Example of Diabetes Using CMS Data

Example of Diabetes Using CMS Data

Patricia Cerrito (University of Louisville, USA) and John Cerrito (Kroger Pharmacy, USA)
DOI: 10.4018/978-1-61520-905-7.ch014
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Abstract

We want to examine the treatment of patients with diabetes, and the reasons these patients are in the hospital. In order to do this, we must consider a cohort of patients who have diabetes and who are treated with medication and with insulin. We also need to know the extent to which compliance with monitoring is related to disease progression. Do patients with organ failure have greater, or less compliance with monitoring? There is so much involved in the treatment of diabetes, that it may be difficult to investigate all aspects of the treatment in one analysis. Therefore, at some point, it becomes important to focus on some one aspect of the disease. Then, you can add a second aspect followed by a third or fourth aspect of the disease.
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Inpatient Data

As discussed in Chapter 1, there are several different datasets available through CMS:

two data sets were used: one is Inpatient_base_claims, including 244,299 data about the claim information for the year 2004; the other is the Beneficiary_summary_file, covering 358,709 observations about the beneficiary information for the year 2004. The variables used are as follows:

  • BENE_ID: Encrypted 723 Beneficiary ID

  • BENE_SEX_IDENT_CD: Sex

  • BENE_RACE_CD: Beneficiary Race Code

  • BENE_AGE_AT_END_REF_YR: Age

  • BENE_ESRD_IND: End Stage Renal Disease indicator

  • ICD9_DGNS_CDn: Claim Diagnosis Code

  • CLM_TOT_CHRG_AMT: Claim Total Charge Amount

  • CLM_UTLZTN_DAY_CNT: Claim Utilization Day Count

  • NCH_DTNT_STATUS_IND_CD: NCH Patient Status Indicator Code

  • ICD9: AN abbreviation for the 9th edition of the International Classification of Diabetes and Related Health Problems.

We want to start looking at the beneficiary file so that we can define the denominator of occurrence. As shown previously, we can find the most frequently occurring procedure codes as well as the most frequently occurring diagnosis codes (Table 1). The most common procedures performed are given in Table 2.

Table 1.
Most frequent diagnosis codes
Primary ICD-9DescriptionCOUNTPERCENT
4280Congestive Heart Failure178437.303755
486Pneumonia, Organism of unspecified type103634.241933
41401Coronary Atherosclerosis; of Native Coronary Artery101964.173574
V5789Other specified rehabilitation procedure59572.438405
49121Obstruction Chronic Bronchitis with acute exacerbation56432.309874
41071Subendocardial Infarction, Initial51692.11585
5990Urinary Tract Infection of unspecified type46891.919369
5849Acute Renal Failure of unspecified type35391.448635
43491Cerebral Artery occlusion, unspecified with Infarction34911.428987
2765Hypovolemia34471.410976
78659Chest Pain of unknown type33961.3901
0389Septicemia of unspecified type32921.347529
42731Atrial Fibrillation29311.199759
51881Respiratory Failure28721.175609
5070Food Vomit Pneumonitis25541.04544
25080Diabetes of unspecified type with other specified manifestations23020.942288
7802Syncope and Collapse22070.903401
6826Cellulitis of Leg21610.884572
4359Unspecified transient cerebral ischemia18310.749491
71536Osteoarthrosis, localized, not specified whether primary or secondary18140.742533

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