Affect Data Quality Research Articles

A cardioimmunologist's toolkit: genetic tools to dissect immune cells in cardiac disease.

Cardioimmunology is a field that encompasses the immune cells and pathways that modulate cardiac function in homeostasis and regulate the temporal balance between tissue injury and repair in disease. Over the past two decades, genetic fate mapping and high-dimensional sequencing techniques have defined increasing functional heterogeneity of innate and adaptive immune cell populations in the heart and other organs, revealing a complexity not previously appreciated and challenging established frameworks for the immune system. Given these rapid advances, understanding how to use these tools has become crucial. However, cardiovascular biologists without immunological expertise might not be aware of the strengths and caveats of immune-related tools and how they can be applied to examine the pathogenesis of myocardial diseases. In this Review, we guide readers through case-based examples to demonstrate how tool selection can affect data quality and interpretation and we provide critical analysis of the experimental tools that are currently available, focusing on their use in models of ischaemic heart injury and heart failure. The goal is to increase the use of relevant immunological tools and strategies among cardiovascular researchers to improve the precision, translatability and consistency of future studies of immune cells in cardiac disease.

Bayesian Cloud Detection over Land for Climate Data Records

Cloud detection is a necessary step in the generation of land surface temperature (LST) climate data records (CDRs) and affects data quality and uncertainty. We present here a sensor-independent Bayesian cloud detection algorithm and show that it is suitable for use in the production of LST CDRs. We evaluate the performance of the cloud detection with reference to two manually masked datasets for the Advanced Along-Track Scanning Radiometer (AATSR) and find a 7.9% increase in the hit rate and 4.9% decrease in the false alarm rate when compared to the operational cloud mask. We then apply the algorithm to four instruments aboard polar-orbiting satellites, which together can produce a global, 25-year LST CDR: the second Along-Track Scanning Radiometer (ATSR-2), AATSR, the Moderate Resolution Spectroradiometer (MODIS Terra) and the Sea and Land Surface Temperature Radiometer (SLSTR-A). The Bayesian cloud detection hit rate is assessed with respect to in situ ceilometer measurements for periods of overlap between sensors. The consistency of the hit rate is assessed between sensors, with mean differences in the cloud hit rate of 4.5% for ATSR-2 vs. AATSR, 4.9% for AATSR vs. MODIS, and 2.5% for MODIS vs. SLSTR-A. This is important because consistent cloud detection performance is needed for the observational stability of a CDR. The application of a sensor-independent cloud detection scheme in the production of CDRs is thus shown to be a viable approach to achieving LST observational stability over time.

Adjoint dynamical kernel density for anomaly detection

Anomalies affect data quality and lead to unexpected analysis results in data mining. Although techniques to handle anomalies do exist, they can fail in considering density changes of object along with incremental neighbors. This paper proposes an outlier factor based on the change of adjoint dynamical kernel density (ADD) to represent the degree of the object being an anomaly. The factor is equal to the ratios of the adjoint dynamical kernel density fluctuation (ADDF) of the object and the average ADDF of its neighborhood. ADDF is estimated by the difference of ADD, which describes the difference of every two consecutive adjoint kernel densities (AKD) of the object. AKD indicates kernel densities of the object while its neighbors are added one by one. Importantly, the kernel function is adopted to measure the distance between objects where the kernel trick improves discriminability between objects and reduces the computational burden of the algorithm. The experiments are performed on eight datasets to evaluate the effectiveness of the proposed method with different kernel functions. The experimental results have shown that the proposed method with the Gaussian kernel function has better performance of anomalies recognition and higher adaption of the parameter k of k-nearest neighbors over some other anomaly detection methods.

Are You…? Asking Questions on Sex with a Third Category in Germany

A question asking for respondents’ sex is one of the standard sociodemographic characteristics collected in a survey. Until now, it typically consisted of a simple question (e.g., “Are you…?”) with two answer categories (“male” and “female”). In 2019, Germany implemented the additional sex designation divers for intersex people. In survey methodology, this has led to an ongoing discussion how to include a third category in questionnaires. We investigate respondents’ understanding of the third category, and whether introducing it affects data quality. Moreover, we investigate the understanding of the German term Geschlecht for sex and gender. To answer our research questions, we implemented different question wordings asking for sex/gender in a non-probability-based online panel in Germany and combined them with open-ended questions. Findings and implications for surveys are discussed.

The need to establish a national vascular surgery registry.

Madam, The integration of information technology (IT) in healthcare and data has resulted in significant medical research advancement. The International Consortium of Vascular Registries is one such example where registries from several countries contribute to the development in vascular surgery via cross-border collaborations.1 Similar successful examples from other fields include the Karachi Cancer registry and Pakistan National joint Registry in our country. 2-3 While Pakistan bears a high burden of vascular diseases, the centres offering state-of-the-art services are limited.4 The lack of accurate statistics makes it more challenging to predict the disease course and its relationship with geography, its complications, and outcomes. Inadequacy of follow-up and poor reporting of patients’ functional status also become barriers to optimal care. The utilisation of a Vascular Registry can gather information regarding all such variables. It makes it possible to analyse data in a large patient population by collecting prospective data routinely.5 This is of great importance in improving the standards of vascular surgery at a national level. Even with long-term follow-up, future studies miss rare incidences and other critical information. However, scientific analysis is more feasible in sufficiently large data sets, such as those found in registries. Such datasets can also be utilised to generate new hypotheses and design more impactful studies.5,6 However, the trend of manual data entry and limited reliance on IT provide a setback to registry development in our country. This adversely affects data quality, and essential information may go missing. There is an intra- and inter-hospital lack of uniformity in reporting, and thus, the development of standardised guidelines becomes pertinent. The digitalisation of information via establishing a national registry will surely aid in this process. This process seems more feasible with the use of computerised national identity cards which are being widely used in patient registrations in major hospitals. Maintaining files in both hard and soft copies is time-consuming until a complete transition to electronic data storage is possible. This calls for taking up the task of computerisation at a rapid pace. A robust software system needs to be used that is user-friendly and poses no threats of security breach. We believe that a collaborative effort by the community's vascular surgeons to build a national registry will result in data centralisation and help in managing resources and improve the quality of care. --Continue

Ponderous, Proficient or Professional? Survey Experience and Smartphone Effects in Stated Preference Research

Stated preference surveys are increasingly conducted online using internet panel samples, where a fast-growing share of respondents answer on smartphones. These panel members range from novices to “professionals” in terms of previous survey experience. Little is known about the potential effects of smartphone responding and survey experience on the data quality of stated preference surveys. This paper uses a discrete choice experiment dataset on the Norwegian population’s willingness to pay to plant climate forests to explore how these two factors affect data quality. These data by type of response device, gathered using a probability-based internet panel, were combined with a unique dataset obtained from the survey company on respondents’ actual experience answering surveys on different types of devices. Our results show that differences in elicited preferences between smartphone and computer respondents are not caused by the device used, suggesting that initial concerns about smartphone responses may be exaggerated. Furthermore, more experience is associated with an increasing scale parameter (indicating lower error variance), but at a decreasing rate; and a higher propensity to choose the status quo (indicating possible simplifying strategies employed by respondents). Combined this suggest some optimal level of experience that is neither too high nor too low. We discuss the implications of our results for stated preference research and provide a few avenues for future research.

Factors affecting data quality of health management information system at township level, Bago region, Myanmar

Background: Health information from Health management information system (HMIS) is the core essential operator for strengthening the health system. The effectiveness of the Myanmar health system is challenged by the poor-quality assurance of healthcare data. Methods: The aim of the quantitative study was to evaluate the township HMIS to assess the factors affecting the data quality assurance through three main aspects; organizational, technical, and behavioral. In this cross-sectional study, eight townships from four districts in Bago Region were randomly picked. Under these townships, from a random sample of 117 public health facilities altogether, 273 public health professionals (PHPs) were culled and 291 HMIS registers and 1270 HMIS monthly reports were reviewed. The researchers applied the PRISM tools developed for assessing district and facility HMIS. SPSS assisted the researchers in computing the frequencies and percentages, practicing cross-tabulation, and analyzing bivariate statistics using the Cox proportional hazards model.Results: Out of 281 PHPs invited, 273 were likely to participate in this study. The overall prevalence of the HMIS data quality was 30.4%. Poor data quality assurance was associated with the burden of workload (95%CI-1.16-2.91), poor management ability of the supervisors (95%CI-1.22-2.54), weak handover practice of the HMIS document (95%CI-1.65-2.22), and unavailability of HMIS resources (95%CI-1.12-2.45). The statistically significant relationships were found between low-quality data and some technical factors such as inexpertness for data analysis (95%CI-1.14-2.19), over-workload of paper-based HMIS (95%CI-1.21-2.44), differences between information systems (95%CI-1.22-2.81), and multiple reporting (95%CI-1.64-2.36). There were significant associations between the unacceptable data quality and the human factors such as lower scores of perceived confidences (95%CI-1.18-2.29), competence (95%CI-1.17-2.77), and promotion of the culture of information (95%CI-1.09-2.33).Conclusions: Current township HMIS data quality is unacceptable. It is necessary to strengthen several factors relating to organization, technology and behaviors of HMIS and to develop the effective township-level strategic plan for improving data quality.

Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation.

Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study.

BackgroundThere is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions.ObjectiveThis study aims to identify data quality dimensions for person-generated wearable device data for research purposes.MethodsThis study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers’ perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions.ResultsIn total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance–related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes.ConclusionsIn this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed.

Level and contributing factors of health data quality and information use in two districts in Northwest Ethiopia: social-ecological perspective

BackgroundThe health management information system has been implemented at all levels of healthcare delivery to ensure quality data production and information use in Ethiopia. Including the capacity-building activities and provision of infrastructure, various efforts have been made to improve the production and use of quality health data though the result is still unsatisfactory. This study aimed to examine the quality of health data and use in Wogera and Tach-Armacheho districts and understand its barriers and facilitators.MethodsThe study utilized a mixed-method; for the quantitative approach, institution-based cross-sectional study was conducted to determine the quality of health data and use by 95 departments in the two districts. The qualitative approach involved 16 in-depth interviewees from Wogera district. A descriptive Phenomenological design was used to explore factors influencing the quality and use of health data. The quantitative data were expressed descriptively with tables, graphs, and percent whereas the qualitative data were analyzed with content analysis guided by the social-ecological model framework.ResultThe average levels of information use for Wogera and Tach-Armacheho districts were estimated at 29 and 35.9, respectively. The overall average level of accuracy of reports for six different health services in the HCs of Wogera and Tach Armacheho districts were 0.95 and 0.86, respectively.The qualitatively identified factors that influence the production and use of quality health data include valuing data, getting staff training, being a patriotic staff, and getting supportive supervision, were identified from individual-level characteristics; similarly, coaching, supportive supervision, and peer-to-peer learning from relational/interpersonal level characteristics, and organizational culture, incentive, infrastructure establishing accountability, and staff turnover, were identified from organizational level characteristics.ConclusionThe quality of data and routine information utilization was low and were influenced by a number of actors presented in and around the health system including individual, interpersonal, and organizational characteristics. Incentive affects data quality and information use directly or indirectly after modifying factors at all levels of the social-ecological model. Therefore, interventions should gear towards addressing multiple social-ecological factors of the health system concomitantly or intervene on incentive which has a multifaceted effect on the outcome.

Challenges to transparency involving intellectual property and privacy concerns in life cycle assessment/costing: A case study of new flame retarded polymers

This work explores the challenges of using life cycle assessment (LCA) and life cycle cost (LCC) analysis to provide easily accessible decision support for early product development in cases where intellectual property (IP) and privacy issues require special consideration. Innovation research projects with partners representing different links along the value chain are potential examples of such cases. A case study in which spreadsheet-based cradle to compounder's gate LCA and LCC screening tools were created for candidate flame retarded polymer formulations exemplifies the need for better solutions to overcome problems associated with lack of transparency due to IP/privacy concerns. These problems affect data quality, scaling up processes, and uncertainty of the results. The consortium in this case study had a common overall goal, although each of the partners had a unique perspective on the polymer development process and different IP/privacy needs. The measures used to overcome the challenges include aggregation, normalisation, and omission of costs and impacts common to all candidate compounds. The resulting LCA and LCC screening tools represent a compromise between providing the requested information at the level of detail required by the partners and reporting results that are as accurate and useful as possible. The findings are: in cases where absolute secrecy must be maintained, no one can learn which materials and processes provide the optimal results; appointing a trusted third party to handle sensitive inventory data can cause increased uncertainty of the results due to lack of peer review; the results of the work cannot be built upon by subsequent research. • Intellectual property and privacy issues can affect transparency of LCA/LCC models. • Poor transparency hinders data quality assessments and peer review processes. • Pros and cons of mitigation methods are assessed regarding IP and privacy issues. • Chosen methods were aggregation, normalisation, and omission of some model aspects. • Transparency level must be balanced between partner needs and usefulness of results.

Use of Non‐Matrix Matched Reference Materials for the Accurate Analysis of Calcium Carbonate by LA‐ICP‐MS

Advances in laser ablation inductively coupled plasma‐mass spectrometry have improved analytical precision for mineral analysis, expanding geological interpretations from LA‐ICP‐MS results. However, with improvements in analytical precision, systematic errors from calibration can become more obvious and affect data quality. To improve the accuracy of the LA‐ICP‐MS technique, a fully quantified method is presented for the analysis of minerals with a CaCO3 matrix. An in‐house calcite sample (P‐Cal) was developed with an independently constrained composition and compared with measurement results from LA‐ICP‐MS analysis, generated using multiple spot sizes, laser fluences and calibration methods. For many elements, calibration against NIST SRM 612 as a calibration reference material and USGS GSD‐1G and USGS BCR‐2G as secondary reference materials (calibration B) provided the best accuracy for CaCO3 analysis. Ablation rates and element yield (ICP‐MS signal intensity in counts per second for a given isotope relative to the mass fraction of the respective element) in calcium carbonate closely resembles NIST SRM 612; however, the down‐hole fractionation curves of USGS BCR‐2G (basaltic glass matrix) are a better fit for carbonate. Variations in accuracy for measurements at different spot sizes are negligible compared with the other effects discussed. Additionally, ablation characteristics for powdered crystals versus large fragments produce a negligible effect on the accuracy of the results for calcium carbonate.

Assessment of quality of routine health information system data and associated factors among departments in public health facilities of Harari region, Ethiopia

BackgroundDespite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.MethodsA cross-sectional study was conducted in all public health facilities in the Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation checklists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P value < 0.05.ResultThe study found good quality data in 51.35% (95% CI 44.6–58.1) of the departments in public health facilities in the Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to those found in the health posts. The presence of trained staffs able to fill reporting formats (AOR = 2.474; 95% CI 1.124–5.445) and provisions of feedbacks (AOR = 3.083; 95% CI 1.549–6.135) were also significantly associated with data quality.ConclusionThe level of good data quality in the public health facilities was less than the expected national level. Lack of trained personnel able to fill the reporting format and feedback were the factors that are found to be affecting data quality. Therefore, training should be provided to increase the knowledge and skills of the health workers. Regular supportive supervision and feedback should also be maintained.

UNGAP best practice for improving solubility data quality of orally administered drugs

An important goal of the European Cooperation in Science and Technology (COST) Action UNGAP (UNderstanding Gastrointestinal Absorption-related Processes, www.ungap.eu) is to improve standardization of methods relating to the study of oral drug absorption. Solubility is a general term that refers to the maximum achievable concentration of a compound dissolved in a liquid medium. For orally administered drugs, relevant information on drug properties is crucial during drug (product) development and at the regulatory level. Collection of reliable and reproducible solubility data requires careful application and understanding of the limitations of the selected experimental method. In addition, the purity of a compound and its solid state form, as well as experimental parameters such as temperature of experimentation, media related factors, and sample handling procedures can affect data quality. In this paper, an international consensus developed by the COST UNGAP network on recommendations for collecting high quality solubility data for the development of orally administered drugs is proposed.

Web-based remote testing as a viable option for measuring binaural hearing abilities

Web-based remote testing can increase access to clinically diverse populations in hearing research. However, whether remote testing affects data quality compared to in-lab testing is unknown. Data for binaural hearing tasks may be particularly vulnerable to commercial-grade hardware limitations and background noise from the testing environment. We replicated two published studies on binaural hearing abilities using a web-based behavioral experimental platform (Gorilla.sc). In Experiment 1, we replicated an experiment by Goupell et al. [JASA 133(4) (2013)] that tested intra-cranial lateralization to interaural time and level difference cues. In Experiment 2, we replicated an experiment by Goupell et al. [JASA 140(3) (2016)] that investigated binaural and contralateral unmasking of speech. Two groups of college-aged young adults were tested: one with verified normal hearing (NH) and one with self-reported NH. Testing took place in participants’ homes using their own computer and audio hardware. Performance was compared between the two groups of participants as well as with published data collected in-lab. For both experiments, performance was similar for in-lab and web-based remote testing. We found no effect of verified versus self-reported NH. These results suggest that remote testing may be a feasible option for binaural hearing experiments. [Work funded by NIH-NIDCD.]

ESTABLISHMENT OF BIG DATA ANALYTICS APPLICATION MODEL FOR MALAYSIAN PUBLIC SECTOR: AN EXPERT VALIDATION

These days, Big Data (BD) and Big Data Analytics (BDA) applications have increased intensively among public and private organisations. Most organisations are aware that BDA has an enormous potential in aiding them to better understand their business environments and their customers’ needs. Nevertheless, many organisations have yet to implement BD as they are concerned that poor quality of data will have an adverse impact on establishing worthful insight, and leading to severe mistakes during their decision-making process. In addition, the different BD characteristics or traits could affect data quality. Therefore, to determine the value of data generated from BD, the collected data must be analysed for accuracy and quality. This paper aims to present findings to better understand quality requirements for BDA implementation in the public sector, specifically in Malaysia. This study explored the influence of Data Quality Dimensions (DQD) on BDA application, identified the influence of Big Data Traits (BDT) on DQD, and evaluated the integration of BDT and DQD in BDA applications using expert validation approach. A conceptual model that incorporates DQD and BDT for BDA application in the public sector was proposed as the study outcome. The conceptual model was developed based on eight BDT (variety, velocity, veracity, validity, volume, value, volatility, and variability) and four data quality categories (intrinsic, contextual, representational, and accessibility). The expert validation results showed that five out of eight BDT are important. The outcomes from this study would deliver important knowledge to the current body of studies that may prove useful for potential use in the future.

Environmental exposure unit simulates natural seasonal birch pollen exposures while maximizing change in allergic symptoms

BackgroundBirch pollen is a prevalent aeroallergen during the springtime allergy season. In field studies, variable allergen exposure and environmental factors can affect data quality while environmental exposure units (EEUs) deliver controlled, standardized, and reproducible allergen exposures. ObjectiveTo inform study design for EEU trials evaluating antiallergic therapies. MethodsIn this prospective study, 76 participants with birch allergy experienced 3 exposures to birch pollen: (1) an out-of-season EEU challenge (two 3-hour sessions on consecutive days); (2) a natural seasonal exposure; and (3) an in-season EEU challenge (3-hour exposure for 2 weeks after birch pollen season initiation). ResultsThe total nasal symptom score, total ocular symptom score, and total symptom score (TSS = total nasal symptom score plus total ocular symptom score) were assessed every 30 minutes and daily during EEU and natural exposures. A high association between TSSs and day 2 of the out-of-season and in-season EEU challenges was noted, with a good association between the maximum TSS during the natural and in-season EEU challenges, and natural season and day 2 of the out-of-season EEU challenge (P < .001 for all). Participants had higher maximum change from the baseline TSS during day 2 of the out-of-season EEU challenge (12.4) vs the following: (1) first day (9.8); (2) in-season EEU challenge (8.4); and (3) natural seasonal exposure (7.6) (P < .001 for all). ConclusionA strong association was seen between the presence of allergy symptoms and exposure to birch pollen in the EEU (maximum change in symptom scores during day 2) and in the field. A hybrid trial design may be useful to demonstrate the clinical efficacy of novel antiallergic therapies requiring fewer participants and shorter timelines and expediting treatment availability.

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.

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.

Agree or Disagree: Does It Matter Which Comes First? An Examination of Scale Direction Effects in a Multi-device Online Survey

Previous research shows that the direction of rating scales can influence participants’ response behavior. Studies also suggest that the device used to complete online surveys might affect the susceptibility to these effects due to the different question layouts (e.g., horizontal grids vs. vertical individual questions). This article contributes to previous research by examining scale direction effects in an online multi-device survey conducted with panelists in Spain. In this experiment, respondents were randomly assigned to two groups where the scale direction was manipulated (incremental vs. decremental). Respondents completed the questionnaire using the device of their choosing (57.8% used PCs; 36.5% used smartphones; and 5.7% used tablets). The results show that scale direction influenced response distributions but did not significantly affect data quality. In addition, our findings indicate that scale direction effects were comparable across devices. Findings are discussed and implications are highlighted.