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1.
Perm J ; 16(3): 37-41, 2012.
Article in English | MEDLINE | ID: mdl-23012597

ABSTRACT

BACKGROUND: Data from the memberships of large, integrated health care systems can be valuable for clinical, epidemiologic, and health services research, but a potential selection bias may threaten the inference to the population of interest. METHODS: We reviewed administrative records of members of Kaiser Permanente Southern California (KPSC) in 2000 and 2010, and we compared their sociodemographic characteristics with those of the underlying population in the coverage area on the basis of US Census Bureau data. RESULTS: We identified 3,328,579 KPSC members in 2000 and 3,357,959 KPSC members in 2010, representing approximately 16% of the population in the coverage area. The distribution of sex and age of KPSC members appeared to be similar to the census reference population in 2000 and 2010 except with a slightly higher proportion of 40 to 64 year olds. The proportion of Hispanics/Latinos was comparable between KPSC and the census reference population (37.5% vs 38.2%, respectively, in 2000 and 45.2% vs 43.3% in 2010). However, KPSC members included more blacks (14.9% vs 7.0% in 2000 and 10.8% vs 6.5% in 2010). Neighborhood educational levels and neighborhood household incomes were generally similar between KPSC members and the census reference population, but with a marginal underrepresentation of individuals with extremely low income and high education. CONCLUSIONS: The membership of KPSC reflects the socioeconomic diversity of the Southern California census population, suggesting that findings from this setting may provide valid inference for clinical, epidemiologic, and health services research.


Subject(s)
Censuses , Delivery of Health Care, Integrated , Patient Selection , Socioeconomic Factors , Adolescent , Adult , Aged , Black People , California , Child , Child, Preschool , Clinical Trials as Topic , Data Collection , Educational Status , Family Characteristics , Female , Health Services Research , Hispanic or Latino , Humans , Income , Infant , Male , Middle Aged , Poverty , Residence Characteristics , United States , Young Adult
2.
BMC Health Serv Res ; 10: 316, 2010 Nov 23.
Article in English | MEDLINE | ID: mdl-21092309

ABSTRACT

BACKGROUND: To understand racial and ethnic disparities in health care utilization and their potential underlying causes, valid information on race and ethnicity is necessary. However, the validity of pediatric race and ethnicity information in administrative records from large integrated health care systems using electronic medical records is largely unknown. METHODS: Information on race and ethnicity of 325,810 children born between 1998-2008 was extracted from health plan administrative records and compared to birth certificate records. Positive predictive values (PPV) were calculated for correct classification of race and ethnicity in administrative records compared to birth certificate records. RESULTS: Misclassification of ethnicity and race in administrative records occurred in 23.1% and 33.6% children, respectively; the majority due to missing ethnicity (48.3%) and race (40.9%) information. Misclassification was most common in children of minority groups. PPV for White, Black, Asian/Pacific Islander, American Indian/Alaskan Native, multiple and other was 89.3%, 86.6%, 73.8%, 18.2%, 51.8% and 1.2%, respectively. PPV for Hispanic ethnicity was 95.6%. Racial and ethnic information improved with increasing number of medical visits. Subgroup analyses comparing racial classification between non-Hispanics and Hispanics showed White, Black and Asian race was more accurate among non-Hispanics than Hispanics. CONCLUSIONS: In children, race and ethnicity information from administrative records has significant limitations in accurately identifying small minority groups. These results suggest that the quality of racial information obtained from administrative records may benefit from additional supplementation by birth certificate data.


Subject(s)
Birth Certificates , Ethnicity/statistics & numerical data , Medical Records Systems, Computerized/statistics & numerical data , Racial Groups/statistics & numerical data , California , Child , Child, Preschool , Confidence Intervals , Delivery of Health Care/statistics & numerical data , Female , Health Planning , Healthcare Disparities , Humans , Infant , Infant, Newborn , Male , Odds Ratio , Predictive Value of Tests , Quality Control
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