AN EPIDEMIOLOGIC CASE-CONTROL ANALYSIS OF CHILDHOOD INTRACRANIAL AND SPINAL CORD TUMORS IN RELATION TO PATERNAL OCCUPATION AT BIRTH (BRAIN, CANCER)

Epidemiologic case-control studies of small groups of childhood nervous system tumor patients have suggested that parental employment in occupations with exposure to hydrocarbons is a risk factor for disease. The main focus of this case-control study was to assess the paternal occupation at the time of birth of offspring who later developed childhood intracranial and spinal tumors. All children under 15 years of age dying of such tumors in Texas, during the period 1964-1980, were selected as cases. Disease and demographic data were abstracted from death certificates. The birth certificate for each child of the final group of 499 cases was located and parental occupation information, as well as demographic and obstetric data, were collected. The comparison group consisted of a random sample from all Texas live births with the same birth year, race and sex distribution as the cases.^ The paternal occupations were categorized into broad classifications of those involving hydrocarbon exposure versus those that did not, based on the occupation criteria used in the previous studies. Odds ratios did not indicate any increased risk associated with general paternal hydrocarbon exposure in the workplace. In prior studies, increased risk estimates were detected with narrower groups of occupations involving exposure to hydrocarbon materials. The data from this study were classified according to these groups, and again, no increased risks were indicated except for a statistically insignificant but elevated odds ratio for fathers who were paper and pulp mill workers.^ Odds ratios were calculated for specific occupations and industries previously implicated as risk factors. Significantly associated odds ratios (OR) were detected for electricians (OR = 3.5), especially those working for construction companies (OR = 10.0), for employment in the printing occupations (OR = 4.5), particularly graphic arts workers (OR = 21.9), and in the electronics and electronic machinery industries (OR = 3.5). Analysis of the petroleum refining and chemical industries, which were not found in previous study populations, revealed significantly elevated odds ratios of 3.0 for occupations with probable heavy exposure to chemicals and petroleum compounds and 10.0 for salesmen of chemical products. ^

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.