Telemedicine Data Research Articles

Assessing Cross-Border Telemedicine Data Exchange in the European Union: A Call to Action.

Background: Telemedicine offers potential benefits for health care delivery. However, evidence of cross-border telemedicine data exchange within the European Union (EU) remains limited. The objective of this communication provides a brief outline of the regulatory framework, initiatives, and challenges associated with cross-border telemedicine data exchange in the EU, setting the stage for a comprehensive evidence assessment. Methods: We explore the current regulatory landscape (European Health Data Space), existing initiatives (the European Electronic Health Record Exchange Format), and interoperability challenges (e.g., legal, technical, semantic) facing EU cross-border telemedicine data exchange. Results: There is a need for thorough evidence assessment of cross-border telemedicine and related data movements. Conclusion: Understanding the current landscape of cross-border telemedicine is crucial. This article highlights the need for evidence assessment through a formal review to inform future research and policy initiatives in this domain.

Telemedicine data secure sharing scheme based on heterogeneous federated learning

The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient’s medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient’s medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost.

Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission.

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master-slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system's effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.

A Blockchain-Based Secure Sharing Scheme for Electrical Impedance Tomography Data

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing.

ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING

In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

Gabaldi et al. utilized telemedicine data, web search trends, hospitalized patient characteristics, and resource usage data to estimate bed occupancy during the COVID-19 pandemic. The results showcase the potential of data-driven strategies to enhance resource allocation decisions for an effective pandemic response. To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources. Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days. Telemedicine calls from COVID-19 patients correlated with the number of patients hospitalized in the next 8 days.

Abstract TP45: A Nurse-Driven Pre-hospital Telemedicine Program Supports Care of Patients With Suspected Stroke

Background: Because of the time-sensitive nature of stroke treatment, extensive efforts focus on reducing treatment time in the hospital and prehospital setting. Recent studies exploring the use of telemedicine within the ambulance setting report positive results. Non-recordable paramedic-assisted videos allowed patients to be examined by an ED physician while in route to the receiving hospital, improving stroke treatment times. Ambulance teleconsultations are reported to be reliable and a good method to aide emergency triage facilitating accurate diagnosis. To the best of our knowledge, these studies have used EMS tele-health services to connect with a physician. In this study we assessed the treatment benefits of a nurse-driven pre-hospital telemedicine program in a community comprehensive stroke center (CSC). Methods: Telemedicine services was installed in 4 ambulance units. A tablet was supplied to highly specialized stroke nurses. Using the current dispatch notification system, the stroke nurse was alerted to an incoming suspected stroke patient and prepared to receive the telemedicine video connection. During the telemedicine video call a paramedic-assisted assessment occurred, patient history, contact information, and pre-thrombolytic consent was obtained. Upon arrival, the stroke alert proceeded as normal. Telemedicine data was retrospectively matched with data extracted from the medical record to evaluate treatment volume (thrombolytics (TNK), endovascular procedure (EP), or both), treatment times [door to CT(D2CT), door to needle (D2N), door to puncture(D2P), door to device(D2D)], and coded stroke volume for all EMS-telemedicine cases between Jan 2020-May 2022. Results: Over the course of the pilot, 41 cases were successfully matched, and pre-hospital video assessments occurred for 17 (41%) cases. Treatment rates were 20% (n=8), 15% (n=6), and 10% (n=4) for TNK, EP, and both respectfully. Median times were 7 min (4-21), 20min (12-32), 58min (48-124), and 82 min (64-140) for D2CT, D2N, D2P, D2D respectively. Lastly, 71% (n=29) received a stroke diagnosis. Conclusion: This study shows that a nurse-driven prehospital telemedicine system can be successfully implemented in a community CRC with excellent treatment time metrics.

Federated learning based futuristic biomedical big-data analysis and standardization.

Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operational methodology unfolds across four strata: the data origin layer, data acquisition layer, data classification layer, and data optimization layer. Central to this endeavor are multi-objective optimal datasets (MooM), characterized by attribute-driven feature cartography and cluster categorization through the conduit of federated learning models. The orchestration of feature synchronization and parameter extraction transpires across multiple tiers of neural networking, culminating in the provisioning of a steadfast remedy through dataset standardization and labeling. The empirical findings reflect the efficacy of the proposed technique, boasting an impressive 97.34% accuracy rate in the disentanglement and clustering of telemedicine data, facilitated by the operational servers within the ambit of the federated model.

Effect of the Announcement of Human-to-Human Transmission on Telemedicine Services in China During COVID-19.

Telemedicine enables patients to communicate with physicians effectively, especially during the coronavirus disease (COVID-19) pandemic. However, few studies have explored the use of online health care platforms for a comprehensive range of specialties during the COVID-19 pandemic. This study aimed to investigate how telemedicine services were affected by the announcement of human-to-human transmission in China. Telemedicine data from haodf.com in China were collected. A difference-in-differences analysis compared the number of telemedicine use and the number of active online physicians for different specialties in 2020 with the numbers in 2019, before and after the announcement of human-to-human transmission. Data from 2 473 734 telemedicine use during the same calendar time in 2020 and 2019 were collected. Telemedicine use in 2020 increased by 349.9% after the announcement of human-to-human transmission in China, and the number of active online physicians increased by 23.2%. The difference-in-differences analysis indicated that the announcement had statistically significant positive effects on the numbers of telemedicine use for almost all specialties, except cosmetic dermatology, pathology, occupational diseases, sports medicine, burn, medical imaging, and interventional medicine. Telemedicine services increased significantly after the announcement of human-to-human transmission of COVID-19. Online activities of most specialties increased, except where providers had to conduct in-person testing and provide bedside therapies.

TELEMEDICINE - A CRITICAL ANALYSIS OF ITS FUTURE IN INDIA

Background:There are enough statistics on the internet for current & future telemedicine usage pattern across geographies in terms of market size, consult volume, mobile penetration, enhanced network bandwidth, advancement in biomedical devices etc. On the other hand, there are enough differences of opinion because of challenges like patient experience, doctor adoption, accessibility, lack of centres, quality of connectivity, setup cost etc. Despite all the concerns pertaining to technology, experience, compliance and effectiveness, during the covid-19 pandemic, patients and caregivers, across countries, relied heavily on the platform to provide essential health care services to patients. During the Covid-19, telemedicine usage was an accessible and cost-effective approach to deliver high-quality care. Objective:To get an overview of the state of affairs for telemedicine in India and its future outlook, i.e., to understand if the usage of the telemedicine platform was a temporary phenomenon during covid-19 only or continue to stay. Further, associated queries were – Is it only for some specific specialties or is it going to be limited to some geo-regions or is it restricted to patients in a particular age group or is it a function the nature of visit (first or follow up).Apart from analysing above considerations, the paper also highlighted important features of a telemedicine platform. Finally, the paper also studied the role of government as a regulatory body as well as an enabler of telemedicine platform in India. Methodology:More than 1 year of telemedicine data for a JCI-accredited tertiary care hospital in India was studied. Additionally, physicians and patient perspective on experience and impact across multiple specialties, age group, gender and regions of the patient was further analysed. Tele-consultation trends were further compared during wave 1 and wave 2 of the covid-19. Doctor and patient experience on the usage of telemedicine were objectively assessed with questionnaires.

DNN based reliability evaluation for telemedicine data.

Telemedicine data are measured directly by untrained patients, which may cause problems in data reliability. Many deep learning-based studies have been conducted to improve the quality of measurement data. However, they could not provide an accurate basis for judgment. Therefore, this study proposed a deep neural network filter-based reliability evaluation system that could present an accurate basis for judgment and verified its reliability by evaluating photoplethysmography signal and change in data quality according to judgment criteria through clinical trials. In the results, the deviation of 3% or more when the oxygen saturation was judged as normal according to each criterion was 0.3% and 0.82% for criteria 1 and 2, respectively, which was very low compared to the abnormal judgment (3.86%). The deviation of diastolic blood pressure (≥ 10 mmHg) according to criterion 3 was reduced by about 4% in the normal judgment compared to the abnormal. In addition, when multiple judgment conditions were satisfied, abnormal data were better discriminated than when only one criterion was satisfied. Therefore, the basis for judging abnormal data can be presented with the system proposed in this study, and the quality of telemedicine data can be improved according to the judgment result.

Backdoor-resistant identity-based proxy re-encryption for cloud-assisted wireless body area networks

The wireless body area network (WBAN) provides users with real-time medical services. Meanwhile, the cloud technology provides greater storage space and computing power for medical data. Both of them have contribute to the development of telemedicine. In a cloud-assisted WBAN, the open network environment and the semi-trust cloud service providers expose the user’s private medical data to backdoor adversaries who can make exfiltration attacks, such as the algorithm substitution attack (ASA) through the process of data sharing. Therefore, it is necessary to find a secure and efficient medical data sharing scheme for the huge amount of medical data. In this paper, we first design an identity-based proxy re-encryption scheme with cryptographic reverse firewall (IBPRE-CRF), then show the application in a multiple-access telemedicine data sharing scenario. Security analysis shows that the IBPRE-CRF scheme provides chosen plaintext attack security and resists exfiltration attacks. Performance analysis shows that the IBPRE-CRF scheme has a significant communication and computational cost advantage while being resistant to exfiltration attacks in clouds. Therefore, our IBPRE-CRF scheme is suitable for telemedicine data sharing in a cloud-assisted WBAN.

Využití telemedicíny v arytmologii

Telemedicine can be defined as a health care service that, specifically in the field of diagnostics, employs remote transfer of a large volume of data from a large number of subjects at the same time. This data is subsequently processed on a central basis and returned to a large number of health care providers by whom the service was ordered on national or international level. In arrhythmology, telemedicine is used particularly in long-term ECG monitoring to diagnose arrhythmias and check out treatment outcome via external recorders, smart watch, and implantable devices. To facilitate analysis of large telemedicine data volume, artificial intelligence is being increasingly exploited.

Identity-Based Designated-Verifier Proxy Signature Scheme with Information Recovery in Telemedicine System

With the promotion of Remote Medical Treatment, the sharing of big telemedicine data becomes more and more popular. Telemedicine based on wireless sensor networks collects blood pressure, pH value, pulse, and other medical information from the telemedicine healthcare terminal. The medical information is sent to the hospital or medical server for processing. The security protection of patient medical data, such as confidentiality and authenticity, has gradually become a critical problem to be solved in the development of cloud medical service platform. A provably secure identity-based designated-verifier proxy signature scheme with information recovery for cloud medical diagnosis network is proposed. The scheme is on the basis of computational Diffie-Hellman difficult problem and existential unforgeable against adaptive chosen message attacks in the random oracle model. The performance analysis shows that the scheme is appropriate to the remote medical diagnosis system.

Digital Health Technology for Remote Care in Primary Care During the COVID-19 Pandemic: Experience from Dubai

Introduction: We aimed to provide an insight on the feasibility and impact of telemedicine use among primary health care (PHC) providers and on nonurgent health care delivery during the coronavirus disease 2019 (COVID-19) outbreak in the United Arab Emirates (UAE). Methods: This observational study was conducted using telemedicine data from Dubai Health Authority from January 2020 to July 2021. Results: Of 123,944 individuals who booked an appointment for the telemedicine service, 106,408 (86%) showed up. Most patients were 19-59 years old, UAE nationals, female, and having government insurance. The telemedicine care volume grew from 188 visits in January 2020 to 11,757 in April 2020. The most common diagnosis was general health consultation (55,672; 52%), followed by COVID-19-related consultation (18,523; 17%) and laboratory test requests (11,064; 10%). Around 4,997 (5%) telemedicine consultations were for a medication refill. A multiple linear regression model predicting the types of diagnosis-related telemedicine needs by age and the interaction of gender and nationality was statistically significant for dermatology (p = 0.0000), respiratory (p = 0.0000), and cardiovascular (p = 0.0000) diseases. Conclusions: Virtual care services in PHC clinics have been highly successful in meeting the needs of patients during the COVID-19 pandemic. Telemedicine has enabled clinicians to continue providing care to patients while maintaining the necessary public health measures adopted in the fight against COVID-19. However, will the switch to telemedicine be sustainable in the long term and what will be the level and degree of telemedicine adoption beyond COVID-19? This requires more studies to explore how the PHC organizations will provide further support and guidance on the suitability of telemedicine in various circumstances and clinical conditions.

What We Know from Telemedicine Data in Indonesia? Study case using Alodokter, Dokter.id, and Honestdocs

The internet and technology development arise in various aspects of life in Indonesia, including in the health sector with e-health. Telemedicine utilization as a form of e-health was still rare among Indonesians because its existence is not as much as e-commerce that is more related to the economic sector. The COVID-19 pandemic has limited people's movement to get health care, but it made people use telemedicine in Indonesia. This research aims to analyze telemedicine utilization in Indonesia and see the health phenomena captured in the data. This research uses descriptive analysis and text mining to determine the utilization of telemedicine with the Named Entity Recognition (NER) and Latent Dirichlet Allocation (LDA) methods. In addition, a literature review is also used to identify the potential use of telemedicine data in collecting health statistics in Indonesia. The results show that telemedicine has been widely used in Indonesia. The clinical teleconsultation data and article titles on telemedicine produce various health topics. Therefore, telemedicine data can potentially be used as a source for collecting health statistics.

Telemedicine in the COVID-19 era: Taking care of children with obesity and diabetes mellitus.

Severe acute respiratory syndrome coronavirus 2 infection was declared a pandemic in January 2020. Since then, several measures to limit virus transmission have been imposed; among them, home confinement has been the most severe, with drastic changes in the daily routines of the general population. The “stay at home” rule has impaired healthcare service access, and patients with chronic conditions were the most exposed to the negative effects of this limitation. There is strong evidence of the worsening of obesity and diabetes mellitus in children during this period. To overcome these issues, healthcare providers have changed their clinical practice to ensure follow-up visits and medical consultation though the use of telemedicine. Telemedicine, including telephone calls, videocalls, data platforms of shared telemedicine data platforms mitigated the negative effect of pandemic restrictions. Published evidence has documented good metabolic control and weight management outcomes in centers that performed extensive telemedicine services last year during the pandemic. This review discusses studies that investigated the use of telemedicine tools for the management of pediatric obesity and diabetes.

Deep learning-enabled block scrambling algorithm for securing telemedicine data of table tennis players

In sports, advance sensing technologies generate massive amount of unstructured telemedicine data that need to be refined for accurate diagnosis of underlying diseases. For accurate prediction of diseases and classification of athletes’ data, deep learning algorithms are frequently used at the cloud. However, the transmission of raw data of athletes to the cloud faces numerous challenges. Among them, security and privacy are a major challenge in view of the sensitive and personal information present within the unstructured data. In this paper, first we present a data block scrambling algorithm (without key management) for secured transmission and storage of ECG (electrocardiogram) data of table tennis players at the cloud. A small piece of original data stored at the cloud is used for scrambling the massive amount of remaining ECG data. The secured telemedicine data is then imported into Hadoop Distributed File System for data management, which is read by Spark framework to form Resilient Distributed Datasets. Finally, a deep learning approach is used that extracts useful features, learns the related information, and weights and sums the feature vectors at different layers for classification. Theoretical analysis proves that our proposed approach is highly robust and resilient to brute force attacks and at the same time has a much better accuracy, sensitivity, and specificity as compared to the existing approaches.

Design of Decentralized Application for Telemedicine Image Record System with Smart Contract on Ethereum

This paper discusses the implementation of smart contracts on the Ethereum blockchain system for telemedicine data storage. Telemedicine is one of the currently developing digital technologies in the health and medical sectors. Telemedicine can be more efficient when seeking treatment because patients do not need to see a doctor face to face. When using blockchain technology, the stored data becomes more transparent for each node in the blockchain network but has verification on every transaction which takes time and gas costs. However, telemedicine has several risks and problems, one of which is long data storage process time because there must be a verification process first to ensure data security. Another problem faced is the issue of the gas fee of the blockchain telemedicine system which is billed in every data storage transaction. In this study, a blockchain system was introduced for managing and securing databases on telemedicine. The implementation of this blockchain system was carried out on a website page that can add data to and retrieve data from the blockchain system. The results of this study showed that blockchain was successfully implemented to store telemedicine data with Ethereum. The analysis in this paper refers to the set and gets functions. The set function is used to send data to the blockchain, and the get function is used to retrieve data from the blockchain. From testing, the Get function has a much faster execution time than the Set function because the Get function does not require verification to retrieve its data. In the iterations carried out—namely 1, 10, and 100—the longest time on average was at 100 iterations when compared to the other iterations. In the tests carried out, the more characters that were stored, the more gas costs must be paid. In the tests, the percentage increase in costs was 0.34% per character.

Concentration-response relationships between hourly particulate matter and ischemic events: A case-crossover analysis of effect modification by season and air-mass origin

Most studies linking cardiovascular disease with particulate matter (PM) exposures have focused on total mass concentrations, regardless of their origin. However, the origin of an air mass is inherently linked to particle composition and possible toxicity. We examine how the concentration-response relation between hourly PM exposure and ischemic events is modified by air-mass origin and season. Using telemedicine data, we conducted a case-crossover study of 1855 confirmed ischemic cardiac events in Israel (2005–2013). Based on measurements at three fixed-sites in Tel Aviv and Haifa, ambient PM with diameter < 2.5 μm (PM2.5) and 2.5–10 μm (PM10–2.5) concentrations during the hours before event onset were compared with matched control periods using conditional logistic regression that allowed for non-linearity. We also examined effect modification of these associations based on the geographical origin of each air mass by season. Independent of the geographical origin of the air mass, we observed concentration-response curves that were supralinear. For example, the overall odds ratios (ORs) of ischemic events for an increase of 10-μg/m3 in the 2-h average of PM10–2.5 were 1.08 (95% confidence interval (CI): 1.03–1.14) and 1.00 (0.99–1.01) at the median (17.8 μg/m3) and 95th percentile (82.3 μg/m3) values, respectively. Associations were strongest at low levels of PM10–2.5 when air comes from central Europe in the summer (OR: 1.27; 95% CI: 1.06, 1.52). Our study demonstrates that hourly associations between PM2.5 and PM10–2.5 and ischemic cardiac events are supralinear during diverse pollution conditions in a single population that experiences a wide range of exposure levels.