Healthcare Claims Data Research Articles

RWD2 Variation in COVID-19 Treatment Cost due to Social Factors in the US – Estimation Using Integration of CDC/ATSDR’s Social Vulnerability Index (SVI) and Healthcare Claims Data

RWD2 Variation in COVID-19 Treatment Cost due to Social Factors in the US – Estimation Using Integration of CDC/ATSDR’s Social Vulnerability Index (SVI) and Healthcare Claims Data

RWD10 Z-Code Documentation to Identify Social Determinants of Health Among Medicaid Beneficiaries in Texas

Social determinants of health (SDoH) are non-medical factors that impact individuals’ health. The importance of integrating SDoH data in healthcare is increasingly recognized by stakeholders. SDoH can be documented in healthcare claims data using International Classification of Disease (ICD) 10 th revision codes Z55 – Z65. The objective of this study was to describe the documentation of SDoH Z-codes amongst Medicaid beneficiaries in Texas.

Caring for Service Members Who Have Been Sexually Assaulted: The Military Health System.

ABSTRACTIntroductionReports of sexual assault (SA) in the U.S. Military have increased in recent years. Given the deleterious effects of military SA, there remains a need for large-scale studies to assess SA-related health care utilization among active duty service members (ADSMs). The present study, therefore, utilized Military Health System (MHS) data to determine the prevalence of SA-related care, sociodemographic characteristics of ADSMs receiving said care, and the type of provider seen during the initial SA-related health encounter.Materials and MethodsUtilizing the MHS Data Repository and Defense Enrollment Eligibility Reporting System, all ADSMs from the Air Force, Army, Navy, and Marine Corps during fiscal years (FY) 2016-2018 were identified. Those with an International Classification of Diseases diagnostic code related to SA during the study period were isolated. Descriptive statistics and multivariable logistic regression analyses were conducted. The study was exempt from human subjects review.ResultsA total of 1,728,433 ADSMs during FY 2016-2018 were identified, of whom 4,113 (0.24%) had an SA-related health encounter. Rates of SA-related health care encounters decreased each FY. Women (odds ratio [OR] = 12.02, P < .0001), those in the Army (reference group), and enlisted personnel (OR = 2.65, P < .0001) were most likely to receive SA-related health care, whereas ADSMs aged 18-25 years had lower odds (OR = 0.70, P < .0001). In addition, higher odds of SA-related care were observed among those identifying as American Indian/Alaskan Native (OR = 1.37, P = .02) and “Other” race (e.g., multiracial) (OR = 4.60, P < .0001). Initial SA-related health encounters were most likely to occur with behavioral health providers (41.4%).ConclusionsThe current study is the first large-scale examination of health care usage by ADSMs in the MHS who have experienced SA. Results indicated that rates of SA-related care decreased throughout the study period, despite the increasing rates of SA documented by the DoD. Inconsistent with previous research and DoD reports indicating that younger ADSMs are at the highest risk for SA, our study observed lower rates of SA-related care among those aged 18-25 years; additional research is warranted to determine if there are barriers preventing younger ADSMs from seeking SA-related health care. Behavioral health providers were most frequently seen for the initial SA-related encounter, suggesting that they may be in a unique position to provide care and/or relevant referrals to ADSMs who have experienced SA. The present study provides key insights about the prevalence of SA-related care within the MHS, not yet reported in previous literature, which could help inform MHS screening practices. The strengths of the study are the inclusion of the entire active duty population without the need for research recruitment given the utilization of de-identified TRICARE claims data. The study is limited by its use of health care claims data, general SA International Classification of Diseases codes as a proxy indicator for military SA, and lack of data on ethnicity. Future research utilizing MHS data should examine mental health outcomes following the documentation of SA and disruptions in SA-related care due to SARS-CoV-2.

EE239 Reduction in Health Care-Related Costs after Initiation of Treatment with a Prescription Digital Therapeutic for Chronic Insomnia: Two-Year Follow up Analysis of Health Care Claims Data

The objective of this study was to evaluate the 2-year effectiveness of the first FDA-authorized prescription digital therapeutic (PDT) for adults with chronic insomnia that delivers evidence-based treatment (Somryst®). PDTs are software-based disease treatments evaluated for safety and effectiveness in randomized clinical trials, and authorized by the US Food and Drug Administration (FDA) to treat disease with approved directions for use (label).

Uncertainty in lung cancer stage for survival estimation via set‐valued classification

The difficulty in identifying cancer stage in health care claims data has limited oncology quality of care and health outcomes research. We fit prediction algorithms for classifying lung cancer stage into three classes (stages I/II, stage III, and stage IV) using claims data, and then demonstrate a method for incorporating the classification uncertainty in survival estimation. Leveraging set‐valued classification and split conformal inference, we show how a fixed algorithm developed in one cohort of data may be deployed in another, while rigorously accounting for uncertainty from the initial classification step. We demonstrate this process using SEER cancer registry data linked with Medicare claims data.

Acute and delayed psychiatric sequelae among patients hospitalised with COVID-19: a cohort study using LIFE study data

BackgroundCharacterising the psychiatric sequelae of coronavirus disease 2019 (COVID-19) can inform the development of long-term treatment strategies. However, few studies have examined these sequelae at different time points after COVID-19...

0480 Reduction in health care-related costs after initiation of treatment with a prescription digital therapeutic for chronic insomnia: two-year follow up analysis of health care claims data

Abstract Introduction The objective of this study was to evaluate the effectiveness of the first FDA-authorized prescription digital therapeutic (PDT) for adults with chronic insomnia that delivers evidence-based treatment (Somryst, previously called SHUTi). FDA considers PDTs to be prescription-only neurobehavioral devices that provide a computerized version of condition-specific behavioral therapy as an adjunct to clinician supervised outpatient treatment. Methods A pre/post multi-payer analysis of claims data was conducted, comparing two-year pre- and post-index healthcare resource utilization (HCRU) in an all-comer population of patients with self-identified sleep problems across the United States who activated the PDT between February 1, 2012 and December 31, 2018 (Index). HCRU categories assessed were: hospitalizations, treat-and-release emergency department (ED) visits, ambulatory surgical center (ASC) visits, hospital outpatient department (HOPD) visits, office visits, number of sleep medication prescriptions, and associated health care costs. Costs were estimated by multiplying HCRU by published average costs for each medical resource. Results 252 patients initiating the PDT were analyzed (mean age 54.2 years, 57.5% female). Compared to the pre-index period, post-index events were reduced for ED (-56.2%; P=0.001), hospitalizations (-20.9%; P=0.4), sleep medication use (-8.9%; P=0. 0.377), HOPD (-8.3%; P=0.522), and ASC (-6.7%; P=0.695). Post-index events were slightly increased for office visits (+0.7%; P=0.891). Total estimated two-year cost savings associated with the reduced rates of all services except office visits was $494,634, or $1,963 per patient. Conclusion In a real-world cohort of patients with self-identified sleep problems, treatment with a PDT delivering digital CBT-I was associated with clinically meaningful net reductions in health-related services and associated costs. Support (If Any) This analysis was funded by Pear Therapeutics, Inc.

Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

SummaryBackgroundLittle is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).MethodsThis prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013–16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017–18 Pennsylvania Medicaid data (n=318 585) and (2) 2015–17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.FindingsA total of 8641 (1·35%) 2013–16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017–18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015–17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013–16 Pennsylvania training dataset and validated on the 2013–16 Pennsylvania internal validation dataset, 2017–18 Pennsylvania external validation dataset, and 2015–17 Arizona external validation dataset were 0·841 (95% CI 0·835–0·847), 0·828 (0·822–0·834), and 0·817 (0·807–0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017–18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38–4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015–17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19–1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.InterpretationA machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.FundingNational Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

The impact of hospital case volume on the outcomes after catheter ablation for atrial fibrillation according to the ablation technology.

The appropriate hospital case volume for catheter ablation (CA) in patients with atrial fibrillation (AF) according to the ablation technology has not been fully examined. This study aimed to investigate the association between the hospital case volume for AF and periprocedural complications and AF recurrence. In this retrospective cohort study, we used data from the National Database of Health Insurance Claims and Specific Health Checkups, which covers almost all healthcare insurance claims data in Japan. We included patients with AF who underwent first-time CA from April 2014 to March 2020. Using mixed-effect logistic regression, we analyzed the effect of the annual case volume for AF ablation on acute periprocedural complications and 1-year success rate off antiarrhythmic drugs according to the ablation technology (radiofrequency ablation or cryoballoon ablation). Among 270 116 patients, 207 839 (77%) patients underwent radiofrequency ablation and 56 648 (21%) patients underwent cryoballoon ablation. Of all patients, acute complications occurred in 5411 (2.0%) patients, and the recurrence at 1 year was 71 511 (27%). In the radiofrequency ablation group, acute complications and 1-year AF recurrence according to case volume decreased as the annual case volume increased to up to 150-200 cases/year. However, in the cryoballoon ablation group, these outcomes were similar regardless of the case volumes. The case-volume effect was noted in the radiofrequency ablation group, but not in the cryoballoon ablation group. Our results may affect the selection of ablation technology, especially in smaller case-volume hospitals.

Protective Effect of Renin-Angiotensin System Inhibitors on Parkinson’s Disease: A Nationwide Cohort Study

Background: Renin-angiotensin system (RAS) inhibitors have been suggested as protective agents in Parkinson’s disease (PD). However, epidemiological evidence on the association between RAS inhibitors and the development of PD is inconsistent. Objectives: To investigate the effect of RAS inhibitors on PD risk in patients with ischemic heart disease (IHD) by type and cumulative duration of RAS inhibitors and their degree of blood-brain barrier (BBB) penetration ability. Methods: This was a propensity score-matched retrospective cohort study using 2008–2019 healthcare claims data from the Korean Health Insurance Review and Assessment database. The association between RAS inhibitor use and PD in patients with IHD was evaluated using multivariate Cox proportional hazard regression analysis. The risks are presented as adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs). Results: Over a 10-year follow-up, 1,086 of 62,228 IHD patients developed PD. The Cox regression model showed that the use of RAS inhibitors was significantly associated with a lower risk of PD (aHR = 0.75; 95% CI 0.66–0.85) than the non-use of RAS inhibitors. Specifically, this reduced risk of PD only remained with the use of BBB-crossing angiotensin II receptor blockers (ARBs) (aHR = 0.62; 95% CI = 0.53–0.74), and this association was more definite with an increasing cumulative duration. A significantly reduced risk of PD was not observed with the use of BBB-crossing angiotensin-converting enzyme inhibitors. Conclusions: The use of ARBs with BBB-penetrating properties and a high cumulative duration significantly reduces the risk of PD in IHD patients. This protective effect could provide insight into disease-modifying drug candidates for PD.

Prevalence of HIV infection among non-elderly individuals with hepatitis C in Japan: a population-based cohort study using a health insurance claim data

BackgroundHepatitis C virus (HCV) has been mainly transmitted through injection drug use, but recently, sexual transmission among men who have sex with men (MSM), which is also a major route of HIV transmission, is increasing. However, the prevalence of HIV and the incidence of other sexually transmitted infections (STIs) among HCV patients have been rarely reported.MethodsUsing a healthcare insurance claim data of employees and their dependents covering seven-million people in Japan, we evaluated HIV prevalence among HCV patients aged 20–59 years. Hemophilia patients were excluded. HIV and HCV were defined by registered diagnoses and receiving viral RNA testing. The time course of HCV and HIV infections was analyzed. Incidences of syphilis, amebiasis, chlamydia, gonorrhea, hepatitis A, and hepatitis B were assessed.ResultsFrom April 2012 to August 2018, 6,422 HCV patients were identified. HIV prevalence was 0.48% (31/6422, 95% CI [confidence interval]: 0.33–0.68%). HIV was diagnosed after HCV in 3.2% (1/31), before HCV in 58.1% (18/31), and concurrently in 38.7% (12/31). Compared with HCV patients without HIV infection, HCV/HIV co-infected patients were younger (median age, 37 vs 51 years, p < 0.001), more likely to be male (30/31 [96.8%] vs 3059/6391 [47.9%], p < 0.001), more likely to have other STIs (38.7% [12/31] vs 0.9% [56/6391], p < 0.001), and live in Tokyo, the most populous capital city in Japan (67.7% [21/31] vs 11.6% [742/6391], p < 0.001). In Tokyo, the HIV prevalence among 20–30 s male with HCV was 18.6% (13/70; 95% CI, 10.3–29.7%).ConclusionsHIV prevalence among young male HCV patients was very high in Tokyo. HCV/HIV co-infected patients were more likely to acquire HIV before HCV, which is a known feature of MSM. They also had a higher incidence of STIs. These findings suggest that HCV might be prevalent as an STI among MSM particularly in Tokyo.

Association Between Changes in Postoperative Opioid Utilization and Long-Term Health Care Spending Among Surgical Patients With Chronic Opioid Utilization.

There is growing interest in identifying and developing interventions aimed at reducing the risk of increased, long-term opioid use among surgical patients. While understanding how these interventions impact health care spending has important policy implications and may facilitate the widespread adoption of these interventions, the extent to which they may impact health care spending among surgical patients who utilize opioids chronically is unknown. This study was a retrospective analysis of administrative health care claims data for privately insured patients. We identified 53,847 patients undergoing 1 of 10 procedures between January 1, 2004, and September 30, 2018 (total knee arthroplasty, total hip arthroplasty, laparoscopic cholecystectomy, open cholecystectomy, laparoscopic appendectomy, open appendectomy, cesarean delivery, functional endoscopic sinus surgery, transurethral resection of the prostate, or simple mastectomy) who had chronic opioid utilization (≥10 prescriptions or ≥120-day supply in the year before surgery). Patients were classified into 3 groups based on differences in opioid utilization, measured in average daily oral morphine milligram equivalents (MMEs), between the first postoperative year and the year before surgery: "stable" (<20% change), "increasing" (≥20% increase), or "decreasing" (≥20% decrease). We then examined the association between these 3 groups and health care spending during the first postoperative year, using a multivariable regression to adjust for observable confounders, such as patient demographics, medical comorbidities, and preoperative health care utilization. The average age of the sample was 62.0 (standard deviation [SD] 13.1) years, and there were 35,715 (66.3%) women. Based on the change in average daily MME between the first postoperative year and the year before surgery, 16,961 (31.5%) patients were classified as "stable," 15,463 (28.7%) were classified as "increasing," and 21,423 (39.8%) patients were classified as "decreasing." After adjusting for potential confounders, "increasing" patients had higher health care spending ($37,437) than "stable" patients ($31,061), a difference that was statistically significant ($6377; 95% confidence interval [CI], $5669-$7084; P < .001), while "decreasing" patients had lower health care spending ($29,990), a difference (-$1070) that was also statistically significant (95% CI, -$1679 to -$462; P = .001). These results were generally consistent across an array of subgroup and sensitivity analyses. Among patients with chronic opioid utilization before surgery, subsequent increases in opioid utilization during the first postoperative year were associated with increased health care spending during that timeframe, while subsequent decreases in opioid utilization were associated with decreased health care spending.

The Perioperative Use of Benzodiazepines for Major Orthopedic Surgery in the United States.

Despite numerous indications for perioperative benzodiazepine use, associated risks may be exacerbated in elderly and comorbid patients. In the absence of national utilization data, we aimed to describe utilization patterns using national claims data from total hip/knee arthroplasty patients (THA/TKA), an increasingly older and vulnerable surgical population. We included data on 1,863,996 TKAs and 985,471 THAs (Premier Healthcare claims data, 2006-2019). Benzodiazepine utilization (stratified by long- and short-acting agents) was assessed by patient- and health care characteristics, and analgesic regimens. Given the large sample size, standardized differences instead of P values were utilized to signify meaningful differences between groups (defined by value >0.1). Among 1,863,996 TKA and 985,471 THA patients, the utilization rate of benzodiazepines was 80.5% and 76.1%, respectively. In TKA, 72.6% received short-acting benzodiazepines, while 7.9% received long-acting benzodiazepines, utilization rates 68.4% and 7.7% in THA, respectively. Benzodiazepine use was particularly more frequent among younger patients (median age [interquartile range {IQR}]: 66 [60-73]/64 [57-71] among short/long-acting compared to 69 [61-76] among nonusers), White patients (80.6%/85.4% short/long-acting versus 75.7% among nonusers), commercial insurance (36.5%/34.0% short/long-acting versus 29.1% among nonusers), patients receiving neuraxial anesthesia (56.9%/56.5% short/long-acting versus 51.5% among nonusers), small- and medium-sized (≤500 beds) hospitals (68.5% in nonusers, and 74% and 76.7% in short- and long-acting benzodiazepines), and those in the Midwest (24.6%/25.4% short/long-acting versus 16% among nonusers) in TKA; all standardized differences ≥0.1. Similar patterns were observed in THA except for race and comorbidity burden. Notably, among patients with benzodiazepine use, in-hospital postoperative opioid administration (measured in oral morphine equivalents [OMEs]) was substantially higher. This was even more pronounced in patients who received long-acting agents (median OME with no benzodiazepines utilization 192 [IQR, 83-345] vs 256 [IQR, 153-431] with short-acting, and 329 [IQR, 195-540] with long-acting benzodiazepine administration). Benzodiazepine use was also more frequent in patients receiving multimodal analgesia (concurrently 2 or more analgesic modes) and regional anesthesia. Trend analysis showed a persistent high utilization rate of benzodiazepines over the last 14 years. Based on a representative sample, 4 of 5 patients undergoing major orthopedic surgery in the United States receive benzodiazepines perioperatively, despite concerns for delirium and delayed postoperative neurocognitive recovery. Notably, benzodiazepine utilization was coupled with substantially increased opioid use, which may project implications for perioperative pain management.

Comparison of Musculoskeletal Injury and Behavioral Health Diagnoses Among U.S. Army Active Duty Servicewomen in Ground Combat and Non-Ground Combat Military Occupational Specialties.

The U.S. Army's Soldier 2020 program, which started in January 2016, was designed to achieve full integration of women in all military occupational specialties. This study was undertaken to determine differences in risk of musculoskeletal injury and behavioral health (BH) disorders among U.S. Army Active Duty Servicewomen (ADSW) in ground combat military occupational specialties (MOS) versus those in non-ground combat MOS since the start of the program until January 2019. Using healthcare claims data from the Military Health System's Data Repository we conducted a cross-sectional study on ADSW from January 1, 2016 to January 1, 2019 and categorized them as either ground combat specialists (GCSs) or non-ground combat specialists (NGCSs). We identified all female soldiers in our cohort with a musculoskeletal injury (MSKI) and/or BH diagnosis during the study period. A multivariable logistic regression, adjusted by pregnancy or delivery status, was used to assess risk factors associated with GCS and included soldier age, race, body mass index (BMI), tobacco use, alcohol/substance use, and MSKI and BH status as predictor variables. A total of 92,443 U.S. Army ADSW were identified, of whom 3,234 (3.5%) were GCS (infantry, field artillery, cavalry/armor, and air defense) and 89,209 (96.5%) were in non-ground combat billets. A large difference was observed when comparing the age of the population by occupation; GCS women were predominantly between the ages of 18-23 years (71.9%), compared to NGCS women aged 18-23 (41.0%). Top MSKI and BH diagnoses for both occupations were joint pain (44.9% GCS, 50.2% NGCS) and adjustment disorders (26.2% GCS, 28.0% NGCS). GCS women had lower odds for musculoskeletal injury (0.86 AOR, 0.79-0.93 CI, P = 0.0002), obesity per BMI classification (0.82 AOR, 0.70-0.97 CI, P = 0.0214), and BH disorders (0.87 AOR, 0.80-0.95 CI, P = 0.0019); and higher odds for tobacco use (1.44 AOR, 1.27-1.63 CI, P < 0.0001), substance use (1.36 AOR, 1.04-1.79 CI, P = 0.0257), and alcohol use (1.18 AOR, 1.02-1.38 CI, P = 0.0308) when compared to NGCS women. With the increasing focus on soldier medical readiness in today's U.S. Army, the health of all soldiers is of paramount concern to command groups, unit leaders, and individual soldiers. The integration of women into ground combat military occupational specialties is a relatively new program; further longitudinal research of these groups should follow, focusing on their progression and improvement in soldier readiness, overall health, and the well-being of all servicewomen.

Patient Factors Associated with Different Hospice Programs in Korea: Analyzing Healthcare Big Data.

The Korean government has implemented a pilot project that introduces a new type of hospice care program called “Consultative Hospice Care” (COHC) since August 2017. The COHC is a new type of hospice program for terminally ill patients in acute care wards, which is different from the Independent Hospice Unit (IHU) care. This study aimed to compare the characteristics of two groups of hospice patients: COHC care only and both IHU care and COHC groups. Healthcare claim data from 1 April 2018 to 31 March 2020 were retrieved from the HIRA data warehouse system. The main outcome variable was patients receiving COHC only or both COHC and IHU care. The total number of hospice patients was 6482. A multivariate logistic regression analysis was used. Of 6482 hospice care recipients, 3789 (58.5%) received both COHC and IHU care. Those who received both COHC and IHU care were significantly associated with several factors: period from the first evaluation to death (adjusted odds ratio (aOR), 1.026; 95% confidence internal (CI), 1.024–1.029; p < 0.0001), disease severity measured by the Charlson Comorbidity Index (aOR, 1.032; CI, 1.017–1.047; p < 0.0001), consciousness (aOR, 3.654; CI, 3.269–4.085; p < 0.0001), and awareness of end-stage disease (aOR, 1.422; CI, 1.226–1.650; p < 0.0001). The COHC program had a critical role in hospice delivery to terminally ill patients. Policymakers on hospice care need to establish plans that promote efficient hospice care delivery systems.

Use of Prescribed Drugs to Treat Chronic Diseases during Pregnancy in Outpatient Care in Switzerland between 2014 and 2018: Descriptive Analysis of Swiss Health Care Claims Data.

Evidence on the use of drugs during pregnancy in Switzerland is lacking. We aimed to evaluate the utilisation of drugs to treat chronic diseases during pregnancy in Switzerland. We identified all pregnancies (excluding abortions) in Swiss Helsana claims data (2014–2018). In those, we identified all claims for drugs to treat a chronic disease, which typically affects women of childbearing age. Potentially teratogenic/fetotoxic drugs were evaluated during specific risk periods. Results were demographically weighted relative to the Swiss population. We identified claims for ≥1 drug of interest during 22% of 369,371 weighted pregnancies. Levothyroxine was most frequently claimed (6.6%). Antihypertensives were claimed during 5.3% (3.9% nifedipine in T3). Renin–Angiotensin–Aldosterone System (RAAS) inhibitors were dispensed to 0.3/10,000 pregnancies during trimester 2 (T2) or trimester 3 (T3). Insulin was claimed during 3.5% of pregnancies, most frequently in T3 (3.3%). Exposure to psychotropic drugs was 3.8% (mostly Selective serotonin reuptake inhibitors (SSRIs)) and to drugs for obstructive airway diseases 3.6%. Traditional immunosuppressants (excluding corticosteroids) were claimed during 0.5% (mainly azathioprine and hydroxychloroquine), biologic immunosuppressants (Tumour necrosis factor-alpha (TNF-alpha) inhibitors and interleukin inhibitors) during 0.2%, and drugs to treat multiple sclerosis during 0.09% of pregnancies. Antiretrovirals were claimed during 0.15% of pregnancies. Patterns of drug claims were in line with treatment recommendations, but relatively rare events of in utero exposure to teratogenic drugs may have had severe implications for those involved.

Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged

The COVID-19 pandemic has disproportionately affected people with diabetes, who are at increased risk of severe COVID-19.* Increases in the number of type 1 diabetes diagnoses (1,2) and increased frequency and severity of diabetic ketoacidosis (DKA) at the time of diabetes diagnosis (3) have been reported in European pediatric populations during the COVID-19 pandemic. In adults, diabetes might be a long-term consequence of SARS-CoV-2 infection (4-7). To evaluate the risk for any new diabetes diagnosis (type 1, type 2, or other diabetes) >30 days† after acute infection with SARS-CoV-2 (the virus that causes COVID-19), CDC estimated diabetes incidence among patients aged <18 years (patients) with diagnosed COVID-19 from retrospective cohorts constructed using IQVIA health care claims data from March 1, 2020, through February 26, 2021, and compared it with incidence among patients matched by age and sex 1) who did not receive a COVID-19 diagnosis during the pandemic, or 2) who received a prepandemic non-COVID-19 acute respiratory infection (ARI) diagnosis. Analyses were replicated using a second data source (HealthVerity; March 1, 2020-June 28, 2021) that included patients who had any health care encounter possibly related to COVID-19. Among these patients, diabetes incidence was significantly higher among those with COVID-19 than among those 1) without COVID-19 in both databases (IQVIA: hazard ratio [HR]=2.66, 95% CI=1.98-3.56; HealthVerity: HR=1.31, 95% CI=1.20-1.44) and 2) with non-COVID-19 ARI in the prepandemic period (IQVIA, HR=2.16, 95% CI=1.64-2.86). The observed increased risk for diabetes among persons aged <18 years who had COVID-19 highlights the importance of COVID-19 prevention strategies, including vaccination, for all eligible persons in this age group,§ in addition to chronic disease prevention and management. The mechanism of how SARS-CoV-2 might lead to incident diabetes is likely complex and could differ by type 1 and type 2 diabetes. Monitoring for long-term consequences, including signs of new diabetes, following SARS-CoV-2 infection is important in this age group.

Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data.

Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis. We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20-October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-30 (Time B), May 1-October 17 (Time C). The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%). Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data.

A digital mobile health platform increasing efficiency and transparency towards universal health coverage in low- and middle-income countries.

In low-and middle-income countries, achieving universal health coverage remains challenging due to insufficient, temporary and fragmented funding as well as limited accessibility to quality healthcare. Leveraging a mobile health platform can be a powerful tool to address these problems. This paper demonstrates how analysing data collected from a mobile health platform helps optimize healthcare provider networks, monitor patient flows and assess the quality and equitability of access to care. The COVID-19 pandemic reinforces the importance of real-time data on health-seeking behaviour. Between 2018 and 2019, as a Kenyan universal health coverage pilot was being planned, Kisumu County, with support from PharmAccess Foundation, implemented household-level digital registration for healthcare and collected socio-economic and healthcare claims data using the M-TIBA platform. In total, 273,350 Kisumu households enrolled. The claims data showed many patients visit higher-level facilities for ailments, that can be treated at primary care levels, unnecessarily. High-level estimate of the disease burden at participating facilities revealed rampant overprescription of pertinent medicines for highly prevalent malaria and respiratory tract infections, exemplifying clinical management deficiencies. M-TIBA data allowed tracking of individual patient trajectories. Analyses of data are shown at the aggregate level. The paper shows how mobile health platforms can be used to generate valuable insights into access to and quality of care. Funding for healthcare can be united through mobile health platforms, limiting the fragmentation in funding. They can be useful for funders, health managers and policymakers to improve the implementation of universal health coverage programs in low-and middle-income countries.

Case Fatality Risk and Attributable Costs in Patients Treated for Community-Acquired Pneumonia in Germany: A Population-Based Healthcare Claims Data Cohort Study

Case Fatality Risk and Attributable Costs in Patients Treated for Community-Acquired Pneumonia in Germany: A Population-Based Healthcare Claims Data Cohort Study