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.

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.

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.

ABSTRACT
ObjectivesThis project builds on previous work linking routinely collected data from birth registration, birth notification, death registration and hospital discharges, extending it to six million births in England and Wales from 2005 to 2014. This linkage is creating a new dataset to investigate previously unanswerable questions about variations in time of birth and its outcome which are highly contested in the health service in England and Wales:
1. How do numbers of births vary according to time of day, day of the week and time of year of birth and how does this relate to modes of onset of labour and delivery?2. How do patterns of birth vary between maternity services in relation to variations in medical and midwifery staffing, patterns of intervention and size of unit?3. How does the outcome of pregnancy, in terms of mortality and morbidity rates at birth and in the first year of life, vary according to time of birth in relation to gestational age, and intervention in the onset of labour and delivery?4. Have the patterns changed over the years 2005 to 2014?
ApproachRoutinely collected national datasets for 2005-14 have been linked using patient identifiable data items. They differ in the way data items are recorded. Methods of linkage, quality assurance and data cleaning have been improved, compared with those developed in the original project.
This unique approach links clinical data from hospital admissions with more reliable data on birthweight from birth registration and with gestational age and time of birth from birth notification. This enables investigation of the associations between day and time of birth with its outcome.
This is also the first study to link mothers' and babies' hospital discharge data for England and Wales and to draw on public and patient involvement to include outcome measures specifically designed to be women-centred. Overall, the linkage has created a fuller range of data.
ResultsResults will be available by the time of the conference. Initial results already clearly illustrate differences in timings of birth by time of day and day of the week by mothers' age, gestational age and birth setting and for singleton and multiple births.
ConclusionThe study has created a valuable linked dataset that will uniquely enable analyses of associations between timing of birth and its outcome. These results will potentially be able to inform and impact NHS service provision.

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.

IntroductionMulti-morbidity is a widely recognised but poorly understood global issue that appears to be increasing in prevalence, according to the UK's Academy of Medical Sciences (AMS) report in 2018. Disease clustering, their determinants and consequences are poorly researched. Better understanding would help drive prevention and improved clinical care, services and patient outcomes.
Objectives and ApproachDevelopment of two comprehensive population-wide e-cohorts, derived utilising data linkage techniques and including multi-sourced anonymised routine health and demographic data held within the SAIL Databank. The objective is to characterise multi-morbidity and its clustering, determinants and outcomes and compare methods using a) prospective cohort design using multiple data sources in Wales and b) retrospective cohort design to examine household level and environment clustering using GP data in demographically diverse populations (Wales and North East London).
The prospective e-cohort focuses on adults living in Wales on 1 st January 2000 and followed up to 2020, including data from the NHS population register, deaths, inpatients, outpatients, Emergency Department, GP, disease registries, laboratory data, and population surveys with QoL measures. This e-cohort will be harmonised with other sites across the UK. The retrospective e-cohort is designed to harmonise with a North East London e-cohort, including all individuals living in Wales on 24 th April 2018 and registered with a GP.
Results2.8 and 2.2 million individuals have been included in the prospective and retrospective cohorts respectively, with 43.6 million person years of follow up. Established comorbidity indices and published phenotypes from libraries are being applied to the data to create initial prevalence and incidence estimates for further analysis. Important clusters will be determined by associations with mortality and excess healthcare utilisation.
Conclusion/ImplicationsBuilding the e-cohorts has involved multiple disciplines across organisations. Multi-morbidity prevalence estimates and study designs will be compared prior to statistical analyses and machine learning methods to evaluate clustering and determinants.

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed