Electronic Health Record Data for Lyme Disease Surveillance, Massachusetts, USA, 2017-2018.

Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.

COVID-19 Severity in People With HIV Compared With Those Without HIV.

BACKGROUND: People with HIV (PWH) may be at risk for more severe COVID-19 outcomes. We compared risk for severe COVID-19 in PWH with matched individuals without HIV. METHODS: We identified adults in Massachusetts with a positive SARS-CoV-2 test, March 2020-July 2022, using electronic medical record data from 3 large clinical practice groups. We then used regression models to compare outcomes among PWH versus propensity score-matched people without HIV (matched 20:1) for severe COVID-19 (pneumonia or acute respiratory distress syndrome), hospitalization, and hospital length of stay. RESULTS: We identified 171,058 individuals with COVID-19; among them, 768 PWH were matched to 15,360 individuals without HIV. Overall, severe COVID-19 and hospitalization were similar in PWH and those without HIV (severe COVID-19: 3.8% vs 3.0%, adjusted odds ratio [OR] 1.27, 95% confidence interval [CI]: 0.86-1.87; hospitalization: 12.1% vs 11.3%, adjusted OR: 1.08, 95% CI: 0.87 to 1.35). Compared with people without HIV, PWH with low CD4 T-cell counts (<200 cells/mm 3 ) had more severe COVID-19 (adjusted OR: 3.99, 95% CI: 2.06 to 7.74) and hospitalization (adjusted OR: 2.26, 95% CI: 1.35 to 3.80), but PWH with high CD4 counts had lower odds of hospitalization (adjusted OR: 0.73, 95% CI: 0.52 to 1.03). CONCLUSIONS: PWH with low CD4 T-cell counts had worse COVID-19 outcomes compared with people without HIV, but outcomes for those with high CD4 counts were similar to, or better than, those without HIV. It is unclear whether these findings are generalizable to settings where PWH have less access to and engagement with health care.

Hepatitis C Cascades: Data to Inform Hepatitis C Elimination in the United States.

The United States has a goal to eliminate hepatitis C as a public health threat by 2030. To accomplish this goal, hepatitis C virus (HCV) care cascades (hereinafter, HCV cascades) can be used to measure progress toward HCV elimination and identify disparities in HCV testing and care. In this topical review of HCV cascades, we describe common definitions of cascade steps, review the application of HCV cascades in health care and public health settings, and discuss the strengths and limitations of data sources used. We use examples from the Massachusetts Department of Public Health as a case study to illustrate how multiple data sources can be leveraged to produce HCV cascades for public health purposes. HCV cascades in health care settings provide actionable data to improve health care quality and delivery of services in a single health system. In public health settings at jurisdictional and national levels, HCV cascades describe HCV diagnosis and treatment for populations, which can be challenging in the absence of a single data source containing complete, comprehensive, and timely data representing all steps of a cascade. Use of multiple data sources and strategies to improve interoperability of health care and public health data systems can advance the use of HCV cascades and speed progress toward HCV elimination.

Community-Based Nutrition Risk Screening in Older Adults (COMRISK): An Exploration of the Experience of Being Screened and Prevalence of Nutrition Risk in Alberta, Canada.

The objectives of this feasibility study were to measure the prevalence of nutrition risk in community-dwelling older adults (CDOA, ages ≥ 65 years) and explore the perspectives of CDOA of the acceptability, value, and effectiveness of nutrition risk screening in primary care and community settings. Using the Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN)© eight-item tool (n = 276), results indicated that moderate and high nutrition risks affected 50 per cent and 8 per cent, respectively, of those screened. Interviewees (n = 16) agreed that screening is acceptable, important, and valuable (Theme One). Effectiveness was unclear, as only 3 of 16 respondents recalled being told their nutrition risk status. When articulating nutrition-related issues, a food security theme, expressed in the third person, was prominent (Theme Two). Screening for nutrition risk and receiving nutrition information in community-based settings are acceptable to CDOA and medically necessary, as evidenced by the high proportion of CDOA at moderate-high nutrition risk.

Syndromic Surveillance for COVID-19, Massachusetts, February 2020-November 2022: The Impact of Fever and Severity on Algorithm Performance.

OBJECTIVES: Syndromic surveillance can help identify the onset, location, affected populations, and trends in infectious diseases quickly and efficiently. We developed an electronic medical record-based surveillance algorithm for COVID-19-like illness (CLI) and assessed its performance in 5 Massachusetts medical practice groups compared with statewide counts of confirmed cases. MATERIALS AND METHODS: Using data from February 2020 through November 2022, the CLI algorithm was implemented in sites that provide ambulatory and inpatient care for about 25% of the state. The initial algorithm for CLI was modeled on influenza-like illness: an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code for COVID-19 and an ICD-10-CM diagnosis code suggesting severe lower respiratory tract infection or ≥1 ICD-10-CM diagnosis code for upper or lower respiratory tract infection plus fever. We generated weekly counts of CLI cases and patients with ≥1 clinical encounter and visually compared trends with those of statewide laboratory-confirmed cases. RESULTS: The initial algorithm tracked well with the spring 2020 wave of COVID-19, but the components that required fever did not clearly detect the November 2020-January 2021 surge and identified <1% of weekly encounters as CLI. We revised the algorithm by adding more mild symptoms and removing the fever requirement; this revision improved alignment with statewide confirmed cases through spring 2022 and increased the proportion of encounters identified as CLI to about 2% to 6% weekly. Alignment between CLI trends and confirmed COVID-19 case counts diverged again in fall 2022, likely because of decreased COVID-19 testing and increases in other respiratory viruses. PRACTICE IMPLICATIONS: Our work highlights the importance of using a broad definition for COVID-19 syndromic surveillance and the need for surveillance systems that are flexible and adaptable to changing trends and patterns in disease or care.

Protein interaction platforms: visualization of interacting proteins in yeast.

Here we describe the protein interaction platform assay, a method for identifying interacting proteins in Saccharomyces cerevisiae. This assay relies on the reovirus scaffolding protein microNS, which forms large focal inclusions in living cells. When a query protein is fused to microNS and potential interaction partners are fused to a fluorescent reporter, interactors can be identified by screening for yeast that display fluorescent foci.

A functional genomic yeast screen to identify pathogenic bacterial proteins.

Many bacterial pathogens promote infection and cause disease by directly injecting into host cells proteins that manipulate eukaryotic cellular processes. Identification of these translocated proteins is essential to understanding pathogenesis. Yet, their identification remains limited. This, in part, is due to their general sequence uniqueness, which confounds homology-based identification by comparative genomic methods. In addition, their absence often does not result in phenotypes in virulence assays limiting functional genetic screens. Translocated proteins have been observed to confer toxic phenotypes when expressed in the yeast Saccharomyces cerevisiae. This observation suggests that yeast growth inhibition can be used as an indicator of protein translocation in functional genomic screens. However, limited information is available regarding the behavior of non-translocated proteins in yeast. We developed a semi-automated quantitative assay to monitor the growth of hundreds of yeast strains in parallel. We observed that expression of half of the 19 Shigella translocated proteins tested but almost none of the 20 non-translocated Shigella proteins nor approximately 1,000 Francisella tularensis proteins significantly inhibited yeast growth. Not only does this study establish that yeast growth inhibition is a sensitive and specific indicator of translocated proteins, but we also identified a new substrate of the Shigella type III secretion system (TTSS), IpaJ, previously missed by other experimental approaches. In those cases where the mechanisms of action of the translocated proteins are known, significant yeast growth inhibition correlated with the targeting of conserved cellular processes. By providing positive rather than negative indication of activity our assay complements existing approaches for identification of translocated proteins. In addition, because this assay only requires genomic DNA it is particularly valuable for studying pathogens that are difficult to genetically manipulate or dangerous to culture.

Yeast functional genomic screens lead to identification of a role for a bacterial effector in innate immunity regulation.

Numerous bacterial pathogens manipulate host cell processes to promote infection and ultimately cause disease through the action of proteins that they directly inject into host cells. Identification of the targets and molecular mechanisms of action used by these bacterial effector proteins is critical to understanding pathogenesis. We have developed a systems biological approach using the yeast Saccharomyces cerevisiae that can expedite the identification of cellular processes targeted by bacterial effector proteins. We systematically screened the viable yeast haploid deletion strain collection for mutants hypersensitive to expression of the Shigella type III effector OspF. Statistical data mining of the results identified several cellular processes, including cell wall biogenesis, which when impaired by a deletion caused yeast to be hypersensitive to OspF expression. Microarray experiments revealed that OspF expression resulted in reversed regulation of genes regulated by the yeast cell wall integrity pathway. The yeast cell wall integrity pathway is a highly conserved mitogen-activated protein kinase (MAPK) signaling pathway, normally activated in response to cell wall perturbations. Together these results led us to hypothesize and subsequently demonstrate that OspF inhibited both yeast and mammalian MAPK signaling cascades. Furthermore, inhibition of MAPK signaling by OspF is associated with attenuation of the host innate immune response to Shigella infection in a mouse model. These studies demonstrate how yeast systems biology can facilitate functional characterization of pathogenic bacterial effector proteins.

The Partnerships for Care Project in Massachusetts: Developing Partnerships and Data Systems to Increase Linkage and Engagement in Care for Individuals Living With HIV.

BACKGROUND: Before implementation of the Massachusetts Partnerships for Care (MA P4C) project, Massachusetts did not routinely provide HIV partner services to newly diagnosed individuals. Identification of individuals disengaged from HIV care and assistance for re-engagement relied on community- and clinic-based HIV medical case management services. Processes to identify individuals out of HIV care used either surveillance or clinic data, but did not combine both. METHODS: The Massachusetts Department of Public Health collaborated with 6 community health centers to implement HIV partner services and out-of-care re-engagement services. Implementation of these services required development of both health department and community health center capacity. Capacity development strategies included the following: development and implementation of operational protocols, establishment of communication strategies and processes, training and education for health department and health center staff, and enhancement of disease surveillance systems. RESULTS: Development of operational protocols supported implementation of public health services and collaboration with health centers. Health department-facilitated meetings, training, and technical assistance enhanced communication with health centers and promoted buy-in for collaboration. A strategy for combining clinic and surveillance data to identify individuals out of HIV care was implemented. Surveillance data system enhancements improved efficiency of out-of-care identification, assignment of cases for public health follow-up, and provided quality improvement tools. CONCLUSIONS: Collaboration between health departments and community health centers to identify and support patient engagement in HIV medical care is feasible and supports improved continuity of care. Use of surveillance and clinic data to identify out-of-care patients promotes efficiency in Data to Care activities.

Using HIV Surveillance and Clinic Data to Optimize Data to Care Efforts in Community Health Centers in Massachusetts: The Massachusetts Partnerships for Care Project.

BACKGROUND: We describe Data to Care processes of the Massachusetts Partnerships for Care (MA P4C) project and identify factors associated with engagement, retention, and viral suppression outcomes. METHODS: The Massachusetts Department of Public Health and participating community health centers generated lists of patients not in care based on a temporal gap in laboratory results, missed clinic visits, and provider concern regarding engagement. The Massachusetts Department of Public Health and community health centers reviewed the lists monthly and identified out-of-care patients in need of linkage or re-engagement. RESULTS: Between October 2015 and June 2017, of 1418 patients potentially out of care, 83 (5.9%) were confirmed to be out of care. Forty-four of those out of care (53%) received services or were re-engaged in care within 90 days, 45 (54%) were retained in care, and 40 (48%) were virally suppressed. The odds of being re-engaged or retained were lower for patients who were 6 months out-of-care (vs. those newly diagnosed). Patients with an AIDS-defining condition had increased odds of retention and viral suppression. The odds of viral suppression were reduced for patients who reported exposure categories other than men who have sex with men and were younger (30-49 years vs. ≥50 years). CONCLUSIONS: Although rates of re-engagement, retention, and viral suppression were low, the MA P4C Data to Care procedures provided a means for accurate ascertainment of out-of-care status. Future Data to Care programs should investigate the factors that contribute to disengagement from care.

Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance.

OBJECTIVES: We compared an electronic health record-based influenza-like illness (ILI) surveillance system with manual sentinel surveillance and virologic data to evaluate the utility of the automated system for routine ILI surveillance. METHODS: We obtained weekly aggregate ILI reports from the Electronic medical record Support for Public Health (ESP) disease-detection and reporting system, which used an automated algorithm to identify ILI visits among a patient population of about 700,000 in Eastern Massachusetts. The percentage of total visits for ILI ("percent ILI") in ESP, percent ILI in the Massachusetts Department of Public Health's sentinel surveillance system, and percentage of laboratory specimens submitted to participating Massachusetts laboratories that tested positive for influenza were compared for the period October 2007-September 2011. We calculated Spearman's correlation coefficients and compared ESP and sentinel surveillance systems qualitatively, in terms of simplicity, flexibility, data quality, acceptability, timeliness, and usefulness. RESULTS: ESP and sentinel surveillance percent ILI always peaked within one week of each other. There was 80% correlation between the two and 71%-73% correlation with laboratory data. Sentinel surveillance percent ILI was higher than ESP percent ILI during influenza seasons. The amplitude of variation in ESP percent ILI was greatest for 5- to 49-year-olds and typically peaked for the 5- to 24-year-old age group before the others. CONCLUSIONS: The ESP system produces percent ILI data of similar quality to sentinel surveillance and offers the advantages of shifting disease reporting burden from clinicians to information systems, allowing tracking of disease by age group, facilitating efficient surveillance for very large populations, and producing consistent and timely reports.