U.S. Women with Invasive Cervical Cancer: Characteristics and Potential Barriers to Prevention

Objectives: Although invasive cervical cancer (ICC) rates have declined since the advent of screening, the annual age-adjusted ICC rate in the United States remains 7.5 per 100,000 women. Failure of recommended screening and management often precedes ICC diagnoses. The study aimed to evaluate characteristics of women with incident ICC, including potential barriers to accessing preventive care. Materials and Methods: We abstracted medical records for patients with ICC identified during 2008-2020 in five U.S. population-based surveillance sites covering 1.5 million women. We identified evidence of adverse social and medical conditions, including uninsured/underinsured, language barrier, substance use disorder, incarceration, serious mental illness, severe obesity, or pregnancy at diagnosis. We calculated descriptive frequencies and compared potential barriers by race/ethnicity, and among women with and without symptoms at diagnosis using chi-square tests. Results: Among 1,606 women with ICC (median age: 49 years; non-White: 47.4%; stage I: 54.7%), the majority (68.8%) presented with symptoms. Forty-six percent of women had at least one identified potential barrier; 15% had multiple barriers. The most common potential barriers among all women were being underinsured/uninsured (17.3%), and language (17.1%). Presence of any potential barrier was more frequent among non-White women and women with than without symptoms (p < 0.05). Conclusions: In this population-based descriptive study of women with ICC, we identified adverse circumstances that might have prevented women from seeking screening and treatment to prevent cancer. Interventions to increase appropriate cervical cancer screening and management are critical for reducing cervical cancer rates.

Error Rates of Data Processing Methods in Clinical Research: A Systematic Review and Meta-Analysis of Manuscripts Identified Through PubMed

Background

In clinical research, prevention of systematic and random errors of data collected is paramount to ensuring reproducibility of trial results and the safety and efficacy of the resulting interventions. Over the last 40 years, empirical assessments of data accuracy in clinical research have been reported in the literature. Although there have been reports of data error and discrepancy rates in clinical studies, there has been little systematic synthesis of these results. Further, although notable exceptions exist, little evidence exists regarding the relative accuracy of different data processing methods. We aim to address this gap by evaluating error rates for 4 data processing methods.

Methods

A systematic review of the literature identified through PubMed was performed to identify studies that evaluated the quality of data obtained through data processing methods typically used in clinical trials: medical record abstraction (MRA), optical scanning, single-data entry, and double-data entry. Quantitative information on data accuracy was abstracted from the manuscripts and pooled. Meta-analysis of single proportions based on the Freeman-Tukey transformation method and the generalized linear mixed model approach were used to derive an overall estimate of error rates across data processing methods used in each study for comparison.

Results

A total of 93 papers (published from 1978 to 2008) meeting our inclusion criteria were categorized according to their data processing methods. The accuracy associated with data processing methods varied widely, with error rates ranging from 2 errors per 10,000 fields to 2,784 errors per 10,000 fields. MRA was associated with both high and highly variable error rates, having a pooled error rate of 6.57% (95% CI: 5.51, 7.72). In comparison, the pooled error rates for optical scanning, single-data entry, and double-data entry methods were 0.74% (0.21, 1.60), 0.29% (0.24, 0.35) and 0.14% (0.08, 0.20), respectively.

Conclusions

Data processing and cleaning methods may explain a significant amount of the variability in data accuracy. MRA error rates, for example, were high enough to impact decisions made using the data and could necessitate increases in sample sizes to preserve statistical power. Thus, the choice of data processing methods can likely impact process capability and, ultimately, the validity of trial results.

Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the Data Quality Literature

Background

Medical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework.

Methods

Using a moderator meta-analysis employed with Q-test, the MRA error rates from the meta-analysis of the literature were compared with the error rate from a recent study that implemented formalized MRA training and continuous QC processes.

Results

The MRA process for data acquisition in clinical research was associated with both high and highly variable error rates (70 - 2,784 errors per 10,000 fields). Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate) (or 104 - 257 errors per 10,000 fields), 4.00 - 5.53 percentage points less than the observed rate from the literature (p<0.0001).

Conclusions

Review of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study.

Challenges of Medical Record Abstraction in a Long-Term Follow-up Study

Medical records are a rich source of information and have tremendous value in epidemiological research. Nevertheless, the process of obtaining and abstracting medical records for a long-term follow-up study is complicated, time-consuming, and resource intensive. We identified the following major challenges during this process. First, widely varying infrastructure of electronic health record systems used by different organizations makes it difficult to ensure that all medical charts from all sources for a particular patient have been received. Second, extensive use of free text by health care providers requires a manual line-by-line search for relevant information, which may result in some missing data due to human error. Third, there are often discrepancies between patients' provided lists of health care providers and the registry data, which may affect the data-collection process. Fourth, providers have varied requirements for medical record release of their patients, which might entail multiple patient contacts. This, in turn, can frustrate patients and discourage them from participating in current or future research studies. Fifth, the use of inconsistent medical terminology by different providers complicates conversion of unstructured text into categorical data for analysis. We have the following recommendations for any future study with similar design to overcome the above challenges. First, the source of medical records best suited for the research objectives should be identified from the beginning. Second, the abstractors should be appropriately trained to accomplish research-specific tasks. Third, a quality data-tracking system for the abstracted elements should be employed to ensure data integrity. Fourth, the abstracted cases should be reviewed by one other abstractor. We also recommend a pilot study with a smaller number of patients to evaluate the required resources before any large-scale study.

Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies

Studies often rely on medical record abstraction as a major source of data. However, data quality from medical record abstraction has long been questioned. Electronic Health Records (EHRs) potentially add variability to the abstraction process due to the complexity of navigating and locating study data within these systems. We report training for and initial quality assessment of medical record abstraction for a clinical study conducted by the IDeA States Pediatric Clinical Trials Network (ISPCTN) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Neonatal Research Network (NRN) using medical record abstraction as the primary data source. As part of overall quality assurance, study-specific training for medical record abstractors was developed and deployed during study start-up. The training consisted of a didactic session with an example case abstraction and an independent abstraction of two standardized cases. Sixty-nine site abstractors from thirty sites were trained. The training was designed to achieve an error rate for each abstractor of no greater than 4.93% with a mean of 2.53%, at study initiation. Twenty-three percent of the trainees exceeded the acceptance limit on one or both of the training test cases, supporting the need for such training. We describe lessons learned in the design and operationalization of the study-specific, medical record abstraction training program.

Muscular Dystrophy Surveillance, Tracking, and Research Network pilot: Population-based surveillance of major muscular dystrophies at four U.S. sites, 2007-2011

BACKGROUND

For 10 years, the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) conducted surveillance for Duchenne and Becker muscular dystrophy (DBMD). We piloted expanding surveillance to other MDs that vary in severity, onset, and sources of care.

METHODS

Our retrospective surveillance included individuals diagnosed with one of nine eligible MDs before or during the study period (January 2007-December 2011), one or more health encounters, and residence in one of four U.S. sites (Arizona, Colorado, Iowa, or western New York) at any time within the study period. We developed case definitions, surveillance protocols, and software applications for medical record abstraction, clinical review, and data pooling. Potential cases were identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 359.0, 359.1, and 359.21 and International Classification of Diseases, Tenth Revision (ICD-10) codes G71.0 and G71.1. Descriptive statistics were compared by MD type. Percentage of MD cases identified by each ICD-9-CM code was calculated.

RESULTS

Of 2,862 cases, 32.9% were myotonic, dystrophy 25.8% DBMD, 9.7% facioscapulohumeral MD, and 9.1% limb-girdle MD. Most cases were male (63.6%), non-Hispanic (59.8%), and White (80.2%). About, half of cases were genetically diagnosed in self (39.1%) or family (6.2%). About, half had a family history of MD (48.9%). The hereditary progressive MD code (359.1) was the most common code for identifying eligible cases. The myotonic code (359.21) identified 83.4% of eligible myotonic dystrophy cases (786/943).

CONCLUSIONS

MD STARnet is the only multisite, population-based active surveillance system available for MD in the United States. Continuing our expanded surveillance will contribute important epidemiologic and health outcome information about several MDs.

Overcoming the Challenges of Unstructured Data in Multisite, Electronic Medical Record-based Abstraction

BACKGROUND

Unstructured data encountered during retrospective electronic medical record (EMR) abstraction has routinely been identified as challenging to reliably abstract, as these data are often recorded as free text, without limitations to format or structure. There is increased interest in reliably abstracting this type of data given its prominent role in care coordination and communication, yet limited methodological guidance exists.

OBJECTIVES

As standard abstraction approaches resulted in substandard data reliability for unstructured data elements collected as part of a multisite, retrospective EMR study of hospital discharge communication quality, our goal was to develop, apply and examine the utility of a phase-based approach to reliably abstract unstructured data. This approach is examined using the specific example of discharge communication for warfarin management.

RESEARCH DESIGN

We adopted a "fit-for-use" framework to guide the development and evaluation of abstraction methods using a 4-step, phase-based approach including (1) team building; (2) identification of challenges; (3) adaptation of abstraction methods; and (4) systematic data quality monitoring.

MEASURES

Unstructured data elements were the focus of this study, including elements communicating steps in warfarin management (eg, warfarin initiation) and medical follow-up (eg, timeframe for follow-up).

RESULTS

After implementation of the phase-based approach, interrater reliability for all unstructured data elements demonstrated κ's of ≥0.89-an average increase of +0.25 for each unstructured data element.

CONCLUSIONS

As compared with standard abstraction methodologies, this phase-based approach was more time intensive, but did markedly increase abstraction reliability for unstructured data elements within multisite EMR documentation.

Field methods in medical record abstraction: assessing the properties of comparative effectiveness estimates

BACKGROUND

Comparative effectiveness studies using Medicare claims data are vulnerable to treatment selection biases and supplemental data from a sample of patients has been recommended for examining the magnitude of this bias. Previous research using nationwide Medicare claims data has typically relied on the Medicare Current Beneficiary Survey (MCBS) for supplemental data. Because many important clinical variables for our specific research question are not available in the MCBS, we collected medical record data from a subsample of patients to assess the validity of assumptions and to aid in the interpretation of our estimates. This paper seeks to describe and document the process used to collect and validate this supplemental information.

METHODS

Medicare claims data files for all patients with fee-for-service Medicare benefits who had an acute myocardial infarction (AMI) in 2007 or 2008 were obtained. Medical records were obtained and abstracted for a stratified subsample of 1,601 of these patients, using strata defined by claims-based measures of physician prescribing practices and drug treatment combinations. The abstraction tool was developed collaboratively by study clinicians and researchers, leveraging important elements from previously validated tools.

RESULTS

Records for 2,707 AMI patients were requested from the admitting hospitals and 1,751 were received for an overall response rate of 65%; 1,601 cases were abstracted by trained personnel at a contracted firm. Data were collected with overall 96% inter-abstractor agreement across all variables. Some non-response bias was detected at the patient and facility level.

CONCLUSION

Although Medicare claims data are a potentially powerful resource for conducting comparative effectiveness analyses, observational databases are vulnerable to treatment selection biases. This study demonstrates that it is feasible to abstract medical records for Medicare patients nationwide and collect high quality data, to design the sampling purposively to address specific research questions, and to more thoroughly evaluate the appropriateness of care delivered to AMI patients.