National Diabetes Surveillance System Research Articles

Enhancing Diabetes Surveillance Across Alberta by Adding Laboratory and Pharmacy Data to the National Diabetes Surveillance System Methods

ObjectivesThe National Diabetes Surveillance System (NDSS) case definition, which identifies a case of diabetes using administrative health records as “two physician claims or one hospital discharge abstract record, within a 2-year period for a diagnosis bearing International Classification of Disease codes for diabetes,” was compared with expanded case definitions, including pharmacy (PHARM) and laboratory (LAB) data. The PHARM definition included any therapeutic antihyperglycemic agents, and the LAB definition included thresholds of ≥1 glycated hemoglobin measurement of ≥6.5%, or 2 instances of random glucose ≥11.1 mmol/L or fasting glucose ≥7.0 mmol/L. MethodsIn this retrospective study we used administrative data from the Diabetes Infrastructure for Surveillance, Evaluation, and Research project. Descriptive statistics were used to characterize participants by several subgroups. ResultsThe NDSS identified 291,242 diabetes cases, indicating a provincial prevalence of 6.83%. Using LAB plus PHARM identified 52,040 additional cases, so the combination of NDSS or LAB or PHARM identified the largest number of cases (n=343,282), increasing the diabetes prevalence estimate to 8.06%. These 3 sources resulted in 7 unique subsets: NDSS only (n=42,606), PHARM only (n=16,310), LAB only (n=32,202), NDSS+LAB (n=32,582), NDSS+PHARM (n=22,503), LAB+PHARM (n=3,528) and NDSS+LAB+PHARM (n=193,551). Refinement using demographic and clinical characteristics allowed presumptive cases of polycystic ovarian syndrome to be excluded. ConclusionsThe widely used NDSS case definition can be enhanced by the addition of LAB and PHARM data. Including PHARM and LAB data identified subsets of the diabetes population, which can maximize the yield for detection of diabetes cases in Alberta and provide a richer understanding of this population to target interventions to improve health outcomes.

At-a-glance - Twenty years of diabetes surveillance using the Canadian Chronic Disease Surveillance System.

In 1999, the Government of Canada, along with the provinces and territories, established the National Diabetes Surveillance System (NDSS) to track rates of diabetes in Canada. The NDSS used a novel method to systematically collect and report national diabetes data using linked administrative health databases. The NDSS has since evolved to become the Canadian Chronic Disease Surveillance System (CCDSS) and provides information on over 20 chronic conditions. This At-a-glance report provides the most up-to-date CCDSS information on diabetes rates in Canada. Currently, 8.8% of Canadians (9.4% male, 8.1% female, aged one year and older) live with diabetes, and approximately 549 new cases are diagnosed each day. Since 2000, the age-standardized prevalence rate has increased by an average of 3.3% per year. The age-standardized incidence rate has remained relatively stable, and all-cause mortality rates among those with diabetes have decreased by an average of 2.1% per year. This suggests that people are living longer with a diabetes diagnosis.

Diabetes surveillance in Germany – strategy for the dissemination of results

Abstract Background Diabetes mellitus is a non-communicable disease with high public health relevance in Germany. Therefore, the Robert Koch Institute startetd to establish a national diabetes surveillance system in Germany in 2016. Essential part of a surveillance system is the dissemination of health information to the relevant stakeholders to facilitate its translation into action. Methods For the development of the dissemination strategy, different sources of information were used. First, an online-survey combined with structured web-research explored dissemination approaches in the international context. Secondly, a workshop with public health experts from seven countries highlighted best practice examples. Based on this information, a dissemination strategy was developed in close alignment with the scientific advisory board of the diabetes surveillance including experts from politics, prevention research, regional health reporting, physicians/diabetologists and public health scientists. Results Three main questions were identified to be considered in the dissemination of health information: Which audience group is addressed? Which content is relevant to this group? What is the preferred format to communicate the information? Therefore, a diabetes report will be generated focusing on health policy makers summarizing the key information on the disease dynamic. A website containing all information including a visualization tool should facilitate access for stakeholders. Additionally, awareness needs to be raised using social media, classical media and direct interactions with relevant stakeholders. Conclusions In alignment with different stakeholder groups, the diabetes surveillance developed a dissemination strategy considering international best practice examples and innovative ideas for health data visualization. The next steps are to refine the dissemination strategy based on feedback from the different stakeholder groups to ensure the use of the provided information. Key messages A dissemination strategy for the diabetes surveillance has been developed in a participatory approach including experts from various stakeholder groups. Next steps include the refinement of the dissemination strategy and its formats based on stakeholders feedback requirements.

62 - 20 Years of Diabetes Surveillance in Canada

This year marks 20 years of diabetes surveillance using the Canadian Chronic Disease Surveillance System (CCDSS). In 1999, the Public Health Agency, along with provinces and territories, established the National Diabetes Surveillance System (NDSS) to track the incidence and prevalence of diabetes in Canada. The NDSS used a novel method to systematically collect and report administrative health data at a national level. The NDSS has since evolved to the CCDSS and is able to provide information on over 20 health conditions.

Anthropometric changes and risk of diabetes: are there sex differences? A longitudinal study of Alberta’s Tomorrow Project

ObjectivesTo characterise the sex-specific difference in the association between anthropometric changes and risk of diabetes in the general population in Canada.Setting and participantsFrom 2000 to 2008, Alberta’s Tomorrow Project (ATP)...

Changes in body mass index and incidence of diabetes: A longitudinal study of Alberta's Tomorrow Project Cohort

Although obesity is a known risk factor for diabetes, the impact of body mass index (BMI) changes over time, especially BMI reduction, on diabetes development is less than clear. The objective of this study is to characterize the association between BMI changes over time and incidence of diabetes in a cohort of adults in Alberta.From 2000 to 2008, Alberta's Tomorrow Project (ATP) enrolled participants aged 35–69 to a population-based prospective cohort study. BMI was calculated from self-reported height and weight; change in BMI (∆BMI) was calculated as the difference between baseline and follow-up measurements. Diabetes cases were identified using the Canadian National Diabetes Surveillance System algorithm applied to linked administrative data (2000–2015). Multivariable Cox regression was used to examine the association between ∆BMI and incidence of diabetes.In a subset of the ATP cohort (n=19,164), 1168 incident cases of diabetes were identified during 198,853person-years of follow-up. Overall, BMI increase was associated with increased risk and BMI reduction was associated with reduced risk of diabetes. Particularly, compared to minimal BMI change (±5%), moderate (5%–10%) reduction in BMI was associated with 34% (95% CI: 12%–51%) reduction in risk of diabetes in participants with obesity; whereas 10% or greater increase in BMI was associated with an increased risk of diabetes of 64% or more in participants with overweight and obesity; in participants with normal and underweight, BMI changes was not apparently associated with risk of diabetes.Public health programs promoting weight loss, even at a moderate extent, would reduce risk of diabetes.

Proceedings of the International Workshop \u2018Development of a National Diabetes Surveillance System in Germany \u2013 Core Indicators and Conceptual Framework\u2019

Proceedings of the International Workshop \u2018Development of a National Diabetes Surveillance System in Germany \u2013 Core Indicators and Conceptual Framework\u2019

Validation of administrative data case definitions for gestational diabetes mellitus

To examine, using administrative data, the validity of two algorithms for identifying gestational diabetes mellitus: 1) the current National Diabetes Surveillance System algorithm for excluding gestational diabetes cases and 2) gestational diabetes-specific ICD codes in the delivery-related hospitalization. This was a retrospective study of all women, aged 18-54 years, residing in Alberta, Canada, with singleton deliveries between 1 April 1999 and 31 March 2010. We linked Alberta Perinatal Health Program data on all deliveries to administrative claims data from Alberta Health using the mother's personal health number. For both gestational diabetes algorithms, we calculated the sensitivity, specificity, positive predictive value, negative predictive value and agreement, using gestational diabetes identified in the Alberta Perinatal Health Program as the 'gold standard'. Our study sample consisted of 411 390 deliveries for 273 152 women. The mean (sd) age was 29.1 (5.6) years and 82.3% of the women were white. Crude rates of gestational diabetes were 3.9% (16 215 cases), 1.3% (5189 cases) and 4.0% (16 440 cases) according to the Alberta Perinatal Health Program, National Diabetes Surveillance System and ICD code-based algorithms, respectively. Compared with the Alberta Perinatal Health Program database, the National Diabetes Surveillance System algorithm had a sensitivity of 25% and specificity of 100%, whereas the gestational diabetes-specific ICD code-based algorithm had a sensitivity of 86% and specificity of 99%. The National Diabetes Surveillance System algorithm underestimates the number of gestational diabetes cases. A more valid mechanism to identify gestational diabetes prevalence using health administrative data is the use of gestational diabetes-specific ICD-9/10 codes in the delivery hospitalization.

The Healthy Diabetes Plate: An Evolving Diabetes Meal Planning Program

Copyright: © 2015 Raidl M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The three components of diabetes self-management are diet, physical activity, and medication (if necessary) [1]. Data collected by the Centers for Disease Control and Prevention (CDC) National Diabetes Surveillance System shows that approximately 57.4% of adults with diabetes in the United States attend at least one diabetes selfmanagement class [2]. Unfortunately, among adults age 40 and over with diagnosed diabetes, only 52% managed to keep their blood sugars under control by achieving a HbA1c of less than 7% [3].

The Relationship Between County-Level Contextual Characteristics and Use of Diabetes Care Services

To examine the relationship between county-level measures of social determinants and use of preventive care among US adults with diagnosed diabetes. To inform future diabetes prevention strategies. Data are from the Behavioral Risk Factor Surveillance System (BRFSS) 2004 and 2005 surveys, the National Diabetes Surveillance System, and the Area Resource File. Use of diabetes care services was defined by self-reported receipt of 7 preventive care services. Our study sample included 46 806 respondents with self-reported diagnosed diabetes. Multilevel models were run to assess the association between county-level characteristics and receipt of each of the 7 preventive diabetes care service after controlling for characteristics of individuals. Results were considered significant if P < .05. Controlling for individual-level characteristics, our analyses showed that 7 of the 8 county-level factors examined were significantly associated with use of 1 or more preventive diabetes care services. For example, people with diabetes living in a county with a high uninsurance rate were less likely to have an influenza vaccination, visit a doctor for diabetes care, have an A1c test, or a foot examination; people with diabetes living in a county with a high physician density were more likely to have an A1c test, foot examination, or an eye examination; and people with diabetes living in a county with more people with less than high-school education were less likely to have influenza vaccination, pneumococcal vaccination, or self-care education (all P < .05). Many of the county-level factors examined in this study were found to be significantly associated with use of preventive diabetes care services. County policy makers may need to consider local circumstances to address the disparities in use of these services.

Re: "Prevalence of Diagnosed and Undiagnosed Type 2 Diabetes Mellitus Among US Adolescents: Results From the Continuous NHANES, 1999-2010"

We read with great interest the article by Demmer et al. (1) recently published in the Journal. We agree that national surveillance efforts for diabetes among this age group are important to assess and monitor the burden of disease. The National Health and Nutrition Examination Survey (NHANES) allows us to monitor and track the nation’s health. In fact, the Centers for Disease Control and Prevention (Atlanta, Georgia) rely on NHANES for our national estimates of the burden of diagnosed and undiagnosed diabetes among adults in the United States (2, 3). Estimates from NHANES for prevalence of diagnosed and undiagnosed diabetes among youth have not been included as part of the National Diabetes Surveillance System because of concerns with the low absolute number of cases precluding comparisons by demographic groups. In fact, we are concerned with the reliability of many of the estimates presented by the authors and, subsequently, with many of the inferences the authors draw from the data. The NHANES Analytic Guidelines, published by the National Center for Health Statistics (Hyattsville, Maryland), state, “The minimum sample size is determined by the statistic to be estimated (e.g., mean, total, proportion …), the reliability criteria (e.g., 20 or 30 percent relative standard error), the Design Effect for the statistics (DEFF defined as the variance inflation factor), and the degrees of freedom for the standard error estimate” (4, p.10). The relative standard error is defined as the standard error divided by the prevalence estimate and multiplied by 100%. Based on our own analysis from the interview sample of adolescents for all diabetes and diabetes type as defined in the article, we found that the relative standard error was greater than 30% for many subgroups presented (Table 1). When the more conservative criterion of a relative standard error of less than 20% is applied, virtually all of the estimates are subject to cautious interpretation and inference (Table 1). Table 1 Relative Standard Errora for Prevalence Estimates of Self-reported Diabetes Mellitus Among Adolescents Aged 12–19 Years, National Health and Nutrition Examination Survey, 1999–2010 We agree with Demmer et al. when they state, “However, neither the sex difference nor the racial/ethnic differences were statistically significant, which makes it possible that these variations were due to chance. Studies with higher numbers of diabetes cases will be required to definitively examine these trends (1, p.6).” Given the poor reliability of the existing data, one could expect no other conclusion. In addition to these concerns about reliability, we also caution interpretation of any absolute numbers. As the authors state in the methods, “Survey weights are necessary to account for nonresponse and oversampling (1, p.2).” Yet, the authors go on to compare absolute numbers of cases in the results and suggest that the estimates and unweighted numbers are difficult to interpret. On the basis of these concerns, we urge readers to interpret the results and inferences with caution.

Type 2 Diabetes in Aboriginal Peoples

Type 2 Diabetes in Aboriginal Peoples

Results of a Pilot National Diabetes Surveillance System for First Nations

Results of a Pilot National Diabetes Surveillance System for First Nations

Application of a national administrative case definition for the identification of pre-existing diabetes mellitus in pregnancy

Accurate ascertainment of pregnant women with pre-existing diabetes allows for the comprehensive surveillance of maternal and neonatal outcomes associated with this chronic disease. To determine the accuracy of case definitions for pre-existing diabetes mellitus when applied to a pregnant population, a cohort of women who were pregnant in Nova Scotia, Canada, between 1991 and 2003 was obtained from a population-based provincial perinatal database, the Nova Scotia Atlee Perinatal Database (NSAPD). Person-level data from administrative databases using hospital discharge abstract data and outpatient physician services data were linked to this cohort. Various algorithms for defining diabetes mellitus from the administrative data, including the algorithm suggested by the National Diabetes Surveillance System (NDSS), were compared to a reference standard definition from the NSAPD. Validation of the NDSS case definition applied to this pregnant population demonstrated a sensitivity of 87% and a positive predictive value (PPV) of 66.4%. Use of ICD-9 and ICD-10 diagnostic codes among hospitalizations with diabetes mellitus in pregnancy showed important increases in sensitivity and PPV, especially for those pregnancies delivered in tertiary centres. In this population, pregnancy-related administrative data from the hospitalization database alone appear to be a more accurate data source for identifying pre-existing diabetes than applying the NDSS case definition, particularly when pregnant women are delivered in a tertiary hospital. Although the NDSS definition of diabetes performs reasonably well compared to a reference standard definition of diabetes, using this definition for evaluating maternal and perinatal outcomes associated with diabetes in pregnancy will result in a certain degree of misclassification and, therefore, biased estimates of outcomes.

Increasing rates of diabetes amongst status Aboriginal youth in Alberta, Canada

Objectives. To track and compare trends in diabetes rates from 1995 to 2007 for Status Aboriginal and general population youth.Study design. Longitudinal observational research study (quantitative) using provincial administrative data.Methods. De-identified data was obtained from Alberta Health and Wellness administrative databases for Status Aboriginal (First Nations and Inuit people with Treaty status) and general population youth (<20 years). Diabetes cases were identified using the National Diabetes Surveillance System algorithm. Crude annual diabetes prevalence and incidence rates were calculated. The likelihood of being a prevalent case and incident case of diabetes for the 2 populations was compared for the year 2007. Average Annual Percent Changes (AAPC) in prevalence and incidence from 1995 to 2007 were determined and compared between the 2 groups to examine trends over time.Results. While the prevalence of diabetes was higher in the general population in 1995, by 2007 there were no between group differences, reflected in the significantly higher AAPC of 6.98 for Status Aboriginal youth. Status Aboriginal males had a lower diabetes risk in 1995 compared with females, and experienced a greater increase in prevalence over the 13 years (AAPC 9.18) so that by 2007 their rates were equivalent to those of the females. Differences in diabetes incidence trends were only observed among male youth, where increases in incidence were greater for Status Aboriginal (AAPC 11.65) compared to general population males (AAPC 4.62) (p = 0.03).Conclusion. Youth-onset diabetes is an increasing problem in Alberta, especially among young Status Aboriginal males.

Validation of classification algorithms for childhood diabetes identified from administrative data

Type 1 diabetes is the most common form of diabetes among children; however, the proportion of cases of childhood type 2 diabetes is increasing. In Canada, the National Diabetes Surveillance System (NDSS) uses administrative health data to describe trends in the epidemiology of diabetes, but does not specify diabetes type. The objective of this study was to validate algorithms to classify diabetes type in children <20 yr identified using the NDSS methodology. We applied the NDSS case definition to children living in British Columbia between 1 April 1996 and 31 March 2007. Through an iterative process, four potential classification algorithms were developed based on demographic characteristics and drug-utilization patterns. Each algorithm was then validated against a gold standard clinical database. Algorithms based primarily on an age rule (i.e., age <10 at diagnosis categorized type 1 diabetes) were most sensitive in the identification of type 1 diabetes; algorithms with restrictions on drug utilization (i.e., no prescriptions for insulin ± glucose monitoring strips categorized type 2 diabetes) were most sensitive for identifying type 2 diabetes. One algorithm was identified as having the optimal balance of sensitivity (Sn) and specificity (Sp) for the identification of both type 1 (Sn: 98.6%; Sp: 78.2%; PPV: 97.8%) and type 2 diabetes (Sn: 83.2%; Sp: 97.5%; PPV: 73.7%). Demographic characteristics in combination with drug-utilization patterns can be used to differentiate diabetes type among cases of pediatric diabetes identified within administrative health databases. Validation of similar algorithms in other regions is warranted.

Optimal strategy to identify incidence of diagnostic of diabetes using administrative data

BackgroundAccurate estimates of incidence and prevalence of the disease is a vital step toward appropriate interventions for chronic disease like diabetes. A growing body of scientific literature is now available on producing accurate information from administrative data. Advantages of use of administrative data to determine disease incidence include feasibility, accessibility and low cost, but straightforward use of administrative data can produce biased information on incident cases of chronic disease like diabetes. The present study aimed to compare criteria for the selection of diabetes incident cases in a medical administrative database.MethodsAn exhaustive retrospective cohort of diabetes cases was constructed for 2002 using the Canadian National Diabetes Surveillance System case definition (one hospitalization or two physician claims with a diagnosis of diabetes over a 2-year period) with the Quebec health service database. To identify previous occurrence of diabetes in the database, a five-year observation period was evaluated using retrograde survival function and kappa agreement. The use of NDSS case definition to identify incident cases was compared to a single occurrence of an ICD-9 code 250 in the records using the McNemar test.ResultsRetrograde survival function showed that the probability of being a true incident case after a 5-year diabetes-free observation period was almost constant and near 0.14. Agreement between 10 years (maximum period) and 5 years and more diabetes-free observation periods were excellent (kappa > 0.9). Respectively 41,261 and 37,473 incident cases were identified using a 5-year diabetes-free observation period with NDSS definition and using a single ICD-9 code 250.ConclusionA 5-year diabetes-free observation period was a conservative time to identify incident cases in an administrative database using one ICD-9 code 250 record.

Diabetes Registries: Where We Are and Where Are We Headed?

Disease registries are a searchable list of all patients with a particular chronic condition that often interface with an electronic medical record. The well-designed registry links all members of the patient's health team and provides key information for patients and physicians. The critical impact of a registry is that it can allow timely identification of high-risk subpopulations permitting the health care team to intensify treatment. Diabetes is a data-driven disease that lends itself well to registry use. This review will examine some current registry uses and highlight some of the respective challenges and benefits. This review compares key examples of registries in different health settings. These include a municipal registry (New York City), academic health centers (Penn State Milton S. Hershey Medical Center), third-party payers (Kaiser Permanente), the Veterans Affairs Health System, and international registries (the DIABCARE Q-NET in Europe and the National Diabetes Surveillance System in Canada). Different aspects are compared and contrasted such as the institutional plan for each and whether care in the "here and now" is impacted.

Using administrative data to define diabetes cases in children and youth

Community Health Survey using the National Diabetes Surveillance System as the gold standard were determined. results: Among people aged 25 years and older, overall agreement between the Canadian Community Health Survey and the National Diabetes Surveillance System was substantial (Kappa = 0.71). Selfreporting of diabetes in the Canadian Community Health Survey was 72% sensitive and showed 98% specificity. Positive predictive value was 77% and negative predictive value was 97%. Discussion/conclusion: This study highlights the importance of having correct estimate of diabetes prevalence for surveillance, research and planning purposes. These findings can support public health decision-making related to diabetes prevention and management.

Development of the Nova scotia Diabetes Repository: a project of the Nova Scotia Diabetes Repository Advisory Committee

Background: The Diabetes Care Program of Nova Scotia (DCPNS) maintains a comprehensive registry of clinically confirmed diabetes (DM) cases; however, only Diabetes Centre attendees are represented (about 70% of DM population in NS). The Nova Scotia Atlee Perinatal Database (NSAPD) and Nova Scotia Seniors' Pharmacare Database (NSSPD) also contain gold standard DM cases, but for a limited population. The National Diabetes Surveillance System (NDSS) derives nationally comparable DM statistics using administrative data; however, these cases are estimated not clinically confirmed. Combining data from various Programme Databases with routinely collected administrative data could greatly enhance DM case ascertainment. In Nova Scotia, all of these data sources are readily linkable, making the province an ideal sentinel surveillance site for DM in Canada.