Proportion of antiviral prescription by sex, race/ethnicity, and US region in Medicaid and CDM influenza episodes aged 18-64, during the 2015/2016 to 2018/2019 influenza seasons.

Abstract

Optum ZIP5 v8.0 database in the OMOP data model (https://www.ohdsi.org/data-standardization/the-common-data-model/). This dataset covers 2003-Q1 to 2020-Q2

Section 10

A Condition Era is defined as a span of time when the Person is assumed to have a given condition. Similar to Drug Eras, Condition Eras are chronological periods of Condition Occurrence. Combining individual Condition Occurrences into a single Condition Era serves two purposes:

• It allows aggregation of chronic conditions that require frequent ongoing care, instead of treating each Condition Occurrence as an independent event.
• It allows aggregation of multiple, closely timed doctor visits for the same Condition to avoid double-counting the Condition Occurrences.

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For example, consider a Person who visits her Primary Care Physician (PCP) and who is referred to a specialist. At a later time, the Person visits the specialist, who confirms the PCP's original diagnosis and provides the appropriate treatment to resolve the condition. These two independent doctor visits should be aggregated into one Condition Era.v

Conventions

• Condition Era records will be derived from the records in the CONDITION_OCCURRENCE table using a standardized algorithm.
• Each Condition Era corresponds to one or many Condition Occurrence records that form a continuous interval.
• Condition Eras are built with a Persistence Window of 30 days, meaning, if no occurrence of the same condition_concept_id happens within 30 days of any one occurrence, it will be considered the condition_era_end_date.

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The text above is taken from the OMOP CDM v5.3 Specification document.

Section 8

The DOMAIN table includes a list of OMOP-defined Domains the Concepts of the Standardized Vocabularies can belong to. A Domain defines the set of allowable Concepts for the standardized fields in the CDM tables. For example, the "Condition" Domain contains Concepts that describe a condition of a patient, and these Concepts can only be stored in the condition_concept_id field of the CONDITION_OCCURRENCE and CONDITION_ERA tables. This reference table is populated with a single record for each Domain and includes a descriptive name for the Domain.

Conventions

• There is one record for each Domain. The domains are defined by the tables and fields in the OMOP CDM that can contain Concepts describing all the various aspects of the healthcare experience of a patient.
• The domain_id field contains an alphanumerical identifier, that can also be used as the abbreviation of the Domain.
• The domain_name field contains the unabbreviated names of the Domain.
• Each Domain also has an entry in the Concept table, which is recorded in the domain_concept_id field. This is for purposes of creating a closed Information Model, where all entities in the OMOP CDM are covered by unique Concept.

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The text above is taken from the OMOP CDM v5.3 Specification document.

Section 12

A Drug Era is defined as a span of time when the Person is assumed to be exposed to a particular active ingredient. A Drug Era is not the same as a Drug Exposure: Exposures are individual records corresponding to the source when Drug was delivered to the Person, while successive periods of Drug Exposures are combined under certain rules to produce continuous Drug Eras.

Conventions

• Drug Eras are derived from records in the DRUG_EXPOSURE table using a standardized algorithm.
• Each Drug Era corresponds to one or many Drug Exposures that form a continuous interval and contain the same Drug Ingredient (active compound).
• The drug_concept_id field only contains Concepts that have the concept_class 'Ingredient'. The Ingredient is derived from the Drug Concepts in the DRUG_EXPOSURE table that are aggregated into the Drug Era record.
• The Drug Era Start Date is the start date of the first Drug Exposure.
• The Drug Era End Date is the end date of the last Drug Exposure. The End Date of each Drug Exposure is either taken from the field drug_exposure_end_date or, as it is typically not available, inferred using the following rules:
• The Gap Days determine how many total drug-free days are observed between all Drug Exposure events that contribute to a DRUG_ERA record. It is assumed that the drugs are "not stockpiled" by the patient, i.e. that if a new drug prescription or refill is observed (a new DRUG_EXPOSURE record is written), the remaining supply from the previous events is abandoned.
• The difference between Persistence Window and Gap Days is that the former is the maximum drug-free time allowed between two subsequent DRUG_EXPOSURE records, while the latter is the sum of actual drug-free days for the given Drug Era under the abo

BackgroundInfluenza-related healthcare utilization among Medicaid patients and commercially insured patients is not well-understood. This study compared influenza-related healthcare utilization and assessed disease management among individuals diagnosed with influenza during the 2015–2019 influenza seasons.MethodsThis retrospective cohort study identified influenza cases among adults (18–64 years) using data from the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Research Identifiable Files (RIF) and Optum’s de-identified Clinformatics® Data Mart Database (CDM). Influenza-related healthcare utilization rates were calculated per 100,000 patients by setting (outpatient, emergency department (ED), inpatient hospitalizations, and intensive care unit (ICU) admissions) and demographics (sex, race, and region). Rate ratios were computed to compare results from both databases. Influenza episode management assessment included the distribution of the index point-of-care, antiviral prescriptions, and laboratory tests obtained.ResultsThe Medicaid population had a higher representation of racial/ethnic minorities than the CDM population. In the Medicaid population, influenza-related visits in outpatient and ED settings were the most frequent forms of healthcare utilization, with similar rates of 652 and 637 visits per 100,000, respectively. In contrast, the CDM population predominantly utilized outpatient settings. Non-Hispanic Blacks and Hispanics exhibited the highest rates of influenza-related ED visits in both cohorts. In the Medicaid population, Black (64.5%) and Hispanic (51.6%) patients predominantly used the ED as their index point-of-care for influenza. Overall, a greater proportion of Medicaid beneficiaries (49.8%) did not fill any influenza antiviral prescription compared to the CDM population (37.0%).ConclusionAddressing disparities in influenza-related healthcare utilization between Medicaid and CDM populations is crucial for equitable healthcare access. Targeted interventions are needed to improve primary care and antiviral access and reduce ED reliance, especially among racial/ethnic minorities and low-income populations.

Abstract

Optum DOD (Date of Death) v8.0 database in the OMOP data model (https://www.ohdsi.org/data-standardization/the-common-data-model/)

Section 10

A Condition Era is defined as a span of time when the Person is assumed to have a given condition. Similar to Drug Eras, Condition Eras are chronological periods of Condition Occurrence. Combining individual Condition Occurrences into a single Condition Era serves two purposes:

• It allows aggregation of chronic conditions that require frequent ongoing care, instead of treating each Condition Occurrence as an independent event.
• It allows aggregation of multiple, closely timed doctor visits for the same Condition to avoid double-counting the Condition Occurrences.

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For example, consider a Person who visits her Primary Care Physician (PCP) and who is referred to a specialist. At a later time, the Person visits the specialist, who confirms the PCP's original diagnosis and provides the appropriate treatment to resolve the condition. These two independent doctor visits should be aggregated into one Condition Era.

Conventions

• Condition Era records will be derived from the records in the CONDITION_OCCURRENCE table using a standardized algorithm.
• Each Condition Era corresponds to one or many Condition Occurrence records that form a continuous interval.
• Condition Eras are built with a Persistence Window of 30 days, meaning, if no occurrence of the same condition_concept_id happens within 30 days of any one occurrence, it will be considered the condition_era_end_date.

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The text above is taken from the OMOP CDM v5.3 Specification document.

Section 5

The CONCEPT_ANCESTOR table is designed to simplify observational analysis by providing the complete hierarchical relationships between Concepts. Only direct parent-child relationships between Concepts are stored in the CONCEPT_RELATIONSHIP table. To determine higher level ancestry connections, all individual direct relationships would have to be navigated at analysis time. The CONCEPT_ANCESTOR table includes records for all parent-child relationships, as well as grandparent-grandchild relationships and those of any other level of lineage.

Using the CONCEPT_ANCESTOR table allows for querying for all descendants of a hierarchical concept. For example, drug ingredients and drug products are all descendants of a drug class ancestor.

Conventions

• The concept_name field contains a valid Synonym of a concept, including the description in the concept_name itself. I.e. each Concept has at least one Synonym in the CONCEPT_SYNONYM table. As an example, for a SNOMED-CT Concept, if the fully specified name is stored as the concept_name of the CONCEPT table, then the Preferred Term and Synonyms associated with the Concept are stored in the CONCEPT_SYNONYM table.
• Only Synonyms that are active and current are stored in the CONCEPT_SYNONYM table. Tracking synonym/description history and mapping of obsolete synonyms to current Concepts/Synonyms is out of scope for the Standard Vocabularies.
• Currently, only English Synonyms are included.

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The text above is taken from the OMOP CDM v5.3 Specification document.

Section 4

The COST table captures records containing the cost of any medical entity recorded in one of the DRUG_EXPOSURE, PROCEDURE_OCCURRENCE, VISIT_OCCURRENCE or DEVICE_OCCURRENCE tables.

The information about the cost is defined by the amount of money paid by the Person and Payer, or as the charged cost by the healthcare provider. So, the COST table can be used to represent both cost and revenue perspectives. The cost_type_concept_id field will use concepts in the Standardized Vocabularies to designate the source of the cost data. A reference to the health plan information in the PAYER_PLAN_PERIOD table is stored in the record that is responsible for the determination of the cost as well as some of the payments.

Convention

The COST table will store information reporting money or currency amounts. There are three types of cost data, defined in the cost_type_concept_id: 1) paid or reimbursed amounts, 2) charges or list prices (such as Average Wholesale Prices), and 3) costs or expenses incurred by the provider. The defined fields are variables found in almost all U.S.-based claims data sources, which is the most common data source for researchers. Non-U.S.-based data holders are encouraged to engage with OHDSI to adjust these tables to their needs.

One cost record is generated for each response by a payer. In a claims databases, the payment and payment terms reported by the payer for the goods or services billed will generate one cost record. If the source data has payment information f

Data specifications and coding details for study outcomes.

Healthcare utilization rates per 100,000 in Medicaid and CDM beneficiaries aged 18-64 years, stratified by setting (Outpatient, ED, Inpatient, ICU) and grouped by sex, race/ethnicity, and US region during the 2015/2016 to 2018/2019 influenza seasons.

Characteristics of Medicaid and CDM beneficiaries aged 18-64 during the 2015/2016 to 2018/2019 influenza seasons.

These are peer-reviewed supplementary materials for the article 'Burden of illness for patients with primary biliary cholangitis: an observational study of clinical characteristics and healthcare resource utilization' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: Codes used for cirrhosis diagnosis and imaging biopsy proceduresAim: To evaluate the clinical characteristics and healthcare resource utilization for acute care and its costs for patients with primary biliary cholangitis (PBC) with or without cirrhosis. Materials & methods: This retrospective observational cohort study was conducted using two datasets (Komodo’s Healthcare Map™ [Komodo Health] and Optum Clinformatics R ? Data Mart [CDM] database) between 2015 and 2023. Patients (≥18 years) with PBC were identified based on ≥1 inpatient or ≥2 outpatient claims. Healthcare resource utilization for acute care (hospitalizations and emergency department [ED] visits [not leading to hospitalization]) were assessed in both datasets, and associated medical costs were evaluated in Optum CDM. Results: In Komodo Health, of the 29,758 patients with PBC (mean age: 59.2 years), 21.6% had cirrhosis and 50.4% of patients with cirrhosis had Medicaid or Medicare coverage. Of the total 8143 patients in Optum CDM (mean age: 67.0 years), 20.7% had cirrhosis, and most were enrolled in Medicare (69.7%). There was a larger proportion of men in the cirrhosis group compared with the no-cirrhosis group in Komodo Health (31.7 vs 16.3%) and Optum CDM (29.7 vs 16.5%). Annually, among patients with cirrhosis who had a hospitalization, 69.3% had additional hospitalizations, and among patients who had an ED visit, 52.9% had additional ED visits in Komodo Health; similar results were observed in Optum CDM. Among patients with at least one acute-care event, the mean annual acute-care costs with and without cirrhosis were $113,568 and $47,436, respectively. Conclusion: Data from two large healthcare claims databases showed that the majority of patients who had at least one acute-care event experienced additional acute-care events, particularly among those with cirrhosis. Timely treatment to avoid hospitalization and disease progression may help mitigate the clinical and economic burden for patients with PBC.

Characteristics of infants with an acute MA RSV LRTI during their first RSV season, by insurance claims database and index diagnosis definition.

Demographic characteristics of the total and stroke population in the CDM dataset 2017–2020.

Objectives: To determine the direct medical cost of illness from essential tremor (ET) from a patient perspective. Methods: Secondary data from the Optum’s de-identified Clinformatics® Data Mart Database (CDM) from 2018-2019 was used to assess medical resource utilization and costs. Propensity score matching was used to match patients age 40+ with to statistically similar controls. Generalized linear models were used to estimate average, adjusted total costs of care per year, by health care setting, and provider specialty. Results: The final sample included 41,200 patients with at least one ET claim and 36,871 matched patients. Overall, ET patients ages 40+ had about $28,217 in direct medical costs per year, which was about $1,601 more than matched comparisons (p < 0.001). This was driven by greater number of outpatient visits overall and with specialists. Extrapolating the estimates from our study and pairing them with published age-specific disease prevalence statistics for ET, we calculated an annual cost for direct medical care of ET patients ages 40+ to be about $9.4 billion. Conclusion: The estimated direct medical costs among adults age 40+ with an ET diagnosis aggregated to the population-level are non-trivial.

Introduction: Essential tremor (ET) is the most common tremor disorder, estimated to affect 7 million individuals in the U.S. There is little empirical evidence on comorbidities among this population beyond higher prevalence of brain-related and stress-related disorders. This study aims to examine differences in the prevalence of the 31 Elixhauser comorbidities among ET patients compared to statistically similar control patients.
Methods: An extract from Optum’s de-identified Clinformatics® Data Mart Database (CDM) from 2018-2019 of adults aged 40 to 80 years with at least one claim with an ET diagnosis was propensity score matched to controls. Logistic regression was used to generate doubly robust adjusted odds ratios for each of the 31 Elixhauser comorbidities.
Results: In these analyses, ET patients had significantly greater adjusted odds of depression, alcohol abuse, and other neurological disorders, as well as chronic pulmonary disease, renal failure, hyperthyroidism, and cardiac arrhythmias relative to controls. They also had lower odds of uncomplicated diabetes, congestive heart failure, metastatic cancer, paralysis, peripheral vascular disease, and fluid and electrolyte disorders. Conclusion: A number of recent studies, including our own, suggest that psychiatric, neurologic and stress-related disorders may be more prevalent among ET patients than controls. Additional differences in the prevalence of a range of medical comorbidities have also been variably reported across studies, suggesting that some combination of these might be more prevalent. Further studies would be of value in sorting through these associations.