Cost Savings Without Increased Risk of Respiratory Hospitalization for Preterm Children after the 2014 Palivizumab Policy Update.

OBJECTIVE: Our objective was to compare rates of hospitalizations for respiratory illnesses in preterm and full-term (FT) children for 4 years before and after the 2014 update to the American Academy of Pediatrics (AAP) respiratory syncytial virus (RSV) immunoprophylaxis guidance, which restricted eligibility among infants born at 29 to 34 weeks in the first winter and all preterm infants in the second winter after neonatal discharge. STUDY DESIGN: We conducted pre-post and interrupted time series analyses on claims data from a commercial national managed care plan. We compared the number of RSV and all respiratory hospital admissions in the first and second RSV seasons after neonatal discharge among a cohort of preterm children, regardless of palivizumab status, in the 4 years before and after the implementation of the 2014 palivizumab eligibility change. A FT group was included for reference. RESULTS: The cohort included 821 early preterm (EP, <29 weeks), 4,790 moderate preterm (MP, 29-34 weeks), and 130,782 FT children. Palivizumab use after the policy update decreased among MP children in the first and second RSV seasons after neonatal discharge, without any change in the odds of hospitalization with RSV or respiratory illness. For the EP group, there was no change in the rate of palivizumab or the odds of hospitalization with RSV or respiratory illness after the policy update. For the FT group, there was a slight decrease in odds of hospitalization post-2014 after the policy update. The interrupted time series did not reveal any secular trends over time in hospitalization rates among preterm children. Following the policy change, there were cost savings for MP children in the first and second RSV seasons, when accounting for the cost of hospitalizations and the cost of palivizumab. CONCLUSION: Hospitalizations for RSV or respiratory illness did not increase, and cost savings were obtained after the implementation of the 2014 AAP palivizumab prophylaxis policy. KEY POINTS: · Palivizumab use decreased among children born moderate preterm (29 to34 weeks) after the 2014 palivizuamb policy update.. · There was no change in odds of hospitalization with respiratory syncitial virus or respiratory illness among preterm infants after the policy update when compared to before.. · There were cost savings, when accounting for the cost of hospitalizations and the cost of palivizumab, after the policy update among children born moderate preterm..

Multi-PheWAS intersection approach to identify sex differences across comorbidities in 59 140 pediatric patients with autism spectrum disorder.

OBJECTIVE: To identify differences related to sex and define autism spectrum disorder (ASD) comorbidities female-enriched through a comprehensive multi-PheWAS intersection approach on big, real-world data. Although sex difference is a consistent and recognized feature of ASD, additional clinical correlates could help to identify potential disease subgroups, based on sex and age. MATERIALS AND METHODS: We performed a systematic comorbidity analysis on 1860 groups of comorbidities exploring all spectrum of known disease, in 59 140 individuals (11 440 females) with ASD from 4 age groups. We explored ASD sex differences in 2 independent real-world datasets, across all potential comorbidities by comparing (1) females with ASD vs males with ASD and (2) females with ASD vs females without ASD. RESULTS: We identified 27 different comorbidities that appeared significantly more frequently in females with ASD. The comorbidities were mostly neurological (eg, epilepsy, odds ratio [OR] > 1.8, 3-18 years of age), congenital (eg, chromosomal anomalies, OR > 2, 3-18 years of age), and mental disorders (eg, intellectual disability, OR > 1.7, 6-18 years of age). Novel comorbidities included endocrine metabolic diseases (eg, failure to thrive, OR = 2.5, ages 0-2), digestive disorders (gastroesophageal reflux disease: OR = 1.7, 6-11 years of age; and constipation: OR > 1.6, 3-11 years of age), and sense organs (strabismus: OR > 1.8, 3-18 years of age). DISCUSSION: A multi-PheWAS intersection approach on real-world data as presented in this study uniquely contributes to the growing body of research regarding sex-based comorbidity analysis in ASD population. CONCLUSIONS: Our findings provide insights into female-enriched ASD comorbidities that are potentially important in diagnosis, as well as the identification of distinct comorbidity patterns influencing anticipatory treatment or referrals. The code is publicly available (https://github.com/hms-dbmi/sexDifferenceInASD).

Medication utilization in children born preterm in the first two years of life.

OBJECTIVE: To compare medications dispensed during the first 2 years in children born preterm and full-term. STUDY DESIGN: Retrospective analysis of claims data from a commercial national managed care plan 2008-2019. 329,855 beneficiaries were enrolled from birth through 2 years, of which 25,408 (7.7%) were preterm (<37 weeks). Filled prescription claims and paid amount over 2 years were identified. RESULTS: In preterm children, the number of filled prescriptions was 1.4 times and cost was 3.8 times that of full-term children. Number and cost of medications were inversely related to gestational age. Differences peak at 4-9 months and resolve by 19 months after discharge. Palivizumab, ranitidine, albuterol, lansoprazole, budesonide, and prednisolone had the greatest differences in utilization. CONCLUSION: Prescription medication utilization among preterm children under 2 years is driven by palivizumab, anti-reflux, and respiratory medications, despite little evidence regarding efficacy for many medications and concern for harm with certain classes.

Association of a Clinician's Antibiotic-Prescribing Rate With Patients' Future Likelihood of Seeking Care and Receipt of Antibiotics.

BACKGROUND: One underexplored driver of inappropriate antibiotic prescribing for acute respiratory illnesses (ARI) is patients' prior care experiences. When patients receive antibiotics for an ARI, patients may attribute their clinical improvement to the antibiotics, regardless of their true benefit. These experiences, and experiences of family members, may drive whether patients seek care or request antibiotics for subsequent ARIs. METHODS: Using encounter data from a national United States insurer, we identified patients <65 years old with an index ARI urgent care center (UCC) visit. We categorized clinicians within each UCC into quartiles based on their ARI antibiotic prescribing rate. Exploiting the quasi-random assignment of patients to a clinician within an UCC, we examined the association between the clinician's antibiotic prescribing rate to the patients' and their spouses' rates of ARI antibiotic receipt in the subsequent year. RESULTS: Across 232,256 visits at 736 UCCs, ARI antibiotic prescribing rates were 42.1% and 80.2% in the lowest and highest quartile of clinicians, respectively. Patient characteristics were similar across the four quartiles. In the year after the index ARI visit, patients seen by the highest-prescribing clinicians received more ARI antibiotics (+3.0 fills/100 patients (a 14.6% difference), 95% CI 2.2-3.8, P < 0.001,) versus those seen by the lowest-prescribing clinicians. The increase in antibiotics was also observed among the patients' spouses. The increase in patient ARI antibiotic prescriptions was largely driven by an increased number of ARI visits (+5.6 ARI visits/100 patients, 95% CI 3.6-7.7, P < 0.001), rather than a higher antibiotic prescribing rate during those subsequent ARI visits. CONCLUSIONS: Receipt of antibiotics for an ARI increases the likelihood that patients and their spouses will receive antibiotics for future ARIs.

Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study.

OBJECTIVE: To quantify the effects of varying opioid prescribing patterns after surgery on dependence, overdose, or abuse in an opioid naive population. DESIGN: Retrospective cohort study. SETTING: Surgical claims from a linked medical and pharmacy administrative database of 37 651 619 commercially insured patients between 2008 and 2016. PARTICIPANTS: 1 015 116 opioid naive patients undergoing surgery. MAIN OUTCOME MEASURES: Use of oral opioids after discharge as defined by refills and total dosage and duration of use. The primary outcome was a composite of misuse identified by a diagnostic code for opioid dependence, abuse, or overdose. RESULTS: 568 612 (56.0%) patients received postoperative opioids, and a code for abuse was identified for 5906 patients (0.6%, 183 per 100 000 person years). Total duration of opioid use was the strongest predictor of misuse, with each refill and additional week of opioid use associated with an adjusted increase in the rate of misuse of 44.0% (95% confidence interval 40.8% to 47.2%, P<0.001), and 19.9% increase in hazard (18.5% to 21.4%, P<0.001), respectively. CONCLUSIONS: Each refill and week of opioid prescription is associated with a large increase in opioid misuse among opioid naive patients. The data from this study suggest that duration of the prescription rather than dosage is more strongly associated with ultimate misuse in the early postsurgical period. The analysis quantifies the association of prescribing choices on opioid misuse and identifies levers for possible impact.

Biases introduced by filtering electronic health records for patients with "complete data".

OBJECTIVE: One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data "completeness" affect the number of patients in the resulting cohort and introduce potential biases. MATERIALS AND METHODS: We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 216 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members. RESULTS: EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete. DISCUSSION AND CONCLUSION: As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort.

Antihyperglycemic Medications: A Claims-Based Estimate of First-line Therapy Use Prior to Initialization of Second-line Medications.

OBJECTIVE: The American Diabetes Association recommends metformin as first-line therapy for type 2 diabetes. However, nonadherence to antihyperglycemic medication is common, and a clinician could confuse nonadherence with pharmacologic failure, potentially leading to premature prescribing of second-line therapies. We measured metformin use prior to second-line therapy initialization. RESEARCH DESIGN AND METHODS: This retrospective cross-sectional study used unidentifiable member claims data from individuals covered from 2010 to 2015 by Aetna, a U.S. health benefits company. Beneficiaries with two physician claims or one hospitalization with a type 2 diabetes diagnosis were included. Recommended use of metformin was measured by the proportion of days covered over 60 days. Through sensitivity analysis, we varied estimates of the percentage of beneficiaries who used low-cost generic prescription medication programs. RESULTS: A total of 52,544 individuals with type 2 diabetes were eligible. Of 22,956 patients given second-line treatment, only 1,875 (8.2%) had evidence of recommended use of metformin in the prior 60 days, and 6,441 (28.0%) had no prior claims evidence of having taken metformin. At the top range of sensitivity, only 49.5% patients could have had recommended use. Patients were more likely to be given an additional second-line antihyperglycemic medication or insulin if they were given their initial second-line medication without evidence of recommended use of metformin (P < 0.001). CONCLUSIONS: Despite published guidelines, second-line therapy often is initiated without evidence of recommended use of first-line therapy. Apparent treatment failures, which may in fact be attributable to nonadherence to guidelines, are common. Point-of-care and population-level processes are needed to monitor and improve guideline adherence.

Predicting Falls in People Aged 65 Years and Older from Insurance Claims.

BACKGROUND: Accidental falls among people aged 65 years and older caused approximately 2,700,000 injuries, 27,000 deaths, and cost more than 34 billion dollars in the US annually in recent years. Here, we derive and validate a predictive model for falls based on a retrospective cohort of those 65 years and older. METHODS: Insurance claims from a 1-year observational period were used to predict a fall-related claim in the following 2 years. The predictive model takes into account a person's age, sex, prescriptions, and diagnoses. Through random assignment, half of the people had their claims used to derive the model, while the remaining people had their claims used to validate the model. RESULTS: Of 120,881 individuals with Aetna health insurance coverage, 12,431 (10.3%) members fell. During validation, people were risk stratified across 20 levels, where those in the highest risk stratum had 10.5 times the risk as those in the lowest stratum (33.1% vs 3.1%). CONCLUSIONS: Using only insurance claims, individuals in this large cohort at high risk of falls could be readily identified up to 2 years in advance. Although external validation is needed, the findings support the use of the model to better target interventions.