Study objective: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression. The aim of this study was to present measures of variation appropriate for the logistic case in a didactic rather than a mathematical way. Design and participants: Data were used from the health survey conducted in 2000 in the county of Scania, Sweden, that comprised 10 723 persons aged 18-80 years living in 60 areas. Conducting multilevel logistic regression different techniques were applied to investigate whether the individual propensity to consult private physicians was statistically dependent on the area of residence (that is, intraclass correlation (ICC), median odds ratio (MOR)), the 80% interval odds ratio (IOR-80), and the sorting out index). Results: The MOR provided more interpretable information than the ICC on the relevance of the residential area for understanding the individual propensity of consulting private physicians. The MOR showed that the unexplained heterogeneity between areas was of greater relevance than the individual variables considered in the analysis (age, sex, and education) for understanding the individual propensity of visiting private physicians. Residing in a high education area increased the probability of visiting a private physician. However, the IOR showed that the unexplained variability between areas did not allow to clearly distinguishing low from high propensity areas with the area educational level. The sorting out index was equal to 82%. Conclusion: Measures of variation in logistic regression should be promoted in social epidemiological and public health research as efficient means of quantifying the importance of the context of residence for understanding disparities in health and health related behaviour. I n the study of contextual determinants of health, considering the extent to which individual health phenomena cluster within areas is not only necessary for obtaining correct estimates in regression analysis. It also provides relevant information that permits assessment of the importance that the context has for different individual health outcomes. 2In multilevel linear regression analysis it is easy to partition the variance between different levels and compute measures of clustering that provide intuitive information for capturing contextual phenomena.3-5 However, for binary outcomes, the partition of variance between different levels does not have the intuitive interpretation of the linear model. Despite these difficulties several methods have been developed in logistic regression to obtain suitable epidemiological information on area level variance and clustering within areas. [6][7][8][9] This paper represents the last of a series of four included in a project aimed to explain in a conceptual rather than a mathematical way how to calculate and interpret multilevel measures of variance and clustering. [3][4][5] This study is focused at measures of variation in...
BackgroundIn recent years, several primary care databases recording information from computerized medical records have been established and used for quality assessment of medical care and research. However, to be useful for research purposes, the data generated routinely from every day practice require registration of high quality. In this study we aimed to investigate (i) the frequency and validity of ICD code and drug prescription registration in the new Skaraborg primary care database (SPCD) and (ii) to investigate the sources of variation in this registration.MethodsSPCD contains anonymous electronic medical records (ProfDoc III) automatically retrieved from all 24 public health care centres (HCC) in Skaraborg, Sweden. The frequencies of ICD code registration for the selected diagnoses diabetes mellitus, hypertension and chronic cardiovascular disease and the relevant drug prescriptions in the time period between May 2002 and October 2003 were analysed. The validity of data registration in the SPCD was assessed in a random sample of 50 medical records from each HCC (n = 1200 records) using the medical record text as gold standard. The variance of ICD code registration was studied with multi-level logistic regression analysis and expressed as median odds ratio (MOR).ResultsFor diabetes mellitus and hypertension ICD codes were registered in 80-90% of cases, while for congestive heart failure and ischemic heart disease ICD codes were registered more seldom (60-70%). Drug prescription registration was overall high (88%). A correlation between the frequency of ICD coded visits and the sensitivity of the ICD code registration was found for hypertension and congestive heart failure but not for diabetes or ischemic heart disease.The frequency of ICD code registration varied from 42 to 90% between HCCs, and the greatest variation was found at the physician level (MORPHYSICIAN = 4.2 and MORHCC = 2.3).ConclusionsSince the frequency of ICD code registration varies between different diagnoses, each diagnosis must be separately validated. Improved frequency and quality of ICD code registration might be achieved by interventions directed towards the physicians where the greatest amount of variation was found.
Major determinants of discontinuation of antihypertensive drug treatment are male sex, young age, mild-to-moderate systolic blood pressure elevation, and birth outside of Sweden.
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