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Cohort Profile: The Epidemiology of Chronic Diseases and Multimorbidity. The EpiChron Cohort Study

Cohort Profile : The Epidemiology of Chronic Diseases and Multimorbidity. The EpiChron Cohort Study


Scope of the cohort and context:
The cohort is an exhaustive data repository of the public health service users in Aragon who represent 98% of total inhabitants in the region, and a demographically stable population. The population of Aragon is representative of the Spanish population in terms of sex, age and nationality distribution, although the population of Aragon is slightly older. As is the case at the national level, almost all inhabitants in Aragon (except for civil servants who are eligible to opt out of the public system, choosing fully private provision) are entitled to the public healthcare network and every individual has a family physician assigned who acts as the gatekeeper to the secondary and tertiary levels of care.

Data sources:
The data collected in the EpiChron Cohort come directly from the primary sources of information, such as electronic health records and administrative databases filled in by healthcare professionals during daily practice. Specifically, the data stem from the Patient Index Database (BDU), the OMI-AP database from primary 2 care, the Basic Minimum Set of Data (CMBD) from hospital care, the registries from specialized care, the PCH database from the Emergency Room, and the Pharmaceutical Consumption Information System (Farmasalud) database. Common data collection software and procedures guarantee standardized data input by all healthcare professionals. The primary care information system in Aragon is regulated by legal purpose, BIFAP has been working for more than 15 years to train physicians to improve the quality of data registry, and they periodically (every six months) send feedback reports about data registry quality to each participant. A number of internal and external validation studies aimed at measuring the quality of diagnosis coding in 3 primary care and at comparing the incidence and prevalence of specific diseases, risk factors and drugs, have shown acceptable results (2). In Aragon, 10% of all general practitioners (85 family physicians and 35 pediatricians) who collect data that feed the EpiChron Cohort are also involved in the BIFAP Project.
Other studies beyond BIFAP conclude along the same lines, and have demonstrated that training of physicians can result in high codification quality in primary care (3). In Aragon, specific training and chart documentation on the use of electronic health record software is provided annually to physicians and nurses. The consistency of primary care electronic health record data is also favored by the high degree of work stability of general practitioners in Aragon.
The validity of Spanish primary care registries for use in research has been assessed in a number of studies. The use of gold standards to validate diabetes mellitus and hypertension diagnosis from primary care electronic health records showed a substantial agreement, which justifies the use of such data for epidemiological studies of these conditions (4). This is also the case of cardiovascular risk factors and vascular disease (5), although heart failure is not so properly codified in primary care (6).
The hospital CMBD registry is filled in by a centralized documentation service, although there is no individual-level ad-hoc study on the reliability and accuracy of hospital registries in Aragon. The need to link information from multiple databases to obtain reliable data for research and for routine monitoring of the prevalence of chronic diseases has been highlighted (7). This is the case of conditions such as COPD, diabetes, hypertension, cerebrovascular disease, ischemic heart disease, asthma, 4 epilepsy and heart failure, in which a low concordance between primary care and hospital diagnoses has been observed (8).

Data request and extraction
Data in the EpiChron Cohort are obtained under request from specific administrative bodies through annual data extraction waves. The systematics of this process is as follows.
The data contained in each of the registries described previously is requested to a specific service of the Department of Health that centralizes all health information in Aragon, through a standardized protocol submitted during the month of January of every year. An independent committee subsequently assesses the data application and, in case of a favorable decision, the specific service performs a pseudonymization of the data to encrypt individual-level identification codes, protecting patients' privacy and complying with data protection laws. This new encrypted code is applied in all registries, enabling the linkage of data at the patient level. One copy of the databases is sent to the researcher and another copy is stored in a central computer server.
Access to these files is restricted to members of the research group by a double entry password. This process is partially regulated by legal order from 1 st April 2013 (Official Aragon Gazette number 88 from 8 th May 2013).

Data processing
Given that original databases are in different formats (e.g. access, excel, plain text), the Structured Query Language (SQL) programming language is employed to extract the data that will later conform the EpiChron Cohort. The Stata Statistical Software 5 (Release 12. College Station, TX: StataCorp LP) is used for data processing, as explained below. Of note, the research group hosting the cohort is a multidisciplinary qualified team including public health specialists, epidemiologists, clinicians (from primary and specialized care), pharmacists, statisticians, and data managers. They are all trained in data management and patient data protection.
The data processing includes a number of systematic steps aimed at improving the quality, accuracy and reliability of the data for research purposes. All changes conducted in the cohort data are kept track of in Stata scripts, and are continuously revised and updated given the dynamic nature of the data processing.

Quality control of diagnoses:
This step aims to verify the correspondence between a diagnostic code and its open-text descriptor (i.e. a separate section of the electronic health record where physicians describe the symptoms and specific features of a diagnostic episode). Non-existent codes are also redefined and/or deleted. It is also common that diagnostic codes assigned by physicians lack accuracy. Such is the case for diabetes, which is often coded in its generic form without specifying the type of diabetes, even if this information is available as free text. For 23,752 of a total of 76,784 diagnoses of diabetes mellitus in the EpiChron Cohort in 2011, the type of diabetes was not coded. Drug prescription and dispensation data can also be considered to determine the presence of a given chronic condition. In the case of diabetes, the absence of dispensation of insulin was used as a complementary criterion for the selection of patients with type II diabetes, as well as the dispensation of sulfonylureas, glucosurics, glitazones, or DDP4 inhibitors, or the treatment with lente 6 insulin therapy without another antidiabetic. This allowed for the relocation of 883 diagnoses of type I diabetes and 15,842 diagnoses of type II diabetes.
Specific algorithms are employed to search for specific key words or roots of words in open-text fields. At the moment, this is exclusively done for specific chronic diseases as it is the case of diabetes, COPD, heart failure, dementia, or stroke, given the time needed to complete this task and the specific focus of the cohort on the epidemiological study of chronic conditions.
The reliability of the diagnoses in the EpiChron Cohort is also enhanced by the combined use of primary care and hospital records. In the case of dementia (9), we have shown the added value of integrating different data sources feeding the cohort to obtain a global and more accurate view of the epidemiology of this chronic condition.

Quality control of patient general data (DGPs):
It refers to data that are not systematically collected for all patients, such as clinical parameters from laboratory analytical tests (e.g. blood lipids -total cholesterol, HDL and LDL-, hematocrit, glycosylated hemoglobin -HbA1c-, urine albumin, and spirometry parameters such as forced vital capacity -FVC-and forced expiratory volume -FEV-), anthropometric measurements and health indicators (e.g. body mass index -BMI-, height, weight, blood pressure, and heart rate), and lifestyle factors (e.g. drinking and smoking habits).
These variables need to be managed carefully given their high rates of missingness that rarely occurs completely at random. Therefore, they are only used in specific studies Clinical thresholds and valid value ranges agreed for each variable are shown in Table   1. The outliers records and those that could not been converted to numerical format were considered as non-logical values and therefore treated as missing data.
Regarding weight, height and BMI records, there were valid measurements available for 27%, 17% and 20% of the total cohort population, respectively. In the case of pulse records, and considering that resting heart rate in a healthy adult ranges from 60 to 100 beats/min, 20% of the cohort had at least one valid record. Similarly, there were valid total cholesterol and hematocrit values for approximately 20% of individuals. In regard to glycosylated hemoglobin and urine albumin values, only 4-5% of the individuals had a valid record, respectively. This percentage decreased to 0.2% for spirometry parameters, which were only available for approximately 3,000 individuals from the cohort. 8 (Table 2). In general, prevalence rates in the EpiChron Cohort are similar to those reported in other sources. 11 The differences observed might be due to the fact that in those reports only population over 15 years of age is taken into account.