Suchergebnisse

ObjectivesHousehold overcrowding is associated with adverse health outcomes, including increased risk of infectious diseases, mental health problems, and poor educational attainment. We investigated inequalities in overcrowding in an urban, ethnically diverse, and disadvantaged London population by pseudonymously linking electronic health records (EHR) to Energy Performance Certificates (EPC) data.
ApproachWe used pseudonymised Unique Property Reference Numbers to link EHRs for 1,066,156 currently registered patients from 321,318 households in north-east London to EPC data.
We measured household occupancy and derived the bedroom standard overcrowding definition (number of rooms relative to occupants' sex and ages) to estimate overcrowding prevalence. We examined associations with: household composition (adults only, single adult+children, ≥2 working-age adults+children, ≥1 retirement-age adults+children, three-generational household); ethnic background (White, South Asian, Black, Mixed, Other, missing); and Index of Multiple Deprivation (IMD) quintile. We used multivariable logistic regression to estimate the adjusted odds (aOR) and 95% Confidence Intervals (CI) of overcrowding.
ResultsOverall, 243,793 (22.9%) people were overcrowded. People living in households with children, or three-generational households were more likely (aOR [95% CI] 3.79 [3.74 - 3.84]; 6.53 [6.41 - 6.66] respectively), and single adults or retirement age adults with children less likely (0.36 [0.35 - 0.38]; 0.36 [0.23 - 0.57] respectively), to be overcrowded. Overcrowding was more likely among people from Asian or Black ethnic backgrounds (1.24 [1.22 - 1.25] and 1.17 [1.15 - 1.19] respectively). There was a dose-response relationship between IMD quintile and overcrowding: OR 0.20 [0.20 - 0.21] in the least deprived compared to most deprived quintile.
ConclusionOne in five people in north-east London live in overcrowded households with marked inequalities by ethnicity, household generational composition, and deprivation. Up-to-date estimates of household overcrowding can be derived from linked housing and health records and used to evaluate the impact of economic policies on health and housing inequalities.

ObjectivesMore than one in four 11-year-old children in England are living with obesity. The implications for future musculoskeletal health remain unclear. We assessed whether general practitioner consultations for knee pain were more likely among children with obesity, and how this varied by sex, ethnic background, and area-level deprivation.
ApproachOf 61,478 11-year-old NCMP participants (2013-19), we linked 60,723 (98.8%) to their primary care records. 58,761 children (50.9% male) had no recorded knee pain consultation (including arthralgia and Osgood-Schlatter's disease) prior to the NCMP measurement date. We calculated the proportion with a consultation for knee pain by ethnic-adjusted weight status (underweight<2nd; overweight≥91st; obese≥98th centile), sex, ethnic background and Index of Multiple Deprivation quintile. We studied time to first general practitioner consultation for knee pain after the NCMP date by fitting Cox proportional hazards models, estimating mutually-adjusted hazard ratios (aHRs) and 95% confidence intervals (CI) for boys and girls separately.
ResultsWe identified 2503 (4.3%) children with at least one consultation for knee pain after the NCMP date. Boys were more likely to consult than girls (mean difference 1.5%; 95% CI: 1.2,1.9). Median time to first knee pain consultation was 1.88 years (IQR: 0.94,2.93). In adjusted analyses, boys with underweight (aHR 0.16; 95% CI: 0.05,0.51), from South Asian ethnic backgrounds (0.80; 0.69,0.92) and living in less deprived areas (Wald test statistic: 11.41; p-value=0.0223) were less likely, and those from Black ethnic backgrounds (1.31; 1.13,1.51) more likely, to consult with knee pain. Girls from South Asian ethnic backgrounds (0.70; 0.59,0.84) and those living in less deprived areas (15.44; p-value=0.0039) were less likely, and those with a BMI considered obese (1.30; 1.10,1.54) more likely to do so.
ConclusionAdolescent girls, but not boys, living with obesity are more likely to consult their general practitioner with knee pain. Ethnic differences in knee pain consultations merit further study. Linkage of primary care and NCMP records enables greater understanding of health service utilisation by children by weight status and demographic characteristics.

Background with rationaleGeneral practitioner (GP) electronic health records (EHRs) are a potentially valuable, but unevaluated, source of child BMI measurements to inform clinical practice and research.
Main AimTo assess representativeness and accuracy of child GP-BMI records.
MethodsWe linked school National Child Measurement Programme (NCMP) records from 29,839 five-year-olds (49.1% girls) and 26,660 11-year-olds (49.1% girls) in City & Hackney (2013-17), Newham (2014-17) and Tower Hamlets (2015-17) to GP EHRs using pseudonymised NHS numbers (94.9% and 95.1% linked, respectively) and identified GP-BMI measurements using Read code "22K..". We estimated adjusted odds ratios (aOR) of at least one GP-BMI by: sex (reference category: male); ethnic background (White); area-level deprivation (most deprived Index of Multiple Deprivation quintile); weight status (healthy weight; clinical UK1990); and long-term condition (none). We estimated mean BMI difference (NCMP-BMI minus GP-BMI kg/m2) and 95% Limits of Agreement (LoA; Bland and Altman method).
ResultsWe identified at least one GP-BMI in 10.5% (2,964/28,330) and 26.0% (6,598/25,365) of 5- and 11-year-olds respectively.
Five-year-old children who were underweight (aOR; 95% CI: 1.70; 1.28,2.25) or obese (1.45; 1.28,1.65), from South Asian backgrounds (1.63; 1.45,1.80) and with long-term conditions (9.58; 8.13,11.28) were more likely, and girls (0.88; 0.81,0.95) and those from less deprived areas (Wald statistic; p-value: 40.06; <0.0001) less likely, to have at least one GP-BMI measurement recorded. Findings among 11-year-olds were similar.
We identified GP-BMI measurements made within one month of NCMP-BMI in 5.4% (160/2,964) of 5-year-olds and 4.0% (263/6,598) of 11-year-olds. There was poor agreement between NCMP-BMI and GP-BMI: mean difference (95% LoA): +0.55 (-2.49,+3.58) and +0.16 (-2.85,+3.18) in five- and 11-year-olds respectively.
ConclusionChild BMI is not comprehensively recorded in UK GP settings. Access to BMI school measurements in GP settings could support discussions about child weight status between children, their families and general practitioners.

ObjectivesWe have developed a dynamic method for identifying household members from Electronic Health Records (EHR). We compared the 2021 Census estimates of household number and demography with similar estimates derived from primary care EHRs on the Census date using primary care EHRs for the population of north east London (NEL).
MethodWe included 2,115,017 patients registered with a general practitioner on the 2021 Census date in NEL and assigned households from encrypted Unique Property Reference Numbers. We compared household number and size by Local Authority (LA), Middle Layer Super Output Area (MSOA) and area's Index of Multiple Deprivation quintiles (IMDq) to Office for National Statistics (ONS) 2021 Census estimates and by LA to ONS Admin Based Housing Stock (ABHS) 2020 estimates. We assessed differences in EHR and Census 2021 populations by sex, age, LA, MSOA and IMDq. Sensitivity analyses will exclude those without a recent recorded clinical encounter.
ResultsEHR population estimates (2,115,017) were 116,346 (5.8%) higher than Census estimates (1,998,671), higher among men (9.2%) than women (2.5%) in almost all age groups, especially men aged 30-50 years and higher in the most (8.7%), than in the least (2.5%) deprived IMDq. EHR household estimates (660,789) were 68,047 (9.3%) lower than Census estimates (728,836), and 19,719 (3.1%) higher than ABHS occupied addresses (641,070). EHR household size estimates were 15.6%, 29.2%,12.5% and 8.4% lower for household sizes 1,2 3 and 4, and 13.3%, 42.1%, 82.1% and 195.8% higher for household sizes 5, 6,7 and 8 respectively when compared to Census estimates. EHR population and household estimates were respectively 5-10% higher and 5-11% lower for almost all NEL local authorities.
ConclusionEHR- and Census-derived population and household estimates differ, mainly in the prevalence of larger households. While data were extracted on the same date, person-level validation was not possible. Differences may reflect deregistration delay in EHR when changing residence. Analyses based on clinical encounters recency may identify registered patients who are no longer residents.

ObjectivesWe used a dynamic method of identifying household members from Electronic Health Records (EHRs) linked to National Child Measurement Programme (NCMP) data to estimate the likelihood of children with obesity sharing a household with an older child with obesity, accounting for individual and household characteristics.
MethodsWe included 126,829 NCMP participants in four London boroughs and assigned households from encrypted Unique Property reference Numbers (UPRNs) at NCMP date for 115,466 (91%). We categorised the ethnic-adjusted body mass index of the youngest and oldest household child (underweight/healthy weight<91st, ≥91st to <98th overweight, obesity≥98th centile) and explored associations of the youngest child's weight status with: oldest child's weight status, number of household children (two, three or ≥4), youngest child's sex, ethnicity and school year of NCMP participation (reception or year 6). We estimated adjusted odds ratios (aOR) and 95% confidence intervals (CI) of obesity in the youngest child.
Results19,702 UPRNs were shared by two or more NCMP participants (youngest children: 51.2% male, 69.5% reception). 10.4% of youngest (95% CI: 10.0,10.9) and 13.0% of oldest (12.5,14.3) children were living with obesity. One third of youngest children with obesity shared a household with another child with obesity (33.2%; 31.2,35.2), compared with 9.2% (8.8,9.7) of those with a healthy weight. Youngest children living with an older child with overweight (aOR: 2.33; 95% CI: 2.06,2.64) or obesity (4.59, 4.10,5.14), those from South Asian ethnic backgrounds (1.89; 1.64,2.19) or taking part in NCMP in year 6 (2.21; 2.00,2.43) were more likely, and girls (0.73; 0.67,0.81), children living with just one other child (0.87; 0.77,0.98) and from Black ethnic backgrounds (0.78; 0.66,0.93) less likely, to be living with obesity.
ConclusionLinked EHRs can provide novel insights into the shared weight status of children sharing the same household. Further qualitative research is needed to understand how household food practices may vary by other household characteristics to improve our understanding of how the home environment influences childhood obesity.

ObjectivesOnly 89.4% of UK children receive MMR1 by age 24 months. We investigated whether children not receiving MMR1 by this age were more likely to share a household with children also not vaccinated by this age, and its variation by sex, ethnic background and area-level deprivation.
ApproachWe identified 208,907 children born between 2013-2019 and eligible for MMR1 from the primary care EHRs of 1,192,630 children registered to a general practitioner in North East London between 2001-2021. We estimated the proportion of children and 95% Confidence Interval (CI) receiving MMR1 between 12 and 24 months of age. We calculated prevalence ratios (PR) by sex, ethnicity, and deprivation (Index of Multiple Deprivation (IMD) quintiles). We identified all children with the same pseudonymised Unique Property Reference Numbers (pUPRNs) at the MMR1 date. We are completing analyses to calculate mutually adjusted PRs and examine household-level associations in timeliness of MMR1.
ResultsOverall, 172,319 (82.5%) of 208,907 eligible children received an MMR1 between age 12-24 months. Non-receipt of MMR1 by 24 months was less likely in children from South Asian ethnic backgrounds (PR 0.74; 95% CI: 0.68,0.79), and more likely in those from Black ethnic backgrounds (1.43;1.31,1.56) and in those living in the most deprived IMD quintile (1.83;1.49,2.23). We identified 137,919 children with the same pUPRNs at the MMR1 date, comprising 69,892 boys (50.7%) and 41,846 (30.3%), 32,631 (23.7%), 10,793 (7.8%), 12,246 (8.9%) and 40,403 (29.3) from White, South Asian, Black, Mixed/Other and Missing ethnic backgrounds respectively. We are calculating adjusted PRs and will estimate associations between non-receipt of MMR1 in the youngest and oldest children within a household.
ConclusionOur findings suggest that non-receipt of MMR1 by 24 months is ethnically patterned and more likely in areas of higher deprivation. Household-level analyses provide actionable insights into the characteristics of measles-susceptible households and opportunities for data-enabled primary care interventions to reduce vaccination inequalities and prevent measles outbreaks.

ObjectiveWe hypothesised that adults living in overcrowded households were more likely to be diagnosed with Serious Mental Illness (SMI) or depression after accounting for individual, household and area-level factors.
ApproachWe linked primary care records of adults currently registered with all general practices in the north-east London region on 21/03/2021 to housing data using pseudonymised unique property reference numbers (pUPRNs), giving a sample of 762,718 adults in 329,948 households. Primary outcomes were a primary care diagnosis of SMI or depression. Overcrowding was defined as <32.5m2 per person. We estimated the adjusted odds (aOR) and 95% confidence intervals (CI) of SMI or depression when living in an overcrowded household, adjusting for individual (age, sex, ethnic group), household (size, composition), and area (Index of Multiple Deprivation) characteristics. We conducted logistic regression models with robust standard errors in RStudio.
ResultsSMI and depression were diagnosed in 6,492 (0.9%) and 45,053 (5.9%) adults respectively; 584,515 (76.6%) adults lived in overcrowded households. In univariable analyses, household overcrowding was inversely related to risk of SMI or depression (aOR[CI]: 0.42 [0.39,0.44]; 0.62 [0.61,0.64]). The direction of effect reversed on adjustment for household size, composition, and area-level deprivation: 1.22 [1.12,1.33]; 1.07 [1.04,1.10] for SMI and depression respectively.
ConclusionsWe found significant associations between SMI and depression and household overcrowding, using a novel method of record linkage to estimate household overcrowding but are not able to establish causality of observed associations in this cross-sectional study.
ImplicationsMore research is needed to understand how housing conditions affect mental health.

IntroductionLinking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers.
ObjectivesTo describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses.
MethodsBest practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables.
ResultsWe found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population.
The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations.
ConclusionsASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other address-based datasets including those with information relevant to the wider determinants of health.

INTRODUCTION: Linking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers. OBJECTIVES: To describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses. METHODS: Best practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables. RESULTS: We found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population. The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations. CONCLUSIONS: ASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other ...

IntroductionRepresenting patient-registered addresses as pseudonymised Unique Property Reference Numbers (UPRNs) enables linkage of environmental and household information to electronic health records (EHRs). However, the accuracy and potential biases in address-matching algorithm results applied to patient addresses is unknown.
Objectives and ApproachTo investigate accuracy, match rate, and biases in assigning UPRNs to general practitioner (GP)-registered patient addresses for a geographically-defined UK population, using a bespoke deterministic address-matching algorithm comprising 213 rules applied in rank order of minimising false-positives, developed for the Discovery Data Service.
We ran this algorithm to match 906,220 adult patient GP-registered addresses (48% female, 47% non-White, 89% 20-64) sampled in mid-2018 from 159 GP practices in four London boroughs to Ordnance Survey's AddressBase Premium database.
We evaluated the error rates using a gold-standard dataset. We used binary logistic regression to estimate the likelihood (Odds Ratio [OR]; 95% Confidence Intervals [CI]) of no UPRN match according to and adjusting for patient age, sex, ethnic background, deprivation, residential mobility and multiple GP registrations.
Results96% of patient addresses were successfully assigned a UPRN. Algorithm sensitivity, specificity, positive and negative predictive-values and F-measure were, respectively: 0.993, 0.019, 0.914, 0.204, and 0.9516.
After mutual adjustment, UPRN assignment was less likely for: men (OR: 0.87; 95%CI: 0.83,0.91); adolescents and the elderly (15-19 years: 0.57;0.43,0.77; ≥90 years: 0.39;0.18,0.84); those from Chinese ethnic backgrounds (0.87;0.8,0.91), living in the least deprived areas (0.25;0.21,0.31), or with two or more distinct UPRNs across multiple registrations (0.37;0.28,0.49); and more likely for: those from Bangladeshi ethnic backgrounds (1.79;1.61,2.00), registered before 2018 (5.10;4.42,5.87), or with multiple GP registrations (2.36;1.82,3.05).
Conclusion / ImplicationsThe Discovery open-source algorithm achieves a high accurate match rate and quantifies the demographic groups that may be under-represented among those successfully matched. This is the first time that bias in matching rates for an address-matching algorithm has been evaluated using patient-registered addresses.

Background with rationalePseudonymised UPRNs based on patient addresses can be used to link environmental information to electronic health records (EHRs), however the representativeness and potential demographic or health-related biases in linkage using existing address-matching algorithms have not been evaluated using patient addresses.
Main AimTo evaluate representativeness and bias in assigning UPRNs using an address-matching algorithm based on general practitioner (GP)-recorded patient addresses for a geographically-defined multi-ethnic inner city population.
MethodsWe evaluated the Discovery Programme deterministic address-matching algorithm, comprising 213 rules applied, in rank order of minimising false positives, to the GP-recorded address of 879,286 (48% female) patients currently registered with all GP practices in four boroughs in inner east London.
We used logistic regression to estimate the adjusted odds (aOR) of an address not being linked to a UPRN by: age band (reference group: <1 year), sex (female), ethnic group (White British), Index of Multiple Deprivation (IMD) quintile (most deprived), number of long-term conditions (none); and timing of GP registration (most recent quartile). We evaluated the linkage and algorithm error rates in an independent validated NHS address dataset using best practice linkage reporting standards.
Results99% of patients had a UPRN assigned. Men (aOR;95%CI:0.87;0.8,0.91), and patients aged 15-19 (0.51;0.39,0.68), 20-24 (0.67;0.51,0.89), or ≥90 years (0.35;0.83,0.91), of Chinese ethnic background (95% CI; 0.50; 0.45,0.56), or living in the least deprived IMD quintile (0.24; 0.20,0.30) were less likely, and those with a GP-registration preceding mid-2016 (p-value0.00) more likely, to have a UPRN assigned. The sensitivity, specificity, positive and negative predictive-values and F-measure of the algorithm were, respectively: 0.993, 0.019, 0.914, 0.204, and 0.9516.
ConclusionWe have demonstrated, for the first time, a high GP-address UPRN match rate and quantified error rates and biases for users. Further work is needed to investigate addresses in patients with more complex address histories.

Background with rationaleNew insights into the wider demographic context of multimorbidity has been prioritised, notably among disadvantaged and ethnically-diverse populations with a high disease burden. We propose an innovative approach to quantify health burden and disease clustering at household level, to enable predictors of household multimorbidity to be investigated and understood.
Main AimTo quantify multi-morbidity at the household level using general practitioner (GP) electronic health records (EHRs) in a geographically-defined ethnically-diverse inner city population.
MethodsWe extracted clinical and patient address data from GP EHRs from four east London boroughs (Tower Hamlets, Newham, Waltham Forest and City & Hackney). We included currently registered patients aged ≥18 years as at July 2018, and excluded those with duplicate or complex registrations, new registrations in the previous 12 months, or registrations without historical clinical data or occurring prior to 1948, as well as inactive patients with no recorded EHR activity in varying years depending on age and gender.
We defined multimorbidity using 16 long-term condition Read codesets defined in the Quality and Outcomes Framework. We grouped patients into households, defined as those sharing the same home address on their GP registration, represented by a pseudonymised Unique Property Reference Number.
ResultsProvisional data are presented. We identified 737,920 patients (51% female) eligible for this study out of a total population of 1,171,483 currently registered patients. Of these, 23% aged <20, 69% aged 20-64, 8% aged >=65, 38% White ethnicity, 3% Mixed, 30% Asian, 14% Black, 5% Other and 12% Not Stated/Null. We identified 312,582 shared households among 737,920 patients. Analyses to derive household-level summary characteristics and relate these to multimorbidity burden are in progress and will be presented.
ConclusionHousehold-level multi-morbidity can be quantified using clinical and patient address data in GP electronic health records.

ObjectiveCoverage for the measles, mumps and rubella (MMR) vaccination in north-east London (NEL) is the lowest in the United Kingdom. It has been hypothesised that children who move home frequently are less likely to receive vaccinations. We examined the association between number of addresses in the first two years of life, and receipt of MMR vaccination by age two.
ApproachWe derived households by pseudonymously linking the addresses of 178,028 children registered with a NEL general practitioner (GP) on their second birthday (2015-2021). We included 135,221 (76.0%) children (51.0% male) after excluding 42,807 with invalid household characteristics at age 24 months. The outcome was MMR vaccination by 24 months. Residential mobility was categorised by number of addresses (1, 2, ≥3) between earliest GP-registration and vaccination date/second birthday. We calculated the percentage receiving MMR by residential mobility and estimated adjusted odds (aOR; 95% confidence intervals [CI]) for MMR receipt.
Results105,392 (77.9%) children received MMR by age two, and 26,652 (19.7%) had more than one address. Receipt of MMR decreased with increasing residential mobility: 80.9% (95% CI: 80.6,81.1), 66.5% (65.9,67.1), and 58.6% (56.2,60.9) for children with one, two, or three or more addresses respectively. Children with multiple addresses were less likely to receive MMR by age two: aOR: 0.53 (0.51,0.55); 0.36 (0.32,0.40) for 2 and ≥3 addresses respectively.
Conclusions Uptake of MMR vaccine by two years of age is lower in children with multiple addresses. Further research is needed to identify actionable opportunities to improve MMR uptake in mobile populations.

IntroductionHouseholds are increasingly studied in population health research as an important context for understanding health and social behaviours and outcomes. Identifying household units of analysis in routinely collected data rather than traditional surveys requires innovative and standardised tools, which do not currently exist.
ObjectivesTo design a utility that identifies households at a point in time from pseudonymised Unique Property Reference Numbers (UPRNs) known as Residential Anonymised Linkage Fields (RALFs) assigned to general practitioner (GP) patient addresses in electronic health records (EHRs) in north east London (NEL).
MethodsRule-based logic was developed to identify households based on GP registration, address date, and RALF validity. The logic was tested on a use case on the household clustering of childhood weight status, and bias in success of identifying households was examined in the use case cohort and in a full population cohort.
Results92.1% of the use case cohort was assigned a household. The most frequent dominant reason (55.3%) for a household not assigned was that a person had no valid household RALFs available across their patient registration address records. Other reasons are having none or multiple valid household RALFs, or not being alive at the event date.
In the use case, children not assigned to a household were more likely to attend schools in City & Hackney and living in the third most deprived quintile of lower super output areas.
88.9% of the population cohort was assigned a household. Patients not assigned to a household were more likely to be aged 18 to 45 years, living in City & Hackney, and living in the second quintile of most deprived lower super output areas.
ConclusionsWe have developed a method for deriving households from primary care EHRs that can be implemented quickly and in real-time, providing timely data to support population health research on households.

BackgroundHarmonisation of different data sources from various electronic health records (EHRs) across systems enhances the potential scope and granularity of data available to health data research.
ObjectiveTo describe data harmonisation of routine electronic healthcare records in Wales and Scotland linked to a UK longitudinal birth cohort, the Millennium Cohort Study (MCS).
MethodsComparable secondary care data was linked, with parental consent, to MCS information for 1838 and 1431 children participating in MCS and residing in Wales and Scotland, by assigning, respectively, unique Anonymised Linkage Fields to personbased records in the privacy protecting Secure Anonymised Information Linkage (SAIL) databank at Swansea University, and by the National Health Service (NHS) Information Standards Division. Survey and non-response weights were created to account for the clustered sample, sample attrition and consent to linkage. Heterogeneous variables from the Patient Episode Dataset for Wales, Emergency Department Data Set for Wales, Scottish Medical Record 01 and Accident and Emergency dataset for Scotland were harmonised enabling data to be pooled and standardised for research.
FindingsOverall linkage to harmonised health care data was achieved for 98.9% (99.9% for Wales and 97.6% for Scotland) of consented MCS participants. 66% of children experienced at least one hospital admission (total 5747 hospital admissions) up totheir 14th birthday, while 60% attended A&E departments at least once (total 5221 attendances) between their 9th and 14th birthday. We managed date granularity by generating random dates of birth, standardising periods of data collection,identifying inconsistencies and then mapping and bridging differences in definitions of periods of care across countries and datasets.
ConclusionsCombining and harmonising data from multiple sources and linking them to information from a longitudinal cohort create useful resources for population health research. These methods are reproducible and can be utilised by other researchersand projects.