bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Shape of the Socioeconomic Gradient: Testing to Functional Form of the Relationship between Socioeconomic Status and Early Child Development
Authors: Webb Simon , Janus Magdalena , Duku Eric , Forer Barry , Minh Anita , Brownell Marni , Muhajarine Nazeem , Guhn Martin 
Published: September 2018

IntroductionThe literature provides abundant evidence of socioeconomic gradients in health outcomes. However, it is unclear, and particularly understudied in early child development research, whether these observed gradients are linear, whether they diminish as socioeconomic status (SES) increases, and if they ultimately reverse in slope at the highest SES values.
Objectives and ApproachWe linked neighbourhood-level Census and Tax Filer data with Early Development Instrument (EDI) data across Canada. The EDI is a kindergarten teacher-completed measure of five domains of early child development. We used this linked database to statistically compare and choose the most appropriate functional form of the relationship between each of the EDI domains (dependent variables), and the Canadian Neighbourhoods and Early Child Development (CanNECD) study's SES index (predictor) in regression models. Model comparison approaches included: visual checks of lines fitted using Generalized Additive Models, Akaike and Bayesian Information Criterions, Ramsay’s RESET, J and Cox tests.
ResultsThe results indicate the optimal functional form of the gradient varies across domains of the EDI. The best model for the Physical Health and Well-Being domain was quadratic, suggesting there may be some reversal in slope at higher values of SES. The best models for the Social Competence and Language and Cognitive Development domains were logarithmic, indicating diminishing returns to SES but with no slope reversal. The best model for the Emotional Maturity domain was linear, suggesting the gradient was consistent across all values of SES. The best fit for the Communication Skills and General Knowledge domain was a cubic ‘S’ curve, suggesting the curve is positive and concave for lower levels of SES but curves upwards beyond a certain SES threshold.
Conclusion/ImplicationsThe results demonstrate the importance of examining functional forms when modeling socioeconomic gradients. Assuming linear relationships between SES and health outcomes (early child development, in this case) may distort and bias the true nature of the relationships, thus leading to misinterpretations, especially at the highest and lowest values of SES.
Exploring public attitudes towards data sharing and linkage in health and social care
Authors: Hobbs Jo , Tully Mary , Ali Iqra 
Published: September 2018

IntroductionThe devolvement of health and social care in Greater Manchester, England provides the private and public sectors with a unique opportunity to build comprehensive information sharing agreements. Shared information governance is key to enabling the rapid sharing of data which will underpin our ability to radically transform population health.
Objectives and ApproachIn 2013 the UK Government introduced a data sharing opt-out programme called Care.data. This programme aimed to extract data at GP level and anonymise data which could then be shared amongst those inside and outside the NHS. This programme was greeted with widespread public mistrust and abandoned in 2016. Care.data demonstrated that public concerns can de-rail efforts to improve data sharing. The key objective for this piece of work was to use public engagement activities as a means of identifying and unpicking public concerns about data-linkage and sharing as part of wider efforts to establish a social license in health.
ResultsDiscursive responses were analysed using thematic analysis and word frequency analysis. Combining thematic analysis and word frequency analysis helped to unpack the data by facilitating the identification and perceived importance of broad themes and sub-themes.
To date, analysis has identified a number of ‘Global Themes’ and 'Sub-themes' which will need to be addressed before we can establish a social license in health and social care with the public:
Global Themes related to:

Trust/mistrust
Privacy and confidentiality/breaches of privacy and confidentiality

Sub-themes related to:

Security of systems
Rest of the world security
What a third party might do with health data
Data ownership
Health data being used for private and or commercial profit
How others perceive you
Access to future treatment

Conclusion/ImplicationsOur deliberative discussions indicated concerns across culture and ethnic groups that data-sharing and linkage might be used to ‘gate-keep’ future access to treatment. Given that congruent social licenses are built on legitimacy, credibility and trust further work is required to allay public fears regarding future access to treatment.
Incidence of First Abortions: Integration of Administrative and Survey Data within a Joint Cohort Life Table Model
Authors: Dutey-Magni Peter , Gilbert Ruth 
Published: September 2018

IntroductionIntegration of administrative and survey data to address sources of error is a fast-growing area of research. This paper examines the case of abortion, where survey data are susceptible to self-report bias, while administrative data provide crude but comprehensive and relatively unbiased information.
Objectives and ApproachAlthough abortion is a common and legal procedure, information is lacking on the proportion of women having one or more abortions during their lifetime. A Bayesian joint cohort life table model estimates age-specific rates of incidence of a first abortion for cohorts of women born between 1936 and 2003 an residing in England and Wales. The model is fitted using (1) waves II and III of the British National Surveys of Sexual Attitudes and Lifestyles (NATSAL) and (2) administrative counts of first ever abortions published by the UK's Office for National Statistics and Department of Health.
ResultsModel parameters controlling for underreporting indicate that survey reports are plausible for abortions occurring before the age of 20 years. Beyond that age, the model shows a fast increasing propensity to underreport abortions depending on the age at which they occurred. Underreporting also appears to be higher in NATSAL III. The study produces corrected estimates of the overall lifetime prevalence of an abortion in England and Wales, which is higher than previously thought.
Conclusion/ImplicationsJoint modelling of survey and administrative data can provide robust statistics, while reducing the need for record linkage where it is not feasible or acceptable. This approach is relevant in other contexts to correct the bias of particular population datasets, when audit data exist (e.g. underascertained diagnoses/causes of death).
Using linked health data in international comparisons of infectious disease burden
Authors: Reeves Rachel 
Published: September 2018

Linking population-level health databases – such as those on hospital admissions, GP consultations, prescriptions, maternal and perinatal data, and laboratory data – provides great opportunities to explore the epidemiology and burden of infectious diseases. Furthermore, comparing the epidemiology and burden of infectious diseases on an international scale is crucial in designing and implementing national and global prevention and control measures. However, substantial differences between countries in national health systems (including thresholds for hospital admission), as well as varying availability and quality of routinely collected data, can pose challenges when using linked population-level health databases to compare estimates of infectious disease burden between countries.
This session aims to highlight and discuss the opportunities and challenges of international comparisons of infectious disease burden using linked population-level health data. This session will facilitate discussion of the methodological, ethical and resource challenges when using linked health data to produce internationally comparable estimates of the burden of infectious diseases. We will use as an example the ongoing work of the REspiratory Syncytial virus Consortium in Europe (RESCEU) – a large-scale collaborative project producing evidence to inform policymaking and regulatory decisions on novel respiratory syncytial virus (RSV) vaccines and therapeutics. The RESCEU project involves at least seven European countries each using linked routinely collected health data to produce national estimates of the health and economic burden of RSV, by age and risk group, for comparison. The results will highlight target populations for future vaccines and therapeutics, and provide a baseline estimate of the pre-vaccine era burden of disease that can be used to measure future vaccine impact. We will share the challenges faced in the RESCEU project with regards to using linked health data in international comparative work. We will then discuss, with relevance to other ongoing or future projects, how these challenges may be overcome.
This session will generate ideas for procedures and tools for international comparative work using routinely collected data to investigate infectious diseases. This session will provide the opportunity to network with other researchers working in this area. We aim to facilitate the generation and dissemination of ideas for current and future projects, and therefore this session is likely to identify areas for potential future international collaborative work.
Data Linkage Methods in Manitoba
Authors: Turner Ken , Walld Randy , Derksen Shelley 
Published: September 2018

IntroductionAt the Manitoba Centre for Health Policy (MCHP), we have been performing data linkage for over 25 years. Over time, the Manitoba Population Research Data Repository (MPRDR) has expanded to over 80 datasets. Data linkage methods are key to bringing all this data together for population-based research.
Objectives and ApproachThe presentation will include a detailed description of the individual steps involved in the data linkage process and provide information about the methods developed and knowledge gained over time at MCHP. We will present different scenarios linking health, education, social and justice data and the choices that are made prior to and during data linkage. The data linkage process and linkage methods, including data validation techniques, will be illustrated with examples from our work.
ResultsThe presentation will describe the different types of data we have in the MPRDR and illustrate how the data are processed in a de-identified manner so that privacy and confidentiality are maintained.
The presentation will provide details on the data linkage methods used, dependent on the type of data sources being linked. This involves identifying and describing a 5-step data linkage process, including:

pre-processing (gaining knowledge about the data and cleaning/standardization techniques);
searching for and selecting the appropriate linkage variables;
applying different linkage techniques (e.g.: deterministic, probabilistic, “fuzzy matching” and manual review) to the data,
“rules” for deciding when data linkage should occur, and
reporting and Interpreting linkage outcome metrics and quality.

Conclusion/ImplicationsOur ability to link different data sources provides the capacity to study questions and complex issues related to health, social, education and justice from a population perspective. The techniques and methods described in this presentation should be applicable to other organizations linking administrative data.
Record linkage of pharmacovigilance and registration databases: a study of biological medicines in Brazil
Authors: Soares Monica , Moreira Antonio , Osorio-de-Castro Claudia , Coeli Claudia 
Published: September 2018

IntroductionBiologic medicines have revolutionized the treatment of many serious and chronic illnesses and they present important challenges for approval and post-market surveillance of drug safety. Pharmacovigilance is an important strategy for monitoring adverse drug events in the post-market. Drug registration is an essential element of medicine regulation.
Objectives and ApproachBrazilian Health Regulatory Agency, ANVISA, is responsible for drug registration and surveillance. In this work we describe the process of linkage of databases covering registration, post-approval changes, and pharmacovigilance of biological medicines in Brazil.
2.429,556 registration dossiers included in the comprehensive national drug registration database, DATAVISA, and 93,391 reports of adverse drug events in the pharmacovigilance database, NOTIVISA, were analyzed for completeness and non biologic dossiers were filtered out. Deterministic linkage technique was used to connect DATAVISA and NOTIVISA data.
ResultsOf the DATAVISA dossiers, 188,830 were registrations and 2,240,726 records were post-approval changes. After filtering out the non-biologic dossiers, 48,144 post-approval changes dossiers for biologic drugs were obtained. Aggregation of 48,144 post-approval biologic products dossiers resulted in 663 product-manufacturer pairs. For pharmacovigilance data, aggregation produced 5,874 pairs. Registration and post-approval changes merging from aggregated pairs resulted in 577 pairs. Trios with pharmacovigilance data resulted in 147 matches. These 147 pairs respond for 5.468 identified adverse drug events reports. Adverse drug events reports of biological medicines account for around 5,0 \% of NOTIVISA.
Conclusion/ImplicationsRegulatory actions should be implemented to stimulate adverse events reports of biological products. A single database with registration, post-approval changes and pharmacovigilance data was produced. Data linkage permitted analysis of biological medicines in the post-market and can provide important information to evaluate risk in drug development.
Improving the Coding Completeness of Hypertension in Inpatient Administrative Health Data Using Machine Learning Methods
Authors: D'Souza Adam , Liang Zhiyang , Williamson Tyler , Smith Tony , Quan Hude , Peng Mingkai 
Published: September 2018

IntroductionThe Discharge Abstract Database (DAD) associates ICD-10-CA diagnosis codes with inpatient care episodes at acute-care facilities. Codes are assigned by human coders, based on chart review. Coding guidelines stipulate mandatory coding of major and fatal conditions but only optional coding of secondary conditions, which results in undercoding for many conditions.
Objectives and ApproachThis research evaluates machine learning approaches for identifying and completing records with missing codes, to improve data quality. The Alberta Hospital DAD for 2013-14 was used in this study. We assumed that the existing ICD-10-CA codes in the DAD are correct, and used them as training examples. Several ML classifiers, including logistic regression and random forest, were used to develop models to assess the coding probability, using existing codes and demographic information. 3300 chart-review records were used as the reference standard. We focused on hypertension-related codes. Validity of raw diagnosis codes in the DAD was used as the baseline.
ResultsA record is deemed to have a missing hypertension diagnosis code if the predicted probability is high, but without the diagnosis codes having been assigned by the coders. In the baseline, the original hypertension codes have high PPV (ranging from 0.902 for the age group 35-54 to 1.000 for the age group 18-34) but low sensitivity (ranging from 0.200 for the age group 18-34 to 0.565 for the age group 75+). The most successful models that we have tested so far have provided improvements of 2-6% in the sensitivity, while maintaining the PPV. More improvement is generally seen for the younger age groups. Initial experiments indicate greater improvements in sensitivity may be possible for other conditions, such as peptic ulcer disease and cerebrovascular disease.
Conclusion/ImplicationsMachine learning approaches can be useful and cost-effective for improving data quality in DAD. While the improvements in sensitivity relative to the baseline are modest at present, further experiments with different models and feature sets are warranted. Experiments with other conditions may also be fruitful.
Inferring sensitivity and specificity of phenotyping algorithms using positive and negative predictive value in validation study in observational health data
Authors: Peng Mingkai , Gini Rosa , Williamson Tyler 
Published: September 2018

IntroductionIn observational health data, phenotyping algorithms are needed to process raw information into clinically relevant features. Validation studies traditionally estimate sensitivity and specificity by comparing the phenotyping algorithm with a reference standard on a population sample. There are challenges to conduct validation studies for conditions with low prevalence.
Objectives and ApproachWe propose a novel and efficient method for conducting validation studies to indirectly estimate the sensitivity and specificity. We simulated datasets with different levels of disease prevalence and phenotyping algorithms with different sensitivities and specificity. We applied both the traditional (direct) and new (indirect) method on simulated data to estimate the sensitivity and specificity and compare the performance of the two methods. We also designed a gate to exclude true negatives to improve study efficiency on conditions with low prevalence and sensitive analysis was conducted on the imperfect gate.
ResultsThe new (indirect) method provided better or comparable accuracy in estimating both sensitivity and specificity compared to the traditional (direct) method. Applying a gate enabled us to conduct validation study in conditions with very low prevalence. An imperfect gate results in the overestimation of sensitivity but has minimal effect on specificity.
Conclusion/ImplicationsThe new (indirect) method provides an alternative way to conduct validation studies in observational health data with improvement in estimating accuracy.
Welsh Government Flying Start Programme Evaluation Using Linked Data
Authors: Lowe Sarah , McGuinn Laura , Quintela Marcos , Player Luke 
Published: September 2018

IntroductionFlying Start (FS) is the Welsh Government’s (WG) flagship Early Years programme for families with children aged less than 4 years of age. Running since 2006, the four entitlements are:

Free part-time childcare for 2-3 year olds
Enhanced Health Visiting
Parenting support
Speech, language, and communication support

Objectives and ApproachCurrently, while we know which areas in Wales are receiving FS support, individual-level data on which child received what entitlements is not available. This means that area-level outcomes can be used as proxy indicators but the individual impact of receiving FS support cannot be examined. This project describes the issues around collecting individual-level data in deprived Welsh regions, a pilot project to facilitate this collection and will report some emerging findings from the analysis of pilot data.
ResultsFollowing from previous studies funded by the ESRC and WG (e.g. Supporting People Data Linking Feasibility Study), a Dataflow Development Project (DDP) has been launched to install Secure Anonymised Information Linkage (SAIL) appliances into 6 pilot Local Authorities in Wales. The SAIL appliance technology will provide Local Authorities with the ability to link individual-level data within a Local Authority or between Local Authorities and to securely anonymise data into the SAIL Databank or ADRC-W.
The DDP will test acquiring the individual level FS data from pilot Local Authorities into SAIL. The project aims to evaluate FS by linking the FS cohort to a range of outcomes including health, education and social care. We will report emerging findings from the analysis of pilot data.
Conclusion/ImplicationsThere is grow interest in using linked administrative data to evaluate government initiatives, and mounting enthusiasm in Local Government. If successful, this model is likely to be adopted by related WG programmes; improving the evidence base, facilitating effective evaluation, and adding to the data available for re-use in Wales.
How do we enhance linked administrative data based chronic disease surveillance in Canada? Results of an environmental scan.
Authors: Duchen Raquel , Lix Lisa , Reimer Kim , Widdifield Jessica , Butt Debra , Smith Mark , Katz Alan , Bartholomew Sharon , Toews Jennette , McRae Louise , Jaakkimainen Liisa 
Published: September 2018

IntroductionThe Canadian Chronic Disease Surveillance System (CCDSS) is a collaboration of provincial and territorial surveillance systems which generates estimates of chronic diseases using linked population-level administrative health databases and standard case definitions. We conducted an environmental scan of administrative data validation studies and identified opportunities for CCDSS case definition enhancement.
Objectives and ApproachThe purpose of this project is to develop a methodology for and conduct an environmental scan, identifying opportunities for enhancing the CCDSS. This multifaceted approach consists of the following elements: 1) key informant interviews and stakeholder consultations to identify new and existing priority conditions for updating/validating within the CCDSS, and new areas of conceptual and methodological relevance for administrative data disease surveillance, 2) a systematic literature review of PubMed, Ovid and Embase from 2013-2017 using MeSH terms and a librarian peer-reviewed search strategy, and 3) a review of the grey literature.
ResultsKey stakeholders identified the following priorities for validation work and/or case definition enhancement: diabetes, mood and anxiety disorders, schizophrenia, obesity, hypertension, chronic obstructive pulmonary disease, osteoarthritis, stroke, early-onset dementia, rheumatoid arthritis and gout. Scientific and grey literature reviews of validation work for these conditions examined the following concepts/methods: 1) evaluating validity of disease-specific case definitions over time, and in different ages, sub-populations and settings, 2) defining incidence versus prevalence using linked administrative data, 3) determining opportunities and constraints of using linked administrative data to conduct surveillance on diseases that are chronic versus episodic in nature and defining active versus lifetime prevalence, and 4) assessing the feasibility of using new sources of data for linkage to enhance case definition validity.
Conclusion/ImplicationsUtilization of linked administrative databases for chronic disease surveillance has expanded across many jurisdictions since the inception of the CCDSS. As disease estimates generated in this manner are increasingly being relied upon by policy makers working to enhance public health, the methodological opportunities and constraints identified here require consideration.