Health services research and data linkages: issues, methods, and directions for the future

Association Between Medicaid Coverage Continuity and Survival in Patients With Newly Diagnosed Pediatric and Adolescent Cancers

PURPOSE

Many patients with cancer do not gain Medicaid coverage until a cancer diagnosis, which can reduce access to early cancer detection and timely treatment, potentially driving inferior survival. Little is known about whether continuous Medicaid coverage prediagnosis through postdiagnosis (v gaining Medicaid at/after diagnosis) provides survival benefits for pediatric/adolescent oncology patients.

MATERIALS AND METHODS

We identified patients newly diagnosed with cancer at age 21 years or younger in a large pediatric health system between 2007 and 2016. Electronic medical records (EMRs) were linked to Medicaid administrative data to differentiate insurance continuity patterns during the 6 months preceding through the 6 months after cancer diagnosis (assessment window): continuous Medicaid, newly gained Medicaid (at or after diagnosis), and other Medicaid enrollment patterns. For patients not linked to Medicaid data, we used EMR-reported insurance types at diagnosis. We followed patients from 6 months postdiagnosis up to 5 years, death, or December 2020, whichever came first. Multivariable regressions estimated all-cause and cancer-specific survival, controlling for sociodemographic and cancer-related factors.

RESULTS

Among 1,800 patients included in the analysis, 1,293 (71.8%) had some Medicaid enrollment during the assessment window; among them, 47.6% had continuous Medicaid and 36.3% had newly gained Medicaid. Patients not linked with Medicaid data had private (26.9%) or other/no insurance (1.2%) at diagnosis. Compared with patients with continuous Medicaid, those with newly gained Medicaid had higher risks of all-cause death (hazard ratio [HR], 1.41 [95% CI, 1.10 to 1.81]; P = .008) and cancer-specific death (HR, 1.46 [95% CI, 1.12 to 1.90]; P = .005).

CONCLUSION

Continuous Medicaid coverage throughout cancer diagnosis is associated with survival benefits for pediatric/adolescent patients. This finding has critical implications as millions of American individuals have been losing coverage since the unwinding of the Medicaid Continuous Enrollment Provision.

Ambulance quality and outcome measures for general non-conveyed populations (AQUA): A scoping review

BACKGROUND

An increasing number of patients receive ambulance care without being conveyed to a definitive care provider. This process has been described as complex, challenging, and lacking in guideline support by EMS clinicians. The use of quality- and outcome measures among non-conveyed patients is an understudied phenomenon.

AIM

To identify current quality- and outcome measures for the general population of non-conveyed patients in order to describe major trends and knowledge gaps.

METHODS

A scoping review of peer-reviewed original articles was conducted to identify quality- and outcome measures for non-conveyance within emergency medical services. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement (PRISMA-ScR) was followed. The PROSPERO and OSF database were checked for pending reviews or protocols. PubMed, CINAHL, Scopus, Web of Science and the Cochrane Library database were searched for relevant articles. Searches were performed in November 2023.

RESULTS

Thirty-six studies fulfilled the inclusion criteria and were included in the review. Mortality was the most used outcome measure, reported in 24 (67%) of the articles. Emergency department attendance and hospital admission were the following most used outcome measures. Follow-up durations varied substantially between both measures and studies. Mortality rates were found to have the longest follow-up times, with a median follow-up duration a little bit over one week.

CONCLUSIONS

This scoping review shows that studies report a wide range of quality and outcome measures in the ambulance setting to measure non-conveyance. Reported quality and outcome measures were also heterogeneous with regard to their follow-up timeframe. The variety of approaches to evaluate non-conveyance poses challenges for future research and quality improvement. A more uniform approach to reporting and measuring non-conveyance is needed to enable comparisons between contexts and formal meta-analysis.

Case study on communicating with research ethics committees about minimizing risk through software: an application for record linkage in secondary data analysis

Objective

In retrospective secondary data analysis studies, researchers often seek waiver of consent from institutional Review Boards (IRB) and minimize risk by utilizing complex software. Yet, little is known about the perspectives of IRB experts on these approaches. To facilitate effective communication about risk mitigation strategies using software, we conducted two studies with IRB experts to co-create appropriate language when describing a software to IRBs.

Materials and Methods

We conducted structured focus groups with IRB experts to solicit ideas on questions regarding benefits, risks, and informational needs. Based on these results, we developed a template IRB application and template responses for a generic study using privacy-enhancing software. We then conducted a three-round Delphi study to refine the template IRB application and the template responses based on expert panel feedback. To facilitate participants' deliberation, we shared the revisions and a summary of participants' feedback during each Delphi round.

Results

11 experts in two focus groups generated 13 ideas on risks, benefits, and informational needs. 17 experts participated in the Delphi study with 13 completing all rounds. Most agreed that privacy-enhancing software will minimize risk, but regardless all secondary data studies have an inherent risk of unexpected disclosures. The majority (84.6%) noted that subjects in retrospective secondary data studies experience no greater risks than the risks experienced in ordinary life in the modern digital society. Hence, all retrospective data-only studies with no contact with subjects would be minimal risk studies.

Conclusion

First, we found fundamental disagreements in how some IRB experts view risks in secondary data research. Such disagreements are consequential because they can affect determination outcomes and might suggest IRBs at different institutions might come to different conclusions regarding similar study protocols. Second, the highest ranked risks and benefits of privacy-enhancing software in our study were societal rather than individual. The highest ranked benefits were facilitating more research and promoting responsible data governance practices. The highest ranked risks were risk of invalid results from systematic user error or erroneous algorithms. These societal considerations are typically more characteristic of public health ethics as opposed to the bioethical approach of research ethics, possibly reflecting the difficulty applying a bioethical approach (eg, informed consent) in secondary data studies. Finally, the development of privacy-enhancing technology for secondary data research depends on effective communication and collaboration between the privacy experts and technology developers. Privacy is a complex issue that requires a holistic approach that is best addressed through privacy-by-design principles. Privacy expert participation is important yet often neglected in this design process. This study suggests best practice strategies for engaging the privacy community through co-developing companion documents for software through participatory design to facilitate transparency and communication. In this case study, the final template IRB application and responses we released with the open-source software can be easily adapted by researchers to better communicate with their IRB when using the software. This can help increase responsible data governance practices when many software developers are not research ethics experts.

Medicare Data Linkages for Conducting Patient-Centered Outcomes Research on Economic Outcomes

BACKGROUND

Medicare patients and other stakeholders often make health care decisions that have economic consequences. Research on economic variables that patients have identified as important is referred to as patient-centered outcomes research (PCOR) and can generate evidence that informs decision-making. Medicare fee-for-service (FFS) claims are widely used for research and are a potentially valuable resource for studying some economic variables, particularly when linked to other datasets.

OBJECTIVE

The aim of this study was to identify and assess the characteristics of federally funded administrative and survey data sources that can be linked to Medicare claims for conducting PCOR on some economic outcomes.

RESEARCH DESIGN

A targeted internet search was conducted to identify a list of relevant data sources. A technical panel and key informant interviews were used for guidance and feedback.

RESULTS

We identified 12 survey and 6 administrative sources of linked data for Medicare FFS beneficiaries. A majority provide longitudinal data and are updated annually. All linked sources provide some data on social determinants of health and health equity-related factors. Fifteen sources capture direct medical costs (beyond Medicare FFS payments); 5 capture indirect costs (eg, lost wages from absenteeism), and 7 capture direct nonmedical costs (eg, transportation).

CONCLUSIONS

Linking Medicare FFS claims data to other federally funded data sources can facilitate research on some economic outcomes for PCOR. However, few sources capture direct nonmedical or indirect costs. Expanding linkages to include additional data sources, and reducing barriers to existing data sources, remain important objectives for increasing high-quality, patient-centered economic research.

Linking health survey data with health insurance data: methodology, challenges, opportunities and recommendations for public health research. An experience from the HISlink project in Belgium

In recent years, the linkage of survey data to health administrative data has increased. This offers new opportunities for research into the use of health services and public health. Building on the HISlink use case, the linkage of Belgian Health Interview Survey (BHIS) data and Belgian Compulsory Health Insurance (BCHI) data, this paper provides an overview of the practical implementation of linking data, the outcomes in terms of a linked dataset and of the studies conducted as well as the lessons learned and recommendations for future links.Individual BHIS 2013 and 2018 data was linked to BCHI data using the national register number. The overall linkage rate was 92.3% and 94.2% for HISlink 2013 and HISlink 2018, respectively. Linked BHIS-BCHI data were used in validation studies (e.g. self-reported breast cancer screening; chronic diseases, polypharmacy), in policy-driven research (e.g., mediation effect of health literacy in the relationship between socioeconomic status and health related outcomes, and in longitudinal study (e.g. identifying predictors of nursing home admission among older BHIS participants). The linkage of both data sources combines their strengths but does not overcome all weaknesses.The availability of a national register number was an asset for HISlink. Policy-makers and researchers must take initiatives to find a better balance between the right to privacy of respondents and society's right to evidence-based information to improve health. Researchers should be aware that the procedures necessary to implement a link may have an impact on the timeliness of their research. Although some aspects of HISlink are specific to the Belgian context, we believe that some lessons learned are useful in an international context, especially for other European Union member states that collect similar data.

Framework for Evaluating and Developing Sustainable Telehealth Programs

During the COVID-19 pandemic and public health emergency, telehealth programs vastly expanded with strong support from various federal and state agencies. However, the uncertainty regarding future reimbursement policies for telehealth services has resulted in concerns about long-term sustainability of innovative health service delivery models beyond the financial support. Given the limited literature on creating telehealth programs with long-term sustainability in consideration, we have developed a framework for gathering appropriate data during various stages of program implementation to evaluate clinical effectiveness and economic sustainability that is applicable across various settings, with additional attention to health equity. Recognizing the difficulty of sustaining telehealth programs solely through a fee-for-service payment model, we encourage all telehealth stakeholders, especially payers and policymakers, to consider cost-effectiveness of telehealth programs and support alternate payment models for ensuring long-term sustainability.

A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information - A merging of two administrative data sources

OBJECTIVE

Despite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders.

METHODS

Data from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims.

RESULTS

A total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition.

CONCLUSION

The integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.

Ethics and legal requirements for data linkage in 14 European countries for children with congenital anomalies

INTRODUCTION

Linking healthcare data sets can create valuable resources for research, particularly when investigating rare exposures or outcomes. However, across Europe, the permissions processes required to access data can be complex. This paper documents the processes required by the EUROlinkCAT study investigators to research the health and survival of children with congenital anomalies in Europe.

METHODS

Eighteen congenital anomaly registries in 14 countries provided information on all the permissions required to perform surveillance of congenital anomalies and to link their data on live births with available vital statistics and healthcare databases for research. Small number restrictions imposed by data providers were also documented.

RESULTS

The permissions requirements varied substantially, with certain registries able to conduct congenital anomaly surveillance as part of national or regional healthcare provision, while others were required to obtain ethics approvals or informed consent. Data linkage and analysis for research purposes added additional layers of complexity for registries, with some required to obtain several permissions, including ethics approvals to link the data. Restrictions relating to small numbers often resulted in a registry's data on specific congenital anomalies being unusable.

CONCLUSION

The permissions required to obtain and link data on children with congenital anomalies varied greatly across Europe. The variation and complexity present a significant obstacle to the use of such data, especially in large data linkage projects. Furthermore, small number restrictions severely limited the research that could be performed for children with specific rare congenital anomalies.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables

OBJECTIVES

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

METHODS

We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.

RESULTS

We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).

CONCLUSIONS

The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Gestational weight gain in triplet pregnancies in the United States

BACKGROUND

The Institute of Medicine has published national recommendations for optimal pregnancy weight gain ranges for singletons and twins but not for higher-order multiples. A common clinical resource suggests weight gain targets for triplet pregnancies, but they are based on a single, small study conducted over 20 years ago.

OBJECTIVE

We sought to describe contemporary maternal weight gain patterns in triplet gestations in the United States, the weight gain patterns associated with good neonatal outcomes, and how these patterns compare with those of healthy twin pregnancies.

STUDY DESIGN

We used data from 7705 triplet pregnancies drawn from the United States live birth and fetal death files (2012‒2018). We calculated total pregnancy weight gain as weight at delivery minus the prepregnancy weight. A good neonatal outcome was defined as delivery at ≥32 weeks' gestation of 3 liveborn infants weighing ≥1500 g with 5-minute Apgar scores of ≥3. We described the weight gain patterns of triplet pregnancies with good neonatal outcomes by calculating week-specific percentiles of the total weight gain distribution for deliveries at 32 to 37 weeks' gestation. For comparative purposes, we plotted these values against the percentiles of a previously published weight gain chart for monitoring and evaluating twin pregnancies from a referent cohort.

RESULTS

Most participants were over weight (26%) or obese (30%), and 42% were normal weight or underweight. The 50th percentile (25th-75th) of total weight gain in triplet pregnancies was 17 (11-23) kg. As the body mass index category increased, the total weight gain declined: underweight or normal weight, median 19 (14-25) kg; overweight, 17 (12-23) kg; obese, 14 (7.7-20) kg. Approximately 46% of triplet pregnancies had a good neonatal outcome (n=3562). For underweight or normal weight triplet pregnancies with good neonatal outcomes, the 50th percentiles of weight gain at 32 weeks' and 36 weeks' gestation were 12.3 kg and 22.7 kg, respectively. The 10th and 90th percentiles were 12.3 kg and 32.7 kg, respectively, at 32 weeks, and 15.0 kg and 34.1 kg, respectively, at 36 weeks. Triplet pregnancies with prepregnancy overweight or obesity and a good neonatal outcome had lower weight gains. Compared with the reference values for pregnancy weight gain from a twin-specific weight gain chart, the median total weight gain in triplet pregnancies with good neonatal outcomes was approximately 3 to 5 kg more than twins, regardless of body mass index.

CONCLUSION

Our study fills an important gap in understanding how much weight gain can be expected among triplet pregnancies by body mass index category. These descriptive data are a necessary first step to inform science-based triplet gestational weight gain guidelines. Additional research is needed to determine whether monitoring triplet pregnancy weight gain is useful for promoting healthy outcomes for pregnant individuals and children and what targets should be used to optimize maternal and neonatal health.

A Methodology to Create Mother-Baby Dyads Using Data From the Pediatric Health Information System

BACKGROUND AND OBJECTIVES

Linking newborn birth records with maternal delivery data is invaluable in perinatal research, though linkage is often challenging or impossible in the context of administrative data. Using data from the Pediatric Health Information System (PHIS), we describe a novel methodology to link maternal delivery data with newborn birth hospitalization records to form mother-baby dyads.

METHODS

We extracted singleton birth discharges and maternal delivery discharges between 2016 and 2020 from hospitals submitting large volumes of maternal delivery discharges and newborn deliveries into PHIS. Birth discharges at these PHIS hospitals included routine births and those requiring specialty care. Newborn discharges were matched to maternal discharges within hospital by date of birth, mode of delivery, and ZIP code.

RESULTS

We identified a matching maternal discharge for 92.1% of newborn discharges (n = 84 593/91 809). Within-hospital match rates ranged from 87.4% to 93.9%. Within the matched cohort, most newborns were normal birth weight (91.2%) and term (61.2%) or early term (27.4%). A total of 88.8% of newborns had birth stays less than 5 days and 14.2% were admitted to the NICU.

CONCLUSIONS

We demonstrate the feasibility of deterministically linking maternal deliveries to newborn discharges forming mother-baby dyads with a high degree of success using data from PHIS. The matched cohort may be used to study a variety of neonatal conditions that are likely to be affected by maternal demographic or clinical factors at delivery. Validation of this methodology is an important next step and area of future work.

Mapping the Research on Health Policy and Services in the Last Decade (2009-2018): A Bibliometric Analysis

Background

Health policy and services is a continuously evolving field of research that can inform prevention and control efforts for a variety of health conditions. The "Healthy China" strategy reflects the demand to formulate health policy that suits China's national needs and goals. Applying bibliometric analysis to grasp the general situation of health policy and services research globally will be conducive to informing China's designated health plans and initiatives.

Method

A bibliometric analysis of 58,065 articles on "Health Policy and Services" topics was conducted. The document type was restricted to journal articles that were published in the Web of Science database between the time parameter of January 1, 2009 to December 31, 2018. Data was collected on indicators such as the annual number of publications in the field of health policy and services, the country where the publication is issued, the publication organization, the source journal, the frequency of citations, research hotspots, and academic areas.

Results

The overall number of articles published in Web of Science on health policy and services research has increased over time. The United States has the largest number of articles in the field. The institution with the highest number of citations in the field is Harvard University and the journal with the most published articles in the field is Health Affairs. Research hotspots in the health policy and services field include topics such as "HIV Infections," "Primary Health Care," "Delivery of Health Care," and "Health Services Accessibility."

Conclusion

Experts in the field of health policy and services globally are dedicated to researching the most effective ways to improve people's health and living standards. There is a certain gap in the depth of health policy and services research between China and developed countries and regions such as Europe or America. China must learn from foreign experience to conduct meaningful and informative research that can aid in the formulation of multi-dimensional health policies in specific areas such as environmental infectious diseases, where attention is needed in areas beyond the medical and health system.

Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias

Pharmacoepidemiologic studies are increasingly conducted within linked databases, often to obtain richer confounder data. However, the potential for selection bias is frequently overlooked when linked data is available only for a subset of patients. We highlight the importance of accounting for potential selection bias by evaluating the association between antipsychotics and type 2 diabetes in youths within a claims database linked to a smaller laboratory database. We used inverse probability of treatment weights (IPTW) to control for confounding. In analyses restricted to the linked cohorts, we applied inverse probability of selection weights (IPSW) to create a population representative of the full cohort. We used pooled logistic regression weighted by IPTW only or IPTW and IPSW to estimate treatment effects. Metabolic conditions were more prevalent in linked cohorts compared with the full cohort. Within the full cohort, the confounding-adjusted hazard ratio was 2.26 (95% CI: 2.07, 2.49) comparing initiation of antipsychotics with initiation of control medications. Within the linked cohorts, a different magnitude of association was obtained without adjustment for selection, whereas applying IPSW resulted in point estimates similar to the full cohort's (e.g., an adjusted hazard ratio of 1.63 became 2.12). Linked database studies may generate biased estimates without proper adjustment for potential selection bias.

Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques

BACKGROUND

The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods.

METHOD

We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents.

RESULTS

We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations.

CONCLUSIONS

This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.

Best Practices in Large Database Clinical Epidemiology Research in Hepatology: Barriers and Opportunities

With advances in computing and information technology, large health care research databases are becoming increasingly accessible to investigators across the world. These rich, population-level data sources can serve many purposes, such as to generate "real-world evidence," to enhance disease phenotyping, or to identify unmet clinical needs, among others. This is of particular relevance to the study of patients with end-stage liver disease (ESLD), a socioeconomically and clinically heterogeneous population that is frequently under-represented in clinical trials. This review describes the recommended "best practices" in the execution, reporting, and interpretation of large database clinical epidemiology research in hepatology. The advantages and limitations of selected data sources are reviewed, as well as important concepts on data linkages. The appropriate classification of exposures and outcomes is addressed, and the strategies needed to overcome limitations of the data and minimize bias are explained as they pertain to patients with ESLD and/or liver transplantation (LT) recipients. Lastly, selected statistical concepts are reviewed, from model building to analytic decision making and hypothesis testing. The purpose of this review is to provide the practical insights and knowledge needed to ensure successful and impactful research using large clinical databases in the modern era and advance the study of ESLD and LT.

Linking Data From the Australian Stroke Clinical Registry With Ambulance and Emergency Administrative Data in Victoria

Objective: In Australia, approximately 3 in 4 people with acute stroke use an ambulance. Few examples of merging ambulance clinical records, hospital government data, and national registry data for stroke exist. We sought to understand the advantages of using linked datasets for describing the full clinical journey of people with stroke and the possibility of investigating their long-term outcomes based on pre-hospital management of stroke. Method: Patient-level data from the Australian Stroke Clinical Registry (AuSCR) (January 2013-October 2017) were linked with Ambulance Victoria (AV) records and Victorian Emergency Minimum Dataset (VEMD). Probabilistic iterative matching on personal identifiers were used and records merged with a project specific identification number. Results: Of the 7,373 episodes in the AuSCR and 6,001 in the AV dataset; 4,569 (62%) were matched. Unmatched records that were positive for “arrival by ambulance” in the AuSCR and VEMD (no corresponding record in AV) were submitted to AV. AV were able to identify 148/435 additional records related to these episodes. The final cohort included 4,717 records (median age: 73 years, female 42%, ischemic stroke 66%). Conclusion: The results of the data linkage provides greater confidence for use of these data for future research related to pre-hospital management of stroke.

Teamwork in clinical genomics: A dynamic sociotechnical healthcare setting

RATIONALE, AIMS AND OBJECTIVES

Teamworking across sociotechnical boundaries in healthcare is growing as technological advances in medicine abound. With this progress, teams need to find new ways of working together in non-traditional settings. The novel field of clinical genomics provides the opportunity to rethink the existing approach to teamworking and how it needs to evolve. Our aim was to identify the key factors influencing teamworking in the emerging field of clinical genomics and how can they be applied in practice.

METHOD

We drew on three qualitative datasets from interviews undertaken in Australia, 2018/2019, that explored determinants of implementation of clinical genomics with laboratory scientists (n = 7), service and programme leads (n = 21), project officers (n = 2), clinical genetics staff (n = 26) and other medical specialists (n = 21). Data were analysed using a theory-informed matrix approach to identify themes related to teamworking.

RESULTS

We identify that teams in clinical genomics work in an elongated adaptive context where there is rapid evolution of the knowledge base, shifting expectations of staff roles, and fast changes of technology. Delivering care in this setting brings additional challenges to teamworking as members strive to stay abreast of current knowledge and technology. We identify four themes: (a) the role of the team in keeping knowledge up-to-date; (b) professional identity; (c) team adaptability, and (d) practical/organisational considerations.

CONCLUSION

Challenges to teamworking that arise in the elongated adaptive context do not always fit traditional ways of working, and innovative strategies will need to be adopted to ensure the diagnostic advances of clinical genomics are realised. Provision of time and permission for team members to share knowledge and evolve, promoting capacity building, nurturing trustful relationships and establishing boundaries are amongst the practice recommendations for organisational and team leaders, even though these activities may disrupt existing ways of working or hierarchical structures.

Challenges in evaluating risks and policy options around endemic establishment or elimination of novel pathogens

When a novel pathogen emerges there may be opportunities to eliminate transmission - locally or globally - whilst case numbers are low. However, the effort required to push a disease to elimination may come at a vast cost at a time when uncertainty is high. Models currently inform policy discussions on this question, but there are a number of open challenges, particularly given unknown aspects of the pathogen biology, the effectiveness and feasibility of interventions, and the intersecting political, economic, sociological and behavioural complexities for a novel pathogen. In this overview, we detail how models might identify directions for better leveraging or expanding the scope of data available on the pathogen trajectory, for bounding the theoretical context of emergence relative to prospects for elimination, and for framing the larger economic, behavioural and social context that will influence policy decisions and the pathogen’s outcome.

Chronic disease surveillance in Alberta's tomorrow project using administrative health data

Introduction

Alberta’s Tomorrow Project (ATP) is the largest population-based prospective cohort study of cancer and chronic diseases in Alberta, Canada. The ATP cohort data were primarily self-reported by participants on lifestyle behaviors and disease risk factors at the enrollment, which lacks sufficient and accurate data on chronic disease diagnosis for longer-term follow-up.

Objectives

To characterize the occurrence rate and trend of chronic diseases in the ATP cohort by linking with administrative healthcare data.

Methods

A set of validated algorithms using ICD codes were applied to Alberta Health (AH) administrative data (October 2000-March 2018) linked to the ATP cohort to determine the prevalence and incidence of common chronic diseases.

Results

There were 52,770 ATP participants (51.2±9.4 years old at enrollment and 63.7% females) linked to the AH data with average follow-up of 10.1±4.4 years. In the ATP cohort, hypertension (18.5%), depression (18.1%), chronic pain (12.8%), osteoarthritis (10.1%) and cardiovascular diseases (8.7%) were the most prevalent chronic conditions. The incidence rates varied across diseases, with the highest rates for hypertension (22.1 per 1000 person-year), osteoarthritis (16.2 per 1000 person-year) and ischemic heart diseases (13.0 per 1000 person-year). All chronic conditions had increased prevalence over time (p < for trend tests), while incidence rates were relatively stable. The proportion of participants with two or more of these conditions (multi-morbidity) increased from 3.9% in 2001 to 40.3% in 2017.

Conclusions

This study shows an increasing trend of chronic diseases in the ATP cohort, particularly related to cardiovascular diseases and multi-morbidity. Using administrative health data to monitor chronic diseases for large population-based prospective cohort studies is feasible in Alberta, and our approach could be further applied in a broader research area, including health services research, to enhance research capacity of these population-based studies in Canada.

Record linkage of population-based cohort data from minors with national register data: a scoping review and comparative legal analysis of four European countries

Background: The GDPR was implemented to build an overarching framework for personal data protection across the EU/EEA. Linkage of data directly collected from cohort participants, potentially serving as a prominent tool for health research, must respect data protection rules and privacy rights. Our objective was to investigate law possibilities of linking cohort data of minors with routinely collected education and health data comparing EU/EEA member states. Methods: A legal comparative analysis and scoping review was conducted of openly accessible published laws and regulations in EUR-Lex and national law databases on GDPR's implementation in Portugal, Finland, Norway, and the Netherlands and its connected national regulations purposing record linkage for health research that have been implemented up until April 30, 2021. Results: The GDPR does not ensure total uniformity in data protection legislation across member states offering flexibility for national legislation. Exceptions to process personal data, e.g., public interest and scientific research, must be laid down in EU/EEA or national law. Differences in national interpretation caused obstacles in cross-national research and record linkage: Portugal requires written consent and ethical approval; Finland allows linkage mostly without consent through the national Social and Health Data Permit Authority; Norway when based on regional ethics committee's approval and adequate information technology safeguarding confidentiality; the Netherlands mainly bases linkage on the opt-out system and Data Protection Impact Assessment. Conclusions: Though the GDPR is the most important legal framework, national legislation execution matters most when linking cohort data with routinely collected health and education data. As national interpretation varies, legal intervention balancing individual right to informational self-determination and public good is gravely needed for health research. More harmonization across EU/EEA could be helpful but should not be detrimental in those member states which already opened a leeway for registries and research for the public good without explicit consent.

Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France

BACKGROUND

The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years.

METHODS

We selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm.

RESULTS

The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66-0.68].

CONCLUSIONS

Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.

Using administrative health data for palliative and end of life care research in Ireland: potential and challenges

Background: This study aims to examine the potential of currently available administrative health and social care data for palliative and end-of-life care (PEoLC) research in Ireland. Objectives include to i) identify data sources for PEoLC research ii) describe the challenges and opportunities of using these and iii) evaluate the impact of recent health system reforms and changes to data protection laws.

Methods: The 2017 Health Information and Quality Authority catalogue of health and social care datasets was cross-referenced with a recognised list of diseases with associated palliative care needs. Criteria to assess the datasets included population coverage, data collected, data dictionary and data model availability, and mechanisms for data access.

Results: Nine datasets with potential for PEoLC research were identified, including death certificate data, hospital episode data, pharmacy claims data,  one national survey, four disease registries (cancer, cystic fibrosis, motor neurone and interstitial lung disease) and a national renal transplant registry.  The ad hoc development of the health system in Ireland has resulted in i) a fragmented information infrastructure resulting in gaps in data collections particularly in the primary and community care sector where much palliative care is delivered, ii) ill-defined data governance arrangements across service providers, many of whom are not part of the publically funded health service and iii) systemic and temporal issues that affect data quality. Initiatives to improve data collections include introduction of i) patient unique identifiers, ii) health entity identifiers and iii) integration of the Eircode postcodes. Recently enacted general data protection and health research regulations will clarify legal and ethical requirements for data use.

Conclusions: Ongoing reform initiatives and recent changes to data privacy laws combined with detailed knowledge of the datasets, appropriate permissions, and good study design will facilitate future use of administrative health and social care data for PEoLC research in Ireland.

Linkage of public health and all payer claims data for population-level opioid research

OBJECTIVE

Our objective is to describe how we combine, at an individual level, multiple administrative datasets to create a Comprehensive Opioid Risk Registry (CORR). The CORR will characterize the role that individual characteristics, household characteristics, and community characteristics have on an individual's risk of opioid use disorder or opioid overdose.

DATA SOURCES

Study data sources include the voluntary Oregon All Payer Claims Database (APCD), American Community Survey Census Data, Oregon Death Certificate data, Oregon Hospital Discharge Data (HDD), and Oregon Prescription Drug Monitoring (PDMP) Data in 2013-2018.

STUDY DESIGN

To create the CORR we first prepared the APCD data set by cleaning and geocoding addresses, creating a community grouper and adding census indices, creating household grouper, and imputing patient race. Then we deployed a probabilistic linkage methodology to incorporate other data sources maintaining compliance with strict data governance regulations.

DATA COLLECTION/EXTRACTION METHODS

Administrative datasets were obtained through an executed data use agreement with each data owner. The APCD served as the population universe to which all other data sources were linked.

PRINCIPAL FINDINGS

There were 3 628 992 unique people in the APCD over the entire study period. We identified 968 767 unique households in 2013 and 1 209 236 in 2018, and geocoded patient addresses representing all census tracts in Oregon. Census, death certificate, HDD, and PDMP datasets were successfully linked to this population universe.

CONCLUSIONS

This methodology can be replicated in other states and may also apply to a broad array of health services research topics.

Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data

BACKGROUND

Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative.

OBJECTIVES

To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs).

METHODS

Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm.

RESULTS

Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV.

CONCLUSION

The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore

Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000-2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists.

Using linked administrative health data for palliative and end of life care research in Ireland: potential and challenges

Background: This study aims to examine the potential of currently available administrative health data for palliative and end-of-life care (PEoLC) research in Ireland. Objectives include to i) identify administrative health data sources for PEoLC research ii) describe the challenges and opportunities of using these and iii) estimate  the impact of recent health system reforms and changes to data protection laws.  Methods: The 2017 Health Information and Quality Authority catalogue of health and social care datasets was cross-referenced with a recognised list of diseases with associated palliative care needs. Criteria to assess the datasets included population coverage, data collected, data dictionary and data model availability and mechanisms for data access. Results: Eight datasets with potential for PEoLC research were identified, including four disease registries, (cancer, cystic fibrosis, motor neurone and interstitial lung disease), death certificate data, hospital episode data, community prescription data and one national survey. The ad hoc development of the health system in Ireland has resulted in i) a fragmented information infrastructure resulting in gaps in data collections particularly in the primary and community care sector where much palliative care is delivered, ii) ill-defined data governance arrangements across service providers, many of whom are not part of the publically funded health service and iii) systemic and temporal issues that affect data quality. Initiatives to improve data collections include introduction of i) patient unique identifiers, ii) health entity identifiers and iii) integration of the eircode postcodes. Recently enacted general data protection and health research regulations will clarify legal and ethical requirements for data  use. Conclusions: With appropriate permissions, detailed knowledge of the datasets and good study design currently available administrative health data can be used for PEoLC research. Ongoing reform initiatives and recent changes to data privacy laws will facilitate future use of administrative health data for PEoLC research.

Characterization of bariatric surgery and outcomes using administrative claims data in the research network of a nationwide commercial health plan

Background

The supplementation of electronic health records data with administrative claims data may be used to capture outcome events more comprehensively in longitudinal observational studies. This study investigated the utility of administrative claims data to identify outcomes across health systems using a comparative effectiveness study of different types of bariatric surgery as a model.

Methods

This observational cohort study identified patients who had bariatric surgery between 2007 and 2015 within the HealthCore Anthem Research Network (HCARN) database in the National Patient-Centered Clinical Research Network (PCORnet) common data model. Patients whose procedures were performed in a member facility affiliated with PCORnet Clinical Research Networks (CRNs) were selected. The outcomes included a 30-day composite adverse event (including venous thromboembolism, percutaneous/operative intervention, failure to discharge and death), and all-cause hospitalization, abdominal operation or intervention, and in-hospital death up to 5 years after the procedure. Outcomes were classified as occurring within or outside PCORnet CRN health systems using facility identifiers.

Results

We identified 4899 patients who had bariatric surgery in one of the PCORnet CRN health systems. For 30-day composite adverse event, the inclusion of HCARN multi-site claims data marginally increased the incidence rate based only on HCARN single-site claims data for PCORnet CRNs from 3.9 to 4.2%. During the 5-year follow-up period, 56.8% of all-cause hospitalizations, 31.2% abdominal operations or interventions, and 32.3% of in-hospital deaths occurred outside PCORnet CRNs. Incidence rates (events per 100 patient-years) were significantly lower when based on claims from a single PCORnet CRN only compared to using claims from all health systems in the HCARN: all-cause hospitalization, 11.0 (95% Confidence Internal [CI]: 10.4, 11.6) to 25.3 (95% CI: 24.4, 26.3); abdominal operations or interventions, 4.2 (95% CI: 3.9, 4.6) to 6.1 (95% CI: 5.7, 6.6); in-hospital death, 0.2 (95% CI: 0.11, 0.27) to 0.3 (95% CI: 0.19, 0.38).

Conclusions

Short-term inclusion of multi-site claims data only marginally increased the incidence rate computed from single-site claims data alone. Longer-term follow up captured a notable number of events outside of PCORnet CRNs. The findings suggest that supplementing claims data improves the outcome ascertainment in longitudinal observational comparative effectiveness studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06074-3.

Building a Data Linkage Foundation for Mother-Child Pharmacoepidemiology Research

BACKGROUND

Expanding our understanding of the effects of maternal medication exposure through research is a public health priority and will help inform both clinical and policy decision making, ultimately improving outcomes for pregnant women and their children.

OBJECTIVE

Our objective was to describe a linked-data research platform that facilitates studies of pregnancy medication exposures and policy changes on maternal and child health outcomes.

METHODS

Mothers receiving Medicaid benefits were probabilistically linked with newborns in the Tennessee Medicaid program (TennCare) through three distinct linkage processes. Medicaid claims data and state birth and fetal death certificate records (vital records) were used to identify and link potential mothers, deliveries, and newborn children. The linkage process started with the creation of a merged pool of potential mothers and eligible deliveries, which was linked to vital records and to children's records. In the last step, linked records from the preceding steps were combined into the final Mother-child linked records. For each data linkage step, rubrics and scoring systems for exact and partial matches and mismatches among key linkage fields were applied and used to examine the strength of the probabilistic linkages. Summary linkage yields for year 2013 are reported for illustration purposes.

RESULTS

Among the 84,253 potential deliveries, 1,761,557 eligible potential mothers, and 51,400 eligible children identified in Tennessee Medicaid records in 2013, a total of 60,265 of these records were uniquely linked to vital records, including 46,172 (77%) with linked mother-child-vital records. Among the 51,400 eligible children records identified in Tennessee Medicaid for that year, 97% (50,053) had at least one link to vital records or a mother-delivery record. In linked records, the median maternal age was 24 years, and the median gestational age was 39 weeks. About 33% of pregnant women underwent cesarean birth, and 1% of births were classified as complicated deliveries.

CONCLUSIONS

Supplementing existing Medicaid claims data with birth certificate records complements administrative claims information and allows for detailed assessments of pregnancy exposures and policy changes on mother and child outcomes.

Methodology for linking Ryan White HIV/AIDS Program Services Report (RSR) client level data over multiple years

BACKGROUND

The Health Resources and Services Administration's (HRSA), HIV/AIDS Bureau (HAB) is responsible for leading the nation's efforts to provide health care, medications, and support services to low-income people living with HIV through the Ryan White HIV/AIDS Program (RWHAP). The RWHAP funds and coordinates with cities, states, and local community-based organizations to deliver efficient and effective HIV care, treatment, and support services for over half a million vulnerable people living with HIV (PLWH) and their families in the United States. The annual RWHAP Services Report (RSR) is an important source of information for monitoring RWHAP's progress towards National HIV/AIDS Strategy goals. Since 2010, HRSA HAB has used the annual client-level RSR data to monitor program-related outcomes, conduct program evaluations, understand service provision, and conduct extensive analysis on disparities in viral suppression and retention in HIV care. HRSA HAB receives annual RSR submissions from RWHAP recipients and sub-recipients. However, the de-identified nature of the data limits HRSA HAB's ability to expand beyond year-to-year analyses and conduct additional analyses to evaluate outcomes for clients who are seen in multiple years. The current paper describes the development and validation of a method to link RSR client-level records across multiple data years.

METHODS AND FINDINGS

Using seven RSR reporting years of data (2010 to 2016), we applied a Fellegi-Sunter (F-S) linkage model that used client demographic characteristics and their providers' geographic locations to calculate matching weights for each record pair based on estimated agreement and disagreement conditional probabilities across RSR years. To validate our methodology, we conducted an internal sample review and external validation to assess the level of accuracy of the linkage, and the extent to which the linked data set corresponds accurately to clinical records of individual clients. The linkage result yielded 70 to 80 percent year-to-year client carry-over rate over seven years of the RSR data; 96 percent linkage ratio from the internal sample review and 79.9 to 94.2 percent of provider network client carry- over rate per year from the external validation.

CONCLUSIONS

This methodology addresses a gap in data analysis capabilities by allowing HRSA HAB to link RWHAP clients across reporting years. Despite weak identifying information and lack of continuity of service reporting, the longitudinal linkage improves HRSA HAB's ability to evaluate the patterns of viral suppression and monitor service utilization over time for individuals who receive services in multiple years. These analyses will support future analytic activities in understanding the impact and outcomes of the RWHAP, and will assist HRSA HAB in monitoring progress toward meeting National HIV/AIDS Strategy goals. For those looking for ways to assess health services data, the F-S unsupervised method combining weak identifying attributes and geographic proximity offers practical solutions to the problem of linking de-identified information about individuals across multiple years and improving longitudinal research.

A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

BACKGROUND

There is significant global policy interest related to enabling a data-driven approach for evidence-based primary care system development. This paper describes the development and initial testing of a prototype tool (the Problem-Oriented Primary Care System Development Record, or PCSDR) that enables a data-driven and contextualized approach to primary care system development.

METHODS

The PCSDR is an electronic record that enables the systematic input, classification, structuring, storage, processing and analysis of different types of data related to the structure, function and performance of primary care systems over time. Data inputted into the PCSDR was coded using the WHO's PHC-IMPACT framework and classification system. The PCSDR's functionalities were tested by using a case study of primary care system development in Tajikistan.

RESULTS

Tajikistan's case study demonstrated that the PCSDR is a potentially effective and conceptually-sound tool for the input, classification, structuring and storage of different data types from myriad sources. The PCSDR is therefore a basic data entry and data management system that enables query and analytics functions for health services research and evidence-based primary care system development functions.

CONCLUSIONS

The PCSDR is a data system that enables a contextualized approach to evidence-based primary care system development. It represents a coherent and effective synthesis of the fields of primary care system development and performance assessment. The PCSDR enables analysts to leverage primary care performance assessment frameworks for a broad range of functions related to health systems analysis, improvement and the development of learning health systems.

Linkage of the CHHiP randomised controlled trial with primary care data: a study investigating ways of supplementing cancer trials and improving evidence-based practice

BACKGROUND

Randomised controlled trials (RCTs) are the gold standard for evidence-based practice. However, RCTs can have limitations. For example, translation of findings into practice can be limited by design features, such as inclusion criteria, not accurately reflecting clinical populations. In addition, it is expensive to recruit and follow-up participants in RCTs. Linkage with routinely collected data could offer a cost-effective way to enhance the conduct and generalisability of RCTs. The aim of this study is to investigate how primary care data can support RCTs.

METHODS

Secondary analysis following linkage of two datasets: 1) multicentre CHHiP radiotherapy trial (ISRCTN97182923) and 2) primary care database from the Royal College of General Practitioners Research and Surveillance Centre. Comorbidities and medications recorded in CHHiP at baseline, and radiotherapy-related toxicity recorded in CHHiP over time were compared with primary care records. The association of comorbidities and medications with toxicity was analysed with mixed-effects logistic regression.

RESULTS

Primary care records were extracted for 106 out of 2811 CHHiP participants recruited from sites in England (median age 70, range 44 to 82). Complementary information included longitudinal body mass index, blood pressure and cholesterol, as well as baseline smoking and alcohol usage but was limited by the considerable missing data. In the linked sample, 9 (8%) participants were recorded in CHHiP as having a history of diabetes and 38 (36%) hypertension, whereas primary care records indicated incidence prior to trial entry of 11 (10%) and 40 (38%) respectively. Concomitant medications were not collected in CHHiP but available in primary care records. This indicated that 44 (41.5%) men took aspirin, 65 (61.3%) statins, 14 (13.2%) metformin and 46 (43.4%) phosphodiesterase-5-inhibitors at some point before or after trial entry.

CONCLUSIONS

We provide a set of recommendations on linkage and supplementation of trials. Data recorded in primary care are a rich resource and linkage could provide near real-time information to supplement trials and an efficient and cost-effective mechanism for long-term follow-up. In addition, standardised primary care data extracts could form part of RCT recruitment and conduct. However, this is at present limited by the variable quality and fragmentation of primary care data.

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

Background

The availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources.

Method

We performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI).

Results

The use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research.

Conclusions

Our results highlight that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.

Big Data in Cancer Research: Real-World Resources for Precision Oncology to Improve Cancer Care Delivery

In oncology, the term "big data" broadly describes the rapid acquisition and generation of massive amounts of information, typically from population cancer registries, electronic health records, or large-scale genetic sequencing studies. The challenge of using big data in cancer research lies in interdisciplinary collaboration and information processing to unify diverse data sources and provide valid analytics to harness meaningful information. This article provides an overview of how big data approaches can be applied in cancer research, and how they can be used to translate information into new ways to ultimately make informed decisions that improve cancer care and delivery.

Role of health plan administrative claims data in participant recruitment for pragmatic clinical trials: An Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE) example

Aim:

The purpose of this study is to evaluate HealthCore/Anthem Research Network recruitment strategies, compare response and enrollment rates for different recruitment strategies, and describe demographic and clinical characteristics of responders and enrollees.

Methods:

HealthCore/Anthem Research Network, a part of the Health Plan Research Network of the Patient-Centered Clinical Data Research Network, used administrative claims data to identify eligible health plan members for potential participation in the Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-term Effectiveness study. We approached health plan members, identified with a validated Patient-Centered Clinical Data Research Network common data model computable phenotype, and their clinical providers during November 2017 to August 2018. Providers were offered the option to exclude their patients’ participation in Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-term Effectiveness prior to our direct patient (member) outreach. Member identification was in two phases: Phase 1: 1 January 2006 to 1 April 2017, and Phase 2: 1 January 2006 to 2 February 2018. Phase 1 consisted of two batches of mail and one phone call per patient. In Phase 2, which included two similar batches of patients, outreach was via either mail or brochure and one phone call.

Results:

Phase 1 and Phase 2 included 133,373 and 51,777 members, respectively. We engaged 28,593 providers in Phase 1, and 5077 in Phase 2. In Phase 1, 264,158 mixed email/mail messages were delivered to 133,373 members, followed by 90,481 phone calls from November 2017 to February 2018. In Phase 2, after simple randomization to letter or brochure, 51,777 members were sent email/mail or mailed brochure in three waves from May 2018 to July 2018. In this 9-week period, 51,623 communications were sent to 25,914 members in the email/mail group, and 50,160 brochures to 25,863 in the brochure group. Following email/mail or mailed brochure outreach, 16,624 and 16,580 calls were made to the groups, respectively. Overall, 1549 health plan members visited the study portal by 1 September 2018; 355 electronically signed the Informed Consent Form and enrolled. Mailed brochures drove more portal visits in Phase 2, but a lower percentage of responders enrolled. Recruitment was better in Phase 2—2.3 enrollees per 1000 outreach members versus 1.8 in Phase 1.

Conclusion:

This study showed the ability of a health plan within Patient-Centered Clinical Data Research Network to identify potential study participants with administrative claims, and use different outreach methods to facilitate recruitment and enrollment for pragmatic clinical trials.

A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

Unlocking the potential of population-based cancer registries

Population-based cancer registries have improved dramatically over the last 2 decades. These central cancer registries provide a critical framework that can elevate the science of cancer research. There have also been important technical and scientific advances that help to unlock the potential of population-based cancer registries. These advances include improvements in probabilistic record linkage, refinements in natural language processing, the ability to perform genomic sequencing on formalin-fixed, paraffin-embedded (FFPE) tissue, and improvements in the ability to identify activity levels of many different signaling molecules in FFPE tissue. This article describes how central cancer registries can provide a population-based sample frame that will lead to studies with strong external validity, how central cancer registries can link with public and private health insurance claims to obtain complete treatment information, how central cancer registries can use informatics techniques to provide population-based rapid case ascertainment, how central cancer registries can serve as a population-based virtual tissue repository, and how population-based cancer registries are essential for guiding the implementation of evidence-based interventions and measuring changes in the cancer burden after the implementation of these interventions.

Unlocking the potential of population-based cancer registries

Population-based cancer registries have improved dramatically over the last 2 decades. These central cancer registries provide a critical framework that can elevate the science of cancer research. There have also been important technical and scientific advances that help to unlock the potential of population-based cancer registries. These advances include improvements in probabilistic record linkage, refinements in natural language processing, the ability to perform genomic sequencing on formalin-fixed, paraffin-embedded (FFPE) tissue, and improvements in the ability to identify activity levels of many different signaling molecules in FFPE tissue. This article describes how central cancer registries can provide a population-based sample frame that will lead to studies with strong external validity, how central cancer registries can link with public and private health insurance claims to obtain complete treatment information, how central cancer registries can use informatics techniques to provide population-based rapid case ascertainment, how central cancer registries can serve as a population-based virtual tissue repository, and how population-based cancer registries are essential for guiding the implementation of evidence-based interventions and measuring changes in the cancer burden after the implementation of these interventions.

Consumer-Mediated Data Exchange for Research: Current State of US Law, Technology, and Trust

A compendium of US laws and regulations offers increasingly strong support for the concept that researchers can acquire the electronic health record data that their studies need directly from the study participants using technologies and processes called consumer-mediated data exchange. This data acquisition method is particularly valuable for studies that need complete longitudinal electronic records for all their study participants who individually and collectively receive care from multiple providers in the United States. In such studies, it is logistically infeasible for the researcher to receive necessary data directly from each provider, including providers who may not have the capability, capacity, or interest in supporting research. This paper is a tutorial to inform the researcher who faces these data acquisition challenges about the opportunities offered by consumer-mediated data exchange. It outlines 2 approaches and reviews the current state of provider- and consumer-facing technologies that are necessary to support each approach. For one approach, the technology is developed and estimated to be widely available but could raise trust concerns among research organizations or their institutional review boards because of the current state of US law applicable to consumer-facing technologies. For the other approach, which does not elicit the same trust concerns, the necessary technology is emerging and a pilot is underway. After reading this paper, the researcher who has not been following these developments should have a good understanding of the legal, regulatory, technology, and trust issues surrounding consumer-mediated data exchange for research, with an awareness of what is potentially possible now, what is not possible now, and what could change in the future. The researcher interested in trying consumer-mediated data exchange will also be able to anticipate and respond to an anticipated barrier: the trust concerns that their own organizations could raise.

Administrative claims data to support pragmatic clinical trial outcome ascertainment on cardiovascular health

BACKGROUND/AIMS

Health plan administrative claims data present a cost-effective complement to traditional trial-specific ascertainment of clinical events typically conducted through patient report or a single health system electronic health record. We aim to demonstrate the value of health plan claims data in improving the capture of endpoints in longitudinal pragmatic clinical trials.

METHODS

This retrospective cohort study paralleled the design of the ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness) trial designed to compare the effectiveness of two doses of aspirin. We applied the ADAPTABLE identification query in claims data from Anthem, an American health insurance company, and identified health plan members who met the ADAPTABLE trial criteria. Among the ADAPTABLE eligible members, we selected overlapping members with PCORnet Clinical Data Research Networks in the 2 years prior to the index date (1 April 2014). PCORnet Clinical Data Research Networks consist of network partners (or healthcare systems) that store their electronic health record data in the same format to support multi-institutional research. ADAPTABLE outcome events-cardiovascular hospitalizations including admissions for myocardial infarction, stroke, or cardiac procedures; hospitalizations for major bleeding; and in-hospital deaths-were evaluated for a 2-year follow-up period. Events were classified as within or outside PCORnet Clinical Data Research Networks using facility identifiers affiliated with each hospital stay. Patient characteristics were examined with descriptive statistics, and incidence rates were reported for available Clinical Data Research Networks and claims data.

RESULTS

Among 884,311 ADAPTABLE eligible health plan members, 11,101 patients overlapped with PCORnet Clinical Data Research Networks. Average age was 70 years, 71% were male, and average follow-up was 20.7 months. Patients had 1521 cardiovascular hospitalizations (571 (37.5%) occurred outside PCORnet Clinical Data Research Networks), 710 for major bleeding (296 (41.7%) outside PCORnet Clinical Data Research Networks), and 196 in-hospital deaths (67 (34.2%) outside PCORnet Clinical Data Research Networks). Incidence rates (events per1000 patient-months) differed between available network partners and claims data: cardiovascular hospitalizations, 4.1 (95% confidence interval: 3.9, 4.4) versus 6.6 (95% confidence interval: 6.3, 7.0), major bleeding, 1.8 (95% confidence interval: 1.6, 2.0) versus 3.1 (95% confidence interval: 2.9, 3.3), and in-hospital death, 0.56 (95% confidence interval: 0.47, 0.67) versus 0.85 (95% confidence interval: 0.74, 0.98), respectively.

CONCLUSION

This study demonstrated the value of supplementing longitudinal site-based clinical studies with administrative claims data. Our results suggest that claims data together with network partner electronic health record data constitute an effective vehicle to capture patient outcomes since >30% of patients have non-fatal and fatal events outside of enrolling sites.

Identifying the Contribution of Nurse Practitioners in the Care of Older Adults With Cancer

OBJECTIVES

To identify the best available dataset that measured the number of nurse practitioners (NPs) and the type of care they provided; patient information, including malignancy type, age, and insurance status; and volume of procedures performed by NPs.

SAMPLE & SETTING

All available national datasets that included patients with cancer and provider types.

METHODS & VARIABLES

Using prespecified consensus-driven criteria, all available administrative datasets were reviewed. The authors evaluated four that met the inclusion criteria.

RESULTS

The authors' analysis identified the Surveillance, Epidemiology, and End Results (SEER) Program linked with Medicare claims dataset as the most appropriate to measure the contribution of NP-provided cancer care to older adults. The Chronic Conditions Data Warehouse was excluded because of the limited number of malignancies included in the data; the SEER-Medicare dataset included all malignancies.

IMPLICATIONS FOR NURSING

Evidence demonstrates that NPs provide an unknown amount of cancer care to older adults. Further research using the SEER-Medicare dataset may yield a solution to the health issue of insufficient oncologists to care for the growing older adult population. Workforce research informs future training needs and influences policymakers' decisions, making secondary data analyses in nursing particularly important.

Creating the first national linked dataset on perinatal and maternal outcomes in Australia: Methods and challenges

BACKGROUND

Data linkage offers a powerful mechanism for examining healthcare outcomes across populations and can generate substantial robust datasets using routinely collected electronic data. However, it presents methodological challenges, especially in Australia where eight separate states and territories maintain health datasets. This study used linked data to investigate perinatal and maternal outcomes in relation to place of birth. It examined data from all eight jurisdictions regarding births planned in hospitals, birth centres and at home. Data linkage enabled the first Australia-wide dataset on birth outcomes. However, jurisdictional differences in data collection created challenges in obtaining comparable cohorts of women with similar low-risk pregnancies in all birth settings. The objective of this paper is to describe the techniques for managing previously linked data, and specifically for ensuring the resulting dataset contained only low-risk pregnancies.

METHODS

This paper indicates the procedures for preparing and merging linked perinatal, inpatient and mortality data from different sources, providing technical guidance to address challenges arising in linked data study designs.

RESULTS

We combined data from eight jurisdictions linking four collections of administrative healthcare and civil registration data. The merging process ensured that variables were consistent, compatible and relevant to study aims. To generate comparable cohorts for all three birth settings, we developed increasingly complex strategies to ensure that the dataset eliminated women with pregnancies at risk of complications during labour and birth. It was then possible to compare birth outcomes for comparable samples, enabling specific examination of the impact of birth setting on maternal and infant safety across Australia.

CONCLUSIONS

Data linkage is a valuable resource to enhance knowledge about birth outcomes from different settings, notwithstanding methodological challenges. Researchers can develop and share practical techniques to address these challenges. Study findings suggest that jurisdictions develop more consistent data collections to facilitate future data linkage.

Building Linked Big Data for Stroke in Korea: Linkage of Stroke Registry and National Health Insurance Claims Data

BACKGROUND

Linkage of public healthcare data is useful in stroke research because patients may visit different sectors of the health system before, during, and after stroke. Therefore, we aimed to establish high-quality big data on stroke in Korea by linking acute stroke registry and national health claim databases.

METHODS

Acute stroke patients (n = 65,311) with claim data suitable for linkage were included in the Clinical Research Center for Stroke (CRCS) registry during 2006-2014. We linked the CRCS registry with national health claim databases in the Health Insurance Review and Assessment Service (HIRA). Linkage was performed using 6 common variables: birth date, gender, provider identification, receiving year and number, and statement serial number in the benefit claim statement. For matched records, linkage accuracy was evaluated using differences between hospital visiting date in the CRCS registry and the commencement date for health insurance care in HIRA.

RESULTS

Of 65,311 CRCS cases, 64,634 were matched to HIRA cases (match rate, 99.0%). The proportion of true matches was 94.4% (n = 61,017) in the matched data. Among true matches (mean age 66.4 years; men 58.4%), the median National Institutes of Health Stroke Scale score was 3 (interquartile range 1-7). When comparing baseline characteristics between true matches and false matches, no substantial difference was observed for any variable.

CONCLUSION

We could establish big data on stroke by linking CRCS registry and HIRA records, using claims data without personal identifiers. We plan to conduct national stroke research and improve stroke care using the linked big database.

Quality and management care improvement of patients with chronic kidney disease: from data analysis to the definition of a targeted clinical pathway in an Italian Region

BACKGROUND

Clinical Diagnostic Care Pathways (CDCP) are management tools widespread throughout the world to improve the quality of patient care through a well-organized care continuum and to enhance the patient's "risk-adjusted" outcomes; indeed they could optimize the management of resources. They are particularly effective in the management of patients with chronic degenerative diseases, such as chronic kidney disease, with increasingly incidence and prevalence, with an estimated 11-13% of the population being affected. The aim of this study is to apply the Health Services Research methods to estimate the relationship between need, demand and supply in patients with stage 5 Chronic Kidney Disease (CKD) for, then to describe the definition of a CDCP dedicated to patients in Lazio Region, so to allow an appropriate patient management, to reduce the likely complications and the patients' migration to facilities outside the region.

METHODS

The study was conducted in 2017 in collaboration between the National Institute of Health, the University of Messina and the S. Giovanni Addolorata Hospital.

RESULTS

We analyzed the data for the CKD in Roma and in the San Giovanni Addolorata Hospital Trust and we found a drop out in the patients' attendance towards other regions and/or hospitals. So we defined a CDCP to be adopted at the San Giovanni Addolorata hospital.

CONCLUSIONS

To define management and care tools to provide adequate, efficient and patient centered care is a nowadays "must", to ensure the sustainability of the Italian NHS, which today is comparable to a "ship that is heading towards a perfect storm".

Social Disadvantage, Healthcare Utilization, and Colorectal Cancer Screening: Leveraging Longitudinal Patient Address and Health Records Data

BACKGROUND

Social disadvantage predicts colorectal cancer outcomes across the cancer care continuum for many populations and places. For medically underserved populations, social disadvantage is likely intersectional-affecting individuals at multiple levels and through membership in multiple disadvantaged groups. However, most measures of social disadvantage are cross-sectional and limited to race, ethnicity, and income. Linkages between electronic health records (EHR) and external datasets offer rich, multilevel measures that may be more informative.

METHODS

We identified urban safety-net patients eligible and due for colorectal cancer screening from the Parkland-UT Southwestern PROSPR cohort. We assessed one-time screening receipt (via colonoscopy or fecal immunochemical test) in the 18 months following cohort entry via the EHR. We linked EHR data to housing and Census data to generate measures of social disadvantage at the parcel- and block-group level. We evaluated the association of these measures with screening using multilevel logistic regression models controlling for sociodemographics, comorbidity, and healthcare utilization.

RESULTS

Among 32,965 patients, 45.1% received screening. In adjusted models, residential mobility, residence type, and neighborhood majority race were associated with colorectal cancer screening. Nearly all measures of patient-level social disadvantage and healthcare utilization were significant.

CONCLUSIONS

Address-based linkage of EHRs to external datasets may have the potential to expand meaningful measurement of multilevel social disadvantage. Researchers should strive to use granular, specific data in investigations of social disadvantage.

IMPACT

Generating multilevel measures of social disadvantage through address-based linkages efficiently uses existing EHR data for applied, population-level research.