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.

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.

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.

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.

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.

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.

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.

Are high school girls' lacrosse players at increased risk of concussion because they are not allowed to wear the same helmet boys' lacrosse players are required to wear?

BACKGROUND

Boys' lacrosse (LAX), a full contact sport allowing body and stick checking, mandates hard shell helmets with full face masks. Girls' LAX, which prohibits body checking and whose sphere rule is supposed to prevent stick checking to the head, allows optional flexible headgear with/without integrated eye protection. Whether the required boys' LAX helmets should also be mandated in girls' LAX has been debated.

METHODS

In this retrospective cohort study we used LAX concussion data from a national high school sports-related injury surveillance study to determine if girls' LAX players were at increased risk of concussion from stick or ball contact due to differences in helmet regulations by calculating the attributable risk and attributable risk percent (AR%) for concussion resulting from ball or stick impacts.

RESULTS

From 2008-09 through 2018-19, boys' LAX players sustained 614 concussions during 1,318,278 athletic exposures (AEs) (4.66 per 10,000 AEs) and girls' LAX players sustained 384 concussions during 983,291 AEs (3.91 per 10,000 AEs). For boys, athlete-athlete contact was the most common mechanism of concussion accounting for 66.4% of all concussions, while stick or ball contact accounted for 23.5%. For girls, stick or ball contact accounted for 72.7% of all concussions, while athlete-athlete contact accounted for 19.8%. Concussion rates from stick or ball contact were significantly higher in girls vs. boys (RR = 2.60, 95% CI 2.12-3.18). The attributable risk associated with playing girls' vs. boys' LAX for concussion resulting from stick or ball contact was 1.75 concussions per 10,000 AEs (95% CI 1.37-2.12) and the AR% was 61.5% (95% CI 52.9-68.5). An estimated 44.7% of all girls' LAX concussions could have been prevented if girls' LAX players wore the helmet mandated in boys' LAX.

CONCLUSIONS

Girls' LAX players who are allowed, but not required, to wear a flexible headgear are at increased risk of concussions from stick or ball impacts compared to boys' LAX players, who are required to wear a hard shell helmet with full face mask. Additional research is needed to determine if there are any defendable arguments to continue justifying restricting girls' LAX players access to this effective piece of protective equipment.

Evaluating the effectiveness of traumatic brain injury state laws among high school athletes

BACKGROUND

Traumatic brain injury legislation varies across states. A comprehensive nationwide evaluation of state traumatic brain injury laws is vital given growing populations of high school athletes. This study evaluates the effectiveness of traumatic brain injury laws by examining longitudinal trends in incident and recurrent concussion rates and determines if state level variations in legislation's language affected the observed trends.

METHODS

In this retrospective ecological study of a large national sample of US high schools from 2005/06 through 2017/18, piecewise regression models along with a profile likelihood approach were utilized to examine longitudinal trends in incident and recurrent concussion rates.

RESULTS

Overall incident concussion rates increased by an additional 1.85%/standardized month (STDM) (95% confidence interval (CI): 1.14, 2.56%) prior to law passage and decreased by an additional 1.08%/ STDM (95%CI: - 1.43, - 0.72%) after law passage. Similar trends were observed for overall recurrent concussion rates. Among states that specified the category of healthcare provider for return to play clearance, post-law recurrent concussion rates decreased on average by an additional 1.59%/STDM (95%CI: - 3.42, 0.22%) compared to states that did not specify the category of healthcare provider.

CONCLUSIONS

The passage of state level traumatic brain injury laws was associated with an increase in overall incident and recurrent concussion rates prior to law passage and a decrease in rates after law passage. Although not statistically significant, states with traumatic brain injury laws specifying the category of healthcare provider for return to play clearance had a greater rate of decline in post-law recurrent concussion rates compared to states not specifying the category of healthcare provider. The findings suggest that state traumatic brain injury laws may benefit from specifying the category of healthcare provider allowed to provide return to play clearance, if they do not already include such language.

Epidemiology of Shoulder and Elbow Injuries Among US High School Softball Players, 2005-2006 Through 2016-2017

Background:

Injury prevalence has been well described among baseball athletes; similarly, a better understanding of injuries in softball athletes is needed.

Purpose:

To examine shoulder and elbow injury epidemiology among high school softball athletes in the United States.

Study Design:

Descriptive epidemiological study.

Methods:

Injury data were obtained from the National High School Sports-Related Injury Surveillance System, which captures data from a large national sample of US high schools. Annually, a random sample of 100 high schools provided a representative sample with respect to the 4 US Census geographic regions and 2 school sizes (cutoff point, 1000 students). Athletic trainers from participating schools reported data for athlete-exposures (AEs; practice or competition) and shoulder and elbow injuries from 2005-2006 through 2016-2017.

Results:

A total of 239 shoulder injuries and 85 elbow injuries occurred within 2,095,329 AEs. The overall shoulder injury rate was 1.14 per 10,000 AEs, whereas the overall elbow injury rate was 0.41 per 10,000 AEs. Injuries to the shoulder were more likely to occur during competition as compared with practice (rate ratio, 1.28; 95% CI, 0.99-1.65). Half of the shoulder (50.4%) and elbow 48.9% injuries were due to an overuse/chronic mechanism. Of the athletes sustaining an injury, 86.8% with shoulder injuries and 93.0% with elbow injuries returned to play within 21 days. Only 16.7% of shoulder injuries and 17.5% of elbow injuries were sustained by pitchers.

Conclusion:

Shoulder and elbow injury rates, time to return, and percentage of injuries among pitchers were far lower in high school softball than previously reported values for high school baseball. There were relatively low incidences of shoulder and elbow injuries in high school softball as compared with baseball, with few injuries requiring lengthy time to return to play.