Deterministic Linkage Research Articles

The application of deterministic and probabilistic methods of data linkage methods to link population and health data in western Uganda

IntroductionThere is increasing interest in record linkage between population-level data and health facility records to support epidemiological research in Africa. However, there is a lack of unique and ubiquitous identification systems for linkage. We applied two common data linkage approaches between population-level data from a health and demographic surveillance system and hospital records in Western Uganda. MethodologyWe established a data linkage system using deterministic and probabilistic linkage on population level data collected from 23 villages with hospital records. Deterministic linkage entails connecting a pair of records from the clinic and census data using a common identifier. Probabilistic linkage classifies a pair of records from two data sources as belonging to the same individual based on the statistical probability that a common set of identifiers drawn from the two data sources belong to the same individual. The preliminary results are on the deterministic approaches. ResultsUsing deterministic linkage, we fully linked 1061 records from 12/23 villages. Further updates of linkage with characteristics of the linked patients will be summarized in the next two months. Probabilistic linkage data will be presented with the characteristics of participants after the completion of the study, and the agreement between the two methods will be compared. Note to the results: Presentations during the conference will be done using finalized data. ConclusionPreliminary findings show that the linkage of population and health data is feasible. Findings could be used by researchers to design data linkage studies and answer different types of health and population research questions using innovative data science methods.

Exploring Multimorbidity Through the Use of Deterministic and Probabilistic Record Linkage of Data in Five Health Facilities in Nairobi

IntroductionData collected by Health Demographic Surveillance Systems (HDSSs) at the community level can support very powerful research questions if it was linked to records at health facilities. In particular, understanding multimorbidity and building predictive models for key health outcomes requires linked data collected both in the community and health facilities. Additionally, the linked data can assist health management teams at the national, county, and local levels in making evidence-based choices. Problemthe inability to link population level data with records at health facilities affects the ability to use data to solve key health research questions. lack of common identifiers affects the possibility of integrating clinic records with population-based data. MethodologyWe intend to link data from five health facilities in Nairobi County serving the population in the Nairobi Urban Health Demographic Surveillance System (NUHDSS) where longitudinal data was collected between 2001 and 2018. The longitudinal data is from a database of about 260,000 individuals. This will result in a merged dataset and will also provide an opportunity to assess the accuracy of the deterministic and probabilistic record linkage algorithm compared with other methods. Note: We will have results by the time the conference is happening as groundwork has been put. ConclusionThe growing burden of multimorbidity on the African continent requires creative and effective strategies of addressing the problem through utilization of existing longitudinal data. By merging clinic records with population-based data, this initiative will promote the use of data evidence informed decision making for interventions to address multimorbidity.

Kids’ Environment and Health Cohort

This presentation discusses Kids’ Environment and Health Cohort (KEHC), a bespoke Administrative Data Research UK (ADR UK) funded linkage project. KEHC will be a comprehensive and yearly updated dataset for children born in England from 2006 onwards, integrating health, education, pregnancy, and environmental data. This integrates data from different administrative systems including the Office for National Statistics, the National Health Service, and the Department for Education as well as open environmental data. This presentation will outline the deterministic and probabilistic linkage methods utilised and the quality assurance undertaken through clerical review and bias analysis. Additionally, we will delve into the challenges encountered in the process of data ingest, linkage, and delivery due to the diverse origins and purposes of the collected data, as well as temporal disparities. In navigating the intricacies of multiple linkages, we will discuss our unique methods, including plans to use an indexing first approach. The first cohort of approximately 10 million children has been created to form the spine and further linkages to the 2011 and 2021 UK census data have been concluded. Linkages between address histories and open environmental data is ongoing. Linkage results have been promising, with high match rate and excellent quality. KEHC is a powerful example of how integrated data can be used for research for the public good. KEHC will provide a unique opportunity for academic researchers investigating the longitudinal effects of local environments on children’s health and educational outcomes and will become one of ADR UK’s flagship Research Ready Datasets.

Using linkage to assess coverage of population estimates

ObjectivesThe Demographic Index (DI) comprises of five linked administrative datasets, used for population estimation. Current linkage methods are not ideal to utilise the power of this asset. Using the 2021 England and Wales Census, we developed an innovative composite linkage method to fully utilise the power of the DI. ApproachUsing non-greedy deterministic and probabilistic linkage methods, we linked the DI to the Census at a composite level where we believe links exist – i.e., linking a Census record with a DI cluster (consisting of linked records from the data sources used to make the DI). Next, pairwise linkage of records within these clusters was performed to ensure that every DI record within the cluster was a link. Clerical review of a sample of the full linkage was performed using clerical resolution and search to resolve uncertain and conflicting links and to inform the quality of our linkage. Due to data quality errors in the administrative data, clerical techniques were not always able to determine true match status, so clerical identified ‘uncertain’ links to estimate precision and recall ranges rather than a set estimate. Results A quality analysis to estimate linkage accuracy was conducted and suggests that the linkage has a precision of between 99.3% and 99.7%, and recall between 99.1% and 99.7%. ConclusionWe have linked the ONS’ composite population-level dataset to the 2021 England and Wales Census. The presentation will showcase the methods developed and how we ensured the highest quality possible. *** Please note that this was subbmited in 2022 but the presenters were unable to present due to the UK Queen's death.

Linking Maternal and Child Health Data to Enhance Public Health Surveillance in Indiana

Indiana faces significant challenges in maternal and infant health, ranking 45th worst in infant mortality with a rate of 6.5 deaths per 1,000 live-births, and 21st in stillbirths. Indiana further has the 3rd highest rate of maternal mortality with 44 deaths per 100,000 live-births. Despite these alarming statistics, comprehensive surveillance of maternal and infant health is absent, and data often remains fragmented across disparate information systems. In response, the Indiana University Fairbanks School of Public Health and Regenstrief Institute secured a cooperative agreement through the CDC's Pregnant People-Infant Linked Longitudinal Surveillance (PILLARS) program, collaborating with the Indiana Health Information Exchange, Indiana Department of Health, and Marion County Public Health Department. The initiative aims to enhance Indiana's infrastructure for surveillance of maternal and child health (MCH) using electronic health records (EHRs), public health, and administrative data. Key efforts have included development, validation, and application of linkage algorithms across records for mothers and children; integration of case data across data sources; design of routine surveillance reports; and design of longitudinal studies to examine determinants and outcomes in MCH populations. Using deterministic linkage algorithms, we have created over 800,000 mother-infant dyads; this number will expand with the implementation of probabilistic approaches. We also have developed a computational phenotype for identifying cases of pregnancy in the EHR even when not explicitly flagged in the EHR. Future efforts will build on this infrastructure to draw from additional public health data sources and/or expand surveillance efforts to include prioritized MCH outcomes.

Collaborating across government agencies to combine linkage processes and algorithms to create an Australian National Linkage Spine (NLS) to facilitate cross-jurisdictional linkage

The Australian Bureau of Statistics (ABS) and the Australian Institute of Health and Welfare (AIHW) are co-technical lead agencies for developing the National Disability Data Asset (NDDA) which will bring together data across jurisdictions to build a more complete picture of the lives of people with disability. ABS and AIHW are collaboratively building a National Linkage Spine (NLS), a high-quality high-coverage population dataset to be shared under new legislation to facilitate cross-jurisdictional linkage and build the NDDA. ABS and AIHW each have extensive data integration experience, however our linkage methods have evolved differently in response to the needs of our respective researcher client bases and the datasets we work with. An ABS-AIHW cross-agency team was established to align terminology and share data processing methods, linkage approaches, and locally built linkage tools. The team collaborated on the design of an NLS that combines the best aspects of each of our approaches. The NLS build methodology delivers aligned processing of input data and applies a combination of ABS’s deterministic linkage algorithm and AIHW’s probabilistic linkage and machine learning algorithms. The NLS was peer reviewed by its future jurisdictional users and is considered fit for purpose for delivering the NDDA. The open sharing of knowledge and tools has significantly benefited both agencies and the broader NDDA project. Takeaways from this project extend beyond it, with improved data process approaches being considered by each agency in other work, and a growing common agreement of linkage best practice.

Leveraging Fuzzy Match: Building an Automated Record Linkage Template in SAS Dataflux

Background ICES has conducted Probabilistic Record Linkage (PRL) for heath and non-health data for over 15 years. The PRL process has made data linkable to many ICES data holdings. However, due to the manual gray area resolution, record linkages for large datasets are time consuming with inconsistent results. ApproachAdapting from many years of experience with PRL methodology, record linkage templates were built in SAS Dataflux. By using combination of deterministic linkage and fuzzy match algorithms, the multi-pass linkage strategy is maintained by passing unlinked records in subsequent linkage comparison cycles. The new template uses personal identifiers of multiple given names, surnames, date of birth, death date (if applicable) and sex. In each pass, the pool of possible matched pairs is created by merging on low sensitive matchcodes with different conditions. Then, using a rule-based approach, possible pairs are examined, flags are assigned, and survival rules are applied to select the best matched records. ResultsBy feeding a pre-prepared linkage file along with updating the input and output file names, the record linkage job will be conducted automatically in SAS Dataflux through submitting a “run” command. It significantly reduces the turnaround time with no clerical review required, achieves a similar linkage rate as PRL consistently, and is utilized by various research projects. ConclusionThe automated SAS Dataflux record linkage template is more efficient than the traditional PRL process. It eliminates the human intervention of gray area resolution, while reducing project turnaround times, and maintaining a comparable accuracy and linkage rate.

Estimating occupation-related crashes in light and medium size vehicles in Kentucky: A text mining and data linkage approach

Occupational motor vehicle (OMV) crashes are a leading cause of occupation-related injury and fatality in the United States. Statewide crash databases provide a good source for identifying crashes involving large commercial vehicles but are less optimal for identifying OMV crashes involving light or medium vehicles. This has led to an underestimation of OMV crash counts across states and an incomplete picture of the magnitude of the problem. The goal of this study was to develop and pilot a systematic process for identifying OMV crashes in light and medium vehicles using both state crash and health-related surveillance databases. A two-fold process was developed that included: 1) a machine learning approach for mining crash narratives and 2) a deterministic data linkage effort with crash state data and workers compensation (WC) claims records and emergency medical service (EMS) data, independently. Overall, the combined process identified 5,302 OMV crashes in light and medium vehicles within one year’s worth of crash data. Findings suggest the inclusion of multi-method approaches and multiple data sources can be implemented and used to improve OMV crash surveillance in the United States.

The Association between Long-term PM2.5 Exposure and Risk for Pancreatic Cancer: An Application of Social Informatics.

There is a profound need to identify modifiable risk factors to screen and prevent pancreatic cancer. Air pollution, including fine particulate matter (PM2.5), is increasingly recognized as a risk factor for cancer. We conducted a case-control study using data from the electronic health record (EHR) of Duke University Health System, 15-year residential history, NASA satellite fine particulate matter (PM2.5) and neighborhood socioeconomic data. Using deterministic and probabilistic linkage algorithms, we linked residential history and EHR data to quantify long term PM2.5 exposure. Logistic regression models quantified the association between a one interquartile range (IQR) increase in PM2.5 concentration and pancreatic cancer risk. The study included 203 cases and 5027 controls (median age of 59 years, 62% female, 26% Black). Individuals with pancreatic cancer had higher average annual exposure (9.4 μg/m3) as compared to IQR increase in average annual PM2.5 was associated with greater odds of pancreatic cancer (OR=1.20; 95% CI: 1.00-1.44). These findings highlight the link between elevated PM2.5 exposure and increased pancreatic cancer risk. They may inform screening strategies for high-risk populations and guide air pollution policies to mitigate exposure.

Real-world lessons: combining cancer registry and retail pharmacy data for oral cancer drugs.

Recent cancer care advances have introduced new oral therapies, and yet population registries lack detailed treatment data, hampering investigations into therapy uptake, adherence, and outcomes. This study aimed to assess the representativeness and completeness of linking Surveillance, Epidemiology, and End Results (SEER) cancer registry data with data from two major retail pharmacy chains, collectively covering a large segment of the US market. A deterministic data linkage between 11 SEER cancer registries and retail pharmacy data (excluding mail order fills) was conducted for individuals diagnosed with selected cancers from 2013 to 2017, with follow-up through 2019. Descriptive characteristics of the linked and unlinked populations were examined. In a selected subcohort of older women (aged ≥65) with first and only primary breast cancer who had Medicare Part D claims for tamoxifen, we further validated the linkage using Medicare Part D event data as the reference standard. Among 758 068 eligible individuals, only 6.4% were linked to CVS/Walgreens data; the linkage percentage varied by age, sex, race, ethnicity, registry, and cancer type. Within the subcohort of 5963 older women with breast cancer and a claim for tamoxifen in Part D data, 25% were identified as tamoxifen users in retail pharmacy data. Out of these 1490 women, 749 (50.3%) had complete longitudinal tamoxifen dispensing information from retail pharmacy data. Retail pharmacy data show promise in identifying oral anticancer treatments, enhancing SEER registry efforts, but they require further validation. We propose an evaluation framework, sharing insights and potential use cases for this resource.

Transition from rehabilitation hospital to the National Disability Insurance Scheme (NDIS) for people with brain injury and spinal cord injury: a data linkage protocol

IntroductionTraumatic brain injury (TBI) and spinal cord injury (SCI) are both major contributors to permanent disability globally, with an estimated 27 million new cases of TBI and 0.93 million new...

Erlotinib or Gefitinib for Treating Advanced Epidermal Growth Factor Receptor Mutation-Positive Lung Cancer in Aotearoa New Zealand: Protocol for a National Whole-of-Patient-Population Retrospective Cohort Study and Results of a Validation Substudy.

Starting in 2010, the epidermal growth factor receptor (EGFR) kinase inhibitors erlotinib and gefitinib were introduced into routine use in Aotearoa New Zealand (NZ) for treating advanced lung cancer, but their impact in this setting is unknown. The study described in this protocol aims to understand the effectiveness and safety of these new personalized lung cancer treatments and the contributions made by concomitant medicines and other factors to adverse outcomes in the general NZ patient population. A substudy aimed to validate national electronic health databases as the data source and the methods for determining patient eligibility and identifying outcomes and variables. This study will include all NZ patients with advanced EGFR mutation-positive lung cancer who were first dispensed erlotinib or gefitinib before October 1, 2020, and followed until death or for at least 1 year. Routinely collected health administrative and clinical data will be collated from national electronic cancer registration, hospital discharge, mortality registration, and pharmaceutical dispensing databases by deterministic data linkage using National Health Index numbers. The primary effectiveness and safety outcomes will be time to treatment discontinuation and serious adverse events, respectively. The primary variable will be high-risk concomitant medicines use with erlotinib or gefitinib. For the validation substudy (n=100), data from clinical records were compared to those from national electronic health databases and analyzed by agreement analysis for categorical data and by paired 2-tailed t tests for numerical data. In the validation substudy, national electronic health databases and clinical records agreed in determining patient eligibility and for identifying serious adverse events, high-risk concomitant medicines use, and other categorical data with overall agreement and κ statistic of >90% and >0.8000, respectively; for example, for the determination of patient eligibility, the comparison of proxy and standard eligibility criteria applied to national electronic health databases and clinical records, respectively, showed overall agreement and κ statistic of 96% and 0.8936, respectively. Dates for estimating time to treatment discontinuation and other numerical variables and outcomes showed small differences, mostly with nonsignificant P values and 95% CIs overlapping with zero difference; for example, for the dates of the first dispensing of erlotinib or gefitinib, national electronic health databases and clinical records differed on average by approximately 4 days with a nonsignificant P value of .33 and 95% CIs overlapping with zero difference. As of May 2024, the main study is ongoing. A protocol is presented for a national whole-of-patient-population retrospective cohort study designed to describe the safety and effectiveness of erlotinib and gefitinib during their first decade of routine use in NZ for treating EGFR mutation-positive lung cancer. The validation substudy demonstrated the feasibility and validity of using national electronic health databases and the methods for determining patient eligibility and identifying the study outcomes and variables proposed in the study protocol. Australian New Zealand Clinical Trials Registry ACTRN12615000998549; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368928. DERR1-10.2196/51381.

Linking an early access program (EAP) to the National Health Data System (NHDS) in France and assessing its performances and potential biases.

11137 Background: Early access programs (EAP) are a unique opportunity to document first use of innovative treatments in real-world settings. The French HTA body requires mandatory patients’ clinical data collection for EAP and since July 2021, has recommended anticipating linkage EAP to the National Health Data System (NHDS). Prior to drug initiation, the collected data is almost complete contrary to follow-up data. To the best of our knowledge, no other study has proposed a methodology to link EAP to the French Cancer cohort (FCC), an extract of the NHDS of over 8 million persons diagnosed or at high risk of cancer, and to assess its assets and downsides. Methods: This cohort study has reused data of informed French EAP patients treated with durvalumab from October 1st, 2017 to December 31st, 2018 (n=457) in locally advanced unresectable NSCLC and of patients who received durvalumab at the time of the EAP in the FCC (n=666). The linkage methodology consists in harmonizing available data across sources, defining matching, control and study variables, testing several indirect deterministic linkage algorithms and assessing their performances. Consistency and differences between data sources were described for the best performing algorithm. Results: Proposed linkage algorithms required matches on a combination of data among sex, birth year and month, treating hospital ID and department. Performance started at 80.9% (370/457) patients from the EAP cohort linked to eligible patients from FCC up to 85.3% (390/457) for the best performing algorithm. The consistency of linkage variables between the two sources was substantial (Cohen’s kappa > 0.8) or excellent (intraclass correlation > 0.9). It was the same for control variables. Comparisons between linked patients and unlinked patients from the EAP cohort showed no statistically significant difference on matching and control variables, except for few cases on history of chemotherapy and history of radiation therapy prior to durvalumab. Similarly, no statistically significant difference was found on matching and control variables between linked patients and unlinked patients from FCC, except for history of radiation therapy and treating hospital department. Median of time difference between foreseen date of first durvalumab administration in EAP versus the effective date in FCC was -1 (IQR = [-9 ; 0]) day. Lack of reported diagnosis related to prior radiation therapy administration in out-of-hospital settings refrained from using them as matching or control variables. Conclusions: The study illustrates the feasibility of matching EAP patients to the French NDHS with indirect deterministic linkage. Additional EAP linkages will be required to ensure the validation and reproducibility of such methodology. Further works in progress will document the adequacy of French NHDS data to address effectiveness.

The Development of the Municipal Registry of People with Diabetes in Porto Alegre, Brazil.

Background/Objective: Diabetes registries that enhance surveillance and improve medical care are uncommon in low- and middle-income countries, where most of the diabetes burden lies. We aimed to describe the methodological and technical aspects adopted in the development of a municipal registry of people with diabetes using local and national Brazilian National Health System databases. Methods: We obtained data between July 2018 and June 2021 based on eight databases covering primary care, specialty and emergency consultations, medication dispensing, outpatient exam management, hospitalizations, and deaths. We identified diabetes using the International Classification of Disease (ICD), International Classification of Primary Care (ICPC), medications for diabetes, hospital codes for the treatment of diabetes complications, and exams for diabetes management. Results: After data processing and database merging using deterministic and probabilistic linkage, we identified 73,185 people with diabetes. Considering that 1.33 million people live in Porto Alegre, the registry captured 5.5% of the population. Conclusions: With additional data processing, the registry can reveal information on the treatment and outcomes of people with diabetes who are receiving publicly financed care in Porto Alegre. It will provide metrics for epidemiologic surveillance, such as the incidence, prevalence, rates, and trends of complications and causes of mortality; identify inadequacies; and provide information. It will enable healthcare providers to monitor the quality of care, identify inadequacies, and provide feedback as needed.

Evaluating the relationship between conditional cash transfer programme on preterm births: a retrospective longitudinal study using the 100 million Brazilian cohort

BackgroundPreterm births increase mortality and morbidity during childhood and later life, which is closely associated with poverty and the quality of prenatal care. Therefore, income redistribution and poverty reduction initiatives may be valuable in preventing this outcome. We assessed whether receipt of the Brazilian conditional cash transfer programme - Bolsa Familia Programme, the largest in the world - reduces the occurrence of preterm births, including their severity categories, and explored how this association differs according to prenatal care and the quality of Bolsa Familia Programme management.MethodsA retrospective cohort study was performed involving the first live singleton births to mothersenrolled in the 100 Million Brazilian Cohort from 2004 to 2015, who had at least one child before cohort enrollment. Only the first birth during the cohort period was included, but born from 2012 onward. A deterministic linkage with the Bolsa Familia Programme payroll dataset and a similarity linkage with the Brazilian Live Birth Information System were performed. The exposed group consisted of newborns to mothers who received Bolsa Familia from conception to delivery. Our outcomes were infants born with a gestational age < 37 weeks: (i) all preterm births, (ii) moderate-to-late (32–36), (iii) severe (28–31), and (iv) extreme (< 28) preterm births compared to at-term newborns. We combined propensity score-based methods and weighted logistic regressions to compare newborns to mothers who did and did not receive Bolsa Familia, controlling for socioeconomic conditions. We also estimated these effects separately, according to the adequacy of prenatal care and the index of quality of Bolsa Familia Programme management.Results1,031,053 infants were analyzed; 65.9% of the mothers were beneficiaries. Bolsa Familia Programme was not associated with all sets of preterm births, moderate-to-late, and severe preterm births, but was associated with a reduction in extreme preterm births (weighted OR: 0.69; 95%CI: 0.63–0.76). This reduction can also be observed among mothers receiving adequate prenatal care (weighted OR: 0.66; 95%CI: 0.59–0.74) and living in better Bolsa Familia management municipalities (weighted OR: 0.56; 95%CI: 0.43–0.74).ConclusionsAn income transfer programme for pregnant women of low-socioeconomic status, conditional to attending prenatal care appointments, has been associated with a reduction in extremely preterm births. These programmes could be essential in achieving Sustainable Development Goals.

Predictive accuracy of boosted regression model in estimating risk of venous thromboembolism following minimally invasive radical surgery in pharmacological prophylaxis-naïve men with prostate cancer.

Venous thromboembolism (VTE) is a potentially life-threatening but preventable complication after urological surgery. Physicians are faced with the challenges of weighing the risks and benefits of thromboprophylaxis given scanty evidence for or against and practice variation worldwide. The primary objective of the study was to explore the possibility of a risk-stratified approach for thromboembolism prophylaxis following radical prostatectomy. A prospective database was accessed to cross-link venous thromboembolism events in 522 men who underwent minimally invasive prostatectomy between February 2010 and October 2021. A deterministic data linkage method was used to record events through electronic systems. Community Health Index (CHI) numbers were used to identify patients via electronic health records. Patient demographics and clinical characteristics such as age, comorbidities, Gleason staging, and readmission details accrued. VTE within 90days and development of a risk-stratified scoring system. All statistical analysis was performed using R-Statistical Software and the risk of VTE within 90days of surgery was estimated via gradient-boosting decision trees (BRT) model. 1.1% (6/522) of patients developed deep vein thrombosis or pulmonary embolism within 3months post-minimally invasive prostatectomy. Statistical analysis demonstrated a significant difference in the body mass index (p = 0.016), duration of hospital stay (p < 0.001), and number of readmissions (p = 0.036) between patients who developed VTE versus patients who did not develop VTE. BRT analysis found 8 variables that demonstrated relative importance in predicting VTE. The receiver operating curves (ROC) were constructed to assess the discrimination power of a new model. The model showed an AUC of 0.97 (95% confidence intervals [CI]: 0.945,0.999). For predicting VTE, a single-center study is a limitation. The incidence of VTE post-minimally invasive prostatectomy in men who did not receive prophylaxis with low molecular weight heparin is low (1.1%). The proposed risk-scoring system may aid in the identification of higher-risk patients for thromboprophylaxis. In this report, we looked at the outcomes of venous thromboembolism following minimally invasive radical prostatectomy for prostate cancer in consecutive men. We developed a new scoring system using advanced statistical analysis. We conclude that the VTE risk is very low and our model, if applied, can risk stratify men for the development of VTE following radical surgery for prostate cancer.

Linking migration and hospital data in England: linkage process and evaluation of bias.

Difficulties ascertaining migrant status in national data sources such as hospital records have limited large-scale evaluation of migrant healthcare needs in many countries, including England. Linkage of immigration data for migrants and refugees, with National Health Service (NHS) hospital care data enables research into the relationship between migration and health for a large cohort of international migrants. We aimed to describe the linkage process and compare linkage rates between migrant sub-groups to evaluate for potential bias for data on non-EU migrants and resettled refugees linked to Hospital Episode Statistics (HES) in England. We used stepwise deterministic linkage to match records from migrants and refugees to a unique healthcare identifier indicating interaction with the NHS (linkage stage 1 to NHS Personal Demographic Services, PDS), and then to hospital records (linkage stage 2 to HES). We calculated linkage rates and compared linked and unlinked migrant characteristics for each linkage stage. Of the 1,799,307 unique migrant records, 1,134,007 (63%) linked to PDS and 451,689 (25%) linked to at least one hospital record between 01/01/2005 and 23/03/2020. Individuals on work, student, or working holiday visas were less likely to link to a hospital record than those on settlement and dependent visas and refugees. Migrants from the Middle East and North Africa and South Asia were four times more likely to link to at least one hospital record, compared to those from East Asia and the Pacific. Differences in age, sex, visa type, and region of origin between linked and unlinked samples were small to moderate. This linked dataset represents a unique opportunity to explore healthcare use in migrants. However, lower linkage rates disproportionately affected individuals on shorter-term visas so future studies of these groups may be more biased as a result. Increasing the quality and completeness of identifiers recorded in administrative data could improve data linkage quality.

Linking crash and breathalyzer data in Connecticut

Objectives To document the process of linking breathalyzer and motor vehicle crash (MVC) data for the State of Connecticut using a unique identifier in the place of personal and private information. Methods Deterministic linkage methodologies were utilized in Microsoft SQL Server to join 5,634 (of 6,650) breathalyzer records to corresponding MVC driver records for the period of January 1, 2017 to December 31, 2022. Differences between the linked and original datasets were documented by comparing the consistency of frequency and proportion distributions of key variables. Results Proportions of annual records, alcohol breath tests, and refusals were nearly unchanged when comparing linked and original breathalyzer data. When examining variables in the original MVC driver records, there were differences in the within-group proportions for sex and age, with an overrepresentation of males and drivers aged 26-to-40 years old. For crash and injury severity, the linked dataset had lower proportions of more severe injury records when compared to the original MVC data. Additionally, 1,007 breathalyzer records were not matched with an associated MVC record. Conclusions Linkage methodology is sound and produced quality matches. The use of a unique identifier provided a strong match qualifier in the absence of personal and private data. Changes in proportions for age, sex, crash and injury severity align with previous research. Potential missed matches may be attributed to several factors outside of the linkage process, including data discrepancies and varied reporting practices. Future studies will further explore these differences and incorporate additional toxicology data as part of a continued effort to fuze crash, citation, toxicology, and public health data. The end result will be a holistic, comprehensive, and multifaceted database for transportation research and education.

A successful linkage of a named patient products of sublingual immunotherapy-dispensing registry to French healthcare insurance database (SNDS): methodological constitution of the EfficAPSI cohort

ABSTRACT Background The only causal treatment for allergic rhinitis (AR) is allergen immunotherapy (AIT) including personalized liquid sublingual AIT (SLIT). We present the methodology for establishing the EfficAPSI cohort to further evaluate the real-life effectiveness and use of SLIT liquid. Research design and methods The EfficAPSI cohort was constituted by deterministic linkage of Stallergenes Greer dispensing and nationwide French healthcare insurance system (SNDS) databases. Data from 2006 to 2018 were extracted. All patients who initiated Stallergenes Greer SLIT liquid between 2010 and 2013 were considered as exposed and those dispensed with AR symptomatic treatment only as control. To limit the impact of confounding, the models will be weighted using the inverse probability of treatment weighting (IPTW). Results A total of 445,574 patients were included; median age was 38 years; 59.1% were female. Exposed patients (n = 112,492) were significantly younger, more frequently males, and less likely to have comorbidities than controls (n = 333,082). After IPTW, patients’ characteristics from both groups were similar. Conclusions To date, the EfficAPSI cohort has the largest number of person-years of follow-up in the field of AIT. The completeness of the data allows to evaluate SLIT liquid effectiveness with rigorous methodology, leading to important insights on personalized medicine in real-life.

The Association between Pediatric Readiness and Mortality for Injured Children Treated at US Trauma Centers.

To use updated 2021 weighted Pediatric Readiness Score (wPRS) data to identify a threshold level of trauma center emergency department (ED) pediatric readiness. Most children in the US receive initial trauma care at non-pediatric centers. The National Pediatric Readiness Project (NPRP) aims to ensure that all EDs are prepared to provide quality care for children. Trauma centers reporting the highest quartile of wPRS on the 2013 national assessment have been shown to have lower mortality. Significant efforts have been invested to improve pediatric readiness in the past decade. A retrospective cohort of trauma centers that completed the NPRP 2021 national assessment and contributed to the National Trauma Data Bank (NTDB) in 2019-21 was analyzed. Center-specific observed-to-expected mortality estimates for children (0-15y) were calculated using Pediatric TQIP models. Deterministic linkage was used for transferred patients to account for wPRS at the initial receiving center. Center-specific mortality odds ratios were then compared across quartiles of wPRS. 66,588 children from 630 centers with a median [IQR] wPRS of 79 [66-93] were analyzed. The average observed-to-expected odds of mortality (1.02 [0.97-1.06]) for centers in the highest quartile (wPRS≥93) was lower than any of the lowest three wPRS quartiles (1.19 [1.14-1.23](Q1), 1.29 [1.24-1.33](Q2), and 1.28 [1.19-1.36](Q3), all P <0.05). The presence of a pediatric-specific quality improvement plan was the domain with the strongest independent association with mortality (standardized beta -0.095 [-0.146--0.044]). Trauma centers should address gaps in pediatric readiness to include a pediatric-specific quality improvement plan and aim to achieve wPRS ≥93.