Social Security Number Research Articles

Measuring the diagnostic management and follow-up imaging for glioma patients across Belgian hospitals between 2016 and 2019.

This study aimed to assess the diagnostic management and follow-up imaging for glioma patients across Belgian hospitals by calculating process indicators. Patients with newly diagnosed glioma in Belgium (2016-2019) were selected from the Belgian Cancer Registry. The National Social Security Number served as unique patient identifier, linking the Registry to vital status and reimbursement data. Nine measurable process related to diagnosis and follow-up imaging were identified, with reformulations for 7 due to data limitations. For each indicator, technical documentation sheets, containing all required details (rationale, numerator and denominator, target, limitations, benchmarking, subgroup analyses) were developed, reviewed by a multidisciplinary expert panel, and validated in six pilot hospitals. Per indicator, patients were assigned to the most relevant hospital per indicator using allocation algorithms. Results for process indicators assessing MRI use in glioma diagnosis and follow-up aligned with predefined targets (90%), except for early postoperative MRI (48.5% vs. target 90%). Mandatory reporting of the WHO performance status (89.3% vs. target 100%) and performance of full-spine (43.6% vs. target 90%) and follow-up MRI (73.5% vs. target 90%) in ependymoma were suboptimal. The largest variability across centers was noted for the indicator on early postoperative MRI. This calculation of process indicators identified opportunities for improvement in diagnosis and follow-up imaging for glioma patients in Belgium. Monitoring indicator results and providing individual feedback reports to the Belgian hospitals invites neuro-oncology care teams and hospital managements to reflect on their results and to take measures to continuously improve care for glioma.

Blocking Fraud, Advertising, or Campaign-Related Calls with a Blockchain-based Mobile App

The use of a person's cell phone to commit fraud is known as cell phone fraud. Such scams are usually carried out through fake phone calls or text messages. The victim receives a call from a cell phone scammer, usually claiming to have an emergency or a legal problem. The purpose of the scam is usually to convince the victim to provide personal or financial information. This may include private information such as social security numbers, bank account details or credit card information. In addition, users are often subjected to unsolicited calls for marketing and information gathering initiatives such as campaigns, advertisements and surveys. In this study, a smartphone application built on the blockchain is created to stop these nuisance actions. Transaction times and performance tests have been rigorously performed according to the difficulty levels of the blockchain structure.

Ten-year trends of delayed sequential bilateral cataract surgery (DSBCS) in Sweden: a register-based study.

To study the trend of delayed sequential bilateral cataract surgery (DSBCS) in Sweden in the past decade. This register-based cohort study utilized data from the Swedish National Cataract Register (NCR) from 2010 through 2019. Register files from patients who underwent cataract surgery in both eyes during the study period were linked using their social security numbers. Bilateral surgeries on different days were classified as DSBCS. The study investigated the association between DSBCS within 3months and several variables with stratification and multivariate logistic regression. The following variables were used: operation year, region, private or public unit, age, sex, indication for surgery, type of intraocular lens (IOL), preoperative visual acuity, ocular comorbidity, posterior capsule rupture and perioperative difficulties. During the study period, 368,106 patients underwent DSBCS, of which 62.6% (n = 230,331) had bilateral surgery within 3months. The median time between the surgeries was 61days (interquartile range 26-161days), showing regional variations. Better visual acuity in the fellow eye, presence of ocular comorbidity, various perioperative events and complications were associated with longer time to surgery of the second eye. Conversely, cataract surgery in more recent years, private clinic, increasing age, anisometropia and multifocal IOL were associated with shorter timespan between surgeries. The majority of DSBCS were conducted within a 3-month timeframe, with the interval between surgeries decreasing throughout the study period. Several rational factors were associated with the time difference, in addition to regional variations. Many patients would probably benefit from less time between the surgeries, and we encourage a clinical practice taking the whole patient's visual function into account.

Abstract A008: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry

Abstract Background: Cancer disparities exist within the Hispanic/Latino/a/x (H/L) population by nativity status. Non-US born H/L colorectal cancer (CRC) patients and Prostate Cancer (PCa) patients in California have a statistically significant lower risk of cancer-specific survival when compared to Non-Hispanic White patients after controlling for confounders. However, birthplace information is largely incomplete in population-based cancer registries. For example, more than 40% of H/L patients have missing information on nativity in the California Cancer Registry (CCR) database. With the goal of better imputing missing birthplace, we built a Machine Learning (ML) model to predict nativity among CCR H/L cancer patients. Methods: H/L cases with primary invasive CRC (n = 75,613) and PCa (n = 93,042) at least 18 years old, diagnosed between 1988 and 2021 contained in the CCR research file were analyzed (46.36% had missing birthplace). A binary indicator variable assigning cases with known birthplace to US-born vs. Non-US born was constructed as the target for prediction. A stratified split by levels of the outcome was used to generate training (70%, n = 63,323) and testing (30%, n = 27,139) datasets. We used a pruned classification tree to perform feature selection, with the 10 features with the highest importance (measured by the overall reduction in impurity for any split containing each feature) used to predict nativity. Missing observations of the quantitative and qualitative features were mean, or mode imputed, respectively. The classification methods considered were classification trees, Random Forest (RF), and Boosting. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and the rate of correctly classified observations. Results: The top three features with the highest importance were the NHIA (NAACCR Hispanic/Latino Identification Algorithm) label, the Hispanic/Spanish identity, and the year of social security number issuance (SSN); the latter is part of a current SSN-based algorithm used to impute nativity status in this population. Classification models showed similar prediction performance. The highest AUC was reported by the RF (AUC = 0.9822), followed by Boosting (AUC = 0.9785), and the pruned classification tree (AUC = 0.9647). A similar rate of correctly classified observations was reported for the Boosting and RF models (∼94%). The RF model ranked feature importance in a similar order as the pruned classification tree. Ultimately, the RF model was the best performing classifier with a sensitivity and specificity of 0.61 and 0.90, respectively. This classifier substantially outperforms the SSN-based algorithm, which reported a correct classification rate of 81%, a sensitivity = 0.54, and a specificity = 0.41 in this dataset. Conclusions: This proof-of-concept analysis showcases a potential for developing a ML model to overcome missing nativity status in H/L cancer patients to enable more accurate classification by nativity in future research using CCR data. Citation Format: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry. Joel Sanchez Mendez, Lihua Liu, Juan P. Lewinger, Laura Fejerman, Mariana C. Stern [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A008.

Linkability measures to assess the data characteristics for record linkage.

Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage. We describe a systematic approach using data fitness ("linkability") measures, defined as metrics that characterize the availability, discriminatory power, and distribution of potential variables for RL. We used the isolation forest algorithm to detect abnormal linkability values from 188 sites in Indiana and Colorado, and manually reviewed the data to understand the cause of anomalies. We calculated 10 linkability metrics for 11 potential linkage variables (LVs) across 188 sites for a total of 20680 linkability metrics. Potential LVs such as first name, last name, date of birth, and sex have low missing data rates, while Social Security Number vary widely in completeness among all sites. We investigated anomalous linkability values to identify the cause of many records having identical values in certain LVs, issues with placeholder values disguising data missingness, and orphan records. The fitness of a variable for RL is determined by its availability and its discriminatory power to uniquely identify individuals. These results highlight the need for awareness of placeholder values, which inform the selection of variables and methods to optimize RL performance. Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.

Comparing the linkage performance of fastLink, Match*Pro and Splink at the Florida Cancer Registry using simulated pseudopeople data

ObjectivesThe objective was to compare the record linkage performance of fastLink, Match*Pro, and Splink using realistic simulated "Rhode Island" data from the Python package pseudopeople. A requirement was no expected wrong matches (false positives, FP) after a limited manual review. ApproachWe incorporated custom noise on (a) Social Security Number (SSN) and (b) address. We first used high-quality data where SSN was available and then low-quality data where SSN was unavailable. Missed matches (false negatives, FN) were acceptable. We used an iterative process of determining the “appropriate” match threshold and the correct matches (true positives, TP). ResultsThe two analysis datasets, df1 and df2, resulted in 660,227 actual matches and 9,987 actual non-matches. The specified match threshold probability was 0.995 for fastLink, 0.999999999 for Splink, and about 0.99 for Match*Pro. When SSN was available, the (rounded) results were: Splink 627,000 TP (4 FP), fastLink 577,000 TP (15 FP), and Match*Pro 436,000 TP (4 FP) in the default frequency-based EpiLink-like option; the EM option crashed. When SSN was unavailable, the results were: Splink 579,000 TP (3 FP), fastLink 582,000 TP (137 FP), and Match*Pro 442,000 TP (3 FP); the EM option had 516,000 TP (442 FP). The fastLink developers are working on adding Active Learning. ConclusionOverall, Splink and fastLink performed better than Match*Pro. A likely explanation is that only Splink and fastLink provide match probability and are open source. Splink (Python) and fastLink (R) require basic user programming skills. The results will be updated once the final report has been approved.

What’s in a name field?

The National Center for Health Statistics (NCHS) has a data linkage program that combines national survey data with key sources of health outcomes and health care utilization. The overall accuracy and quality of a data linkage depends on the quality of the data fields. This applies in a variety of data linkage methods, including clear text and privacy preserving record linkage. Data pre-processing and cleaning are essential to address data quality issues in most linkage tasks. Automating data cleaning prevents time-consuming manual reviews particularly when linkages involve a large number of records. For some data fields, these activities are relatively straightforward. For example, dates typically have a limited number of plausible values that make checking and cleaning relatively easy and straightforward. Unique identifiers (e.g., social security number) often conform to some set format or have restrictions on the values that would be expected. Other data fields such as first name and last name present greater challenges with respect to automating the cleaning process. For some data sources, first name and last name fields may contain non-name text that needs to be identified and removed. In this project, we examined the use of an artificial intelligence (AI) based large language model (LLM) and a simple rule-based algorithm to identify non-name text in name fields that should be removed prior to linkage. We also investigated the impact of automated name clean-up algorithms on the quality of an example linkage.

Data Quality and Legal Barriers to Linking US Government Social Program Data

To link social program data, such as employment, welfare, education, and human services data, a set of high-quality variables or fields are needed to facilitate rigorous linkage. The siloed nature of data collection in the US leads to a lack of consistency in the data that agencies collect. The data that one needs to make high quality linkages are often exactly those that government agencies are unwilling and unable to collect, or those that have poor quality. This has significant implications for privacy-protecting record-linkage and other techniques whereby privacy and confidentiality can be appropriately addressed. There are some obvious examples. For example, in the US, many agencies are unlikely to collect or share Social Security Number because of privacy concerns. A different type of example many agencies collect race or ethnicity poorly, so that the data is not consistent over time or simply inaccurate at the point of collection. Some programs may only collect age or month and year of birthdate. This presentation will discuss strategies to address these barriers short of changing agency practice around data sharing. It will also address how policies are a barrier to better linkage and data use. Also, how the proposed National Secure Data Service may affect state capacity to conduct linkage will also be discussed. This presentation is based on 40 years of experience working with the US federal government, states, and local agencies.

Increased Traumatic Brain Injury Severity and Mortality in Undocumented Immigrants.

Health disparities related to traumatic brain injury (TBI) have focused on socioeconomic status, race, and ethnicity. We sought to characterize TBI patterns and outcomes based on undocumented status. Patients who presented to University of California, San Diego Health Trauma Center with a TBI between 2019 and 2022 were identified and stratified based on undocumented status. Undocumented immigrants were identified using validated methods of absent or invalid social security number and key terms through chart review. Demographic information, injury characteristics, and neurosurgical interventions were recorded. Univariable and multivariable analyses were performed to determine the impact of patient factors on outcomes. Of 1654 patients with TBI, 76 (4.6%) were undocumented. Undocumented immigrants were younger (50 vs 60 years; P < .001) and had higher Injury Severity Score (17 vs 13; P < .001). They presented from farther distances (12.8 vs 5.3 miles, P < .001) with greater midline shift (1.49 vs 0.91 mm; P = .003). A greater proportion had basal cistern compression/effacement (14% vs 4.6%; P = .001) and required neurosurgical intervention (18% vs 9.6%; P = .012). Undocumented immigrants had higher hospital charges ($208 403 vs $128 948; P < .001), length of stay (5 vs 4 days; P = .002), and were discharged to a health facility at a lower rate (18% vs 32%; P = .012). They had nearly double the mortality rate (14% vs 7.3%; P = .021), with undocumented status trending as a predictor on multivariable regression (odds ratio = 2.87; P = .052). Undocumented immigrants presented from farther distances with increased TBI severity, likely from both more severe trauma and delayed presentation, requiring more neurosurgical intervention. They also had greater length of stay, charges, and nearly double the mortality rate. Importantly, undocumented status was a strong predictor for mortality. Despite worse outcomes, they were discharged to a health care facility at a lower rate. Advocacy efforts should be directed at increasing health care coverage and migrant community engagement and education.

Exploring Libyan Higher Education Institutions Employees’ Information Security Policy Awareness

Incidents of data breach in higher education could harm students since the exposure of sensitive student data (e.g., social security numbers) could twist students into victims of identity theft and endanger their identities. Regrettably, while most of the developed countries have standard information security laws apply to higher education institutions, such laws are still in its infant stage in developing countries like Libya. However, there are established policies documented by Libyan authorities to guide employees against information security breach, yet existing higher education institutions’ information security awareness models did not investigate these policies into the awareness programs. Consequently, employees will rely on policies that can subvert the security best practice. This study aimed to explore the level of understanding of Libyan information security policies among employees of higher education institutions. The quantitative technique is applied in this research, 196 employees of Libyan higher education institutions were randomly surveyed to respond to Linker information security policy awareness questionnaire. Using SPSS statistics tools, a descriptive analysis of the data is achieved. The findings of the study revealed that employees of Libyan higher education institutions are much aware of Internet Usage and Workstation security policies.

Detection and Classification of Personally Identifiable Information in Images Using Artificial Intelligence

Personally, Identifiable Information (PII) is any content that is sensitive that needs to be treated as secure and private. When data pieces such as a person's name, address, Social Security number, phone number, email address, and so on may be used to identify a specific individual, they are deemed PII. As organizations grow, so does their volume of data. This makes identifying and protecting such sensitive resources at a scale quite complex. In this project, we demonstrate where and how PII can be discovered and how we developed a working prototype of a tool that can easily detect PII images using advanced artificial intelligence (AI) techniques such Optical Character Recognition (OCR) and image classification using Convolutional Neural Networks (CNN).

IoT Privacy Risks Revealed.

A user's devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user's devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user's personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident's consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user's device, UTCID PPA offers actionable recommendations enhancing the user's control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control.

Prevalence and Incidence of Medication-Treated Diabetes and Pattern of Glucose-Lowering Treatment During the COVID-19 Pandemic: Real-World Data from the Electronic Greek Prescription Database.

This study aimed to investigate the prevalence and incidence of medication-treated diabetes mellitus and the evolving patterns of glucose-lowering treatments the year before and during the first two years of the coronavirus disease 2019 (COVID-19) pandemic. Data from the Greek electronic prescription database were analyzed for 2019, 2020, and 2021. The study population included individuals with active social security numbers. Prevalence and incidence rates were calculated based on the dispensing of glucose-lowering medications according to their unique anatomical therapeutic chemical (ATC) code. The study population comprised 10,289,140 individuals in 2019, 10,630,726 in 2020, and 11,246,136 in 2021. Diabetes prevalence rates were 8.06%, 6.89%, and 7.91%, and incidence rates were 16.8/1000, 8.6/1000, and 13.4/1000 individuals, respectively. Metformin was the most prescribed medication, and newer classes, like sodium-glucose cotransporter-2 inhibitors 2 (SGLT-2) inhibitors and glucagon like peptide-1 (GLP-1) receptor agonists exhibited increasing trends. The study identified a decrease in medication-prescribed diabetes prevalence and incidence during the initial year of the COVID-19 pandemic, attributed to healthcare access restrictions. Subsequently, figures returned close to baseline levels. Glucose-lowering medication trends reflected adherence to local and international guidelines, with metformin as the cornerstone, and increasing preference for newer classes such as GLP-1 receptor agonists and SGLT-2 inhibitors.

Phishing URL Detection: A Basic Machine Learning Approach

Phishing is currently one of the most trending scam attacks in information transmission worldwide. Such occurrences can cause severe network disruption to corporate organizations, financial and/or educational institutions and even individual network subscribers. Phishing attacks would normally involve developing a malicious webpage mimicking a legitimate website. This lures unsuspecting users into giving out personal information, such as banking details, social security numbers, passwords, usernames, etc. A phishing fraudster sends an e-mail or text message to a target webpage that contains its Universal Resource Locator (URL). The result could be monetary loss, data breach, intellectual property theft, damaged reputation, and loss of customers. In this paper, we propose a simple phishing detection approach. Moreover, because the impostor is always evolving attacking techniques in a bid to evade detection, our method addresses this using a supervised machine learning system. From the website content, we extract the stochastic dynamical patterns and then use this to predict the authenticity of the website. The Extreme Gradient Boosting (XGBoost) algorithm is used to model the website features to obtain a better prediction result. The proposed technique can detect phishing websites with an accuracy of 86.6%.

SECURITY IN SOCIAL MEDIA: AWARNESS OF PHISHING ATTACKS TECHNIQUES AND COUNTER MEASURES

As time and technology progresses, the social media security is one of the basic and most important aspects to consider when browsing the internet.Nowadays all people using social media mostly, using social media is widely spread among different age groups to meet several purposes including education, entertainment, or even pursue a research. With the increase of using smartphones and social media, the cyber security threats also increased. The most widely- perpetrated form of social media attacks is phishing; it is used to gather sensitive personal and professional information such as social security numbers, credit card numbers, bank account numbers, user logins and passwords, as well as other information entered through a web site or online sources. Furthermore, many users of different age groups and mainly the old age people have been exposed to social media security violations most times due to a lack of awareness about phishing attacks and how to address with them. Our paper focuses primarily to make people of all age group aware of exposure to this type of attack while using social media networks. Key Words: Cyber security, Social media, Phishing attack

Regional Inequalities in Diagnosis and Therapies in Greece regarding Autism Spectrum Disorders

IntroductionAutism spectrum disorders (ASD) represent a major public health concern on a global scale. The increasing prevalence of ASD worldwide, coupled with the arising demand for treatments, underscores its important role in the public mental health discourse. Ensuring the equitable integration of children with ASD and their families into all aspects of society becomes an imperative task, in order to eradicate the stigma associated with the broad spectrum of autism, encompassing both visible and concealed dimensions.ObjectivesThe primary objective of this study was to determine the crude prevalence of ASD in Greece nationwide, while also examining regional disparities in both prevalence and therapies. The study spanned a three-year period from February 2019 to February 2022 and relied on retrospective data sourced from the Greek National Organization for Healthcare Services Provision (EOPYY).MethodsEOPYY provided de-identified data, including information such as sex, age, diagnosis, and treatment for each child, facilitated by hashed social security numbers. Statistical analysis of the dataset was performed using the open-source statistical program R.ResultsA total of 18,245 children aged 2 -17 years were diagnosed with ASD in Greece, representing a nationwide crude prevalence rate of 1.16%. Regional disparities were evident, with prevalence rates ranging from 0.49% in the North Aegean to 1.57% in Crete. Over the three-year study period, a total of 15,328,327 non-medical therapies were prescribed, corresponding to an annual average of 264 therapies per child. Statistically significant differences between the thirteen regions in Greece were observed, ranging from 230 to 323 annual therapies per child.ConclusionsOur findings align Greece’s ASD prevalence with the global estimate of 1 in 100 children, as per the World Health Organization. Disparities between rural and urban areas in Greece may be attributed to differences in diagnostic procedures and the availability and accessibility of specialized services for autistic individuals. Thus, the establishment of a national surveillance system for ASD is recommended to enhance our understanding of the autism spectrum, monitor changes in prevalence, and identify potential contributing factors to autism conditions. Furthermore, these evidence-based results offer invaluable insights for crafting policies concerning healthcare, education, and employment for individuals with ASD in order to ensure the development of people with autism, their wellbeing, and a good quality of life.Disclosure of InterestNone Declared

Pharmacist-driven outreach initiative to increase prescribing of sodium-glucose cotransporter-2 inhibitors in eligible VHA patients with chronic kidney disease: a study protocol

BackgroundPatients with chronic kidney disease (CKD) are at increased risk for multiple adverse events, several of which have been proven to be less likely with the use of sodium-glucose cotransporter-2 inhibitors (SGLT2i). As a result, guidelines now recommend SGLT2i be given to those with mild to moderate CKD and type 2 diabetes. The objective of this study is to evaluate if a pharmacist-driven SGLT2i prescribing initiative among eligible patients with CKD and diabetes within the VA could more rapidly improve the adoption of SGLT2i via a pragmatic approach aligned with learning health systems.MethodsEligible patients will be identified through an established VA diabetes dashboard. Veterans with an odd social security number (SSN), which is effectively a random number, will be the intervention group. Those with even SSNs will serve as the control while awaiting a second iteration of the same interventional program. The intervention will be implemented in a rolling fashion across one Veterans Integrated Service Network. Our primary outcome is initiation of an SGLT2i. Secondary outcomes will include medication adherence and safety-related outcomes.DiscussionThis project tests the impact of a pharmacist-driven medication outreach initiative as a strategy to accelerate initiation of SGLT2i. The results of this work will not only illustrate the effectiveness of this strategy for SGLT2is but may also have implications for increasing other guideline-concordant care. Furthermore, the utilization of SSNs to select Veterans for the first wave of this program has created a pseudo-randomized interventional trial supporting a pragmatic learning health system approach.Trial registrationISRCTN12374636.

U.S. public perceptions of the sensitivity of brain data.

As we approach an era of potentially widespread consumer neurotechnology, scholars and organizations worldwide have started to raise concerns about the data privacy issues these devices will present. Notably absent in these discussions is empirical evidence about how the public perceives that same information. This article presents the results of a nationwide survey on public perceptions of brain data, to inform discussions of law and policy regarding brain data governance. The survey reveals that the public may perceive certain brain data as less sensitive than other 'private' information, like social security numbers, but more sensitive than some 'public' information, like media preferences. The findings also reveal that not all inferences about mental experiences may be perceived as equally sensitive, and perhaps not all data should be treated alike in ethical and policy discussions. An enhanced understanding of public perceptions of brain data could advance the development of ethical and legal norms concerning consumer neurotechnology.

Newcard telemonitoring system improves survival at a lower cost than traditional approaches in chronic heart failure patients as assessed by data extract from the French National Health Data System

Abstract Background Despite widespread use of telemonitoring (TLM) for chronic heart failure (CHF) patients, outcome data remain scarce and medico – economic assessment not routinely addressed. In this setting, the French National Health Data System (SNDS) can be used to comprehensively collect health care costs for the entire French population whatever the type of TLM system. Purpose The aim of this study is to assess the health economic impact of telemonitoring among 1057 patients presenting heart failure matched with a control group selected in the SNDS Methods All patients TLM at least 3 months between 2017 and 2020 were included. For each patient identified by his single social security number (case), anonymised data was retrieved from the SNDS. A control group (CG) was randomly selected from an exhaustive extraction of SNDS of patients with heart failure without TLM. This CG was matched in a 1:3 ratio to TLM patients using a propensity score including baseline characteristics (i.e. gender, age group, social disadvantage index, presence of comorbidities (renal insufficiency, coronary disease, diabetes, cancer), presence of treatments (beta blockers, Sacubitril, aldosterone antagonists, aspirin/Clopidogrel, anticoagulants)). Costs were evaluated using French National Health Insurance perspective using €2022. Binomial negative GEE models were used on matched data sets to assess differences. Results The matching rate between patients included in the TLM arm and the SNDS database was excellent at 99%. Hence, 1,057 TLM patients were matched to 3,171 patients with heart failure. In the telemonitoring group (TLM), patients were 74 years old (±12,3), 68.4 % male, mean left ventricular ejection fraction 41 % and the mean follow-up by the TLM system was 618 days. At 1-year, 2-year and 3-year follow-up, survival rates were respectively of 96%, 91% and 87% for TLM patients vs 93%, 83% and 76% among CG patients (p&amp;lt;0.0001, figure 1). At 6 months, the monthly cost for managing patients was 1,403.2 € in the TLM group vs 1,251.5 € in the CG (p=0.0064). At 12 months, 949.52 € in the TLM group vs 1,114.40 in the CG (p&amp;lt;0.0001). At 24 months, 591.31 € in the TLM group vs 1,064.20 in the CG (p&amp;lt;0.0001). Conclusion While using common evaluation methods seems to be unsatisfactory, economic evaluation both costs and outcomes of an intervention could be more appropriate. The French SNDS is a powerful tool to implement such a type of study. The results presented here show on one hand a significant decrease in mortality in the TLM. On the economic hand, we show here an increase in cost during the first 6 months of the telemonitoring balanced with a significant decrease at 12 and 24 months. Especially, those data place telemonitoring of heart failure as a real improvement in the care pathway which needs time to be effective but is able to have an effect even on the mortality rate

Evolving availability and standardization of patient attributes for matching.

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.