HealthCore
HealthCore Integrated Research Database (HIRD) has one of the largest commercially insured population databases in the nation. Information is available on nearly 60 million individuals from multiple health plans across the U.S and over 175,000 physicians. 44+ million private U.S. commercial lives with medical and pharmacy claims, spanning 14 states from Anthem’s affiliated health plans dating back to 2006. This would include 14 WellPoint affiliated Blue Cross and/or Blue Shield licensed plans and 2 non-WellPoint affiliated Blue Cross and/or Blue Shield licensed plans. Regions represented include: Northeast, Southeast, Mid-Atlantic, Midwest, Central, and West. Lab results are also available for 13+ million lives integrated with claims data as well as clinical oncology data. Other data captured includes demographic data, enrollment data, outpatient prescription data, outpatient diagnosis data and procedures, hospital discharge data, deaths – captured using National Death Index or Social Security Administration’s Death Master File (deaths in the hospital may also be captured), and facility information.
Linkages available include standing linkage with SSA Master Death files and National Death Index, state cancer registries, state immunization registries, patient and provider surveys.
Strong RQ – What are the treatment patterns and cost of erythropoiesis stimulating agents in patients with cancer receiving myelosuppressive chemotherapy?
This sample includes medical and pharmacy claims data, clinical oncology data, lab results covering 13+ million individuals, and also a wide geographical representation from the Northeast, Southeast, Mid-Atlantic, Midwest, Central, and West. Therefore, it is advantageous to use this data source due to broad geographic representation for assessing outcomes in specific populations and comparison of regional practice pattern variations. Also, there would be the ability to validate claims versus EMR data for a subset of the population without going to paper charts and ability to validate claims with EMRs in other regions and provider/hospital systems (where EMRs are available). Also, this data source would be good to study broad population representative of the commercially insured in the United States.
Weak RQ – What are the inpatient treatment patterns and cost of erythropoiesis stimulating agent treatment in elderly patients ≥65 years of age with cancer receiving myelosuppressive chemotherapy?
This would not be a great research question to answer using this data source because complete capture of elderly care is also not possible, since Medicare is the primary payer, however it does capture those on Medicare Advantage Plans. Moreover, there is no linkage available to Medicare FFS data, so capture of elderly patients (≥ 65 years old) most likely will be incomplete. Also, formularies can be specific to the network and may not be representative of the population treatment patterns.
The Surveillance, Epidemiology, and End Results (SEER) – Medicare linked Database
The SEER-Medicare data reflect the linkage of two large population-based sources of data that provide detailed information about Medicare beneficiaries with cancer. The data come from the Surveillance, Epidemiology and End Results (SEER) External Web Site Policy program of cancer registries that collect clinical, demographic and cause of death information for persons with cancer and the Medicare claims for covered health care services from the time of a person's Medicare eligibility until death. The linkage of these two data sources results in a unique population-based source of information that can be used for an array of epidemiological and health services research.
The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI) collects data on cancer incidence and survival from population-based cancer registries throughout the United States. Data collection began in 1973 with a limited amount of registries and has expanded over time to include registries that cover 28% of the United States population (updated annually and data is available through 2011). The SEER Program registries routinely collect data on patient demographics, primary tumor site, tumor morphology and stage at diagnosis, first course of treatment, and follow-up for vital status. The SEER data do not capture information about surgery and radiation provided past four months of diagnosis, nor is there information about recurrence or metastasis that is detected subsequent to the initial diagnosis.
The Medicare data available as part of SEER-Medicare include claims from hospital, outpatient, physician, home health, and hospice providers. Each Medicare file varies in the data elements included and the types of procedure and diagnostic codes used, either International Classification of Diseases (ICD-9) codes for procedures and diagnoses or HCFA Common Procedure Coding System (HCPCS) codes for procedures. HCPCS are the AMA's Common Procedure Terminology codes (CPT-4) with additional codes used exclusively by CMS. In general, all Medicare files have fields for race, sex, and date of birth or age, the date(s) of service, diagnostic codes (for many files), and procedure codes in addition to the amounts for charges and reimbursement. In addition, every Medicare file contains a provider identification number for the hospital or physician.
Strong RQ – What is the comparative effectiveness of carboplatin and paclitaxel (with and without) bevacizumab in older patients with advanced non-small cell lung cancer?
It is advantageous to use this data source to answer this research question because the linkage to SEER data provides the identification of incident cases (which is not possible from the Medicare claims) with detailed site and stage reporting and information about the cause of death. The Medicare data also offers a longitudinal perspective, making it possible to look at medical services before, during, and after diagnosis. Claims before diagnosis can also be used to measure preexisting co-morbidities that might influence treatment decisions and cancer screening, to a limited extent. Also, this data source is appropriate as Medicare would include health insurance information for the elderly (65+), as well as those with end-stage renal disease and some disabled and is pertinent to this population.
Weak RQ – Identification of cancer recurrence in colorectal and breast cancer patients (in the general population).
Neither SEER nor Medicare collect information about recurrence. Medicare claims can be used to identify recurrences indirectly only if the patient receives treatment for the recurrence. It is not possible to identify recurrence through diagnosis codes only. Investigators who have used claims to identify treated recurrence have used an approach that involves reviewing claims longitudinally for cancer related treatment (cancer-related surgery, chemotherapy, RT) after the initial care period. The later surgeries should be selected carefully (ex. hepatic resection for a colon cancer patient likely shows recurrence while a hemicolectomy may be for disease recurrence or adhesions).
Using a treatment-based approach is dependent on the patient receiving additional treatment in the event of a recurrence. However, many elderly patients are not offered/decline additional treatment if their cancer recurs. While it is possible to use SEER-Medicare to identify patients with "treated recurrence", this approach can miss a large number of cases and the cases identified are a biased sample of the elderly.
Also, Medicare data limited to those over the age of 64 and the disabled and oral medications are not covered (prior to Part D data). Moreover, it is also important to note that SEER registry areas may not be totally representative of patterns of care and there is also a long-time lag to obtain data.
Hunger in America (HIA 2014)
The Hunger in America survey, the largest of its kind, is a series of quadrennial studies providing comprehensive demographic profiles of people receiving food assistance through the charitable sector, and offers in-depth analyses of the partner agencies in the Feeding America Network. Feeding America is a nationwide network consisting of 200-member food banks serving all 50 states, the District of Columbia, and Puerto Rico. The Feeding America network of food banks provides food assistance to an estimated 46.5 million Americans in need each year, including 12 million children. Data collected from these surveys, last completed in 2014 (HIA 2014), help guide the development of programs and solutions that improve FI for individuals and their households and inform public awareness and policy development for addressing hunger in the United States. Of note, this survey was available to be administered in English, Spanish, Vietnamese, Russian, and Mandarin, and could be completed either by the client independently or with assistance from a proxy.
The HIA 2014 Client Survey was fielded from April, 2013 through August, 2013, and was implemented through a force of data collectors recruited by each participating food bank. Based on pre-testing, the survey was revised, finalized, and then programmed into a computerized version of the survey to be implemented using a touchscreen tablet device and Audio Computer-Assisted Self-Interview (ACASI) technology. A second pretest was then performed on the digital/audio survey, and client responses were used to make additional improvements before the final survey was created. HIA 2014 represented the first HIA client survey utilizing this computerized technology.
HIA 2014 aimed to collect information directly from Feeding America clients, and to describe the numbers and characteristics of clients using the networks for charitable food assistance. Because conducting interviews with every client served by every program over an extended period of time was not feasible, probability sampling was used to select a subset of programs at which data collection should occur, the days on which data collection should occur at those programs, and the clients who should be asked to complete the survey. As it applies to HIA 2014, probability sampling is an approach in which each client has a known, positive chance of being selected to complete the survey. This technique makes it possible to use the sample to estimate population-level information. As the full population of Feeding America clients in the US is unknown, it was not possible to select from a known list of clients, as is sometimes possible in probability sampling. Consequently, the HIA 2014 was designed with a multistage design to facilitate selection of the probability sample.
At least 6,000 data collectors were trained and registered to carry out client data collection. Data collectors followed a prescribed study plan in order to select a random sample of clients at nearly 12,500 assistance programs across the Feeding America network.
Strong RQ – Are individuals seeking assistance at food pantries who have a personal or household history of diabetes mellitus able to obtain diabetes appropriate foods in 2014 (cross-sectional study)? Is the prevalence of individuals with a personal or household history of DM seeking assistance at food pantries requesting and unable to obtain fresh fruits, vegetables, proteins, grains, and dairy the same as the prevalence of individuals without a personal or household history of DM seeking assistance at food pantries?
This is an appropriate database for this research question because the target population would be adults seeking assistance at food pantries with diabetes or at risk for developing diabetes. Moreover, the presence of food insecurity (FI) makes diabetes mellitus (DM) management more challenging; a common coping strategy is shifting dietary intake towards cheaper and more obesogenic foods. There is clearly a role for dietary education in the management of DM, and having knowledge and availability of proper nutrition is important to improving glycemic control and preventing the development of T2D in at-risk individuals.
This survey is also advantageous because participants surveyed included grocery (i.e. food-bank operated pantry programs, food panties, community gardens, school pantries); meal (i.e. community kitchen, group home, shelter, transitional housing); food-related benefits (i.e. WIC outreach, SNAP); and non-food programs (i.e. financial assistance, GED programs, health clinics, job training programs).
Weak RQ – Are the types of food requested and unable to be obtained from assistance programs in the US between those with a personal or household history of DM and those with no personal or household history of DM associated with increased mortality rates due to DM? Is the lack of access to proper cooking and refrigeration amenities associated with increased mortality due to DM?
This would not be a great research question to answer using this data source because of the cross-sectional nature of the data source. Moreover, mortality record data was not captured and it would not be possible to address this question. One would also need to validate cause-of-death determinations (via death record information with hospital records, etc).