Association Between Biologic Exposure and the Risk of Depression in Patients with Psoriasis: A Retrospective Analysis of Large US Administrative Claims Data.
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SUBMITTER: Strober B
PROVIDER: S-EPMC11358220 | biostudies-literature | 2024 Sep
REPOSITORIES: biostudies-literature
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Association Between Biologic Exposure and the Risk of Depression in Patients with Psoriasis: A Retrospective Analysis of Large US Administrative Claims Data.
Strober Bruce B Soliman Ahmed M AM Truong Bang B Patel Manish B MB Barqawi Yazan K YK Gisondi Paolo P
American journal of clinical dermatology 20240627 5
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Project description:IntroductionAdverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness.ObjectiveThis study aimed to identify methods for the early detection of a wide range of ADR signals.MethodsFirst, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance.ResultsWe created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method.ConclusionsARM of claims data may be effective in the early detection of a wide range of ADR signals.
| S-EPMC10113351 | biostudies-literature
Project description:BackgroundLimited data exist on correlates of psychological outcomes after kidney donation.MethodsWe used a database integrating Organ Procurement and Transplantation Network registrations for 4650 living kidney donors from 1987 to 2007 with administrative data of a U.S. private health insurer (2000-2007 claims) to identify depression diagnoses among prior living donors. The burden and demographic correlates of depression after enrollment in the insurance plan were estimated by Cox regression. Graft failure and death of the donor's recipient were examined as time-varying exposures.ResultsAfter start of insurance benefits, the cumulative frequency of depression diagnosis was 4.2% at 1 year and 11.5% at 5 years, and depression among donors was less common than among age- and gender-matched general insurance beneficiaries (rate ratio, 0.70; 95% confidence intervals [CI], 0.60-0.81). Demographic and clinical correlates of increased likelihood of depression diagnoses among the prior donors included female gender, white race, and some perioperative complications. After adjustment for donor demographic factors, recipient death (adjusted hazard ratio (aHR), 2.23; 95% CI, 1.11-4.48) and death-censored graft failure (aHR, 3.30; 95% CI, 1.49-7.34) were associated with two to three times the relative risk of subsequent depression diagnosis among nonspousal unrelated donors. There were trends toward increased depression diagnoses after recipient death and graft failure among spousal donors but no evidence of associations of these recipient events with the likelihood of depression diagnosis among related donors.ConclusionsRecipient death and graft loss predict increased depression risk among unrelated living donors in this privately insured sample. Informed consent and postdonation care should consider the potential impact of recipient outcomes on the psychological health of the donor.
| S-EPMC4447542 | biostudies-literature
Project description:BackgroundCerebral palsy is the most common cause of childhood physical disability, with multiple associated comorbidities. Administrative claims data provide population-level prevalence estimates for cerebral palsy surveillance; however, their diagnostic accuracy has never been validated in Quebec. This study aimed to assess the accuracy of administrative claims data for the diagnosis of cerebral palsy.MethodsWe conducted a retrospective cohort study of children with cerebral palsy born between 1999 and 2002 within 6 health administrative regions of Quebec. Provincial cerebral palsy registry data (reference standard) and administrative physician claims were linked. We explored differences between true-positive and false-negative cases using subgroup sensitivity analysis.ResultsA total of 301 children were identified with confirmed cerebral palsy from the provincial registry, for an estimated prevalence of 1.8 (95% confidence interval [CI] 1.6-2.1) per 1000 children 5 years of age. The sensitivity and specificity of administrative claims data for cerebral palsy were 65.5% (95% CI 59.8%-70.8%) and 99.9% (95% CI 99.9%-99.9%), respectively, yielding a prevalence of 2.0 (95% CI 1.9-2.3) per 1000 children 5 years of age. The positive and negative predictive values were 58.8% (95% CI 53.3%-64.1%) and 99.9% (95% CI 99.9%-99.9%), respectively. The κ value was 0.62 (95% CI 0.57-0.67). Administrative claims data were more sensitive for children from rural regions, born preterm, with spastic quadriparesis and with higher levels of motor impairment.InterpretationAdministrative claims data do not capture the full spectrum of children with cerebral palsy. This suggests the need for a more sensitive case definition and caution when using such data without validation.
| S-EPMC5621965 | biostudies-literature
Project description:Routine cytomegalovirus (CMV) screening during pregnancy is not recommended in the United States and the extent to which it is performed is unknown. Using a medical claims database, we computed rates of CMV-specific testing among pregnant women.We used medical claims from the 2009 Truven Health MarketScan® Commercial databases. We computed CMV-specific testing rates using CPT codes.We identified 77,773 pregnant women, of whom 1,668 (2%) had a claim for CMV-specific testing. CMV-specific testing was significantly associated with older age, Northeast or urban residence, and a diagnostic code for mononucleosis. We identified 44 women with a diagnostic code for mononucleosis, of whom 14% had CMV-specific testing.Few pregnant women had CMV-specific testing, suggesting that screening for CMV infection during pregnancy is not commonly performed. In the absence of national surveillance for CMV infections during pregnancy, healthcare claims are a potential source for monitoring practices of CMV-specific testing.
| S-EPMC3582420 | biostudies-other
Project description:ObjectivesHidradenitis suppurativa (HS) causes substantial morbidity and quality-of-life impairment. We examined demographic/clinical characteristics of patients with HS and treatment patterns, prevalence and healthcare resource utilisation/expenditures related to HS in the real-world.DesignRetrospective claims data of MarketScan Commercial, Medicare Supplemental and Medicaid databases (2009-2014).SettingUSA.ParticipantsPatients aged ≥12 years with ≥3 non-diagnostic outpatient or inpatient claims with an HS diagnosis code and ≥12 months continuous enrolment with medical and pharmacy benefits before (preindex) and after (postindex) the earliest diagnosis of HS (index) were included.ResultsThere were 11 325 Commercial/Medicare patients (mean age 37.4 years) and 5164 Medicaid patients (mean age 28.3 years). HS was more common in Medicaid than Commercial/Medicare patients (0.301% and 0.098%, respectively, in 2014). Cellulitis and psychiatric disorders were the most common comorbidities and oral antibiotics and narcotics were the most frequently prescribed drugs preindex, with ≥10% increase postindex in both populations. HS-related inpatient costs decreased while outpatient costs increased from preindex to postindex. Medicaid patients had several risk factors that may be associated with poor outcomes (eg, high rates of prescription pain medication use, comorbidities, drug discontinuation/interruption/holiday, emergency department (ED) visits and hospitalisation).ConclusionsCommercial/Medicare and Medicaid HS beneficiaries experience high comorbidity burden but use different treatment modalities to manage HS. Results suggest a substantial unmet need exists among this patient population, with Medicaid patients experiencing a particularly high burden of disease and expensive healthcare resource utilisation.
| S-EPMC6797383 | biostudies-literature
Project description:Colonoscopy outcomes, such as polyp detection or complication rates, may differ by procedure indication.To develop methods to classify colonoscopy indications from administrative data, facilitating study of colonoscopy quality and outcomes.We linked 14,844 colonoscopy reports from the Clinical Outcomes Research Initiative, a national repository of endoscopic reports, to the corresponding Medicare Carrier and Outpatient File claims. Colonoscopy indication was determined from the procedure reports. We developed algorithms using classification and regression trees and linear discriminant analysis (LDA) to classify colonoscopy indication. Predictor variables included ICD-9CM and CPT/HCPCS codes present on the colonoscopy claim or in the 12 months prior, patient demographics, and site of colonoscopy service. Algorithms were developed on a training set of 7515 procedures, then validated using a test set of 7329 procedures.Sensitivity was lowest for identifying average-risk screening colonoscopies, varying between 55% and 86% for the different algorithms, but specificity for this indication was consistently over 95%. Sensitivity for diagnostic colonoscopy varied between 77% and 89%, with specificity between 55% and 87%. Algorithms with classification and regression trees with 7 variables or LDA with 10 variables had similar overall accuracy, and generally lower accuracy than the algorithm using LDA with 30 variables.Algorithms using Medicare claims data have moderate sensitivity and specificity for colonoscopy indication, and will be useful for studying colonoscopy quality in this population. Further validation may be needed before use in alternative populations.
| S-EPMC3387505 | biostudies-literature
Project description:ObjectiveTo examine the association between non-adherence to clinical practice guidelines (CPGs) and time to return to work (RTW) for patients with workplace injuries.Data sources/study settingSecondary analysis of medical billing and disability data for 148,199 for shoulder and back injuries from a workers' compensation insurer.Study designCox proportional hazard regression is used to estimate the association between time to RTW and receipt of guideline-discordant care. We test the robustness of our findings to an omitted confounding variable.Data collectionCollected by the insurer from the time an injury was reported, through recovery or last follow-up.Principal findingsReceiving guideline-discordant care was associated with slower RTW for only some guidelines. Early receipt of care, and getting less than the recommended amount of care, were correlated with faster RTW. Excessive physical therapy, bracing, and injections were associated with slower RTW.ConclusionsThere is not a consistent relationship between performance on CPGs and RTW. The association between performance on CPG and RTW is difficult to measure in observational data, because analysts cannot control for omitted variables that affect a patient's treatment and outcomes. CPGs supported by observational studies or randomized trials may have a more certain relationship to health outcomes.
| S-EPMC4874815 | biostudies-literature
Project description:BackgroundChronic obstructive pulmonary disease (COPD) is the fourth leading cause of death among US adults and is projected to be the third by 2020. In anticipation of the increasing burden imposed on healthcare systems and payers by patients with COPD, a means of identifying COPD patients who incur higher healthcare utilization and costs is needed.MethodsThis retrospective, cross-sectional analysis of US managed care administrative claims data describes a practical way to identify COPD patients. We analyze 7.79 million members for potential inclusion in the COPD cohort, who were continuously eligible during a 1-year study period. A younger commercial population (7.7 million) is compared with an older Medicare population (0.115 million). We outline a novel approach to stratifying COPD patients using "complexity" of illness, based on occurrence of claims for given comorbid conditions. Additionally, a unique algorithm was developed to identify and stratify COPD exacerbations using claims data.ResultsA total of 42,565 commercial (median age 56 years; 51.4% female) and 8507 Medicare patients (median 75 years; 53.1% female) were identified as having COPD. Important differences were observed in comorbidities between the younger commercial versus the older Medicare population. Stratifying by complexity, 45.0%, 33.6%, and 21.4% of commercial patients and 36.6%, 35.8%, and 27.6% of older patients were low, moderate, and high, respectively. A higher proportion of patients with high complexity disease experienced multiple (≥2) exacerbations (61.7% commercial; 49.0% Medicare) than patients with moderate- (56.9%; 41.6%), or low-complexity disease (33.4%; 20.5%). Utilization of healthcare services also increased with an increase in complexity.ConclusionIn patients with COPD identified from Medicare or commercial claims data, there is a relationship between complexity as determined by pulmonary and non-pulmonary comorbid conditions and the prevalence of exacerbations and utilization of healthcare services. Identification of COPD patients at highest risk of exacerbations using complexity stratification may facilitate improved disease management by targeting those most in need of treatment.
| S-EPMC3050697 | biostudies-literature
Project description:BackgroundThe purpose of this study was to describe demographic and clinical characteristics among patients who have medical encounters for weight management treatments and to investigate the association of those characteristics with treatment modality.MethodsThis was a retrospective database study using medical claims, pharmacy claims, and enrollment information from commercial and Medicare Advantage with Part D members in the Optum Research Database from 01/01/2011-2/29/2020. Adult patients with a claim for a weight management treatment from 01/01/2012-2/28/2019 were categorized into cohorts according to the highest intensity intervention received. To examine the association between patient characteristics and treatment modality received, a multinomial logit model was performed.ResultsCohorts by increasing intensity included lifestyle intervention (LSI, n = 67,679), weight reduction pharmacotherapy (WRRx) with an anti-obesity medication (AOM, n = 6,905), weight reduction procedure (WRP, n = 1,172), and weight reduction surgery (WRS, n = 18,036). Approximately 32.1% and 16.6% of patients who received WRS or WRP had an LSI during the 12-month baseline, and only 0.6% and 0.4% had treatment with long-term AOMs. In a multinomial logit model, patients with type 2 diabetes (not including WRRx cohort), respiratory disorders, cardiovascular risk factors, pain disorders, and mental health conditions had increased odds of treatment with higher intensity intervention versus LSI. Patients who were male, received an intervention more recently (2016-2019), or had a Charlson comorbidity score of 1 (compared to 0) had decreased odds of treatment with higher intensity interventions.ConclusionIn this study, age, sex, body mass index, obesity-related complications, and Charlson comorbidity score appeared to influence the type of weight management treatment modality received. This study improves understanding of weight management treatment utilization and identifies gaps and opportunities to improve obesity care with the appropriate use of different treatment modalities.
| S-EPMC10661997 | biostudies-literature
Project description:BackgroundTo define a method for identifying neonatal intensive care unit (NICU) admissions using administrative claims data.MethodsThis was a retrospective cohort study using claims from Optum's de-identified Clinformatics® Data Mart Database (CDM) from 2016 -2020. We developed a definition to identify NICU admissions using a list of codes from the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), and revenue codes frequently associated with NICU admissions. We compared agreement between codes using Kappa statistics and calculated positive predictive values (PPV) and 95% confidence intervals (CI).ResultsOn average, revenue codes (3.3%) alone identified more NICU hospitalizations compared to CPT codes alone (1.5%), whereas the use of CPT and revenue (8.9%) and CPT or revenue codes (13.7%) captured the most NICU hospitalizations, which aligns with rates of preterm birth. Gestational age alone (4.2%) and birthweight codes alone (2.0%) identified the least number of potential NICU hospitalizations. Setting CPT codes as the standard and revenue codes as the "test,", revenue codes resulted in identifying 86% of NICU admissions (sensitivity) and 97% of non-NICU admissions (specificity).ConclusionsUsing administrative data, we developed a robust definition for identifying neonatal admissions. The identified definition of NICU codes is easily adaptable, repeatable, and flexible for use in other datasets.
| S-EPMC10916318 | biostudies-literature