were associated with increased risk for hospital-onset CDI, and how risk varied with time between exposure and hospitalization. METHODS: Records were collected from a large insurance-claims database from 2001 to 2017 for hospitalized adult patients. Prior healthcare exposures were identified using inpatient, outpatient, emergency department, and prescription drug claims; results were compared between various CDI case definitions. RESULTS: Hospitalized patients with CDI had significantly more frequent healthcare exposures prior to admission. Healthcare visits, antibiotic use, and family exposures were associated with greater likelihood of CDI during hospitalization. The degree of association diminished with time between exposure and hospitalization. Results were consistent across CDI case definitions. CONCLUSIONS: Many different prior healthcare exposures appear to increase risk for CDI presenting during hospitalization. Moreover, patients with CDI typically have multiple exposures prior to admission, confounding the ability to attribute cases to a particular stay.

Many Opportunities to Record, Diagnose, or Treat Injection Drug-related Infections Are Missed: A Population-based Cohort Study of Inpatient and Emergency Department Settings.

BACKGROUND: Injection drug use (IDU) and IDU-related infections have increased dramatically. However, the incidence of IDU-related infections may be underreported because drug use is not recorded in diagnostic records where associated infections are identified. Our goal was to estimate a more accurate incidence of IDU-related infections by including IDU-related infections not recorded at the time infections are diagnosed. METHODS: We performed a retrospective cohort study using inpatient and emergency department visits from the Healthcare Cost and Utilization Project for California, Florida, and New York. We identified all patients diagnosed with bacteremia or sepsis, endocarditis, osteomyelitis or septic arthritis, and skin or soft tissue infection. We estimated the incidence of IDU-related infections by identifying cases where drug use was recorded at the time of an infection and cases where drug use was not recorded at the time of infection but within 6 months before or after the infection diagnosis. We also analyzed factors associated with unrecorded IDU. RESULTS: There has been an increasing trend in the number of IDU-related infections. The annual number of IDU-related infections increased between 105% and 218% after incorporating infections in which drug use was unrecorded. Factors associated with drug use being unrecorded included emergency department diagnosis, the level of hospital experience treating drug use, age <18 years, and having Medicare as the primary payer. CONCLUSIONS: More than half of all IDU-related infections may be unrecorded in existing surveillance estimates. There may be many missed opportunities to record, diagnose, or treat underlying drug abuse among patients presenting with IDU-related infections.

A Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance.

Background: Smartphone-based sensors may enable real-time surveillance of infectious diseases at population and household levels. This study evaluates the use of data from commercially available "smart thermometers," connected to a mobile phone application, for surveillance of influenza-like illness (ILI). Methods: At a population level, we analyzed the correlation between thermometer recordings and Centers for Disease Control and Prevention-reported ILI activity nationally and by age group and region. We developed time-series models to forecast ILI activity in real time and up to 3 weeks in advance. We analyzed the ability of thermometer readings to track the duration of fevers and identify biphasic fever patterns. We also investigated potential transmission of febrile illness within households among device users. Results: Thermometer readings were highly correlated with national ILI activity (r > 0.95) and activity patterns across regions and age groups. Thermometer readings also significantly improved forecasts of ILI activity in real time and up to 3 weeks in advance. We found that fevers lasting between 3 and 6 days and biphasic fever episodes occurred more frequently during the influenza season. In addition, potential cases of in-household transmission of febrile illness originated more frequently from children than adults. Conclusions: Smart thermometers represent a novel source of information for influenza surveillance and forecasting. Thermometer readings capture real-time ILI activity at a population level, and they can also be used to generate improved forecasts. Moreover, the widespread deployment of these smart thermometers may also allow for more rapid and efficient surveillance at the household level.

Optimal screening strategies for healthcare associated infections in a multi-institutional setting.

Health institutions may choose to screen newly admitted patients for the presence of disease in order to reduce disease prevalence within the institution. Screening is costly, and institutions must judiciously choose which patients they wish to screen based on the dynamics of disease transmission. Since potentially infected patients move between different health institutions, the screening and treatment decisions of one institution will affect the optimal decisions of others; an institution might choose to "free-ride" off the screening and treatment decisions of neighboring institutions. We develop a theoretical model of the strategic decision problem facing a health care institution choosing to screen newly admitted patients. The model incorporates an SIS compartmental model of disease transmission into a game theoretic model of strategic decision-making. Using this setup, we are able to analyze how optimal screening is influenced by disease parameters, such as the efficacy of treatment, the disease recovery rate and the movement of patients. We find that the optimal screening level is lower for diseases that have more effective treatments. Our model also allows us to analyze how the optimal screening level varies with the number of decision makers involved in the screening process. We show that when institutions are more autonomous in selecting whom to screen, they will choose to screen at a lower rate than when screening decisions are more centralized. Results also suggest that centralized screening decisions have a greater impact on disease prevalence when the availability or efficacy of treatment is low. Our model provides insight into the factors one should consider when choosing whether to set a mandated screening policy. We find that screening mandates set at a centralized level (i.e. state or national) will have a greater impact on the control of infectious disease.

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.

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.

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.

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.

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.

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.