Information Seeking by Under-Represented Communities

Final project for INST490: Integrative Capstone (Fall 2019). University of Maryland, College Park.Prince George's County Parks and Recreation offers recreation programs, facilities, and services throughout the entire county to its residents and visitors. The county operates and maintains more than 27,000 acres of parkland for parks, picnic areas, athletic fields, historic sites, community centers, and recreation facilities. The county also seeks to preserve parts of this parkland as buffers and natural open space. Parks and Recreation services include art and nature programs, fitness and sports programs, live performances, more than 90 miles of hiker/biker/equestrian trails, outdoor festivals, self-improvement classes, summer camps, teen and senior activities, and trips. While the county offers these various services and programs, more initiatives are needed to understand how Prince George's County residents are using these programs and services, or even if they are using them. Understanding how members of underrepresented communities use these services will allow the Department to better tailor what they offer for the benefit of all its residents. Through this project, the Parks and Recreation Department seeks to better understand how its residents, in particular how underrepresented communities, use its services and programs. The Department wants to know their information seeking behavior, whether different groups use the parks differently, and if certain parks features are being used more or less. To gather this information, an online and in-person survey, developed by the team member authors, was administered at various events, beginning in September 2019 and finishing in December 2019. The main point of contact throughout this project was Edith Michel, a certified Parks and Recreation Professional with more than 20 years of experience in local government within the Maryland-National Capital Park and Planning Commission. Other key Parks and Recreation staff who we worked with are Alex Teaff, the Community Outreach Manager and Kira Lewis, the Acting Chief of the Public Affairs & Marketing Division.Prince George’s Count

EClinicalMedicine

BACKGROUND: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. METHODS: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. FINDINGS: Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. INTERPRETATION: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. FUNDING: Authors are supported by various funders, with full details stated in the acknowledgement section

Meta-analysis of the risk of adverse clinical outcomes stratified by concurrent neurological status and outcome during acute COVID-19 hospitalizations in adults.

Adverse outcomes include lower risk of hospital discharge and higher risk of mortality. Neurological status during COVID-19 hospitalization included any central nervous system (CNS) diagnosis (A, C) or any peripheral nervous system (PNS) diagnosis (B, D). Black circles indicate the local healthcare system-level hazard ratio derived from the Cox proportional hazards model. The red diamond represents the pooled effect size derived from the random-effects meta-analysis. The effect size and associated p-value derived from meta-analysis are reported in Table 2 of the main text. We also report the following metrics: I2 (95% CI), the estimated proportion of variance due to differences among healthcare systems; (Tau) τ2, the between-healthcare system variance; Prediction Interval, the predicted effect size if we were to add a new healthcare system to the analysis. We excluded two adult healthcare systems (NUH and UKFR) from the meta-analysis due to low frequency of neurological diagnoses in their patient populations ( (PDF)</p

Descriptions of the parent category ICD-10 diagnosis codes mapped to each condition of the Elixhauser Comorbidity Index.

Notes: 1. Complicated and uncomplicated diabetes were combined as one condition. Likewise, complicated and uncomplicated hypertension were combined as one condition. 2. ICD-10 descriptions were curated using the icd R package [1]. Supplemental Citation 1. Wasey JO, Frank SM, Rehman MA. icd: Efficient Computation of Comorbidities from ICD Codes Using Sparse Matrix Multiplication in R. Journal of Statistical Software. 2018. Available: https://jackwasey.github.io/icd/articles/efficiency-prebuilt.pdf (PDF)</p

Random-effects meta-analysis of the risk of adverse clinical outcomes in adults with concurrent CNS or PNS diagnosis during the acute COVID-19 hospitalization from the Cox-proportional hazard models locally run at each healthcare system.

Random-effects meta-analysis of the risk of adverse clinical outcomes in adults with concurrent CNS or PNS diagnosis during the acute COVID-19 hospitalization from the Cox-proportional hazard models locally run at each healthcare system.</p

Logistic Principal Component Analysis.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients </div

Protecting Patient Confidentiality.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients </div

Study Population Characteristics.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients </div

Study design and federated learning approach.

A. We constructed Cox proportional hazard models to evaluate clinical endpoints in acute COVID-19 patients with concurrent neurological diagnoses. Patients were followed up to 90 days after the first acute COVID-19 hospital admission. Models were adjusted for pre-existing comorbidity burden and prior neurological conditions as well as baseline demographics, including age group, sex, race/ethnicity. B. The analysis plan was provided as a standardized R package and containerized with Docker to facilitate local deployment at each participating healthcare system. Cox proportional hazards statistics (summary.coxph) were extracted from the analysis at each healthcare system and included in a random-effects meta-analysis to pool the summary statistics. NNC: No Neurological Condition; CNS: Central Nervous System diagnosis; PNS: Peripheral Nervous System diagnosis.</p

Pediatric Population Characteristics.

Notes: 1. Neurological status during acute COVID-19 hospitalization: NNC = No Neurological Condition; CNS = Central Nervous System diagnosis; PNS = Peripheral Nervous System diagnosis. 2. P-values were adjusted with the Benjamini-Hochberg method to control the false discovery rate when evaluating the distribution (categorical variables) or means (continuous variables) of characteristics stratified by neurological status. P-values i.e., comorbidities) in the pediatric population. Please see S7 Table for all pre-admission health conditions stratified by neurological status during acute COVID-19 hospitalization. (PDF)</p