Browsing by Author "Esperanca, Alvaro"

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    A distinct symptom pattern emerges for COVID-19 long-haul: a nationwide study
    (Springer Nature, 2022-09-23) Pinto, Melissa D.; Downs, Charles A.; Huang, Yong; El‑Azab, Sarah A.; Ramrakhiani, Nathan S.; Barisano, Anthony; Yu, Lu; Taylor, Kaitlyn; Esperanca, Alvaro; Abrahim, Heather L.; Hughes, Thomas; Giraldo Herrera, Maria; Rahamani, Amir M.; Dutt, Nikil; Chakraborty, Rana; Mendiola, Christian; Lambert, Natalie; Biostatistics, School of Public Health
    Long-haul COVID-19, also called post-acute sequelae of SARS-CoV-2 (PASC), is a new illness caused by SARS-CoV-2 infection and characterized by the persistence of symptoms. The purpose of this cross-sectional study was to identify a distinct and significant temporal pattern of PASC symptoms (symptom type and onset) among a nationwide sample of PASC survivors (n = 5652). The sample was randomly sorted into two independent samples for exploratory (EFA) and confirmatory factor analyses (CFA). Five factors emerged from the EFA: (1) cold and flu-like symptoms, (2) change in smell and/or taste, (3) dyspnea and chest pain, (4) cognitive and visual problems, and (5) cardiac symptoms. The CFA had excellent model fit (x2 = 513.721, df = 207, p < 0.01, TLI = 0.952, CFI = 0.964, RMSEA = 0.024). These findings demonstrate a novel symptom pattern for PASC. These findings can enable nurses in the identification of at-risk patients and facilitate early, systematic symptom management strategies for PASC.
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    COVID-19 Survivors’ Reports of the Timing, Duration, and Health Impacts of Post-Acute Sequelae of SARS-CoV-2 (PASC) Infection
    (Cold Spring Harbor Laboratory Press, 2021) Lambert, Natalie; Survivor Corps; El-Azab, Sarah A.; Ramrakhiani, Nathan S.; Barisano, Anthony; Yu, Lu; Taylor, Kaitlyn; Esperanca, Alvaro; Downs, Charles A.; Abrahim, Heather L.; Rahmani, Amir M.; Borelli, Jessica L.; Chakraborty, Rana; Pinto, Melissa D.; Biostatistics, School of Public Health
    IMPORTANCE Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) is a major public health concern. Studies suggest that 1 in 3 infected with SARS-CoV-2 may develop PASC, including those without initial symptoms or with mild COVID-19 disease.1, 2 OBJECTIVE To evaluate the timing, duration, and health impacts of PASC reported by a large group of primarily non-hospitalized COVID-19 survivors. DESIGN, SETTING, AND PARTICIPANTS A survey of 5,163 COVID-19 survivors reporting symptoms for more than 21 days following SARS-CoV-2 infection. Participants were recruited from Survivor Corps and other online COVID-19 survivor support groups. MAIN OUTCOMES AND MEASURES Participants reported demographic information, as well as the timing, duration, health impacts, and other attributes of PASC. The temporal distribution of symptoms, including average time of onset and duration of symptoms were determined, as well as the perceived distress and impact on ability to work. RESULTS On average, participants reported 21.4 symptoms and the number of symptoms ranged from 1 to 93. The most common symptoms were fatigue (79.0%), headache (55.3%), shortness of breath (55.3%), difficulty concentrating (53.6%), cough (49.0%), changed sense of taste (44.9%), diarrhea (43.9%), and muscle or body aches (43.5%). The timing of symptom onset varied and was best described as happening in waves. The longest lasting symptoms on average for all participants (in days) were “frequently changing” symptoms (112.0), inability to exercise (106.5), fatigue (101.7), difficulty concentrating (101.1), memory problems (100.8), sadness (99.2), hormone imbalance (99.1), and shortness of breath (96.9). The symptoms that affected ability to work included the relapsing/remitting nature of illness (described by survivors as “changing symptoms”), inability to concentrate, fatigue, and memory problems, among others. Symptoms causing the greatest level of distress (on scale of 1 “none” to 5 “a great deal”) were extreme pressure at the base of the head (4.4), syncope (4.3), sharp or sudden chest pain (4.2), brain pressure (4.2), headache (4.2), persistent chest pain or pressure (4.1), and bone pain in extremities (4.1). CONCLUSIONS AND RELEVANCE PASC is an emerging public health priority characterized by a wide range of changing symptoms, which hinder survivors’ ability to work. PASC has not been fully characterized and the trajectory of symptoms and long-term outcomes are unknown. There is no treatment for PASC, and survivors report distress in addition to a host of ongoing symptoms. Capturing patient reports of symptoms through open-ended inquiry is a critical first step in accurately and comprehensively characterizing PASC to ensure that medical treatments and management strategies best meet the needs of individual patients and help mitigate health impacts of this new disease.
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    Social Media Sensing Framework for Population Health
    (IEEE, 2019) Esperanca, Alvaro; Miled, Zina Ben; Mahoui, Malika; Electrical and Computer Engineering, School of Engineering and Technology
    Conducting large health population studies is expensive. For instance, collecting field information about the efficacy of health campaigns or the impact of a disease may require the involvement of many health providers over an extended period of time and sometimes may not reach the target population. In fact, due to the aforementioned difficulties, health-related population statistics may be unavailable or lag by several years. Recently, social media networks have emerged as a source of sensory data for various aspects of social behavior. This source of information is used to drive marketing campaigns, conduct threat analysis and profile groups of individuals among numerous other applications. However, these applications are usually limited to specific case studies and do not provide a systematic approach to translating social media data into knowledge. In this paper, we propose a framework that can extract knowledge from social media networks in support of large scale health studies. The framework consists of an automated workflow designed to collect data from social media platforms, filter the data based on geographical criteria, and extract information relevant to a target hypothesis. The framework is demonstrated in the case of mortality and incidence of three chronic diseases, namely asthma, cancer, and diabetes. Twitter data is extracted over the period 2010 to 2015 for each target geographical region and classified based on its relevance to each of the aforementioned diseases. Due to the large number of extracted records, a simple random sampling approach is used to support the supervised training and testing of the classifier in the framework. Despite the limited number of records used for the training of the classifiers as a result of this approach, high classification accuracies are achieved for all three diseases. While the focus of the case studies in this paper is on the three chronic diseases asthma, diabetes and cancer, the utility of the proposed framework extends to other areas in the health sector. The proposed framework can help automate data-driven hypothesis validation for social media health-related studies. This paper describes the underlying methodology as well as the limitations associated with using social media data as a sensor for trends in population health.