Performance assessment across different care settings of a heart failure hospitalisation risk-score for type 2 diabetes using administrative claims

Predicting the risk of cardiovascular complications, in particular heart failure hospitalisation (HHF), can improve the management of type 2 diabetes (T2D). Most predictive models proposed so far rely on clinical data not available at the higher Institutional level. Therefore, it is of interest to assess the risk of HHF in people with T2D using administrative claims data only, which are more easily obtainable and could allow public health systems to identify high-risk individuals. In this paper, the administrative claims of > 175,000 patients with T2D were used to develop a new risk score for HHF based on Cox regression. Internal validation on the administrative data cohort yielded satisfactory results in terms of discrimination (max AUROC = 0.792, C-index = 0.786) and calibration (Hosmer-Lemeshow test p value < 0.05). The risk score was then tested on data gathered from two independent centers (one diabetes outpatient clinic and one primary care network) to demonstrate its applicability to different care settings in the medium-long term. Thanks to the large size and broad demographics of the administrative dataset used for training, the proposed model was able to predict HHF without significant performance loss concerning bespoke models developed within each setting using more informative, but harder-to-acquire clinical variables.

Cardiovascular outcomes after initiating GLP-1 receptor agonist or basal insulin for the routine treatment of type 2 diabetes: a region-wide retrospective study

Aim

We aimed to compare cardiovascular outcomes of patients with type 2 diabetes (T2D) who initiated GLP-1 receptor agonists (GLP-1RA) or basal insulin (BI) under routine care.

Methods

We accessed the administrative claims database of the Veneto Region (Italy) to identify new users of GLP-1RA or BI in 2014–2018. Propensity score matching (PSM) was implemented to obtain two cohorts of patients with superimposable characteristics. The primary endpoint was the 3-point major adverse cardiovascular events (3P-MACE). Secondary endpoints included 3P-MACE components, hospitalization for heart failure, revascularizations, and adverse events.

Results

From a background population of 5,242,201 citizens, 330,193 were identified as having diabetes. PSM produced two very well matched cohorts of 4063 patients each, who initiated GLP-1RA or BI after an average of 2.5 other diabetes drug classes. Patients were 63-year-old and only 15% had a baseline history of cardiovascular disease. During a median follow-up of 24 months in the intention-to-treat analysis, 3P-MACE occurred less frequently in the GLP-1RA cohort (HR versus BI 0.59; 95% CI 0.50–0.71; p < 0.001). All secondary cardiovascular endpoints were also significantly in favor of GLP-1RA. Results were confirmed in the as-treated approach and in several stratified analyses. According to the E-value, confounding by unmeasured variables were unlikely to entirely explain between-group differences in cardiovascular outcomes.

Conclusions

Patients with T2D who initiated a GLP-1RA experienced far better cardiovascular outcomes than did matched patients who initiated a BI in the same healthcare system. These finding supports prioritization of GLP-1RA as the first injectable regimen for the management of T2D.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01414-3.

A Deep Learning Approach to Predict Diabetes' Cardiovascular Complications From Administrative Claims

People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate great volumes of data across several domains, from clinical to administrative. Difficulties in accessing and processing these data hinder their secondary use in an institutional setting, even for highly desirable applications, such as the prediction of cardiovascular disease, the main driver of excess mortality in diabetes. Hence, in the present work, we propose a deep learning model for the prediction of major adverse cardiovascular events (MACE), developed and validated using the administrative claims of 214,676 diabetic patients of the Veneto region, in North East Italy. Specifically, we use a year of pharmacy and hospitalisation claims, together with basic patient's information, to predict the 4P-MACE composite endpoint, i.e., the first occurrence of death, heart failure, myocardial infarction, or stroke, with a variable prediction horizon of 1 to 5 years. Adapting to the time-to-event nature of this task, we cast our problem as a multi-outcome (4P-MACE and components), multi-label (1 to 5 years) classification task with a custom loss to account for the effect of censoring. Our model, purposefully specified to minimise data preparation costs, exhibits satisfactory performance in predicting 4P-MACE at all prediction horizons: AUROC from 0.812 (C.I.: 0.797 - 0.827) to 0.792 (C.I.: 0.781 - 0.802); C-index from 0.802 (C.I.: 0.788 - 0.816) to 0.770 (C.I.: 0.761 - 0.779). Components' prediction performance is also adequate, ranging from death's 0.877 1-year AUROC to stroke's 0.689 5-year AUROC.

Outcomes of patients with type 2 diabetes treated with SGLT-2 inhibitors versus DPP-4 inhibitors. An Italian real-world study in the context of other observational studies

AIMS

We compared cardiovascular outcomes of patients with type 2 diabetes (T2D) receiving sodium glucose cotransporter-2 inhibitors (SGLT2i) or dipeptidyl peptidase-4 inhibitors (DPP4i) under routine care.

METHODS

From an administrative claims database of >5.2M citizen, we identified patients with T2D who initiated SGLT2i or DPP4i from 2014 to 2018. Patients were matched by propensity scores. The primary outcome was the 3-point major adverse cardiovascular events (3P-MACE).

RESULTS

After matching, we included 3216 patients/group, with mean age of 63 years, diabetes duration of 8.7 years, and 20% had cardiovascular disease. During a median follow-up of 18 months, the rate of 3P-MACE was lower among patients who initiated SGLT2i versus DPP4i (HR 0.74; 95 %C.I. 0.58-0.94). Initiators of SGLT2i also showed significantly lower rates of myocardial infarction (HR 0.75; 95 %C.I. 0.56-1.00), hospitalization for heart failure (HR 0.44; 95 %C.I. 0.25-0.95) or cardiovascular causes (HR 0.72; 95 %C.I. 0.60-0.87), and all-cause death (HR 0.49; 95 %C.I. 0.25-0.95). Renal failure was less common with SGLT2i than with DPP4i. Results were consistent to those obtained in a meta-analysis of 10 observational studies on ~1.5M patients.

CONCLUSIONS

Patients with T2D who initiated SGLT2i under routine care had better cardio-renal outcomes and lower all-cause mortality than similar patients who initiated DPP4i.

Cardiovascular effectiveness of human-based vs. exendin-based glucagon like peptide-1 receptor agonists: a retrospective study in patients with type 2 diabetes

AIMS

Glucagon like peptide-1 (GLP-1) receptor agonists (GLP-1RA) are effective to control type 2 diabetes (T2Ds) and can protect from adverse cardiovascular outcomes. GLP-1RA are based on the human GLP-1 or the exendin-4 sequence. We compared cardiovascular outcomes of patients with T2D who received human-based or exendin-based GLP-1RA in routine clinical practice.

METHODS AND RESULTS

We performed a retrospective study on the administrative database of T2D patients from the Veneto Region (North-East Italy). We identified patients who initiated a human-based or exendin-based GLP-1RA from 2011 to 2018. The primary outcome was occurrence of major adverse cardiovascular events (MACE). Secondary outcomes were individual MACE components, revascularization, hospitalization for heart failure, or for cardiovascular causes. From 330 193 patients with diabetes, 6620 were new users of GLP-1RA. After propensity score matching, we analysed 1098 patients in each group, who were on average 61 years old, 59.5% males, 13% with established cardiovascular disease, had an estimated diabetes duration of 8.4 years, and a baseline HbA1c of 7.9%. During a median follow-up of 18 months, patients treated with human-based GLP-1RA as compared to those treated with exendin-based GLP-1RA, showed lower rates of MACE [hazard ratio 0.61; 95% confidence interval (CI) 0.39-0.95], myocardial infarction (0.51; 95% CI 0.28-0.94), and hospitalization for cardiovascular causes (0.66; 95% CI 0.47-0.92).

CONCLUSION

We observed better cardiovascular outcomes among patients treated with human-based vs. exendin-based GLP-1RA under routine care. In the absence of comparative trials and in view of the limitations of retrospective studies, this finding provides a moderate level of evidence to guide clinical decision.

Exposure to dipeptidyl-peptidase-4 inhibitors and COVID-19 among people with type 2 diabetes: A case-control study

Because other coronaviruses enter the cells by binding to dipeptidyl-peptidase-4 (DPP-4), it has been speculated that DPP-4 inhibitors (DPP-4is) may exert an activity against severe acute respiratory syndrome coronavirus 2. In the absence of clinical trial results, we analysed epidemiological data to support or discard such a hypothesis. We retrieved information on exposure to DPP-4is among patients with type 2 diabetes (T2D) hospitalized for COVID-19 at an outbreak hospital in Italy. As a reference, we retrieved information on exposure to DPP-4is among matched patients with T2D in the same region. Of 403 hospitalized COVID-19 patients, 85 had T2D. The rate of exposure to DPP-4is was similar between T2D patients with COVID-19 (10.6%) and 14 857 matched patients in the region (8.8%), or 793 matched patients in the local outpatient clinic (15.4%), 8284 matched patients hospitalized for other reasons (8.5%), and when comparing 71 patients hospitalized for COVID-19 pneumonia (11.3%) with 351 matched patients with pneumonia of another aetiology (10.3%). T2D patients with COVID-19 who were on DPP-4is had a similar disease outcome as those who were not. In summary, we found no evidence that DPP-4is might affect hospitalization for COVID-19.

Exposure to dipeptidyl-peptidase 4 inhibitors and the risk of pneumonia among people with type 2 diabetes: Retrospective cohort study and meta-analysis

AIM

Concerns have been raised that dipeptidyl-peptidase 4 inhibitors (DPP-4i) may increase the risk of pneumonia. We analysed observational data and clinical trials to explore whether use of DPP-4i modifies the risk of pneumonia.

METHODS

We identified patients with diabetes in the Veneto region administrative database and performed propensity score matching between new users of DPP-4 inhibitors and new users of other oral glucose-lowering medications (OGLMs). We compared the rate of hospitalization for pneumonia between matched cohorts using the Cox proportional hazard model. The same analysis was repeated using the database of a local diabetes outpatient clinic. We retrieved similar observational studies from the literature to perform a meta-analysis. Results from trials reporting pneumonia rates among patients randomized to DPP-4 inhibitors versus placebo/active comparators were also meta-analysed.

RESULTS

In the regional database, after matching 6495 patients/group, new users of DPP-4 inhibitors had a lower rate of hospitalization for pneumonia than new users of other OGLMs (HR 0.76; 95% CI 0.61-0.95). In the outpatient database, after matching 867 patients/group, new users of DPP-4 inhibitors showed a non-significantly lower rate of hospitalization for pneumonia (HR 0.65; 95% CI 0.41-1.04). The meta-analysis of observational studies yielded an overall non-significant lower risk of hospitalization for pneumonia among DPP-4 inhibitor users (RR 0.81; 95% CI 0.65-1.01). The meta-analysis of randomized controlled trials showed no overall effect of DPP-4 inhibitors on pneumonia risk (RR 1.06; 95% CI 0.93-1.20).

CONCLUSION

The use of DPP-4 inhibitors can be considered as safe with regard to the risk of pneumonia.

Better cardiovascular outcomes of type 2 diabetic patients treated with GLP-1 receptor agonists versus DPP-4 inhibitors in clinical practice

Background

Cardiovascular outcome trials in high-risk patients showed that some GLP-1 receptor agonists (GLP-1RA), but not dipeptidyl-peptidase-4 inhibitors (DPP-4i), can prevent cardiovascular events in type 2 diabetes (T2D). Since no trial has directly compared these two classes of drugs, we performed a comparative outcome analysis using real-world data.

Methods

From a database of ~ 5 million people from North-East Italy, we retrospectively identified initiators of GLP-1RA or DPP-4i from 2011 to 2018. We obtained two balanced cohorts by 1:1 propensity score matching. The primary outcome was the 3-point major adverse cardiovascular events (3P-MACE; a composite of death, myocardial infarction, or stroke). 3P-MACE components and hospitalization for heart failure were secondary outcomes.

Results

From 330,193 individuals with T2D, we extracted two matched cohorts of 2807 GLP-1RA and 2807 DPP-4i initiators, followed for a median of 18 months. On average, patients were 63 years old, 60% male; 15% had pre-existing cardiovascular disease. The rate of 3P-MACE was lower in patients treated with GLP-1RA compared to DPP4i (23.5 vs. 34.9 events per 1000 person-years; HR: 0.67; 95% C.I. 0.53–0.86; p = 0.002). Rates of myocardial infarction (HR 0.67; 95% C.I. 0.50–0.91; p = 0.011) and all-cause death (HR 0.58; 95% C.I. 0.35–0.96; p = 0.034) were lower among GLP-1RA initiators. The as-treated and intention-to-treat approaches yielded similar results.

Conclusions

Patients initiating a GLP-1RA in clinical practice had better cardiovascular outcomes than similar patients who initiated a DPP-4i. These data strongly confirm findings from cardiovascular outcome trials in a lower risk population.

Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study

Background

The detection of infectious diseases through the analysis of free text on electronic health reports (EHRs) can provide prompt and accurate background information for the implementation of preventative measures, such as advertising and monitoring the effectiveness of vaccination campaigns.

Objective

The purpose of this paper is to compare machine learning techniques in their application to EHR analysis for disease detection.

Methods

The Pedianet database was used as a data source for a real-world scenario on the identification of cases of varicella. The models’ training and test sets were based on two different Italian regions’ (Veneto and Sicilia) data sets of 7631 patients and 1,230,355 records, and 2347 patients and 569,926 records, respectively, for whom a gold standard of varicella diagnosis was available. Elastic-net regularized generalized linear model (GLMNet), maximum entropy (MAXENT), and LogitBoost (boosting) algorithms were implemented in a supervised environment and 5-fold cross-validated. The document-term matrix generated by the training set involves a dictionary of 1,871,532 tokens. The analysis was conducted on a subset of 29,096 tokens, corresponding to a matrix with no more than a 99% sparsity ratio.

Results

The highest predictive values were achieved through boosting (positive predicative value [PPV] 63.1, 95% CI 42.7-83.5 and negative predicative value [NPV] 98.8, 95% CI 98.3-99.3). GLMNet delivered superior predictive capability compared to MAXENT (PPV 24.5% and NPV 98.3% vs PPV 11.0% and NPV 98.0%). MAXENT and GLMNet predictions weakly agree with each other (agreement coefficient 1 [AC1]=0.60, 95% CI 0.58-0.62), as well as with LogitBoost (MAXENT: AC1=0.64, 95% CI 0.63-0.66 and GLMNet: AC1=0.53, 95% CI 0.51-0.55).

Conclusions

Boosting has demonstrated promising performance in large-scale EHR-based infectious disease identification.

ADVANCE database characterisation and fit for purpose assessment for multi-country studies on the coverage, benefits and risks of pertussis vaccinations

INTRODUCTION

The public-private ADVANCE consortium (Accelerated development of vaccine benefit-risk collaboration in Europe) aimed to assess if electronic healthcare databases can provide fit-for purpose data for collaborative, distributed studies and monitoring of vaccine coverage, benefits and risks of vaccines.

OBJECTIVE

To evaluate if European healthcare databases can be used to estimate vaccine coverage, benefit and/or risk using pertussis-containing vaccines as an example.

METHODS

Characterisation was conducted using open-source Java-based (Jerboa) software and R scripts. We obtained: (i) The general characteristics of the database and data source (meta-data) and (ii) a detailed description of the database population (size, representatively of age/sex of national population, rounding of birth dates, delay between birth and database entry), vaccinations (number of vaccine doses, recording of doses, pattern of doses by age and coverage) and events of interest (diagnosis codes, incidence rates). A total of nine databases (primary care, regional/national record linkage) provided data on events (pertussis, pneumonia, death, fever, convulsions, injection site reactions, hypotonic hypo-responsive episode, persistent crying) and vaccines (acellular pertussis and whole cell pertussis) related to the pertussis proof of concept studies.

RESULTS

The databases contained data for a total population of 44 million individuals. Seven databases had recorded doses of vaccines. The pertussis coverage estimates were similar to those reported by the World Health Organisation (WHO). Incidence rates of events were comparable in magnitude and age-distribution between databases with the same characteristics. Several conditions (persistent crying and somnolence) were not captured by the databases for which outcomes were restricted to hospital discharge diagnoses.

CONCLUSION

The database characterisation programs and workflows allowed for an efficient, transparent and standardised description and verification of electronic healthcare databases which may participate in pertussis vaccine coverage, benefit and risk studies. This approach is ready to be used for other vaccines/events to create readiness for participation in other vaccine related studies.

Detecting Undiagnosed Diabetes: Proof-of-Concept Based on the Health-Information Exchange System of the Veneto Region (North-East Italy)

Diabetes is a chronic illness characterised by elevated blood glucose levels, driving excess mortality. Its prompt detection and accurate management are critical for delaying complications. Nevertheless, diabetes can remain undiagnosed for years from the onset. The identification of undiagnosed diabetes is a public health priority: in Italy, it is estimated that up to 30% of diabetes cases remain undetected, i.e., that ~1.8 million citizens may be unaware they need medical help. Sometimes, this happens even though these subjects undergo routine or emergency check-ups. Veneto, a region in North-East Italy with 4.9 million residents, implements a regional Health Information Exchange system (rHIE) to collect healthcare data, including laboratory reports, and integrate them with administrative claims. Their combination may be instrumental in finding otherwise undetected cases of diabetes. On the one hand, known diabetic patients should have disease management-generated claims; on the other, laboratory test results can be independently evaluated against diagnostic criteria. In the present work, we examined the anonymised claims and laboratory data, extracted from the rHIE, of 23,376 citizens of the Veneto region. We compared their exemptions, diabetes-related hospitalisation discharge codes, and antidiabetic drugs between 2012 and 2018 to the results of their fasting glucose, glycated haemoglobin, and oral glucose tolerance tests in 2017-2018. We identified 1,407 (6.02%) subjects who, according to administrative claims, appear to be free from diabetes, but met at least one laboratory diagnostic criterion. Such a discrepancy suggests that these people may be undiagnosed diabetic patients. To the best of our knowledge, this is the first proof of concept of an automatic system for the detection of undiagnosed diabetes in Italy. Its full integration in the rHIE and its consequent capillary application could potentially reveal thousands of hidden cases throughout Veneto.

Advance system testing: Vaccine benefit studies using multi-country electronic health data - The example of pertussis vaccination

The Accelerated Development of VAccine benefit-risk Collaboration in Europe (ADVANCE), a public-private consortium, implemented and tested a distributed network system for the generation of evidence on the benefits-risks of marketed vaccines in Europe. We tested the system by estimating the incidence rate (IR) of pertussis and pertussis-related complications in children vaccinated with acellular (aP) and whole-cell (wP) pertussis vaccine. Data from seven electronic databases from four countries (Denmark: AUH and SSI, Spain: SIDIAP and BIFAP, UK: THIN and RCGP RSC and Italy: Pedianet) were included in a retrospective cohort analysis. Exposure was defined as any pertussis vaccination (aP or wP). The follow-up time started 14 days after the first dose. Children who had received any pertussis vaccine from January 1990 to December 2015 were included (those who switched type, or had unknown type were excluded). The outcomes of interest were confirmed or suspected pertussis and pertussis-related pneumonia and generalised convulsions within one month of pertussis diagnosis and death within three months of pertussis diagnosis. The cohort comprised 2,886,367 children ≤5 years of age. Data on wP and aP vaccination were available in three and seven databases, respectively. The IRs (per 100,000 person-years) for pertussis varied largely and ranged between 0.15 (95% CI: 0.12; 0.19) and 1.15 (95% CI: 1.07; 1.23), and the trends over time was consistent with those observed from national surveillance databases for confirmed pertussis. The pertussis IRs decreased as the number of wP and aP vaccine doses increased. Pertussis-related complications were rare (89 pneumonia, 7 generalised convulsions and no deaths) and their relative risk (vs. non-pertussis) could not be reliably estimated. The study demonstrated the feasibility of the ADVANCE system to estimate the change in pertussis IRs following pertussis vaccination. Larger sample sizes would provide additional power to compare the risk for complications between children with and without pertussis. The feasibility of vaccine-type specific effectiveness studies may be considered in the future.

ADVANCE system testing: Benefit-risk analysis of a marketed vaccine using multi-criteria decision analysis and individual-level state transition modelling

BACKGROUND

The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines using electronic health record (eHR) databases in Europe. Proof-of-concept studies were designed to assess the proposed processes and system for generating the required evidence to perform B/R assessment and near-real time monitoring of vaccines. We aimed to test B/R methodologies for vaccines, using the comparison of the B/R profiles of whole-cell (wP) and acellular pertussis (aP) vaccine formulations in children as an example.

METHODS

We used multi-criteria decision analysis (MCDA) to structure the B/R assessment combined with individual-level state transition modelling to build the B/R effects table. In the state transition model, we simulated the number of events in two hypothetical cohorts of 1 million children followed from first pertussis dose till pre-school-entry booster (or six years of age, whichever occurred first), with one cohort receiving wP, and the other aP. The benefits were reductions in pertussis incidence and complications. The risks were increased incidences of febrile convulsions, fever, hypotonic-hyporesponsive episodes, injection-site reactions and persistent crying. Most model parameters were informed by estimates (coverage, background incidences, relative risks) from eHR databases from Denmark (SSI), Spain (BIFAP and SIDIAP), Italy (Pedianet) and the UK (RCGP-RSC and THIN). Preferences were elicited from clinical and epidemiological experts.

RESULTS

Using state transition modelling to build the B/R effects table facilitated the comparison of different vaccine effects (e.g. immediate vaccine risks vs long-term vaccine benefits). Estimates from eHR databases could be used to inform the simulation model. The model results could be easily combined with preference weights to obtain B/R scores.

CONCLUSION

Existing B/R methodology, modelling and estimates from eHR databases can be successfully used for B/R assessment of vaccines.

Quantifying outcome misclassification in multi-database studies: The case study of pertussis in the ADVANCE project

BACKGROUND

The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines using European healthcare databases. Event misclassification can result in biased estimates. Using different algorithms for identifying cases of Bordetella pertussis (BorPer) infection as a test case, we aimed to describe a strategy to quantify event misclassification, when manual chart review is not feasible.

METHODS

Four participating databases retrieved data from primary care (PC) setting: BIFAP: (Spain), THIN and RCGP RSC (UK) and PEDIANET (Italy); SIDIAP (Spain) retrieved data from both PC and hospital settings. BorPer algorithms were defined by healthcare setting, data domain (diagnoses, drugs, or laboratory tests) and concept sets (specific or unspecified pertussis). Algorithm- and database-specific BorPer incidence rates (IRs) were estimated in children aged 0-14 years enrolled in 2012 and 2014 and followed up until the end of each calendar year and compared with IRs of confirmed pertussis from the ECDC surveillance system (TESSy). Novel formulas were used to approximate validity indices, based on a small set of assumptions. They were applied to approximately estimate positive predictive value (PPV) and sensitivity in SIDIAP.

RESULTS

The number of cases and the estimated BorPer IRs per 100,000 person-years in PC, using data representing 3,173,268 person-years, were 0 (IR = 0.0), 21 (IR = 4.3), 21 (IR = 5.1), 79 (IR = 5.7), and 2 (IR = 2.3) in BIFAP, SIDIAP, THIN, RCGP RSC and PEDIANET respectively. The IRs for combined specific/unspecified pertussis were higher than TESSy, suggesting that some false positives had been included. In SIDIAP the estimated IR was 45.0 when discharge diagnoses were included. The sensitivity and PPV of combined PC specific and unspecific diagnoses for BorPer cases in SIDIAP were approximately 85% and 72%, respectively.

CONCLUSION

Retrieving BorPer cases using only specific concepts has low sensitivity in PC databases, while including cases retrieved by unspecified concepts introduces false positives, which were approximately estimated to be 28% in one database. The share of cases that cannot be retrieved from a PC database because they are only seen in hospital was approximately estimated to be 15% in one database. This study demonstrated that quantifying the impact of different event-finding algorithms across databases and benchmarking with disease surveillance data can provide approximate estimates of algorithm validity.

ADVANCE system testing: Can coverage of pertussis vaccination be estimated in European countries using electronic healthcare databases: An example

INTRODUCTION

The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines, using existing healthcare databases in Europe. The objective of this paper was to assess the feasibility of using electronic healthcare databases to estimate dose-specific acellular pertussis (aP) and whole cell pertussis (wP) vaccine coverage.

METHODS

Seven electronic healthcare databases in four European countries (Denmark (n = 2), UK (n = 2), Spain (n = 2) and Italy (n = 1)) participated in this study. Children were included from birth and followed up to age six years. Vaccination exposure was obtained from the databases and classified by type (aP or wP), and dose 1, 2 or 3. Coverage was estimated using period prevalence. For the 2006 birth cohort, two estimation methods for pertussis vaccine coverage, period prevalence and cumulative incidence were compared for each database.

RESULTS

The majority of the 2,575,576 children included had been vaccinated at the country-specific recommended ages. Overall, the estimated dose 3 coverage was 88-97% in Denmark (birth cohorts from 2003 to 2014), 96-100% in the UK (2003-2014), 95-98% in Spain (2004-2014) and 94% in Italy (2006-2007). The estimated dose 3 coverage per birth cohort in Denmark and the UK differed by 1-6% compared with national estimates, with our estimates mostly higher. The estimated dose 3 coverage in Spain differed by 0-2% with no consistent over- or underestimation. In Italy, the estimates were 3% lower compared with the national estimates. Except for Italy, for which the two coverage estimation methods generated the same results, the estimated cumulative incidence coverages were consistently 1-10% lower than period prevalence estimates.

CONCLUSION

This study showed that it was possible to provide consistent estimates of pertussis immunisation coverage from the electronic healthcare databases included, and that the estimates were comparable with the national estimates.

Impact of a vaccination programme in children vaccinated with ProQuad, and ProQuad-specific effectiveness against varicella in the Veneto region of Italy

Background

Monovalent varicella vaccines have been available in the Veneto Region of Italy since 2004. In 2006, a single vaccine dose was added to the immunisation calendar for children aged 14 months. ProQuad®, a quadrivalent measles-mumps-rubella-varicella vaccine, was introduced in May 2007 and used, among other varicella vaccines, until October 2008. This study aimed to evaluate the effectiveness of a single dose of ProQuad, and the population impact of a vaccination program (VP) against varicella of any severity in children who received a first dose of ProQuad at 14 months of age in the Veneto Region,

Methods

All children born in 2006/2007, i.e., eligible for varicella vaccination after ProQuad was introduced, were retrospectively followed through individual-level data linkage between the Pedianet database (varicella cases) and the Regional Immunization Database (vaccination status). The direct effectiveness of ProQuad was estimated as the incidence rate of varicella in ProQuad-vaccinated children aged < 6 years compared to children with no varicella vaccination from the same birth cohort. The impact of the VP on varicella was measured by comparing children eligible for the VP to an unvaccinated historical cohort from 1997/1998. The vaccine impact measures were: total effect (the combined effect of ProQuad vaccination and being covered by the Veneto VP); indirect effect (the effect of the VP on unvaccinated individuals); and overall effect (the effect of the VP on varicella in the entire population of the Veneto Region, regardless of their vaccination status).

Results

The adjusted direct effectiveness of ProQuad was 94%. The vaccine impact measures total, indirect, and overall effect were 97%, 43%, and 90%, respectively.

Conclusions

These are the first results on the effectiveness and impact of ProQuad against varicella; data confirmed its high effectiveness, based on immunological correlates for protection. Direct effectiveness is our only ProQuad-specific measure; all impact measures refer at least partially to the VP and should be interpreted in the context of high vaccine coverage and the use of various varicella vaccines in this region. The Veneto Region offered a unique opportunity for this study due to an individual data linkage between Pedianet and the Regional Immunization database.

Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93–100%), while drug-based components were the main contributors in RLDs (81–100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.

From laboratory to clinic: the development of web-based tools for the estimation of retinal diagnostic parameters

Over the years, tools for the analysis of retinal images have been developed by several research groups but their usage has been mainly confined within the developing institutions. One possibility to foster their adoption is to develop them as web-based tools. We present here three such systems we recently developed. They are specifically focused on the estimation of retinal vascular parameters, such as arteriolar narrowing (AVR parameter), vessel tortuosity, and vessel caliber narrowing and tortuosity in retinopathy of prematurity (ROP) images. These systems have been successfully evaluated as regards their reliability and will soon be publicly available to interested health care providers.

A Web-based system for the quantitative and reproducible assessment of clinical indexes from the retinal vasculature

A novel system for the vascular tree identification and the quantitative estimation of arteriolar venular ratio clinical index in retinal fundus images is presented. The system is composed of a module for automatic vascular tracking, an interactive editing interface to correct errors and set the required parameters of analysis, and a module for the computation of clinical indexes. The system was organized as a client-server structure to allow clinicians and researchers from all over the world to work remotely. The system was evaluated by three graders analyzing 30 fundus images. The evaluation of the Pearson's correlation coefficient and p-value of a paired t-test for each pair of graders demonstrates the high reproducibility of the measures provided by the system.

An improved system for the automatic estimation of the Arteriolar-to-Venular diameter Ratio (AVR) in retinal images

The Arteriolar-to-Venular diameter Ratio (AVR), a parameter derived from vessel caliber measurements in a specific region of retinal images, is used as a descriptor of generalized arteriolar narrowing, an eye fundus sign often seen in patients affected by hypertensive or diabetic retinopathies. We developed an improved system to compute AVR in a totally automatic way. Images are at first enhanced to highlight the vessel network, which is then traced by a vessel tracking algorithm. From the detected vessel structure, the position of the optic disc is derived and the region inside which the AVR data are to be measured is determined. Vessels within this region are classified as either arteries or veins, their caliber is estimated and the AVR parameter is eventually computed. Improvements with respect to the previous version are related to post-processing algorithms to enhance vessel tracking and a totally new artery/vein discrimination technique. Results provided by the new system have been compared with manually derived AVR values on 20 eye fundus images, resulting in a final correlation coefficient of 0.88.