Investigating concordance in diabetes diagnosis between primary care charts (electronic medical records) and health administrative data: a retrospective cohort study

Identification of patients undergoing chronic kidney replacement therapy in primary and secondary care data: validation study based on OpenSAFELY and UK Renal Registry

Objective

To validate primary and secondary care codes in electronic health records to identify people receiving chronic kidney replacement therapy based on gold standard registry data.

Design

Validation study using data from OpenSAFELY and the UK Renal Registry, with the approval of NHS England.

Setting

Primary and secondary care electronic health records from people registered at 45% of general practices in England on 1 January 2020, linked to data from the UK Renal Registry (UKRR) within the OpenSAFELY-TPP platform, part of the NHS England OpenSAFELY covid-19 service.

Participants

38 745 prevalent patients (recorded as receiving kidney replacement therapy on 1 January 2020 in UKRR data, or primary or secondary care data) and 10 730 incident patients (starting kidney replacement therapy during 2020), from a population of 19 million people alive and registered with a general practice in England on 1 January 2020.

Main outcome measures

Sensitivity and positive predictive values of primary and secondary care code lists for identifying prevalent and incident kidney replacement therapy cohorts compared with the gold standard UKRR data on chronic kidney replacement therapy. Agreement across the data sources overall, and by treatment modality (transplantation or dialysis) and personal characteristics.

Results

Primary and secondary care code lists were sensitive for identifying the UKRR prevalent cohort (91.2% (95% confidence interval (CI) 90.8% to 91.6%) and 92.0% (91.6% to 92.4%), respectively), but not the incident cohort (52.3% (50.3% to 54.3%) and 67.9% (66.1% to 69.7%)). Positive predictive values were low (77.7% (77.2% to 78.2%) for primary care data and 64.7% (64.1% to 65.3%) for secondary care data), particularly for chronic dialysis (53.7% (52.9% to 54.5%) for primary care data and 49.1% (48.0% to 50.2%) for secondary care data). Sensitivity decreased with age and index of multiple deprivation in primary care data, but the opposite was true in secondary care data. Agreement was lower in children, with 30% (295/980) featuring in all three datasets. Half (1165/2315) of the incident patients receiving dialysis in UKRR data had a kidney replacement therapy code in the primary care data within three months of the start date of the kidney replacement therapy. No codes existed whose exclusion would substantially improve the positive predictive value without a decrease in sensitivity.

Conclusions

Codes used in primary and secondary care data failed to identify a small proportion of prevalent patients receiving kidney replacement therapy. Codes also identified many patients who were not recipients of chronic kidney replacement therapy in UKRR data, particularly dialysis codes. Linkage with UKRR kidney replacement therapy data facilitated more accurate identification of incident and prevalent kidney replacement therapy cohorts for research into this vulnerable population. Poor coding has implications for any patient care (including eligibility for vaccination, resourcing, and health policy responses in future pandemics) that relies on accurate reporting of kidney replacement therapy in primary and secondary care data.

A Project ECHO and Community Health Worker Intervention for Patients with Diabetes

BACKGROUND

Both community health workers and the Project ECHO model of specialist telementoring are innovative approaches to support primary care providers in the care of complex patients with diabetes. We studied the effect of an intervention that combined these 2 approaches on glycemic control.

METHODS

Patients with diabetes were recruited from 10 federally qualified health centers in New Mexico. We used electronic health record (EHR) data to compare HbA1c levels prior to intervention enrollment with HbA1c levels after 3 months (early follow-up) and 12 months (late follow-up) following enrollment. We propensity matched intervention patients to comparison patients from other sites within the same electronic health records databases to estimate the average treatment effect.

RESULTS

Among 557 intervention patients with HbA1c data, mean HbA1c decreased from 10.5% to 9.3% in the pre- versus postintervention periods (P < .001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% confidence interval [CI] -0.4%-0.5%) and -0.3 (95% CI -0.5-0.0) in the early and late follow-up cohorts, respectively. The intervention was associated with a significant increase in percentage of patients with HbA1c <8% in the late follow-up cohort (8.1%, 95% CI 2.2%-13.9%) but not the early follow-up cohort (3.6%, 95% CI -1.5% to 8.7%) DISCUSSION: The intervention was associated with a substantial decrease in HbA1c in intervention patients, although this improvement was not different from matched comparison patients in early follow-up. Although combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

High concordance between chart review adjudication and electronic medical record data to identify prevalent and incident diabetes mellitus among persons with and without HIV

BACKGROUND

Electronic medical records (EMR) represent a rich source of data, but the value of EMR for health research relies on accurate ascertainment of clinical diagnoses. Identifying diabetes in EMR is complicated by the variety of accepted diagnostic criteria, some of which can be confounded by conditions such as HIV infection. We compared EMR-based criteria for estimating diabetes prevalence and incidence in the Veterans Health Administration (VHA), overall and by HIV status, against physician chart review and adjudication.

RESEARCH DESIGN AND METHODS

We used laboratory values (serum glucose and hemoglobin A1c% [HbA1c]), ICD-9 codes, and medication records from the United States Veterans Aging Cohort Study Biomarker Cohort to identify veterans with any indication of diabetes in the EMR for subsequent physician adjudication. Sensitivity, specificity, PPV, NPV, and kappa statistics were used to evaluate agreement of EMR-based diabetes diagnoses with chart review adjudicated diagnoses.

RESULTS

EMR entries were reviewed for 1546 persons with HIV (PWH) and 843 HIV-negative participants through 2015. Agreement was at least moderate overall (kappa ≥ 0.42) for all pre-specified measures and among PWH vs HIV-negative, and African-American vs white sub-groups. Having at least one HbA1c ≥6.5% provided substantial agreement with chart adjudication for prevalent and incident diabetes (kappa = 0.89 and 0.73).

CONCLUSIONS

Identification of those with diabetes nationally within the VHA can be used in future studies to evaluate treatments, health outcomes, and adjust for diabetes in epidemiologic studies. Our methodology may provide insights for other organizations seeking to use EMR data for accurate determination of diabetes.

Impact of the diabetes Canada guideline dissemination strategy on dispensed vascular protective medications for older patients in Ontario, Canada: a linked EMR and administrative data study

BACKGROUND

The 2013 Diabetes Canada guidelines recommended routinely using vascular protective medications for most patients with diabetes. These medications included statins and angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs). Antiplatelet agents were only recommended for secondary prevention of cardiovascular disease. Using Electronic Medical Record (EMR) data, we previously found that guideline dissemination efforts were not associated with an increase in the rate of primary care prescriptions of these medications. However, this needs confirmation: patients can receive prescriptions from different sources including specialists and they may not always fill these prescriptions. Using both EMR and administrative health data, we examined whether guideline dissemination impacted the dispensing of vascular protective medications to patients.

METHODS

The study population included patients with diabetes aged 66 or over in Ontario, Canada. We created two cohorts using two different approaches: an Electronic Medical Record (EMR) algorithm for diabetes using linked EMR-administrative data and an administrative algorithm using population level administrative data. We examined data from January 2010 to December 2016. Patients with diabetes were deemed to be likely taking a medication (or covered) during a quarter if the daily amount for a dispensed medication would last for at least 75% of days in any given quarter. An interrupted time series analysis was used to assess the proportion of patients covered by each medication class. Proton pump inhibitors (PPIs) were used as a reference.

RESULTS

There was no increase in the rate of change for medication coverage following guideline release in either the EMR or the administrative diabetes cohorts. For statins, the change in trend was - 0.03, p = 0.7 (EMR) and - 0.12, p = 0.04(administrative). For ACEI/ARBs, this was 0.03, p = 0.6 (EMR) and 0, p = 1(administrative). For antiplatelets, this was 0.001, P = .97 (EMR) and - 0.03, p = 0.03 (administrative). The comparator PPI was - 0.07, p = 0.4 (EMR) and - 0.11, p = 0.002 (administrative).

CONCLUSIONS

Using both EMR and administrative health data, we confirmed that the Diabetes Canada 2013 guideline dissemination strategy did not lead to an increased rate of coverage for vascular protective medications. Alternative strategies are needed to effect change in practice.

Prevalence of diabetes mellitus at the end of life: An investigation using individually linked cause-of-death and medical register data

AIMS

Although diabetes mellitus at the end of life is associated with complex care, its end-of-life prevalence is uncertain. Our aim is to estimate diabetes prevalence in the end-of-life population, to evaluate which medical register has the largest added value to cause-of-death data in detecting diabetes cases, and to assess the extent to which reporting of diabetes as a cause of death is associated with disease severity.

METHODS

Our study population consisted of deaths in the Netherlands (2015-2016) included in Nivel Primary Care Database (Nivel-PCD; N = 18,162). The proportion of deaths with diabetes (Type 1 or 2) within the last two years of life was calculated using individually linked cause-of-death, general practice, medication, and hospital discharge data. Severity status of diabetes was defined with dispensed medicines.

RESULTS

According to all data sources combined, 28.7% of the study population had diabetes at the end of life. The estimated end-of-life prevalence of diabetes was 7.7% using multiple cause-of-death data only. Addition of general practice data increased this estimate the most (19.7%-points). Of the cases added by primary care data, 76.3% had a severe or intermediate status.

CONCLUSIONS

More than one fourth of the Dutch end-of-life population has diabetes. Cause-of-death data are insufficient to monitor this prevalence, even of severe cases of diabetes, but could be enriched particularly with general practice data.

Enhancing Health Surveillance: Validation of a Novel Electronic Medical Records-Based Definition of Cases of Pediatric Type 1 and Type 2 Diabetes Mellitus

OBJECTIVES

To compose and validate an electronic medical records-based case definition for pediatric diabetes in primary care.

METHODS

Data from the electronic medical records of 221 primary care providers participating in the Manitoba Primary Care Research Network were extracted from April 1, 1998, to March 31, 2015. We assessed agreement among the 3 case definitions of pediatric diabetes and compared the performance of each with the clinical database of the Manitoba Diabetes Education Resource for Children and Adolescents.

RESULTS

Our reference dataset included 41,055 pediatric patients. Electronic medical records-based case definitions, which included billing records, health conditions lists, prescription records and laboratory results, showed substantially higher sensitivity compared to the administration-based case definition that relied on billing and prescription records (96.9% and 94.9% vs 48.5%). Our study suggests a higher prevalence of pediatric diabetes in Manitoba than was previously reported through administration-based case definitions or in patients whose data were captured in the Manitoba Diabetes Education Resource for Children and Adolescents clinical database.

CONCLUSIONS

We describe a novel method of calculating the prevalence of pediatric diabetes in a primary care population. This case definition will improve the surveillance of pediatric diabetes and enhance service planning and the development of strategies to support prevention and management.

Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric population using electronic medical records and claims data

PURPOSE

To develop and validate algorithms to classify diabetes type in newly diagnosed pediatric patients with DM.

METHOD

Data from the United States Department of Defense health system were used to identify patients aged 10 to 18 years with incident DM. Two independent sets of 200 children were randomly sampled for algorithm development and validation. Algorithms were developed based on clinical insight, published literature, and quantitative approaches. The actual DM type was ascertained via chart review. Finally, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated.

RESULTS

Among the 400 patients, mean age was 14.2 (±2.5 years), and 50% were female. The best performing algorithms were based on data available in claims. They consisted of several logical expressions based on one predictor or more, which classified patients by use of glucose-lowering drugs or testing, DM ICD-9 diagnosis codes, and comorbidities. The best performing T2DM and T1DM algorithms achieved 90% and 98% sensitivity, 95% and 95% specificity, 87% and 98% PPV, and 96% and 96% NPV, respectively.

CONCLUSIONS

Our results suggest that claims algorithms can accurately identify newly diagnosed T1DM and T2DM pediatric patients, which can facilitate large database studies in children with T1DM and T2DM. However, external validation in other data sources is needed.

Using Routine Data Sources to Feed an Immunization Information System for High-Risk Patients-A Pilot Study

Background

Vaccine-preventable diseases among high-risk patients are a public health priority in high-income countries. Most national immunization programs have included vaccination recommendations for these population groups but they remain hard-to-reach and coverage data are poorly available. In a pilot study, we developed and tested an automated approach for identifying individuals with underlying medical conditions to feed an immunization information system (IIS).

Methods

We reviewed published recommendations on medical conditions that indicate vaccination against influenza, pneumococcal disease, meningococcal disease, hepatitis A, and hepatitis B. For each medical condition, we identified the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedure codes, the user fee exempt codes and the Anatomical Therapeutic Chemical Classification System codes and we reported these data in correspondence tables. Using these tables, we extracted three lists of patients recorded in three current data sources between 2001 and 2010 in the Apulia region of Italy: the hospital discharge registry, the user fee exempt registry, and the drug prescription registry. Using a unique personal identification number, we linked these three lists of patients with the regional IIS (2012 database), obtaining a list of patients with chronic diseases eligible for vaccination. We tested completeness, sensitivity, and positive predictive value (PPV) of this approach by asking a sample of 28 general practitioners (GPs) to evaluate the matching between a sublist of patients with clinical recommendations for influenza vaccination and the GPs individual subjects medical records.

Results

We included a total of 1,204,496 subjects with underlying medical conditions eligible to receive any of the aforementioned vaccinations. Of these, 9% were identified in all three data sources, 18% in two sources, and 73% in one source. The completeness of this automated process in identifying GPs high-risk patients eligible for influenza vaccination was 88.9% [95% confidence intervals (95% CI): 88.1–89.8%], with a sensitivity of 69.2% (95% CI: 67.7–70.6%) and a PPV of 85.7% (95% CI: 84.4–86.8%).

Conclusion

The high completeness of the methodology used for identifying high-risk patients in current data sources encouraged us to apply this approach for feeding the regional IIS.

Using a stakeholder-engaged approach to develop and validate electronic clinical quality measures

Objective

To describe the stakeholder-engaged processes used to develop, specify, and validate 2 oral health care electronic clinical quality measures.

Materials and Methods

A broad range of stakeholders were engaged from conception through testing to develop measures and test feasibility, reliability, and validity following National Quality Forum guidance. We assessed data element feasibility through semistructured interviews with key stakeholders using a National Quality Forum-recommended scorecard. We created test datasets of synthetic patients to test measure implementation feasibility and reliability within and across electronic health record (EHR) systems. We validated implementation with automated reporting of EHR clinical data against manual record reviews, using the kappa statistic.

Results

A stakeholder workgroup was formed and guided all development and testing processes. All critical data elements passed feasibility testing. Four test datasets, representing 577 synthetic patients, were developed and implemented within EHR vendors' software, demonstrating measure implementation feasibility. Measure reliability and validity were established through implementation at clinical practice sites, with kappa statistic values in the "almost perfect" agreement range of 0.80-0.99 for all but 1 measure component, which demonstrated "substantial" agreement. The 2 validated measures were published in the United States Health Information Knowledgebase.

Conclusion

The stakeholder-engaged processes used in this study facilitated a successful measure development and testing cycle. Engaging stakeholders early and throughout development and testing promotes early identification of and attention to potential threats to feasibility, reliability, and validity, thereby averting significant resource investments that are unlikely to be fruitful.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.

Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature

BACKGROUND

Primary care data gathered from Electronic Health Records are of the utmost interest considering the essential role of general practitioners (GPs) as coordinators of patient care. These data represent the synthesis of the patient history and also give a comprehensive picture of the population health status. Nevertheless, discrepancies between countries exist concerning routine data collection projects. Therefore, we wanted to identify elements that influence the development and durability of such projects.

METHODS

A systematic review was conducted using the PubMed database to identify worldwide current primary care data collection projects. The gray literature was also searched via official project websites and their contact person was emailed to obtain information on the project managers. Data were retrieved from the included studies using a standardized form, screening four aspects: projects features, technological infrastructure, GPs' roles, data collection network organization.

RESULTS

The literature search allowed identifying 36 routine data collection networks, mostly in English-speaking countries: CPRD and THIN in the United Kingdom, the Veterans Health Administration project in the United States, EMRALD and CPCSSN in Canada. These projects had in common the use of technical facilities that range from extraction tools to comprehensive computing platforms. Moreover, GPs initiated the extraction process and benefited from incentives for their participation. Finally, analysis of the literature data highlighted that governmental services, academic institutions, including departments of general practice, and software companies, are pivotal for the promotion and durability of primary care data collection projects.

CONCLUSION

Solid technical facilities and strong academic and governmental support are required for promoting and supporting long-term and wide-range primary care data collection projects.

Effects of Integrated Care on Disease-Related Hospitalisation and Healthcare Costs in Patients with Diabetes, Cardiovascular Diseases and Respiratory Illnesses: A Propensity-Matched Cohort Study in Switzerland

Background:

There is an ongoing discussion on the further promotion of integrated care models in many healthcare systems. Only a few data, which examine the effect of integrated care models on medical expenditures and quality of care in chronically ill patients, exist.

Aims:

To investigate the effect of integrated care models on disease-related hospitalisations as a quality indicator and healthcare costs in patients with either diabetes, cardiovascular diseases or respiratory illnesses.

Methods:

A propensity-matched retrospective cohort study based on a large Swiss health insurance database (2012–2013) was performed for three chronic patient groups (diabetes, cardiovascular diseases, respiratory illnesses), who were enrolled in an integrated care model and compared to individuals in a standard care model. Multivariate regression models were applied to estimate the effect of integrated care models on disease-related hospitalisations and healthcare costs.

Results:

The matched cohorts included a total of 12,526 patients with diabetes, 71,778 with cardiovascular diseases and 17,498 with respiratory illnesses, in which each one half was enrolled in integrated care models and the other half in standard care models. Diabetes and cardiovascular patients with integrated care models had a significantly lower probability of disease-related hospitalisation compared to those with standard care models (p < 0.01). Healthcare costs were statistically significant lower in all three patient groups with integrated care, but with the highest effect in patients with diabetes (Swiss francs (CHF) –778).

Conclusions:

Integrated care may provide an effective strategy to improve the quality of care and to reduce healthcare costs in chronically ill patients. Study findings intend to contribute to the ongoing political discussion on integrated care and provide evidence for improved and more effective care of patients with chronic diseases.

Preventable major cardiovascular events associated with uncontrolled glucose, blood pressure, and lipids and active smoking in adults with diabetes with and without cardiovascular disease: a contemporary analysis

OBJECTIVE

The objective of this study was to assess the incidence of major cardiovascular (CV) hospitalization events and all-cause deaths among adults with diabetes with or without CV disease (CVD) associated with inadequately controlled glycated hemoglobin (A1C), high LDL cholesterol (LDL-C), high blood pressure (BP), and current smoking.

RESEARCH DESIGN AND METHODS

Study subjects included 859,617 adults with diabetes enrolled for more than 6 months during 2005-2011 in a network of 11 U.S. integrated health care organizations. Inadequate risk factor control was classified as LDL-C ≥100 mg/dL, A1C ≥7% (53 mmol/mol), BP ≥140/90 mm Hg, or smoking. Major CV events were based on primary hospital discharge diagnoses for myocardial infarction (MI) and acute coronary syndrome (ACS), stroke, or heart failure (HF). Five-year incidence rates, rate ratios, and average attributable fractions were estimated using multivariable Poisson regression models.

RESULTS

Mean (SD) age at baseline was 59 (14) years; 48% of subjects were female, 45% were white, and 31% had CVD. Mean follow-up was 59 months. Event rates per 100 person-years for adults with diabetes and CVD versus those without CVD were 6.0 vs. 1.7 for MI/ACS, 5.3 vs. 1.5 for stroke, 8.4 vs. 1.2 for HF, 18.1 vs. 40 for all CV events, and 23.5 vs. 5.0 for all-cause mortality. The percentages of CV events and deaths associated with inadequate risk factor control were 11% and 3%, respectively, for those with CVD and 34% and 7%, respectively, for those without CVD.

CONCLUSIONS

Additional attention to traditional CV risk factors could yield further substantive reductions in CV events and mortality in adults with diabetes.

Evaluating the performance of the Framingham Diabetes Risk Scoring Model in Canadian electronic medical records

OBJECTIVE

The objective of this study was to evaluate the performance of the Framingham Diabetes Risk Scoring Model (FDRSM) in a Canadian population, using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database.

METHODS

We analyzed the records of 571 631 patients, between the ages of 45 and 64, between 2002 and 2005, by extracting the most recent laboratory and examination results, including age, sex, body mass index, fasting blood glucose, high-density lipoprotein, triglycerides and blood pressure. We calculated the risk scores of these patients based on the FDRSM. We tracked these patients for 8 years to find out whether or not they were diagnosed with diabetes. We used the area under the receiver operating characteristics curve (AROC) to estimate the discrimination capability of the FDRSM on our study sample and compared it with the AROC reported in the original Framingham diabetes study.

RESULTS

The AROC for our main research sample of 1970 patients for whom all risk factors and follow-up data were available was 78.6% compared to the AROC of 85% reported in the FDRSM. We found that 70.1% of our main sample had risks lower than 3%; 16.3% had risks between 3% and 10%; and 13.6% had risks greater than 10% for diabetes over the following 8-year period.

CONCLUSIONS

The discrimination capability of the FDRSM Canadian electronic medical records is fair. However, building a more accurate model for predicting diabetes based on the characteristics of Canadian patients is highly recommended.

Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study

BACKGROUND

The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics.

OBJECTIVE

This study evaluated the potential of administrative and electronic health record (EHR) data from a large academic care delivery system to conduct diabetes case ascertainment in youth according to type, age, and race/ethnicity.

SUBJECTS

Of 57 767 children aged <20 yr as of 31 December 2011 seen at University of North Carolina Health Care System in 2011 were included.

METHODS

Using an initial algorithm including billing data, patient problem lists, laboratory test results, and diabetes related medications between 1 July 2008 and 31 December 2011, presumptive cases were identified and validated by chart review. More refined algorithms were evaluated by type (type 1 vs. type 2), age (<10 vs. ≥10 yr) and race/ethnicity (non-Hispanic White vs. 'other'). Sensitivity, specificity, and positive predictive value were calculated and compared.

RESULTS

The best algorithm for ascertainment of overall diabetes cases was billing data. The best type 1 algorithm was the ratio of the number of type 1 billing codes to the sum of type 1 and type 2 billing codes ≥0.5. A useful algorithm to ascertain youth with type 2 diabetes with 'other' race/ethnicity was identified. Considerable age and racial/ethnic differences were present in type-non-specific and type 2 algorithms.

CONCLUSIONS

Administrative and EHR data may be used to identify cases of childhood diabetes (any type), and to identify type 1 cases. The performance of type 2 case ascertainment algorithms differed substantially by race/ethnicity.

Prevalence and epidemiology of diabetes in Canadian primary care practices: a report from the Canadian Primary Care Sentinel Surveillance Network

OBJECTIVE

The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a large, validated national primary care Electronic Medical Records (EMR)-based database. Our objective was to describe the epidemiology of diabetes in this Canadian sample.

METHODS

We analyzed the records of 272 469 patients10 years of age and older, with at least 1 primary care clinical encounter between January 1, 2011, and December 31, 2012. We calculated the age-gender standardized prevalence of diabetes. We compared health care utilization and comorbidities for 7 selected chronic conditions in patients with and without diabetes. We also examined patterns of medication usage.

RESULTS

The estimated population prevalence of diabetes was 7.6%. Specifically, we studied 25 425 people with diabetes who had at least 1 primary care encounter in 2 years. On average, patients with diabetes had 1.42 times as many practice encounters as patients without diabetes (95% CI 1.42 to 1.43, p<0.0001). Patients with diabetes had 1.29 times as many other comorbid conditions as those without diabetes (95% CI 1.27 to 1.31, p<0.0001). We found that 85.2% of patients taking hypoglycemic medications were taking metformin, and 51.8% were taking 2 or more classes of medications.

CONCLUSIONS

This study is the first national Canadian report describing the epidemiology of diabetes using primary care EMR-based data. We found significantly higher rates of primary care use, and greater numbers of comorbidities in patients with diabetes. Most patients were on first-line hypoglycemic medications. Data routinely recorded in EMRs can be used for surveillance of chronic diseases such as diabetes in Canada. These results can enable comparisons with other national EMR-based datasets.

Exploring the feasibility of using electronic health records in the surveillance of fetal alcohol syndrome

BACKGROUND

Explore the use of electronic health records (EHRs) in fetal alcohol syndrome (FAS) surveillance systems.

METHODS

Using EHRs we identified diagnoses and anthropometric measurements related to the FAS criteria developed by the Fetal Alcohol Syndrome Surveillance Network (FASSNet) among children aged 0 to 12 years.

RESULTS

There were 143,393 distinct children aged between 0 and 12 years enrolled in Kaiser Permanente, Georgia, during the study period. Based on diagnoses and anthropometric measurements, 20,101 children met at least one criterion of interest, and when grouped into combinations of different criteria there were 2285 who met GROWTH+CNS criteria, 76 children who met GROWTH+FACE criteria, 107 children who met CNS+FACE criteria, and 93 children who met GROWTH+CNS+FACE criteria. The prevalence of FAS as defined by FASSNet is 1.92 per 1000 children. We linked 17,084 (85.0%) children to their mothers in the health plan; only 3% of mothers of children in the GROWTH+CNS+FACE group had an indication of alcohol or drugs use, but they had the highest rate of depression (39%).

CONCLUSION

Data of utility in identification of FAS are readily available in EHRs and may serve as a basis for intervention with at-risk children and in planning of future FAS surveillance programs.

Building a population-based diabetes register: an Italian experience

AIMS

To describe the methodology used to set up the Reggio Emilia (northern Italy) Diabetes Register. The prevalence estimates on December 31st, 2009 are also provided.

METHODS

The Diabetes Register covers all residents in the Reggio Emilia province. The register was created by deterministic linkage of six routinely collected data sources through a definite algorithm able to ascertain cases and to distinguish type of diabetes and model of care: Hospital Discharge, Drug Dispensation, Biochemistry Laboratory, Disease-specific Exemption, Diabetes Outpatient Clinics, and Mortality databases. Using these data, we estimated crude prevalence on December 31st, 2009 by sex, age groups, and type of diabetes.

RESULTS

There were 25,425 ascertained prevalent cases on December 31st, 2009. Drug Dispensation and Exemption databases made the greatest contribution to prevalence. Analyzing overlapping sources, more than 80% of cases were reported by at least two sources. Crude prevalence was 4.8% and 5.9% for the whole population and for people aged 18 years and over, respectively. Males accounted for 53.6%. Type 1 diabetes accounted for 3.8% of cases, while people with Type 2 diabetes were the overriding majority (91.2%), and Diabetes Outpatient Clinics treated 75.4% of people with Type 2 diabetes.

CONCLUSION

The Register is able to quantify the burden of disease, the first step in planning, implementing, and monitoring appropriate interventions. All data sources contributed to completeness and/or accuracy of the Register. Although all cases are identified by deterministic record linkage, manual revision and General Practitioner involvement are still necessary when information is insufficient or conflicting.

Estimating the prevalence of comorbid conditions and their effect on health care costs in patients with diabetes mellitus in Switzerland

BACKGROUND

Estimating the prevalence of comorbidities and their associated costs in patients with diabetes is fundamental to optimizing health care management. This study assesses the prevalence and health care costs of comorbid conditions among patients with diabetes compared with patients without diabetes. Distinguishing potentially diabetes- and nondiabetes-related comorbidities in patients with diabetes, we also determined the most frequent chronic conditions and estimated their effect on costs across different health care settings in Switzerland.

METHODS

Using health care claims data from 2011, we calculated the prevalence and average health care costs of comorbidities among patients with and without diabetes in inpatient and outpatient settings. Patients with diabetes and comorbid conditions were identified using pharmacy-based cost groups. Generalized linear models with negative binomial distribution were used to analyze the effect of comorbidities on health care costs.

RESULTS

A total of 932,612 persons, including 50,751 patients with diabetes, were enrolled. The most frequent potentially diabetes- and nondiabetes-related comorbidities in patients older than 64 years were cardiovascular diseases (91%), rheumatologic conditions (55%), and hyperlipidemia (53%). The mean total health care costs for diabetes patients varied substantially by comorbidity status (US$3,203-$14,223). Patients with diabetes and more than two comorbidities incurred US$10,584 higher total costs than patients without comorbidity. Costs were significantly higher in patients with diabetes and comorbid cardiovascular disease (US$4,788), hyperlipidemia (US$2,163), hyperacidity disorders (US$8,753), and pain (US$8,324) compared with in those without the given disease.

CONCLUSION

Comorbidities in patients with diabetes are highly prevalent and have substantial consequences for medical expenditures. Interestingly, hyperacidity disorders and pain were the most costly conditions. Our findings highlight the importance of developing strategies that meet the needs of patients with diabetes and comorbidities. Integrated diabetes care such as used in the Chronic Care Model may represent a useful strategy.

Distinguishing incident and prevalent diabetes in an electronic medical records database

PURPOSE

To develop a method to identify incident diabetes mellitus (DM) using an electronic medical records (EMR) database and test this classification by comparing incident and prevalent DM with common outcomes related to DM duration.

METHODS

Incidence rates (IRs) of DM (defined as a first diagnosis or prescription) were measured in 3-month intervals through 36 months after registration in The Health Improvement Network, a primary care database, from 1994 to 2012. We used Joinpoint regression to identify the point where a statistically significant change in the trend of IRs occurred. Further analyses used this point to distinguish those likely to have incident (n = 50 315) versus prevalent (n = 28 337) DM. Incident and prevalent cohorts were compared using Cox regression for all-cause mortality, cardiovascular disease (CVD), diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy. Analyses were adjusted for age, sex, smoking, obesity, hyperlipidemia, hypertension, and calendar year.

RESULTS

Trends in DM IRs plateaued 9 months after registration (p = 0.04). All cause-mortality was increased (hazard ratio (HR) 1.62, 95% CI 1.53-1.70) among patients diagnosed with DM prior to 9 months following registration (prevalent DM) compared to those diagnosed after 9 months (incident DM). Similarly, the risk of DM-related complications was higher in prevalent versus incident DM patients [CVD, HR 2.24 (2.08-2.40); diabetic retinopathy, HR 1.31 (1.24-1.38); diabetic nephropathy, HR 2.30 (1.95-2.72); diabetic neuropathy, HR 1.28 (1.16-1.41)].

CONCLUSION

Joinpoint regression can be used to identify patients with newly diagnosed diabetes within EMR data. Failure to exclude patients with prevalent DM can lead to exaggerated associations of DM-related outcomes.

Systematic review and meta-analysis of validation studies on a diabetes case definition from health administrative records

OBJECTIVES

Health administrative data are frequently used for diabetes surveillance. We aimed to determine the sensitivity and specificity of a commonly-used diabetes case definition (two physician claims or one hospital discharge abstract record within a two-year period) and their potential effect on prevalence estimation.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Medline (from 1950) and Embase (from 1980) databases for validation studies through August 2012 (keywords: "diabetes mellitus"; "administrative databases"; "validation studies"). Reviewers abstracted data with standardized forms and assessed quality using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) criteria. A generalized linear model approach to random-effects bivariate regression meta-analysis was used to pool sensitivity and specificity estimates. We applied correction factors derived from pooled sensitivity and specificity estimates to prevalence estimates from national surveillance reports and projected prevalence estimates over 10 years (to 2018).

RESULTS

The search strategy identified 1423 abstracts among which 11 studies were deemed relevant and reviewed; 6 of these reported sensitivity and specificity allowing pooling in a meta-analysis. Compared to surveys or medical records, sensitivity was 82.3% (95%CI 75.8, 87.4) and specificity was 97.9% (95%CI 96.5, 98.8). The diabetes case definition underestimated prevalence when it was ≤10.6% and overestimated prevalence otherwise.

CONCLUSION

The diabetes case definition examined misses up to one fifth of diabetes cases and wrongly identifies diabetes in approximately 2% of the population. This may be sufficiently sensitive and specific for surveillance purposes, in particular monitoring prevalence trends. Applying correction factors to adjust prevalence estimates from this definition may be helpful to increase accuracy of estimates.

Electronic health records as a tool for recruitment of participants' clinical effectiveness research: lessons learned from tobacco cessation

Translating tobacco dependence treatments that are effective in research settings into real-world clinical settings remains challenging. Electronic health record (EHR) technology can facilitate this process. This paper describes the accomplishments and lessons learned from a translational team science (clinic/research) approach to the development of an EHR tool for participant recruitment and clinic engagement in tobacco cessation research. All team stakeholders-research, clinical, and IT-were engaged in the design and planning of the project. Results over the first 17 months of the study showed that over one half of all smokers, coming in for any type of clinic appointment, were offered participation in the study, a very high level of adherent use of the EHR. Study recruitment over this period was 1,071 individuals, over 12 % of smokers in the participating clinics.

Measuring Diabetes Care Performance Using Electronic Health Record Data: The Impact of Diabetes Definitions on Performance Measure Outcomes

The objective was to examine the use of electronic health record (EHR) data for diabetes performance measurement. Data were extracted from the EHR of a health system to identify patients with diabetes using 8 different EHR data-based methods of identification. These EHR-based methods were compared to the gold standard of a manual medical record review. The study team then assessed whether the method of identifying patients with diabetes could affect performance measurement scores. The sensitivity of the 8 EHR-based methods of identifying patients with diabetes ranged from moderate to high. The use of certain data elements in the EHR to identify patients with diabetes selectively identified those who had better performance measures. Diabetes performance measures are influenced by the data elements used to identify patients. As EHR data are used increasingly to measure performance, continuing to improve our understanding of how EHR data are collected and used will be critical.

Caveats for the use of operational electronic health record data in comparative effectiveness research

The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols. However, the quantity and real-world nature of these data provide impetus for their use, and we develop a list of caveats to inform would-be users of such data as well as provide an informatics roadmap that aims to insure this opportunity to augment comparative effectiveness research can be best leveraged.

Risk of incident diabetes in relation to long-term exposure to fine particulate matter in Ontario, Canada

BACKGROUND

Laboratory studies suggest that fine particulate matter (≤ 2.5 µm in diameter; PM(2.5)) can activate pathophysiological responses that may induce insulin resistance and type 2 diabetes. However, epidemiological evidence relating PM2.5 and diabetes is sparse, particularly for incident diabetes.

OBJECTIVES

We conducted a population-based cohort study to determine whether long-term exposure to ambient PM(2.5) is associated with incident diabetes.

METHODS

We assembled a cohort of 62,012 nondiabetic adults who lived in Ontario, Canada, and completed one of five population-based health surveys between 1996 and 2005. Follow-up extended until 31 December 2010. Incident diabetes diagnosed between 1996 and 2010 was ascertained using the Ontario Diabetes Database, a validated registry of persons diagnosed with diabetes (sensitivity = 86%, specificity = 97%). Six-year average concentrations of PM2.5 at the postal codes of baseline residences were derived from satellite observations. We used Cox proportional hazards models to estimate the associations, adjusting for various individual-level risk factors and contextual covariates such as smoking, body mass index, physical activity, and neighborhood-level household income. We also conducted multiple sensitivity analyses. In addition, we examined effect modification for selected comorbidities and sociodemographic characteristics.

RESULTS

There were 6,310 incident cases of diabetes over 484,644 total person-years of follow-up. The adjusted hazard ratio for a 10-µg/m(3) increase in PM(2.5) was 1.11 (95% CI: 1.02, 1.21). Estimated associations were comparable among all sensitivity analyses. We did not find strong evidence of effect modification by comorbidities or sociodemographic covariates.

CONCLUSIONS

This study suggests that long-term exposure to PM2.5 may contribute to the development of diabetes.

Glycemic control and diabetes-related health care costs in type 2 diabetes; retrospective analysis based on clinical and administrative databases

Background

Diabetes is one of the most prevalent chronic diseases, and its prevalence is predicted to increase in the next two decades. Diabetes imposes a staggering financial burden on the health care system, so information about the costs and experiences of collecting and reporting quality measures of data is vital for practices deciding whether to adopt quality improvements or monitor existing initiatives. The aim of this study was to quantify the association between health care costs and level of glycemic control in patients with type 2 diabetes using clinical and administrative databases.

Methods

A retrospective analysis using a large administrative database and a clinical registry containing laboratory results was performed. Patients were subdivided according to their glycated hemoglobin level. Multivariate analyses were used to control for differences in potential confounding factors, including age, gender, Charlson comorbidity index, presence of dyslipidemia, hypertension, or cardiovascular disease, and degree of adherence with antidiabetic drugs among the study groups.

Results

Of the total population of 700,000 subjects, 31,022 were identified as being diabetic (4.4% of the entire population). Of these, 21,586 met the study inclusion criteria. In total, 31.5% of patients had very poor glycemic control and 25.7% had excellent control. Over 2 years, the mean diabetes-related cost per person was: €1291.56 in patients with excellent control; €1545.99 in those with good control; €1584.07 in those with fair control; €1839.42 in those with poor control; and €1894.80 in those with very poor control. After adjustment, compared with the group having excellent control, the estimated excess cost per person associated with the groups with good control, fair control, poor control, and very poor control was €219.28, €264.65, €513.18, and €564.79, respectively.

Conclusion

Many patients showed suboptimal glycemic control. Lower levels of glycated hemoglobin were associated with lower diabetes-related health care costs. Integration of administrative databases and a laboratory database appears to be suitable for showing that appropriate management of diabetes can help to achieve better resource allocation.

Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey

BACKGROUND

Administrative databases are widely available and have been extensively used to provide estimates of chronic disease prevalence for the purpose of surveillance of both geographical and temporal trends. There are, however, other sources of data available, such as medical records from primary care and national surveys. In this paper we compare disease prevalence estimates obtained from these three different data sources.

METHODS

Data from general practitioners (GP) and administrative transactions for health services were collected from five Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the three macroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data source and region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease (COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey. When necessary, estimates were adjusted for completeness of data ascertainment.

RESULTS

Crude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lower than corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends were similar in the three sources and estimates based on treatment were the same, while estimates adjusted for completeness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrative and GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to 4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs' estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates from administrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates in four regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrative data vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher than the corresponding estimates from the other two sources.

CONCLUSION

This study supports the use of data from Italian administrative databases to estimate geographic differences in population prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. The algorithm for COPD used in this study requires further refinement.

Improving diabetes outcomes using a web-based registry and interactive education: a multisite collaborative approach

INTRODUCTION

To support the adoption of guideline concordant care by primary care practices, the New York Diabetes Coalition (NYDC) promoted use of an electronic diabetes registry and developed an interactive educational module on using the registry and improving patient communication. The NYDC hypothesized that use of a registry with immediate feedback would achieve measurable and clinically meaningful improvement in the proportion of patients at goal for diabetes health metrics.

RESEARCH DESIGN AND METHODS

In 2006-2007, the NYDC recruited 7 small to midsized primary care practices to implement the registry and to receive education and coaching on registry use, practice work flow, and patient engagement. The patient cohort included those with 2 or more visits with a diagnosis of diabetes within a 12-month period. Each patient's health measure status (at goal, above goal, not recorded) was assessed quarterly for hemoglobin A1C , low-density lipoprotein (LDL), and blood pressure (BP), and most recent A1C value was noted. A cohort analysis was performed using random effects regression models to assess the impact of the registry over time for each diabetes health metric.

RESULTS

After controlling for variability between sites, with each subsequent quarter during the registry period patients were 1.4 times more likely to have A1C ≤ 9, almost twice (OR = 1.8) as likely to have LDL < 100, and 1.3 times more likely to have BP < 140/90. These improvements in compliance were statistically significant. Average A1C also improved over time, though this did not reach statistical significance.

DISCUSSION

Utilizing a Web-based registry and interactive education, the project demonstrated improved patient outcomes, as well as the feasibility of collecting aggregate data from unrelated, independent practices.

Diabetes and asthma case identification, validation, and representativeness when using electronic health data to construct registries for comparative effectiveness and epidemiologic research

BACKGROUND

Advances in health information technology and widespread use of electronic health data offer new opportunities for development of large scale multisite disease-specific patient registries. Such registries use existing data, can be constructed at relatively low cost, include large numbers of patients, and once created can be used to address many issues with a short time between posing a question and obtaining an answer. Potential applications include comparative effectiveness research, public health surveillance, mapping and improving quality of clinical care, and others.

OBJECTIVE AND DISCUSSION

This paper describes selected conceptual and practical challenges related to development of multisite diabetes and asthma registries, including development of case definitions, validation of case identification methods, variation in electronic health data sources; representativeness of registry populations, including the impact of attrition. Specific challenges are illustrated with data from actual registries.

Construction of a multisite DataLink using electronic health records for the identification, surveillance, prevention, and management of diabetes mellitus: the SUPREME-DM project

Introduction

Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research.

Methods

We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment.

Results

The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases.

Conclusion

The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.

Satisfaction with referral relationships between general practice and allied health professionals in Australian primary health care

Chronic diseases require a multidisciplinary approach to provide patients with optimal care in general practice. This often involves general practitioners (GPs) referring their patients to allied health professionals (AHPs). The Team-link study explored the impact of an intervention to enhance working relationships between GPs and AHPs in general practice regarding the management of two chronic diseases: diabetes and ischaemic heart disease (IHD) or hypertension. The Measure of Multidisciplinary Linkages (MoML) questionnaire was developed to assess professional interactions and satisfaction with various aspects of the multidisciplinary relationship. Questionnaires were completed at baseline and 6 months by GPs (n=29) participating in the Team-link project and by AHPs (n=39) who had a current working relationship with these GPs. The Chronic Care Team Profile (CCTP) and Clinical Linkages Questionnaire (CLQ) were also completed by GPs. There were significant changes from baseline to 6 months after the intervention measures for individual items and overall MoML scores for GPs, especially items assessing 'contact', 'shared care' and 'satisfaction with communication'. The comparable item in the CLQ, 'Shared Care', also showed significant improvement. However, there were no statistically significant correlations between the change in overall 'Referral Satisfaction' scores in the GP MoML and the CLQ. The CCTP also improved and was a weak negative correlation between the GP MoML and two of the subscores of this instrument. There were no changes in AHP measure. This study demonstrates that the instrument is sensitive to differences between providers and conditions and is sensitive to change over time following an intervention. There were few associations with the other measures suggesting that the MoML might assess other aspects of teamwork involving practitioners who are not collocated or in the same organisation.

Primary health care service delivery networks for the prevention and management of type 2 diabetes: using social network methods to describe interorganisational collaboration in a rural setting

Adults with type 2 diabetes or with behavioural risk factors require comprehensive and well coordinated responses from a range of health care providers who often work in different organisational settings. This study examines three types of collaborative links between organisations involved in a rural setting. Social network methods were employed using survey data on three types of links, and data was collected from a purposive sample of 17 organisations representing the major provider types. The analysis included a mix of unconfirmed and confirmed links, and network measures. General practices were the most influential provider group in initiating referrals, and they referred to the broadest range of organisations in the network. Team care arrangements formed a small part of the general practice referral network. They were used more for access to private sector allied health care providers and less for sharing care with public sector health services. Involvement in joint programs/activities was limited to public and non-government sector services, with no participation from the private sector. The patterns of interactions suggest that informal referral networks provide access to services and coordination of care for individual patients with diabetes. Two population subgroups would benefit from more proactive approaches to ensure equitable access to services and coordination of care across organisational boundaries: people with more complex health care needs and people at risk of developing diabetes.

Data standards in diabetes patient registries

Widespread adoption of electronic health records (EHRs) and expansion of patient registries present opportunities to improve patient care and population health and advance translational research. However, optimal integration of patient registries with EHR functions and aggregation of regional registries to support national or global analyses will require the use of standards. Currently, there are no standards for patient registries and no content standards for health care data collection or clinical research, including diabetes research. Data standards can facilitate new registry development by supporting reuse of well-defined data elements and data collection systems, and they can enable data aggregation for future research and discovery. This article introduces standardization topics relevant to diabetes patient registries, addresses issues related to the quality and use of registries and their integration with primary EHR data collection systems, and proposes strategies for implementation of data standards in diabetes research and management.