Use of name recognition software, census data and multiple imputation to predict missing data on ethnicity: application to cancer registry records.

Publication Date: 2012 Jan 23 PMID: 22269985
Authors: Ryan, R. - Vernon, S. - Lawrence, G. - Wilson, S.
Journal: BMC Med Inform Decis Mak

ABSTRACT: BACKGROUND: Information on ethnicity is commonly used by health services and researchers to plan services, ensure equality of access, and for epidemiological studies. In common with other important demographic and clinical data it is often incompletely recorded. This paper presents a method for imputing missing data on the ethnicity of cancer patients, developed for a regional cancer registry in the UK. METHODS: Routine records from cancer screening services, name recognition software (Nam Pehchan and Onomap), Census data, and multiple imputation were used to predict the ethnicity of the 23% of cases that were still missing following linkage with self-reported ethnicity from inpatient hospital records. RESULTS: The name recognition software were good predictors of ethnicity for South Asian cancer cases when compared with data on ethnicity derived from hospital inpatient records, especially when combined (sensitivity 90.5%; specificity 99.9%; PPV 93.3%). Onomap was a poor predictor of ethnicity for other minority ethnic groups (sensitivity 4.4% for Black cases and 0.0% for Chinese/Other ethnic groups). Area-based data derived from the national Census was also a poor predictor non-White ethnicity (sensitivity: South Asian 7.4%; Black 2.3%; Chinese/Other 0.0%; Mixed 0.0%). CONCLUSIONS: Currently, neither method for assigning individuals to an ethnic group (name recognition and ethnic distribution of area of residence) performs well across all ethnic groups. We recommend further development of name recognition applications and the identification of additional methods for predicting ethnicity to improve their precision and accuracy for comparisons of health outcomes. However, real improvements can only come from better recording of ethnicity by health services.

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Is increasing complexity of algorithms the price for higher accuracy? Virtual comparison of three algorithms for tertiary level management of chronic cough in people living with HIV in a low-income country.

Publication Date: 2012 Jan 19 PMID: 22260242
Authors: Mukabatsinda, C. - Nguyen, J. - Bisig, B. - Lynen, L. - Coppens, Y. D. - Asiimwe, A. - Van den Ende, J.
Journal: BMC Med Inform Decis Mak

ABSTRACT: BACKGROUND: The algorithmic approach to guidelines has been introduced and promoted on a large scale since the 1970s. This study aims at comparing the performance of three algorithms for the management of chronic cough in patients with HIV infection, and at reassessing the current position of algorithmic guidelines in clinical decision making through an analysis of accuracy, harm and complexity. METHODS: Data were collected at the University Hospital of Kigali (CHUK) in a total of 201 HIV-positive hospitalised patients with chronic cough. We simulated management of each patient following the three algorithms. The first was locally tailored by clinicians from CHUK, the second and third were drawn from publications by Medecins sans Frontieres (MSF) and the World Health Organisation (WHO). Semantic analysis techniques known as Clinical Algorithm Nosology were used to compare them in terms of complexity and similarity. For each of them, we assessed the sensitivity, delay to diagnosis and hypothetical harm of false positives and false negatives. RESULTS: The principle diagnoses were tuberculosis (21%) and pneumocystosis (19%). Sensitivity, representing the proportion of correct diagnosis made by each algorithm, was 95.7%, 88% and 70% for CHUK, MSF and WHO, respectively. Mean time to appropriate management was 1.86 days for CHUK and 3.46 for the MSF algorithm. The CHUK algorithm was the most complex, followed by MSF and WHO. Total harm was by far the highest for the WHO algorithm, followed by MSF and CHUK. CONCLUSIONS: This study confirms our hypothesis that sensitivity and patient safety (i.e. less expected harm) are proportional to the complexity of algorithms, though increased complexity may make them difficult to use in practice.

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BMC Medical Informatics and Decision Making

Use of name recognition software, census data and multiple imputation to predict missing data on ethnicity: application to cancer registry records.

Is increasing complexity of algorithms the price for higher accuracy? Virtual comparison of three algorithms for tertiary level management of chronic cough in people living with HIV in a low-income country.