Printer Friendly

Diabetes among young American Indians--Montana and Wyoming, 2000-2002.

Type 2 diabetes is increasing among young American Indians (AIs) and other populations (1-4), and accurate surveillance is important to monitor trends in diabetes prevalence. The Indian Health Service (IHS) patient database has been used to identify cases of diabetes and estimate diabetes prevalence among AIs aged [greater than or equal to]15 years (5). However, limited studies have assessed the use of health databases to ascertain diabetes cases in young persons. The Montana Department of Public Health and Human Services (MDPHHS), in collaboration with the Billings Area IHS, conducted a study to assess use of the IHS patient database to identify AIs aged <20 years with diabetes in Montana and Wyoming. This report summarizes the results of that study, which found that diabetes cases were identified more accurately by using at least two patient visits for diabetes rather than only one patient visit. To reduce misclassification of diabetes, health-care systems and managed care organizations that use patient databases for diabetes surveillance should evaluate the accuracy of case ascertainment periodically and ensure adequate training for staff responsible for coding health-care visits and database entry.

During 2000-2002, AIs aged <20 years with at least one outpatient visit or hospitalization coded for diabetes (i.e., using International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM-CM] codes 250.0-250.9) at one of six IHS facilities were identified from the IHS database. Medical records of each person were reviewed to confirm the diagnosis and classify the type of diabetes (6). MDPHHS collected demographic and clinical data and assessed the diagnoses of diabetes. A case of confirmed diabetes was defined as a case with documented diagnostic blood glucose values (7) or a record of treatment with antidiabetic therapies (e.g., insulin or oral medication). To assess the accuracy of case ascertainment, the study compared the percentage of false positives (i.e., for which persons were determined not to have diabetes) for cases based on only one health-care visit with the percentage for cases based on at least two health-care visits during 2000-2002. Diagnostic codes or reason-for-visit narratives that might have led to case misclassification were identified for the false-positive cases.

The study identified 93 persons classified with diabetes based on one coded health-care visit. Assessment of the diagnoses by MDPHHS found that 40 persons (43%) did not have diabetes. No statistically significant differences by sex or by mean age were found when confirmed cases were compared with false positives. Wide variation was observed in the proportion of false-positive cases across the six clinical facilities: 0%, 25%, 27%, 50%, 67%, and 89%. Among the false-positive cases, the most common reason (15 cases out of 40) for a health-care visit was diabetes screening or a school health assessment; for 19 of the cases, no specific reason was identified (Table). On the basis of the 93 database cases with one coded health-care visit, the prevalence of diabetes in young AIs was 4.0 per 1,000 population (estimated population of AIs aged <20 years = 23,035) (8), and 2.3 per 1,000 population by using only the 53 confirmed cases.

On the basis of two health-care visits, the study identified 61 persons classified with diabetes; 12 (20%) were false positives. Once again, no statistically significant differences by sex or by mean age were found. Of the 12 persons with false-positive cases, seven had been referred for a health-care visit through diabetes screening or a school health assessment. On the basis of the 61 database cases with at least two coded visits, the prevalence of diabetes in young AIs was 2.9 per 1,000 population, and 2.1 per 1,000 population by using only the 49 confirmed cases.

Editorial Note: Accurate surveillance of type 1 and type 2 diabetes in young persons is important to monitor trends in prevalence and incidence. The findings in this report suggest that using only one ICD-9-CM coded visit during a 3-year period to ascertain diabetes cases among young AIs was accurate in only 57% of cases; therefore, the number of cases was probably overestimated by approximately 40%. The use of at least two ICD-9-CM coded visits for case ascertainment was substantially more accurate (80%). Because of the low national prevalence of diabetes in young AIs (less than five cases per 1,000 persons) (3), an increase in false-positive cases has little effect on the estimated rates; however, the number of affected young persons will be overestimated.

The findings in this report are subject to at least one limitation. This analysis included only six IHS facilities. The accuracy of case ascertainment in other IHS areas and facilities might vary by facility and by the prevalence of disease in young persons.

Patient databases have been useful for monitoring diabetes care in adults and can be helpful in monitoring diabetes prevalence in adolescents (5,9,10). This report highlights the importance of evaluating the use of patient databases for ascertaining diabetes cases among young persons and emphasizes the need to update and maintain case registries based on patient databases. In addition, adequate training of staff responsible for coding and database entry of patient diagnoses, particularly related to diabetes screening and school health assessments, probably will reduce misclassification of diabetes in young persons.
TABLE. Diagnosis or reason for health-care visits by American
Indian aged <20 years found to be false positive * for diabetes,
by number of ICD-9-CM ([dagger]) coded visits--Montana and
Wyoming, 2000-2002

                                                           Two or more
                                    One visit                 visits
Diagnosis/Reason for visit          No.   (%)              No.     (%)

Diabetes screening/
  School health assessment          15   (38)               7     (58)
Impaired glucose tolerance/
  Insulin resistance syndrome        3    (8)               3     (25)
Hypothyroidism                       1    (3)              --       --
Medical nutrition therapy            1    (3)              --       --
Otitis media                         1    (3)              --       --
Other/Unknown                       19   (48)               2       17
Total                               40   100 ([section])   12      100

* Did not have diabetes.

([dagger]) International Classification of Diseases, Ninth Revision,
Clinical Modification.

([section]) Total >100% because of rounding.


(1.) Rosenbloom AL, Joe JR, Young RS, Winter WE. Emerging epidemic of type 2 diabetes in youth. Diabetes Care 1999;22:345-54.

(2.) Fagot-Campagna A, Pettitt DJ, Engelgau MM, et al. Type 2 diabetes among North American children and adolescents: an epidemiologic review and a public health perspective. J Pediatr 2000;136:664-72.

(3.) Acton KJ, Burrows NR, Moore K, Querec L, Geiss LS, Engelgau MM. Trends in diabetes prevalence among American Indian and Alaska Native children, adolescents, and young adults. Am J Public Health 2002;92:1485-90.

(4.) Dabelea D, Hanson RL, Bennett PH, Roumain J, Knowler WC, Pettitt DJ. Increasing prevalence of type II diabetes in American Indian children. Diabetologia 1998;41:904-10.

(5.) Wilson C, Susan L, Lynch A, Saria R, Peterson D. Patients with diagnosed diabetes mellitus can be accurately identified in an Indian Health Service patient registration database. Public Health Rep 2001;116:45-9.

(6.) Harwell TS, McDowall JM, Moore K, Fagot-Campagna A, Helgerson SD, Gohdes D. Establishing surveillance for diabetes in American Indian youth. Diabetes Care 2001;24:1029-32.

(7.) American Diabetes Association. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 2003;26(suppl 1):S5-S20.

(8.) U.S. Department of Health and Human Services. User population estimates 1995 to 1997. Available at facilitiesservices/areaoffices/billings/stats/population.asp.

(9.) Hebert PL, Geiss LS, Tierney EF, Engelgau MM, Yawn BP, McBean AM. Identifying persons with diabetes using Medicare claims data. Am J Med Qual 1999;14:270-7.

(10.) Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 2002;25:512-6.

Reported by: KR Moore, MD, Billings Area Indian Health Svc, Billings; TS Harwell, MPH, JM McDowall, CS Oser, MPH, SD Helgerson, MD, D Gohdes, MD, Montana Dept of Public Health and Human Svcs. NR Burrows, MPH, Div of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, CDC.
COPYRIGHT 2003 U.S. Government Printing Office
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2003 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Moore, KR; Harwell, TS; McDowall, JM; Oser, CS; Helgerson, SD; Gohdes, D; Burrows, NR
Publication:Morbidity and Mortality Weekly Report
Geographic Code:1U8WY
Date:Nov 21, 2003
Previous Article:Direct and indirect costs of arthritis and other rheumatic conditions--United States, 1997.
Next Article:Investigation of a ricin-containing envelope at a postal facility--South Carolina, 2003.

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters