Influenza has been associated with the risk of developing Parkinson disease, but the association is controversial.
To examine whether prior influenza and other infections are associated with ...Parkinson disease more than 10 years after infection.
This case-control study used data from 1977 to 2016 from the Danish National Patient Registry. All individuals with Parkinson disease, excluding those with drug-induced parkinsonism, were included and matched to 5 population controls on sex, age, and date of Parkinson diagnosis. Data were analyzed from December 2019 to September 2021.
Infections were ascertained between 1977 and 2016 and categorized by time from infection to Parkinson disease diagnosis. To increase specificity of influenza diagnoses, influenza exposure was restricted to months of peak influenza activity.
Parkinson disease diagnoses were identified between January 1, 2000, and December 31, 2016. Crude and adjusted odds ratios (ORs) and 95% CIs were calculated by conditional logistic regression overall and stratified by time between infection and Parkinson disease (5 years or less, more than 5 to 10 years, more than 10 years).
Of 61 626 included individuals, 23 826 (38.7%) were female, and 53 202 (86.3%) were older than 60 years. A total of 10 271 individuals with Parkinson disease and 51 355 controls were identified. Influenza diagnosed at any time during a calendar year was associated with Parkinson disease more than 10 years later (OR, 1.73; 95% CI, 1.11-2.71). When influenza exposure was restricted to months of highest influenza activity, an elevated OR with a wider confidence interval was found (OR, 1.52; 95% CI, 0.80-2.89). There was no evidence of an association with any type of infection more than 10 years prior to Parkinson disease (OR, 1.04; 95% CI, 0.98-1.10). Several specific infections yielded increased odds of Parkinson disease within 5 years of infection, but results were null when exposure occurred more than 10 years prior.
In this case-control study, influenza was associated with diagnoses of Parkinson disease more than 10 years after infection. These observational data suggest a link between influenza and Parkinson disease but do not demonstrate causality. While other infections were associated with Parkinson disease diagnoses soon after infection, null associations after more than 10 years suggest these shorter-term associations are not causal.
Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified ...potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.
Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system ...applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.
People with HIV (PWH) may be at risk for more severe COVID-19 outcomes. We compared risk for severe COVID-19 in PWH with matched individuals without HIV.
We identified adults in Massachusetts with a ...positive SARS-CoV-2 test, March 2020-July 2022, using electronic medical record data from 3 large clinical practice groups. We then used regression models to compare outcomes among PWH versus propensity score-matched people without HIV (matched 20:1) for severe COVID-19 (pneumonia or acute respiratory distress syndrome), hospitalization, and hospital length of stay.
We identified 171,058 individuals with COVID-19; among them, 768 PWH were matched to 15,360 individuals without HIV. Overall, severe COVID-19 and hospitalization were similar in PWH and those without HIV (severe COVID-19: 3.8% vs 3.0%, adjusted odds ratio OR 1.27, 95% confidence interval CI: 0.86-1.87; hospitalization: 12.1% vs 11.3%, adjusted OR: 1.08, 95% CI: 0.87 to 1.35). Compared with people without HIV, PWH with low CD4 T-cell counts (<200 cells/mm 3 ) had more severe COVID-19 (adjusted OR: 3.99, 95% CI: 2.06 to 7.74) and hospitalization (adjusted OR: 2.26, 95% CI: 1.35 to 3.80), but PWH with high CD4 counts had lower odds of hospitalization (adjusted OR: 0.73, 95% CI: 0.52 to 1.03).
PWH with low CD4 T-cell counts had worse COVID-19 outcomes compared with people without HIV, but outcomes for those with high CD4 counts were similar to, or better than, those without HIV. It is unclear whether these findings are generalizable to settings where PWH have less access to and engagement with health care.
A fundamental question in using real‐world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort‐defining ...characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit‐for‐purposefulness of real‐world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.
Purpose
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.
Methods
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).
Results
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%).
Conclusion
Our results support the use of code U07.1 to identify hospitalized COVID‐19 patients in U.S. claims data.
We examined annual outpatient antibiotic dispensings within a health insurance plan covering ∼970,000 members per year during 2000-2016. The proportion of members with antibiotic dispensings ...decreased from 33.3% in 2000 to 25.9% in 2016. This trend was consistent in all stratifications of age, race/ethnicity, sex, and comorbidities.
Potentially inappropriate prescribing of medications in older adults, particular those with dementia, can lead to adverse drug events including falls and fractures, worsening cognitive impairment, ...emergency department visits, and hospitalizations. Educational mailings from health plans to patients and their providers to encourage deprescribing conversations may represent an effective, low-cost, "light touch", approach to reducing the burden of potentially inappropriate prescription use in older adults with dementia.
The objective of the Developing a PRogram to Educate and Sensitize Caregivers to Reduce the Inappropriate Prescription Burden in Elderly with Alzheimer's Disease (D-PRESCRIBE-AD) trial is to evaluate the effect of a health plan based multi-faceted educational outreach intervention to community dwelling patients with dementia who are currently prescribed sedative/hypnotics, antipsychotics, or strong anticholinergics.
The D-PRESCRIBE-AD is an open-label pragmatic, prospective randomized controlled trial (RCT) comparing three arms: 1) educational mailing to both the health plan patient and their prescribing physician (patient plus physician arm, n = 4814); 2) educational mailing to prescribing physician only (physician only arm, n = 4814); and 3) usual care (n = 4814) among patients with dementia enrolled in two large United States based health plans. The primary outcome is the absence of any dispensing of the targeted potentially inappropriate prescription during the 6-month study observation period after a 3-month black out period following the mailing. Secondary outcomes include dose-reduction, polypharmacy, healthcare utilization, mortality and therapeutic switching within targeted drug classes.
This large pragmatic RCT will contribute to the evidence base on promoting deprescribing of potentially inappropriate medications among older adults with dementia. If successful, such light touch, inexpensive and highly scalable interventions have the potential to reduce the burden of potentially inappropriate prescribing for patients with dementia. ClinicalTrials.gov Identifier: NCT05147428.
We described care received by hospitalized children with COVID-19 or multi-system inflammatory syndrome (MIS-C) prior to the 2021 COVID-19 Omicron variant surge in the US. We identified hospitalized ...children <18 years of age with a COVID-19 or MIS-C diagnosis (COVID-19 not required), separately, from February 2020-September 2021 (n = 126 hospitals). We described high-risk conditions, inpatient treatments, and complications among these groups. Among 383,083 pediatric hospitalizations, 2,186 had COVID-19 and 395 had MIS-C diagnosis. Less than 1% had both COVID-19 and MIS-C diagnosis (n = 154). Over half were >6 years old (54% COVID-19, 70% MIS-C). High-risk conditions included asthma (14% COVID-19, 11% MIS-C), and obesity (9% COVID-19, 10% MIS-C). Pulmonary complications in children with COVID-19 included viral pneumonia (24%) and acute respiratory failure (11%). In reference to children with COVID-19, those with MIS-C had more hematological disorders (62% vs 34%), sepsis (16% vs 6%), pericarditis (13% vs 2%), myocarditis (8% vs 1%). Few were ventilated or died, but some required oxygen support (38% COVID-19, 45% MIS-C) or intensive care (42% COVID-19, 69% MIS-C). Treatments included: methylprednisolone (34% COVID-19, 75% MIS-C), dexamethasone (25% COVID-19, 15% MIS-C), remdesivir (13% COVID-19, 5% MIS-C). Antibiotics (50% COVID-19, 68% MIS-C) and low-molecular weight heparin (17% COVID-19, 34% MIS-C) were frequently administered. Markers of illness severity among hospitalized children with COVID-19 prior to the 2021 Omicron surge are consistent with previous studies. We report important trends on treatments in hospitalized children with COVID-19 to improve the understanding of real-world treatment patterns in this population.
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