Browsing by Author "Grannis, Shaun"
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Item A simple two-step procedure using the Fellegi-Sunter model for frequency-based record linkage(Taylor & Francis, 2021-05-04) Xu, Huiping; Li, Xiaochun; Grannis, Shaun; Biostatistics, School of Public HealthThe widely used Fellegi-Sunter model for probabilistic record linkage does not leverage information contained in field values and consequently leads to identical classification of match status regardless of whether records agree on rare or common values. Since agreement on rare values is less likely to occur by chance than agreement on common values, records agreeing on rare values are more likely to be matches. Existing frequency-based methods typically rely on knowledge of error probabilities associated with field values and frequencies of agreed field values among matches, often derived using prior studies or training data. When such information is unavailable, applications of these methods are challenging. In this paper, we propose a simple two-step procedure for frequency-based matching using the Fellegi-Sunter framework to overcome these challenges. Matching weights are adjusted based on frequency distributions of the agreed field values among matches and non-matches, estimated by the Fellegi-Sunter model without relying on prior studies or training data. Through a real-world application and simulation, our method is found to produce comparable or better performance than the unadjusted method. Furthermore, frequency-based matching provides greater improvement in matching accuracy when using poorly discriminating fields with diminished benefit as the discriminating power of matching fields increases.Item Alliances to disseminate addiction prevention and treatment (ADAPT): A statewide learning health system to reduce substance use among justice-involved youth in rural communities(Elsevier, 2021) Aalsma, Matthew C.; Aarons, Gregory A.; Adams, Zachary W.; Alton, Madison D.; Boustani, Malaz; Dir, Allyson L.; Embi, Peter J.; Grannis, Shaun; Hulvershorn, Leslie A.; Huntsinger, Douglas; Lewis, Cara C.; Monahan, Patrick; Saldana, Lisa; Schwartz, Katherine; Simon, Kosali I.; Terry, Nicolas; Wiehe, Sarah E.; Zapolski, Tamika C. B.; Pediatrics, School of MedicineBackground: Youth in the justice system (YJS) are more likely than youth who have never been arrested to have mental health and substance use problems. However, a low percentage of YJS receive SUD services during their justice system involvement. The SUD care cascade can identify potential missed opportunities for treatment for YJS. Steps along the continuum of the cascade include identification of treatment need, referral to services, and treatment engagement. To address gaps in care for YJS, we will (1) implement a learning health system (LHS) to develop, or improve upon, alliances between juvenile justice (JJ) agencies and community mental health centers (CMHC) and (2) present local cascade data during continuous quality improvement cycles within the LHS alliances. Methods/design: ADAPT is a hybrid Type II effectiveness implementation trial. We will collaborate with JJ and CMHCs in eight Indiana counties. Application of the EPIS (exploration, preparation, implementation, and sustainment) framework will guide the implementation of the LHS alliances. The study team will review local cascade data quarterly with the alliances to identify gaps along the continuum. The study will collect self-report survey measures longitudinally at each site regarding readiness for change, implementation climate, organizational leadership, and program sustainability. The study will use the Stages of Implementation Completion (SIC) tool to assess the process of implementation across interventions. Additionally, the study team will conduct focus groups and qualitative interviews with JJ and CMHC personnel across the intervention period to assess for impact. Discussion: Findings have the potential to increase SUD need identification, referral to services, and treatment for YJS.Item Better patient identification could help fight the coronavirus(Nature Research, 2020-06-01) Moscovitch, Ben; Halamka, John D.; Grannis, Shaun; BioHealth Informatics, School of Informatics and ComputingItem Development and Assessment of a Public Health Alert Delivered through a Community Health Information Exchange(2010-10) Gamache, Roland; Stevens, Kevin C; Merriwether, Rico; Dixon, Brian E.; Grannis, ShaunTimely communication of information to health care providers during a public health event can improve overall response to such events. However, current methods for sending information to providers are inefficient and costly. Local health departments have traditionally used labor-intensive, mail-based processes to send public health alerts to the provider community. This article describes a novel approach for delivering public health alerts to providers by leveraging an electronic clinical messaging system within the context of a health information exchange. Alerts included notifications related to the 2009 H1N1 flu epidemic, a syphilis outbreak, and local rabies exposure. We describe the process for sending electronic public health alerts and the estimated impact on efficiency and cost effectiveness.Item Effectiveness of Covid-19 Vaccines in Ambulatory and Inpatient Care Settings(Massachusetts Medical Society, 2021-10-07) Thompson, Mark G.; Stenehjem, Edward; Grannis, Shaun; Ball, Sarah W.; Naleway, Allison L.; Ong, Toan C.; DeSilva, Malini B.; Natarajan, Karthik; Bozio, Catherine H.; Lewis, Ned; Dascomb, Kristin; Dixon, Brian E.; Birch, Rebecca J.; Irving, Stephanie A.; Rao, Suchitra; Kharbanda, Elyse; Han, Jungmi; Reynolds, Sue; Goddard, Kristin; Grisel, Nancy; Fadel, William F.; Levy, Matthew E.; Ferdinands, Jill; Fireman, Bruce; Arndorfer, Julie; Valvi, Nimish R.; Rowley, Elizabeth A.; Patel, Palak; Zerbo, Ousseny; Griggs, Eric P.; Porter, Rachael M.; Demarco, Maria; Blanton, Lenee; Steffens, Andrea; Zhuang, Yan; Olson, Natalie; Barron, Michelle; Shifflett, Patricia; Schrag, Stephanie J.; Verani, Jennifer R.; Fry, Alicia; Gaglani, Manjusha; Azziz-Baumgartner, Eduardo; Klein, Nicola P.; Family Medicine, School of MedicineBACKGROUND There are limited data on the effectiveness of the vaccines against symptomatic coronavirus disease 2019 (Covid-19) currently authorized in the United States with respect to hospitalization, admission to an intensive care unit (ICU), or ambulatory care in an emergency department or urgent care clinic. METHODS We conducted a study involving adults (≥50 years of age) with Covid-19–like illness who underwent molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We assessed 41,552 admissions to 187 hospitals and 21,522 visits to 221 emergency departments or urgent care clinics during the period from January 1 through June 22, 2021, in multiple states. The patients’ vaccination status was documented in electronic health records and immunization registries. We used a test-negative design to estimate vaccine effectiveness by comparing the odds of a positive test for SARS-CoV-2 infection among vaccinated patients with those among unvaccinated patients. Vaccine effectiveness was adjusted with weights based on propensity-for-vaccination scores and according to age, geographic region, calendar time (days from January 1, 2021, to the index date for each medical visit), and local virus circulation. RESULTS The effectiveness of full messenger RNA (mRNA) vaccination (≥14 days after the second dose) was 89% (95% confidence interval [CI], 87 to 91) against laboratory-confirmed SARS-CoV-2 infection leading to hospitalization, 90% (95% CI, 86 to 93) against infection leading to an ICU admission, and 91% (95% CI, 89 to 93) against infection leading to an emergency department or urgent care clinic visit. The effectiveness of full vaccination with respect to a Covid-19–associated hospitalization or emergency department or urgent care clinic visit was similar with the BNT162b2 and mRNA-1273 vaccines and ranged from 81% to 95% among adults 85 years of age or older, persons with chronic medical conditions, and Black or Hispanic adults. The effectiveness of the Ad26.COV2.S vaccine was 68% (95% CI, 50 to 79) against laboratory-confirmed SARS-CoV-2 infection leading to hospitalization and 73% (95% CI, 59 to 82) against infection leading to an emergency department or urgent care clinic visit. CONCLUSIONS Covid-19 vaccines in the United States were highly effective against SARS-CoV-2 infection requiring hospitalization, ICU admission, or an emergency department or urgent care clinic visit. This vaccine effectiveness extended to populations that are disproportionately affected by SARS-CoV-2 infection. Methods: We conducted a study involving adults (≥50 years of age) with Covid-19-like illness who underwent molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We assessed 41,552 admissions to 187 hospitals and 21,522 visits to 221 emergency departments or urgent care clinics during the period from January 1 through June 22, 2021, in multiple states. The patients' vaccination status was documented in electronic health records and immunization registries. We used a test-negative design to estimate vaccine effectiveness by comparing the odds of a positive test for SARS-CoV-2 infection among vaccinated patients with those among unvaccinated patients. Vaccine effectiveness was adjusted with weights based on propensity-for-vaccination scores and according to age, geographic region, calendar time (days from January 1, 2021, to the index date for each medical visit), and local virus circulation. Results: The effectiveness of full messenger RNA (mRNA) vaccination (≥14 days after the second dose) was 89% (95% confidence interval [CI], 87 to 91) against laboratory-confirmed SARS-CoV-2 infection leading to hospitalization, 90% (95% CI, 86 to 93) against infection leading to an ICU admission, and 91% (95% CI, 89 to 93) against infection leading to an emergency department or urgent care clinic visit. The effectiveness of full vaccination with respect to a Covid-19-associated hospitalization or emergency department or urgent care clinic visit was similar with the BNT162b2 and mRNA-1273 vaccines and ranged from 81% to 95% among adults 85 years of age or older, persons with chronic medical conditions, and Black or Hispanic adults. The effectiveness of the Ad26.COV2.S vaccine was 68% (95% CI, 50 to 79) against laboratory-confirmed SARS-CoV-2 infection leading to hospitalization and 73% (95% CI, 59 to 82) against infection leading to an emergency department or urgent care clinic visit. Conclusions: Covid-19 vaccines in the United States were highly effective against SARS-CoV-2 infection requiring hospitalization, ICU admission, or an emergency department or urgent care clinic visit. This vaccine effectiveness extended to populations that are disproportionately affected by SARS-CoV-2 infection. (Funded by the Centers for Disease Control and Prevention.).Item Electric Scooters (e-scooters): Assessing the Threat to Public Health and Safety in Setting Policies: Assessing e-scooter policies(Society of Practitioners of Health Impact Assessment, 2020-11) Comer, Amber R.; Apathy, Nate; Waite, Carly; Bestmann, Zoe; Bradshaw, Jacob; Burchfield, Emily; Harmon, Brittany; Legg, Rebekah; Meyer, Star; O'Brien, Patrick; Sabec, Micha; Sayeed, Jami; Weaver, Alexis; D'Cruz, Lynn; Bartlett, Stephanie; Marchand, McKenzi; Zepeda, Isabel; Endri, Katelyn; Finnell, John T.; Grannis, Shaun; Silverman, Ross D.; Embi, Peter J.; Health Sciences, School of Health and Human SciencesObjective: To determine self-reported incidences of health and safety hazards among persons who ride rentable electric scooters (e-scooters), knowledge of e-scooter laws, and attitudes and perceptions of the health and safety of e-scooter usage. Methods: A cross-sectional survey of n= 561 e-scooter riders and non-riders was conducted during June of 2019. Results: Almost half of respondents (44%) report that e-scooters pose a threat to the health and safety of riders. Riders and non-riders disagree regarding the hazards that e-scooters pose to pedestrians. Among riders, 15% report crashing or falling off an e-scooter. Only 2.5% of e-scooter riders self-report that they always wear a helmet while riding. Conclusions: E-scooter riders report substantial rates of harmful behavior and injuries. Knowledge of e-scooter laws is limited, and e-scooters introduce threats to the health and safety of riders, pedestrians on sidewalks, and automobile drivers. Enhanced public health interventions are needed to educate about potential health risks and laws associated with e-scooter use and to ensure health in all policies. Additionally, greater consideration should be given to public health, safety, and injury prevention when passing relevant state and local e-scooter laws.Item Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet®, the National Patient-Centered Clinical Research Network(BMC, 2022-10-31) Kiernan, Daniel; Carton, Thomas; Toh, Sengwee; Phua, Jasmin; Zirkle, Maryan; Louzao, Darcy; Haynes, Kevin; Weiner, Mark; Angulo, Francisco; Bailey, Charles; Bian, Jiang; Fort, Daniel; Grannis, Shaun; Krishnamurthy, Ashok Kumar; Nair, Vinit; Rivera, Pedro; Silverstein, Jonathan; Marsolo, Keith; Medicine, School of MedicineObjective: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network. Results: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet®). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens. We queried the sites for patients with a clinical encounter in 2018 or 2019 and matched their tokens to determine whether overlap existed. We described patient overlap among the sites and generated a "deduplicated" table of patient demographic characteristics. Overlapping patients were found in 3 of the 6 site-pairs. Following deduplication, the total patient count was 3,108,515 (0.11% reduction), with the largest reduction in count for patients with an "Other/Missing" value for Sex; from 198 to 163 (17.6% reduction). The PPRL solution successfully links patients across data sources using distributed queries without directly accessing patient PII. The overlap queries and analysis performed in this pilot is being replicated across the full network to provide additional insight into patient linkages among a distributed research network.Item Evaluating the effect of data standardization and validation on patient matching accuracy(Oxford, 2019-05) Grannis, Shaun; Xu, Huiping; Vest, Josh; Kasthurirathne, Suranga; Bo, Na; Moscovitch, Ben; Torkzadeh, Rita; Rising, Josh; Family Medicine, School of MedicineObjective This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. Materials and Methods We used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance was evaluated using 4 metrics: sensitivity, specificity, positive predictive value, and accuracy. Results Standardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, date of birth, or social security number) showed no matching improvements. As standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset. Conclusions Data standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety.Item Evaluating the Variation on Public Health’s Perceived Field Need of Communicable Disease Reports(2013-04) Kirbiyik, Uzay; Gamache, Roland; Dixon, Brian E.; Grannis, ShaunItem Evaluating Two Approaches for Parameterizing the Fellegi-Sunter Patient Matching Algorithm to Optimize Accuracy(Medinfo conference proceedings, 2019-08-25) Grannis, Shaun; Kasthurirathne, Suranga; Bo, Na; Huiping, Xu
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