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international comparisons of electronic health record cohorts in England and New Zealand
AbstractObjectives Electronic health records offer the opportunity to discover new clinical implications for established blood tests, but international comparisons have been lacking. We tested the association of total white cell count (WBC) with all cause mortality in England and New Zealand. High WBC within the reference range (8.65 10.05109/L) was associated with significantly increased mortality compared to the middle quintile (6.25 7.25109/L); adjusted HR 1.51 (95% CI 1.43 to 1.59) in CALIBER and 1.33 (95% CI 1.06 to 1.65) in PREDICT. WBC outside the reference range was associated with even greater mortality. The association was stronger over the first 6months of follow up, but similar across ethnic groups.
MortalityLeukocyte countElectronic health recordsCohort studiesStrengths and limitations of this studyThe main strength of this study is that we showed similar associations of total white cell count with mortality in two ethnically different populations from different countries.
Both cohorts were large and population based, avoiding the selection bias inherent in bespoke cohorts with low response rates.
A limitation of this study is that white cell count and other covariates were measured only when thought to be clinically necessary, so some covariate data were missing.
As this is an observational study it can be used to infer association but not causation, and residual confounding may partly account for the observed association between total white cell count and mortality.
IntroductionA fundamental question in clinical medicine is 'what does this blood test result mean?' Relevant evidence in answering this question may come from examining the prognostic significance of blood tests recorded in usual clinical care. The direct clinical applicability, large sample sizes and population base of electronic health record cohorts, which are increasingly available for research in different countries, might provide opportunities to discover and replicate associations between clinically recorded measurements and patient outcomes. However, to date there have been few international comparisons of the prognostic validity of blood tests performed in primary care, partly because of the challenge of accessing such data, and harmonising the structure and coding of electronic healthcare records between countries. Nevertheless, such international comparisons might help to evaluate the robustness of associations among patients with different ethnic backgrounds and different profiles of risk.
We chose to compare England and New Zealand because they have different healthcare systems and ethnically different populations (the English population is predominantly Caucasian, with small proportions of South Asian or black ethnicities1 whereas New Zealand contains a sizeable proportion of Mori, Pacific, Chinese and South Asian people2). However, both countries have pseudonymised primary care data linked with other data sources available for research on a large scale,replique arpels and van cleef, making this type of study feasible. Despite the ubiquity of this test, it is unclear how strongly a clinically recorded 'normal' value in the general population is associated with subsequent short term and long term mortality. White cell counts vary between ethnic groups3 and are associated with inflammation, smoking, obesity and high systolic blood pressure.4 Previous bespoke cohort studies with white cell counts measured under research conditions suggest a link between high white cell count and increased risk of coronary disease5 ,6 and long term mortality (see online supplementary table S2).7 17 The largest previous study involved 438500 government employees and their families in South Korea and accrued 48757 events,7 but the largest study to compare ethnic groups was much smaller, with only 1062 events (Reasons for Geographical and Racial Differences in Stroke (REGARDS) study).8 As far as we are aware, there are no general population studies of white cell counts and mortality that used clinical rather than research measures of white cell counts. However, it is important to know the prognostic significance of white cell counts measured for diverse indications in usual clinical care. White cell count even within the normal range can be affected by a range of chronic and acute illnesses18 which prompts the question, not answered in previous studies, as to whether it is more strongly associated with short term than long term prognosis.
Our objectives were (1) to use clinically recorded white cell counts in diverse populations to replicate previous observations of the association of white cell count with mortality and (2) to extend these observations by comparing short term and long term associations, and investigating interactions with ethnicity, age, sex and smoking. This study was a new collaboration between the CALIBER (ClinicAl research using LInked Bespoke studies and Electronic health Records) programme in England19 (primary care data linked to hospitalisation, mortality and acute coronary syndrome registries) and PREDICT in New Zealand20 (cardiovascular risk assessments from primary care linked to hospital admissions, mortality,van der cleef arpels replique, dispensed medication and laboratory results).
MethodsWe carried out a cohort study using information recorded in usual clinical care in electronic health records in England and New Zealand. Characteristics of data sources analysed in two countries are summarised in online supplementary table S1.
CALIBER study population (England)The study population was drawn from the CALIBER,19 which links four sources of electronic health data in England: primary care health records (coded diagnoses, clinical measurements, laboratory results and prescribed medication) from general practices contributing to the Clinical Practice Research Datalink (CPRD),21 coded hospital discharges (Hospital Episode Statistics, HES), the Myocardial Ischaemia National Audit Project (MINAP)22 and death registrations. CALIBER contains data from 244 general practices which consented to the data linkage; these practices contained 3.9% of the population of England in 2006. The linkage was carried out in October 2010 by a trusted third party, using a deterministic match between National Health Service number, date of birth and sex. CALIBER studies have demonstrated associations of age, sex,23 blood pressure,24 neutrophil,van cleef and arpels replique, eosinophil and lymphocyte counts,25 ,26 and type 2 diabetes27 with initial presentation of cardiovascular diseases.
The study period was January 1997 to March 2010, and patients were eligible for inclusion when they had been registered for at least 1year with a practice meeting research data recording standards.
PREDICT study population (New Zealand)In New Zealand, approximately one third of general practitioners use PREDICT, a web based clinical decision support application to assess cardiovascular risk for primary prevention, and the PREDICT software captures this information centrally including commonly measured risk factors for cardiovascular disease (smoking status, diabetes status, gender, age, systolic blood pressure).20 ,28 These risk assessment records are linked to national databases of hospital admission data and mortality using an encrypted New Zealand National Health Index (NHI) number. Records were also linked with the New Zealand Pharmaceutical Information database,29 a national register of community dispensing, and TestSafe, a repository of laboratory test results for the Auckland and Northland region of the North Island of New Zealand. TestSafe contains community and hospital laboratory results from July 2006 onwards; prior to this date only hospital test results were available from this source, or community tests which were also copied to hospital services. White cell count results were linked to PREDICT information using the encrypted NHI. In PREDICT, prior cardiovascular disease was ascertained by the cardiovascular risk assessment questionnaire and hospitalisation records; in CALIBER prior cardiovascular disease was identified in primary care (Read codes for diagnoses) or hospitalisation records (International Classification of Diseases, Tenth Revision (ICD 10) codes) or an entry in the acute coronary syndrome registry.
In CALIBER, patients entered the study on the date of the first white cell count measurement after study eligibility. In PREDICT,imitation van cleef, patients entered the study on the date of the cardiovascular risk assessment. The most recent measure of total white cell count up to 5years before or 2weeks after cardiovascular risk assessment was chosen. If no white cell count was available during this period, it was considered missing.
OutcomesThe primary outcome of the study was all cause mortality. We identified patients who had died by linkage to the relevant national death registry, with follow up in New Zealand until 26 July 2012 and in CALIBER until 25 March 2010.
Other covariatesCardiovascular risk factor information such as smoking status, blood pressure and total:high density lipoprotein (HDL) cholesterol ratio were collected in PREDICT as part of the cardiovascular risk assessment and were largely complete. In CALIBER, we derived smoking status (current smoker or non smoker, to match the PREDICT categories) using primary care records prior to study entry, and extracted the most recent value of continuous risk factors (blood pressure, total cholesterol and HDL cholesterol) up to 1year prior to study entry.
Total white cell counts can be affected by factors such as infections, autoimmune diseases, medication and haematological conditions. Similar to our recent CALIBER studies on differential white cell counts,25 ,26 we sought to differentiate between a patient's long term 'stable' white cell count, and values obtained when the patient had an 'acute' condition which may alter white cell counts. We adapted a set of validated criteria published by the eMERGE consortium30 (electronic Medical Records and Genomics) for studying genetic determinants of the stable white cell counts, which takes into account cancer diagnoses, haematological diagnoses, use of steroids or immune modulating medication, recent vaccination and recent symptoms or diagnoses of infection. We used prescription, symptom, diagnosis and hospitalisation data in the primary care and secondary care records in CALIBER to assess whether the patient was clinically 'acute' or 'stable' at the time of the blood test; see online supplementary material methods for more details.
Diabetes status was assessed in CALIBER by a diagnosis of diabetes recorded prior to study entry in primary care (as a Read code) or in a hospital admission (as an ICD 10 code in Hospital Episode Statistics).27 In PREDICT, diabetes status was entered by the general practitioner into the web based cardiovascular risk assessment form. In addition, we considered patients to be diabetic if they had been dispensed an oral hypoglycaemic agent or insulin in the 6months before assessment, or if they had been hospitalised with a primary diagnosis of diabetes within the previous 5years.
New Zealand ethnicity data were recorded in PREDICT and national data sources, and were classified according to a standardised protocol,31 which prioritises Mori then Pacific ethnic groups if more than one ethnic group is recorded. Individuals were coded as Mori, Pacific, Indian or other. 'Others' were mainly of New Zealand European descent. In CALIBER, we classified ethnicity as white, black, South Asian (comprising Indian, Pakistani or Bangladeshi) or 'other', according to ethnicity recorded in primary care or during a hospital admission.1
AbstractObjectives Electronic health records offer the opportunity to discover new clinical implications for established blood tests, but international comparisons have been lacking. We tested the association of total white cell count (WBC) with all cause mortality in England and New Zealand. High WBC within the reference range (8.65 10.05109/L) was associated with significantly increased mortality compared to the middle quintile (6.25 7.25109/L); adjusted HR 1.51 (95% CI 1.43 to 1.59) in CALIBER and 1.33 (95% CI 1.06 to 1.65) in PREDICT. WBC outside the reference range was associated with even greater mortality. The association was stronger over the first 6months of follow up, but similar across ethnic groups.
MortalityLeukocyte countElectronic health recordsCohort studiesStrengths and limitations of this studyThe main strength of this study is that we showed similar associations of total white cell count with mortality in two ethnically different populations from different countries.
Both cohorts were large and population based, avoiding the selection bias inherent in bespoke cohorts with low response rates.
A limitation of this study is that white cell count and other covariates were measured only when thought to be clinically necessary, so some covariate data were missing.
As this is an observational study it can be used to infer association but not causation, and residual confounding may partly account for the observed association between total white cell count and mortality.
IntroductionA fundamental question in clinical medicine is 'what does this blood test result mean?' Relevant evidence in answering this question may come from examining the prognostic significance of blood tests recorded in usual clinical care. The direct clinical applicability, large sample sizes and population base of electronic health record cohorts, which are increasingly available for research in different countries, might provide opportunities to discover and replicate associations between clinically recorded measurements and patient outcomes. However, to date there have been few international comparisons of the prognostic validity of blood tests performed in primary care, partly because of the challenge of accessing such data, and harmonising the structure and coding of electronic healthcare records between countries. Nevertheless, such international comparisons might help to evaluate the robustness of associations among patients with different ethnic backgrounds and different profiles of risk.
We chose to compare England and New Zealand because they have different healthcare systems and ethnically different populations (the English population is predominantly Caucasian, with small proportions of South Asian or black ethnicities1 whereas New Zealand contains a sizeable proportion of Mori, Pacific, Chinese and South Asian people2). However, both countries have pseudonymised primary care data linked with other data sources available for research on a large scale,replique arpels and van cleef, making this type of study feasible. Despite the ubiquity of this test, it is unclear how strongly a clinically recorded 'normal' value in the general population is associated with subsequent short term and long term mortality. White cell counts vary between ethnic groups3 and are associated with inflammation, smoking, obesity and high systolic blood pressure.4 Previous bespoke cohort studies with white cell counts measured under research conditions suggest a link between high white cell count and increased risk of coronary disease5 ,6 and long term mortality (see online supplementary table S2).7 17 The largest previous study involved 438500 government employees and their families in South Korea and accrued 48757 events,7 but the largest study to compare ethnic groups was much smaller, with only 1062 events (Reasons for Geographical and Racial Differences in Stroke (REGARDS) study).8 As far as we are aware, there are no general population studies of white cell counts and mortality that used clinical rather than research measures of white cell counts. However, it is important to know the prognostic significance of white cell counts measured for diverse indications in usual clinical care. White cell count even within the normal range can be affected by a range of chronic and acute illnesses18 which prompts the question, not answered in previous studies, as to whether it is more strongly associated with short term than long term prognosis.
Our objectives were (1) to use clinically recorded white cell counts in diverse populations to replicate previous observations of the association of white cell count with mortality and (2) to extend these observations by comparing short term and long term associations, and investigating interactions with ethnicity, age, sex and smoking. This study was a new collaboration between the CALIBER (ClinicAl research using LInked Bespoke studies and Electronic health Records) programme in England19 (primary care data linked to hospitalisation, mortality and acute coronary syndrome registries) and PREDICT in New Zealand20 (cardiovascular risk assessments from primary care linked to hospital admissions, mortality,van der cleef arpels replique, dispensed medication and laboratory results).
MethodsWe carried out a cohort study using information recorded in usual clinical care in electronic health records in England and New Zealand. Characteristics of data sources analysed in two countries are summarised in online supplementary table S1.
CALIBER study population (England)The study population was drawn from the CALIBER,19 which links four sources of electronic health data in England: primary care health records (coded diagnoses, clinical measurements, laboratory results and prescribed medication) from general practices contributing to the Clinical Practice Research Datalink (CPRD),21 coded hospital discharges (Hospital Episode Statistics, HES), the Myocardial Ischaemia National Audit Project (MINAP)22 and death registrations. CALIBER contains data from 244 general practices which consented to the data linkage; these practices contained 3.9% of the population of England in 2006. The linkage was carried out in October 2010 by a trusted third party, using a deterministic match between National Health Service number, date of birth and sex. CALIBER studies have demonstrated associations of age, sex,23 blood pressure,24 neutrophil,van cleef and arpels replique, eosinophil and lymphocyte counts,25 ,26 and type 2 diabetes27 with initial presentation of cardiovascular diseases.
The study period was January 1997 to March 2010, and patients were eligible for inclusion when they had been registered for at least 1year with a practice meeting research data recording standards.
PREDICT study population (New Zealand)In New Zealand, approximately one third of general practitioners use PREDICT, a web based clinical decision support application to assess cardiovascular risk for primary prevention, and the PREDICT software captures this information centrally including commonly measured risk factors for cardiovascular disease (smoking status, diabetes status, gender, age, systolic blood pressure).20 ,28 These risk assessment records are linked to national databases of hospital admission data and mortality using an encrypted New Zealand National Health Index (NHI) number. Records were also linked with the New Zealand Pharmaceutical Information database,29 a national register of community dispensing, and TestSafe, a repository of laboratory test results for the Auckland and Northland region of the North Island of New Zealand. TestSafe contains community and hospital laboratory results from July 2006 onwards; prior to this date only hospital test results were available from this source, or community tests which were also copied to hospital services. White cell count results were linked to PREDICT information using the encrypted NHI. In PREDICT, prior cardiovascular disease was ascertained by the cardiovascular risk assessment questionnaire and hospitalisation records; in CALIBER prior cardiovascular disease was identified in primary care (Read codes for diagnoses) or hospitalisation records (International Classification of Diseases, Tenth Revision (ICD 10) codes) or an entry in the acute coronary syndrome registry.
In CALIBER, patients entered the study on the date of the first white cell count measurement after study eligibility. In PREDICT,imitation van cleef, patients entered the study on the date of the cardiovascular risk assessment. The most recent measure of total white cell count up to 5years before or 2weeks after cardiovascular risk assessment was chosen. If no white cell count was available during this period, it was considered missing.
OutcomesThe primary outcome of the study was all cause mortality. We identified patients who had died by linkage to the relevant national death registry, with follow up in New Zealand until 26 July 2012 and in CALIBER until 25 March 2010.
Other covariatesCardiovascular risk factor information such as smoking status, blood pressure and total:high density lipoprotein (HDL) cholesterol ratio were collected in PREDICT as part of the cardiovascular risk assessment and were largely complete. In CALIBER, we derived smoking status (current smoker or non smoker, to match the PREDICT categories) using primary care records prior to study entry, and extracted the most recent value of continuous risk factors (blood pressure, total cholesterol and HDL cholesterol) up to 1year prior to study entry.
Total white cell counts can be affected by factors such as infections, autoimmune diseases, medication and haematological conditions. Similar to our recent CALIBER studies on differential white cell counts,25 ,26 we sought to differentiate between a patient's long term 'stable' white cell count, and values obtained when the patient had an 'acute' condition which may alter white cell counts. We adapted a set of validated criteria published by the eMERGE consortium30 (electronic Medical Records and Genomics) for studying genetic determinants of the stable white cell counts, which takes into account cancer diagnoses, haematological diagnoses, use of steroids or immune modulating medication, recent vaccination and recent symptoms or diagnoses of infection. We used prescription, symptom, diagnosis and hospitalisation data in the primary care and secondary care records in CALIBER to assess whether the patient was clinically 'acute' or 'stable' at the time of the blood test; see online supplementary material methods for more details.
Diabetes status was assessed in CALIBER by a diagnosis of diabetes recorded prior to study entry in primary care (as a Read code) or in a hospital admission (as an ICD 10 code in Hospital Episode Statistics).27 In PREDICT, diabetes status was entered by the general practitioner into the web based cardiovascular risk assessment form. In addition, we considered patients to be diabetic if they had been dispensed an oral hypoglycaemic agent or insulin in the 6months before assessment, or if they had been hospitalised with a primary diagnosis of diabetes within the previous 5years.
New Zealand ethnicity data were recorded in PREDICT and national data sources, and were classified according to a standardised protocol,31 which prioritises Mori then Pacific ethnic groups if more than one ethnic group is recorded. Individuals were coded as Mori, Pacific, Indian or other. 'Others' were mainly of New Zealand European descent. In CALIBER, we classified ethnicity as white, black, South Asian (comprising Indian, Pakistani or Bangladeshi) or 'other', according to ethnicity recorded in primary care or during a hospital admission.1
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