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Background Public health surveillance requires timely access to actionable data at every level. Current approaches for accessing chronic disease surveillance data are not sufficient, and health departments are increasingly looking to augment surveillance efforts using electronic health records (EHRs). While proven effective for acute syndromic surveillance, the utilization of EHR systems and health data networks for monitoring chronic conditions remains sparse. This study tested the generalizability of a previously validated hypertension computable phenotype. Methods A previously developed phenotype was used to estimate prevalence of hypertension in a geographically and clinically distinct region from its development. To test validity, the results were compared to available, statewide Behavioral Risk Factor Surveillance System (BRFSS) data using the two one-sided t-test (TOST) of equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race/ethnicity. Results Compared to statewide hypertension prevalence of 34.5% in the BRFSS, an EHR-based phenotype estimated an overall prevalence of 24.1%. Estimates were not equivalent overall or across most subpopulations. Like BRFSS, we observed higher prevalence among Black men and women as well as increasing prevalence with age. Conclusion With caveats, this study demonstrates that EHR-derived prevalence estimates may serve as a complement for population-based survey estimates. Utilizing available EHR data should increase timeliness of surveillance as well as enhance the ability of states and local health agencies to more readily address the burden of chronic disease in their respective jurisdictions.

Timely surveillance of diabetes mellitus remains a challenge for public health agencies. In this study, researchers compared type 2 diabetes (T2D) prevalence estimates using electronic health record (EHR) data and computable phenotypes (CPs) as defined and applied by 2 independent networks. One network, Diabetes in Children, Adolescents, and Young Adults, was a research consortium, and the other, the Multi-State EHR-Based Network for Disease Surveillance, is a practice-based public health surveillance network. This study sought to determine the equivalence of T2D prevalence estimates generated by 2 distinct, yet conceptually related, CPs using EHR data. Each network used diagnostic, laboratory, and medication data for young adults (aged 18-44 years) extracted from the Indiana Network for Patient Care (INPC) to independently calculate prevalence of T2D using distinct CPs for the year 2022. The INPC is a statewide health information exchange that receives EHR data from multiple health care systems and supports public health use cases such as surveillance. The two one-sided tests method for independence with a predefined margin of -2.5 to +2.5 percentage points was used to compare the estimated prevalence as previously derived from the Multi-State EHR-Based Network for Disease Surveillance and Diabetes in Children, Adolescents, and Young Adults networks. The two one-sided tests for equivalence show that any observed difference between 2 estimates is small and practically insignificant. Results at the overall level, and stratified by sex, age, and race or ethnicity, were examined. Overall prevalence estimates for 2022 were 4.1% for CP 1 and 2.4% for CP 2. Although prevalence estimates for CP 1 were consistently higher than those for CP 2, absolute differences were generally less than 2.5 percentage points, which did not result in a statistically significant (P<.001) difference between estimates. The only exception was for Hispanic individuals, where prevalence was significantly different (P=0.2) for CP 1 (5.4%) versus CP 2 (3.0%), yielding a margin of 2.4 (95% CI 2.2-2.6) percentage points. Other groups that had relatively higher but statistically nonsignificant prevalence included male individuals (4.6% for CP 1 vs 2.3% for CP 2), individuals aged 35-44 years (6.9% for CP 1 vs 4.9% for CP 2), and African American individuals (5.5% for CP 1 vs 3.7% for CP 2). Therefore, we concluded that the 2 CPs largely produced equivalent estimates of T2D prevalence. The 2 independent CPs demonstrated equivalent T2D prevalence estimates, except in Hispanic individuals. Although the CPs can be considered statistically equivalent, the data driving each CP may impact accuracy and completeness. CP 1 was broader, incorporating clinical diagnoses, laboratory data, and medication, whereas CP 2 used clinical diagnostic codes alone. These results have implications for improving harmonization of CPs for public health surveillance.

Electronic health record (EHR) systems are widely used in the United States to document care delivery and outcomes. Health information exchange (HIE) networks, which integrate EHR data from the various health care providers treating patients, are increasingly used to analyze population-level data. Existing methods for population health surveillance of essential hypertension by public health authorities may be complemented using EHR data from HIE networks to characterize disease burden at the community level. We aimed to derive and validate computable phenotypes (CPs) to estimate hypertension prevalence for population-based surveillance using an HIE network. Using existing data available from an HIE network, we developed 6 candidate CPs for essential (primary) hypertension in an adult population from a medium-sized Midwestern metropolitan area in the United States. A total of 2 independent clinician reviewers validated the phenotypes through a manual chart review of 150 randomly selected patient records. We assessed the precision of CPs by calculating sensitivity, specificity, positive predictive value (PPV), F -score, and validity of chart reviews using prevalence-adjusted bias-adjusted κ. We further used the most balanced CP to estimate the prevalence of hypertension in the population. Among a cohort of 548,232 adults, 6 CPs produced PPVs ranging from 71% (95% CI 64.3%-76.9%) to 95.7% (95% CI 84.9%-98.9%). The F -score ranged from 0.40 to 0.91. The prevalence-adjusted bias-adjusted κ revealed a high percentage agreement of 0.88 for hypertension. Similarly, interrater agreement for individual phenotype determination demonstrated substantial agreement (range 0.70-0.88) for all 6 phenotypes examined. A phenotype based solely on diagnostic codes possessed reasonable performance (F -score=0.63; PPV=95.1%) but was imbalanced with low sensitivity (47.6%). The most balanced phenotype (F -score=0.91; PPV=83.5%) included diagnosis, blood pressure measurements, and medications and identified 210,764 (38.4%) individuals with hypertension during the study period (2014-2015). We identified several high-performing phenotypes to identify essential hypertension prevalence for local public health surveillance using EHR data. Given the increasing availability of EHR systems in the United States and other nations, leveraging EHR data has the potential to enhance surveillance of chronic disease in health systems and communities. Yet given variability in performance, public health authorities will need to decide whether to seek optimal balance or declare a preference for algorithms that lean toward sensitivity or specificity to estimate population prevalence of disease.

Objectives. To assess the effectiveness of vaccine-induced immunity against new infections, all-cause emergency department (ED) and hospital visits, and mortality in Indiana. Methods. Combining statewide testing and immunization data with patient medical records, we matched individuals who received at least 1 dose of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines with individuals with previous SARS-CoV-2 infection on index date, age, gender, race/ethnicity, zip code, and clinical diagnoses. We compared the cumulative incidence of infection, all-cause ED visits, hospitalizations, and mortality. Results. We matched 267 847 pairs of individuals. Six months after the index date, the incidence of SARS-CoV-2 infection was significantly higher in vaccine recipients (6.7%) than the previously infected (2.9%). All-cause mortality in the vaccinated, however, was 37% lower than that of the previously infected. The rates of all-cause ED visits and hospitalizations were 24% and 37% lower in the vaccinated than in the previously infected. Conclusions. The significantly lower rates of all-cause ED visits, hospitalizations, and mortality in the vaccinated highlight the real-world benefits of vaccination. The data raise questions about the wisdom of reliance on natural immunity when safe and effective vaccines are available. (Am J Public Health. 2023;113(1):96–104. https://doi.org/10.2105/AJPH.2022.307112 )

Respiratory syncytial virus vaccines first recommended for use during 2023 were efficacious against lower respiratory tract disease in clinical trials. Limited real-world data regarding respiratory syncytial virus vaccine effectiveness are available. To inform vaccine policy and address gaps in evidence from the clinical trials, we aimed to assess the effectiveness against respiratory syncytial virus-associated hospitalisations and emergency department encounters among adults aged at least 60 years. We conducted a test-negative design analysis in an electronic health records-based network in eight states in the USA, including hospitalisations and emergency department encounters with respiratory syncytial virus-like illness among adults aged at least 60 years who underwent respiratory syncytial virus testing from Oct 1, 2023, to March 31, 2024. Respiratory syncytial virus vaccination status at the time of the encounter was derived from electronic health record documentation, state and city immunisation registries, and, for some sites, medical claims. Vaccine effectiveness was estimated by immunocompromise status, comparing the odds of vaccination among respiratory syncytial virus-positive case patients and respiratory syncytial virus-negative control patients, and adjusting for age, race and ethnicity, sex, calendar day, social vulnerability index, number of underlying non-respiratory medical conditions, presence of respiratory underlying medical conditions, and geographical region. Among 28 271 hospitalisations for respiratory syncytial virus-like illness among adults aged at least 60 years without immunocompromising conditions, vaccine effectiveness was 80% (95% CI 71–85) against respiratory syncytial virus-associated hospitalisations, and vaccine effectiveness was 81% (52–92) against respiratory syncytial virus-associated critical illness (ICU admission or death, or both). Among 8435 hospitalisations for respiratory syncytial virus-like illness among adults with immunocompromising conditions, vaccine effectiveness was 73% (48–85) against associated hospitalisation. Among 36 521 emergency department encounters for respiratory syncytial virus-like illness among adults aged at least 60 years without an immunocompromising condition, vaccine effectiveness was 77% (70–83) against respiratory syncytial virus-associated emergency department encounters. Vaccine effectiveness estimates were similar by age group and product type. Respiratory syncytial virus vaccination was effective in preventing respiratory syncytial virus-associated hospitalisations and emergency department encounters among adults aged at least 60 years in the USA during the 2023–24 respiratory syncytial virus season, which was the first season after respiratory syncytial virus vaccine was approved. The Centers for Disease Control and Prevention.

Infectious disease modeling and forecasting tools are crucial for outbreak management. However, variability exists in the capacity of state and local health departments to effectively utilize these tools, influenced by factors such as infrastructure, funding, staff capacity, and data access. This study aims to identify the current priorities, needs, and capacities of state and local public health departments regarding infectious disease modeling and forecasting tools. Key informant interviews were conducted with epidemiologists, informaticists, and leadership across state and local health departments from Montana, Utah, and Washington. Thematic coding and axial coding were used for thematic analysis. Three themes emerged: (1) models and tools must be adaptable based on the jurisdiction type (rural, urban, state); (2) building trust in models and tools is an important precursor to adoption; and (3) there are concerns about the availability and quality of data. This study highlights the need for adaptable modeling tools that are tailored to specific public health jurisdictions. Building trust in modeling and forecasting tools and addressing data quality issues are essential for successful tool implementation and adoption across diverse public health settings.

Background: Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health. Objective: This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources. Methods: We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported. Results: A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location. Conclusions: A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Lay Summary Social factors, such as an individual’s housing, food, employment, and income situations, affect their overall health and well-being. As a result, data on patients’ social factors aid in clinical decision making, planning by hospital administrators and policy-makers, and enrich research studies with data representative of more factors influencing the life of an individual. Data on social factors can be collected at the time of a healthcare visit through screening questionnaires or are often documented in the clinical text as part of the social narrative. This study examines the use of natural language processing—a machine method to identify certain text within a larger document—to identify housing instability, financial insecurity, and unemployment from within the clinical notes. Using a relatively unsophisticated methodology, this study demonstrates strong performance in identifying these social factors, which will enable stakeholders to utilize these details in support of improved clinical care. Abstract Objective This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability. Materials and Methods Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Results PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Discussion We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics. Conclusion The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution.

This study aimed to assess COVID-19 vaccine effectiveness against death (VE), controlling for healthy vaccinee bias. We link all adult deaths through year-end 2022 in the State of Indiana, U.S.A., to vaccination records and identify which deceased received primary vaccination (measured as either one or two initial doses) and which received one or two booster doses. We measure COVID-19 mortality with the COVID Excess Mortality Percentage (CEMP). CEMP is calculated, for a group defined by various characteristics (age, sex, time period), as COVID-19 deaths divided by non-COVID natural deaths. The CEMP outcome measure accounts for healthy vaccinee bias by using non-COVID natural mortality to control for differences in population health. We find a large healthy vaccinee bias. Controlling for this bias, we find substantial VE for primary vaccination and the first booster dose during the first five vaccine-available calendar quarters, from 1Q2021 through 1Q2022 (end of Omicron infection wave). However, over 2Q-4Q2022, we find no evidence for primary-vaccination VE, and find moderate but statistically insignificant booster VE, which largely wanes by 4Q2022. It is known that by 2Q2022, most people had natural immunity from prior COVID-19 infection. Thus, our results for 2Q-4Q2022 largely reflect comparing hybrid (infection plus vaccination) immunity to infection-only immunity. In this period, we find negligible mortality benefit from primary vaccination, and moderate but waning benefit from a booster dose. Controlling for healthy vaccinee bias is crucial when estimating VE. We found limited VE against COVID-19 mortality over 2Q-4Q2022, but lacked data for more recent periods.

SARS-CoV-2 continues to evolve, population immunity changes, and COVID-19 vaccine formulas have been updated, necessitating ongoing COVID-19 vaccine effectiveness (VE) monitoring. To evaluate the VE of 2023-2024 COVID-19 vaccines against COVID-19-associated emergency department (ED) and urgent care (UC) encounters, hospitalizations, and critical illness, including during XBB- and JN.1-predominant periods. This test-negative design VE case-control study was conducted using data from September 21, 2023, to August 22, 2024, from EDs, UC centers, and hospitals in 6 US health care systems. Eligible adults 18 years or older with COVID-19-like illness and molecular or antigen testing for SARS-CoV-2 were studied. Case patients were those with a positive molecular or antigen test result; control patients were those with a negative molecular test result. Receipt of 2023-2024 (monovalent XBB.1.5) COVID-19 vaccination with products approved or authorized for use in the US. Main outcomes were COVID-19-associated ED and UC encounters, hospitalizations, and critical illness (admission to the intensive care unit or in-hospital death). VE was estimated comparing the odds of receipt of the 2023-2024 COVID-19 vaccine with no receipt among case and control patients. Among 345 639 eligible ED and UC encounters in immunocompetent adults 18 years or older with COVID-19-like illness and available test results (median [IQR] age, 53 [34-71] years; 209 087 [60%] female), 37 096 (11%) had a positive SARS-CoV-2 test result. VE against COVID-19-associated ED and UC encounters was 24% (95% CI, 21%-26%) during 7 to 299 days after vaccination. Among 111 931 eligible hospitalizations in immunocompetent adults 18 years or older with COVID-19-like illness and available test results (median [IQR] age, 71 [58-81] years), 10 380 (9%) had a positive SARS-CoV-2 test result. During 7 to 299 days after vaccination, VE was 29% (95% CI, 25%-33%) against COVID-19-associated hospitalization and 48% (95% CI, 40%-55%) against COVID-19-associated critical illness. VE was highest 7 to 59 days after vaccination (VE against ED and UC encounters 49%; 95% CI, 46%-52%; hospitalization, 51%; 95% CI, 46%-56%; critical illness, 68%; 95% CI, 56%-76%) and then waned (VE 180-299 days after vaccination against ED and UC encounters, -7% [95% CI, -13% to -2%]; hospitalization, -4% [95% CI, -14% to 5%]; and critical illness, 16% [95% CI, -6 to 34%]). In this case-control study of VE, 2023-2024 COVID-19 vaccines were estimated to provide additional effectiveness against medically attended COVID-19, with the highest and most sustained estimates against critical illness. These results highlight the importance of receiving recommended COVID-19 vaccination for adults 18 years or older.