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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.

Importance of the global collaboration for the sake of regional development is very well understood by the rising economic structure of China as it has played a very important role in developing such initiates such as Belt and Road Initiative for regional advancement in Middle Eastern countries. The importance of One Belt One road initiative for the economic, cultural, and regional development cannot be overemphasized and thus it is going to bridge the gap between China and Middle Eastern countries. This research is focused on those specific requisite areas, which need a systematic scenario for the implementation of this global progressive initiative. Moreover, this research also exploits the relationship between imperative areas. Although these highlighted associations do not play a stand‐alone role, though they are and will be affected by other prompting factors. The major findings of this research study expose the dependency and mutual exclusion of these factors such as the significant change in our main variable (IPR Paradigm) due to change in Trademark, IPR institution, ownership, and confidentiality adoption because of the Sig. value is 0.014, 0.015, 0.003, and 0.002 respectively, which is less than the acceptable value of 0.05. With a 1% increase in the adoption of trademark, IPR institution, ownership, and confidentiality, the IPR Paradigm will increase by 0.183%, 0.173%, 0.197%, and 0.197% (B value) respectively.

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.