Literature DB >> 34847164

From pandemic response to portable population health: A formative evaluation of the Detroit mobile health unit program.

Phillip Levy1, Erin McGlynn1, Alex B Hill1, Liying Zhang2, Steven J Korzeniewski2, Bethany Foster1, Jasmine Criswell3, Caitlin O'Brien3, Katee Dawood3, Lauren Baird3, Charles J Shanley4.   

Abstract

This article describes our experience developing a novel mobile health unit (MHU) program in the Detroit, Michigan, metropolitan area. Our main objectives were to improve healthcare accessibility, quality and equity in our community during the novel coronavirus pandemic. While initially focused on SARS-CoV-2 testing, our program quickly evolved to include preventive health services. The MHU program began as a location-based SARS-CoV-2 testing strategy coordinated with local and state public health agencies. Community needs motivated further program expansion to include additional preventive healthcare and social services. MHU deployment was targeted to disease "hotspots" based on publicly available SARS-CoV-2 testing data and community-level information about social vulnerability. This formative evaluation explores whether our MHU deployment strategy enabled us to reach patients from communities with heightened social vulnerability as intended. From 3/20/20-3/24/21, the Detroit MHU program reached a total of 32,523 people. The proportion of patients who resided in communities with top quartile Centers for Disease Control and Prevention Social Vulnerability Index rankings increased from 25% during location-based "drive-through" SARS-CoV-2 testing (3/20/20-4/13/20) to 27% after pivoting to a mobile platform (4/13/20-to-8/31/20; p = 0.01). The adoption of a data-driven deployment strategy resulted in further improvement; 41% of the patients who sought MHU services from 9/1/20-to-3/24/21 lived in vulnerable communities (Cochrane Armitage test for trend, p<0.001). Since 10/1/21, 1,837 people received social service referrals and, as of 3/15/21, 4,603 were administered at least one dose of COVID-19 vaccine. Our MHU program demonstrates the capacity to provide needed healthcare and social services to difficult-to-reach populations from areas with heightened social vulnerability. This model can be expanded to meet emerging pandemic needs, but it is also uniquely capable of improving health equity by addressing longstanding gaps in primary care and social services in vulnerable communities.

Entities:  

Mesh:

Year:  2021        PMID: 34847164      PMCID: PMC8631611          DOI: 10.1371/journal.pone.0256908

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Difficult-to-reach populations from areas with heightened vulnerability related to socioeconomic status have suffered disproportionately from SARS-CoV-2 and its sequelae (i.e., COVID-19) [1-3]. Multiple community-level risk factors including poor access to healthcare and social services, household overcrowding [4, 5], increased reliance on public transportation [6] and other correlates of poverty (e.g., chronic cardiometabolic disease) appear to contribute to elevated risks [1-3]. Thus, there has been accelerated interest in alternative community-based healthcare strategies that target vulnerable populations. Outreach into communities using vehicle-based platforms offers tremendous flexibility and enhanced capacity to help meet the needs of vulnerable populations. In comparison to temporary shelters and “pop up” healthcare clinics, mobile health units (MHUs) can adapt to evolving community needs more easily and they are readily accessible to people without transportation. Moreover, by bringing care directly to people in their neighborhoods, MHUs can help improve health equity by filling gaps in primary/preventive care and chronic disease management (e.g., by integrating with a rapidly expanding telehealth ecosystem). This article describes our experience developing and deploying a fleet of five MHUs in the Detroit, Michigan, metropolitan area. First, recognizing the potential value of MHUs early on in the COVID-19 pandemic, we partnered with the Ford Motor Company to field-test a novel mobile SARS-CoV-2 testing platform. Second, we leveraged the Population Health Outcomes and Information Exchange (PHOENIX [7]) program at Wayne State University to “hotspot” areas with heightened social vulnerability. Third, as the pandemic progressed, we expanded our MHU program to include additional preventive services in hopes of mitigating the impact of COVID-19 on our local community. Indeed, what began as a mobile SARS-CoV-2 testing strategy evolved into a demonstrated capacity for bringing portable healthcare to populations from disadvantaged communities.

Methods

We performed a formative process evaluation by applying the Centers for Disease Control and Prevention (CDC) framework for public health program evaluation [8]. Our main objective was to determine the success of processes that were implemented to improve health equity by increasing access to difficult-to-reach populations from areas with heightened social vulnerability. We further examined whether the additional screening identified patients at elevated risk of poor health outcomes. Lastly, we determined whether patients requested and received assistance with referral to social services. The Wayne State University Institutional Review Board (IRB) deemed this work to be not human participant research and thus IRB review was waived (WSU IRB 2020 092).

Mobile health unit program development

The Detroit MHU program began as a rapidly deployed drive-through SARS-CoV-2 testing clinic that was housed in temporary canopy tent shelters at two fixed locations; one in Detroit and the other in Dearborn, Michigan during the first wave of the pandemic. As the local health department established its own large-scale drive-through testing site, our team pivoted towards a focus on patients from socially vulnerable areas who might lack transportation or otherwise be unable to access such services. Working with Ford X, Ford Motor Company’s incubator program, we began by using stock Ford Transit vans to implement a mobile “drive-to” SARS-CoV-2 testing program. Next, the results of field-testing form and function assessments prompted the Ford X team to fully upfit their Ford Transit van platform to better meet our program needs (Fig 1a–1d). Through a grant from the Michigan Department of Health and Human Services (MDHHS) and generous support from the philanthropic community, we were able to purchase and deploy five upfitted vehicles. Program funding also covered the cost of personnel and materials.
Fig 1

Mobile health unit design.

Fig 1a-1d show design features of mobile health units. A. Initial Version Based on Stock Ford Transit Platform. B. Upfitted Fleet With Custom Wrap. C. Upfitted Vehicle With Built In Side Awning in Use. D. Overview of Upfitted Vehicle Features.

Mobile health unit design.

Fig 1a-1d show design features of mobile health units. A. Initial Version Based on Stock Ford Transit Platform. B. Upfitted Fleet With Custom Wrap. C. Upfitted Vehicle With Built In Side Awning in Use. D. Overview of Upfitted Vehicle Features.

Healthcare and social services

At the outset of our efforts, we partnered with Patient Education Genius (PEG; Troy, Michigan), to develop a de novo, text-message-based, closed loop, HIPAA-compliant system for patient intake, reporting SARS-COV-2 test results, and data sharing. Electronic informed consent for all services was obtained via PEG, with a parent or guardian consenting for minors, during the registration process. Patients initiated SARS-CoV-2 testing encounters via text messages, services were rendered, and test results were delivered via text message sent automatically by the PEG system. Importantly, these lines of communication remain open as nearly all patients consented to future follow-up contact. Nasopharyngeal swab SARS-CoV-2 testing was offered to symptomatic healthcare workers and first responders beginning on 3/20/20, ten days after the first two cases were reported in Michigan. The mobile “drive-to” program was launched on 4/13/20 and then subsequently revised as of 9/1/20 to include a more intensive data-drive deployment strategy. Additional program expansions involved antibody testing (Abbott Architect IGG), HIV testing, hypertension screening, additional serology testing, linkage to additional healthcare/social services, and most recently the administration of COVID19 vaccinations. The period of observation and number of patients served are reported in the Results section.

Data-directed MHU deployment

Site selection for MHU deployment was initially based on publicly available COVID-19 data. “Hot spots” were identified in the surrounding seven counties by calculating SARS-CoV-2 positivity rates per 100,000 people using five-year population estimates from the 2018 US Census American Community Survey. In the absence of open machine-readable data sharing, case data were aggregated from the public dashboards of Local Health Departments (LHDs) via application program interfaces (APIs). Later as the MHU program expanded, we increasingly relied on real-time data collected onsite. We began using data provided by the PHOENIX program to target high-risk communities based on emerging evidence that patients from areas with increased social vulnerability might disproportionately suffer adverse COVID-19 outcomes [9]. Our primary measure of community social vulnerability is provided by the CDC Social Vulnerability Index (SVI) [10]. The CDC SVI ranks census tracts on fifteen social factors (e.g., unemployment, minority status, and disability) that are subclassified under four themes: i) Socioeconomic, ii) Household Composition and Disability, iii) Minority Status and Language, and iv) Housing Type and Transportation. We used the SVI for deployment purposes to identify Census Tracts with “racially concentrated poverty” (>40% poverty, >50% non-white), but we also considered chronic disease burden estimates provided by the CDC 500 Cities project [11]. We chose to consider information about chronic disease based on evidence of comorbid cardiometabolic disorders in COVID19, and because neighborhood disadvantage might play a role in the pathogenesis of atherosclerotic/cardiovascular disease-related events [12].

Statistical evaluation

We performed a formative program evaluation to determine whether our MHU model was able to access people from areas with increased social vulnerability. Patients were classified based on residence in census tracts that received bottom, middle or top quartile CDC SVI rankings. The Chi-Square test and the Cochrane Armitage test were used to test for differences in the proportion of patients from communities with top quartile SVI rankings during three phases of program implementation. Statistical significance was defined with alpha set at 5%. Hypothesis testing was performed using SAS V9.4 (Cary, NC).

Results

From 3/20/20-3/24/21, the program reached a total of 32,523 people, through 510 total events (491 testing, 19 vaccination) conducted with 218 unique community partners. The median (interquartile range) age was 48 (33–60) years; children comprised 11% of the cohort. Of patients who reported sex, a small majority was female (58%, n = 7339). The most frequently reported race/ethnic category was Black or African American (43%, n = 3395), followed by White (19.3%, n = 1519), Middle Eastern/North African (13%, n = 1059), Hispanic/Latino (12%, n = 964), Asian (6%, n = 472), Multiracial (3%, n = 248) and Native Hawaiian/Pacific Islander (0.2%, n = 14). Among the 11,088 patients who self-reported their medical history, 28% had one or more chronic health conditions.

SARS-CoV-2 testing

Fig 2 displays the MHU locations overlaid on background SARS-CoV-2 prevalence rates during the observation period (Fig 2). Patients from areas with heightened social vulnerability were over-represented among the patient population as a whole; 33% lived in census tracts with top quartile CDC SVI rankings, while only 25% was expected based on the frequency distribution in the general population. The proportion of patients who resided in communities with top quartile SVI scores increased significantly during the observation period. At the outset during location-based “drive-through” SARS-CoV-2 testing (3/20/20-3/24/21), 25% of the patients were from communities with increased vulnerability. After pivoting to a mobile platform, from 4/13/20-to-8/31/20 the fraction of patients from vulnerable areas increased to 27% (p = 0.01). The adoption of a data-driven deployment strategy resulted in further improvement; from 9/1/20-to-3/24/21, 41% of the patients who sought services from the MHUs lived in communities with top quartile SVI scores (Cochrane Armitage test for trend across the three periods, p<0.001).
Fig 2

COVID-19 case rate and mobile health unit testing sites.

Patients from areas with increased vulnerability tested positive for SARS-CoV-2 more frequently than patients whose community did not receive a top quartile SVI ranking; however, the pattern was detected only after the data-driven deployment strategy was implemented and background proportion of patients from disadvantaged areas increased (Fig 3).
Fig 3

Mobile health unit testing encounters and SARS-CoV-2 positivity rate.

Positivity rate by residence in an area with top quartile CDC Social Vulnerability Index rankings.

Mobile health unit testing encounters and SARS-CoV-2 positivity rate.

Positivity rate by residence in an area with top quartile CDC Social Vulnerability Index rankings.

Additional services

A timeline of additional program services and numbers of patients reached is provided in Table 1. Through 3/24/21, n = 1,837 patients have received social services assistance (Table 2). The most common request for social services was for food resources (n = 653, 36%). No new cases were identified by HIV testing. Of patients screened for hypertension, nearly half had elevated (>130 mm Hg) systolic blood pressure (46.4%). Fifty-five patients requested linkage to a primary care provider. A total of 4,605 patients received at least one dose of a COVID-19 vaccine from the MHUs.
Table 1

Number of patients served overall and by service type.

ServiceStart DatePatients Served (N)
SARS-CoV-2 Nasal Swab Diagnostic Testing3/20/202029,406
SARS-CoV-2 IGG Antibody Testing4/28/202011,654
HIV Testing5/19/2020400
Hypertension Screening6/6/2020896
Other Serology Testing (A1c and lipid panel)9/26/2020565
Linkage to Care for Social and Medical Services10/1/20201,837
COVID-19 Vaccinations3/15/20214,605
Total Encounters 32,523
Table 2

Number of social service referrals, follow up attempts and completions .

Referral CategoryTotal Assisted# Attempted Follow Ups# Completed Follow Ups
Number of individuals assisted with social service referrals onsite18371500822
Food Assistance653545321
Public Benefits Assistance400285152
Unemployment Assistance308256142
Health Insurance Navigation17614282
Utility Assistance505050
Voter Registration393322
PCP Referral252525
Transportation Assistance555

a some patients received more than one service referral.

a some patients received more than one service referral.

Discussion

While MHUs have been used in various forms for decades, we developed a novel platform by, i) partnering with community stakeholders to develop custom vehicles, ii) innovating a mobile phone-based application for HIPAA-compliant patient intake and correspondence, and iii) implementing a data-driven deployment strategy. Our experience demonstrates the capacity for MHUs not only to deliver SARS-CoV-2 testing to difficult-to-reach populations, but also to provide preventive healthcare and social services coordination in vulnerable communities. What began as a program focused on providing SARS-CoV-2 testing, evolved into a portable population healthcare delivery system that can address a multitude of community needs. Importantly, by adopting a data-driven deployment strategy, we further demonstrated that a combination of publicly available information and real time clinical data can be used effectively to increase MHU access to patients from vulnerable communities. Our findings are consistent with pre-SARS-CoV-2 evidence that supports the utility of geospatial solutions as a means to improve crisis response and community resilience [13]. We feel that MHUs will be particularly useful in Michigan, where telehealth uptake is the lowest in the nation during the first year of the novel coronavirus pandemic (15.1% of visits) [14]. Fewer overall clinic visits in-turn resulted in a 50% decline in routine blood pressure assessments and a 37% decline in cholesterol screening in 2020 compared to 2018–2019. This is particularly concerning in Detroit, given the high prevalence of hypertension, kidney problems and other chronic diseases [11]. Indeed, our finding that nearly half of the patients served by our program had elevated systolic blood pressure is concerning and indicative of the need for outreach efforts that extend beyond COVID-19 itself. As indicated by our connectivity with over 200 community partners, we’ve had broad support for our program from the outset and such partnerships, along with legislation that evolved during the COVID-19 pandemic, helped foster our efforts. Based on our experience, we feel that our MHU model can serve as a mechanism to reduce risk in difficult to reach populations and warrants further investigation as a potentially reimbursable healthcare delivery model. We envision the possibility of a nationwide mobile health corps that is deployed to improve health equity by filling gaps in primary care and chronic disease management in vulnerable areas. Evidence that supports our view comes from previous studies that reported considerable return on investment under similar contexts prior to [15, 16] and during the pandemic [17].

Strengths and limitations

The major strength of this evaluation is that we applied the CDC Framework for Public Health Program Evaluation to examine whether our processes met the desired objectives. Nevertheless, we do not know if our experience will generalize to similarly vulnerable communities.

Conclusions

Our descriptive study demonstrates the feasibility of using MHUs to deliver SARS-CoV-2 testing, and additional healthcare/social services, to people from vulnerable areas who are at elevated risk of COVID-19 and its sequelae. Importantly, MHU deployment in at-risk communities created an opportunity to collect information on health and social service deficits that in-turn enabled us to address those very needs. 18 Aug 2021 From pandemic response to portable population health: A formative evaluation of the Detroit mobile health unit program PONE-D-21-11698 Dear Dr. McGlynn, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Joseph Telfair, DrPH, MSW, MPH Academic Editor PLOS ONE Journal requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2.  We note that Figure 2 in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright. We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission: a. You may seek permission from the original copyright holder of Figure 2 to publish the content specifically under the CC BY 4.0 license. We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text: “I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.” Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission. In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].” b. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only. The following resources for replacing copyrighted map figures may be helpful: USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/ The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/ Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/ Landsat: http://landsat.visibleearth.nasa.gov/ USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/# Natural Earth (public domain): http://www.naturalearthdata.com/. Additional Editor Comments (optional): The Academic Editor served as the second reviewer for this manuscript and agree it should be accepted, pending any secondary editorial corrections. Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The paper presents the results of a formative evaluation that will benefit other health care and public health professionals who are considering mobile health strategies or other innovative, portable forms of health care. It is an excellent example of data-informed program development and continuous improvement. The paper is well-written and uses sound methods. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Mollie Williams 17 Nov 2021 PONE-D-21-11698 From pandemic response to portable population health: a formative evaluation of the Detroit mobile health unit program Dear Dr. McGlynn: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Joseph Telfair Academic Editor PLOS ONE
  14 in total

1.  Mobile clinic in Massachusetts associated with cost savings from lowering blood pressure and emergency department use.

Authors:  Zirui Song; Caterina Hill; Jennifer Bennet; Anthony Vavasis; Nancy E Oriol
Journal:  Health Aff (Millwood)       Date:  2013-01       Impact factor: 6.301

2.  The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease.

Authors:  Steven J Korzeniewski; Carla Bezold; Jason T Carbone; Shooshan Danagoulian; Bethany Foster; Dawn Misra; Maher M El-Masri; Dongxiao Zhu; Robert Welch; Lauren Meloche; Alex B Hill; Phillip Levy
Journal:  Online J Public Health Inform       Date:  2020-05-16

3.  Mobile health clinic model in the COVID-19 pandemic: lessons learned and opportunities for policy changes and innovation.

Authors:  Sharon Attipoe-Dorcoo; Rigoberto Delgado; Aditi Gupta; Jennifer Bennet; Nancy E Oriol; Sachin H Jain
Journal:  Int J Equity Health       Date:  2020-05-19

Review 4.  Geospatial Science and Point-of-Care Testing: Creating Solutions for Population Access, Emergencies, Outbreaks, and Disasters.

Authors:  Gerald J Kost
Journal:  Front Public Health       Date:  2019-11-26

Review 5.  Crowding has consequences: Prevention and management of COVID-19 in informal urban settlements.

Authors:  Lorenz von Seidlein; Graham Alabaster; Jacqueline Deen; Jakob Knudsen
Journal:  Build Environ       Date:  2020-11-22       Impact factor: 6.456

6.  Prediction of the Transition From Subexponential to the Exponential Transmission of SARS-CoV-2 in Chennai, India: Epidemic Nowcasting.

Authors:  Kamalanand Krishnamurthy; Bakiya Ambikapathy; Ashwani Kumar; Lourduraj De Britto
Journal:  JMIR Public Health Surveill       Date:  2020-09-18

7.  SARS-CoV-2 prevalence associated to low socioeconomic status and overcrowding in an LMIC megacity: A population-based seroepidemiological survey in Lima, Peru.

Authors:  Mary F Reyes-Vega; M Gabriela Soto-Cabezas; Fany Cárdenas; Kevin S Martel; Andree Valle; Juan Valverde; Margot Vidal-Anzardo; María Elena Falcón; César V Munayco
Journal:  EClinicalMedicine       Date:  2021-03-30

8.  Elevated COVID19 mortality risk in detroit area hospitals among patients from census tracts with extreme socioeconomic vulnerability.

Authors:  Avnish Sandhu; Steven J Korzeniewski; Jordan Polistico; Harshita Pinnamaneni; Sushmitha Nanja Reddy; Ahmed Oudeif; Jessica Meyers; Nikki Sidhu; Phillip Levy; Lobelia Samavati; M Safwan Badr; Jack D Sobel; Robert Sherwin; Teena Chopra
Journal:  EClinicalMedicine       Date:  2021-04-06

9.  Use and Content of Primary Care Office-Based vs Telemedicine Care Visits During the COVID-19 Pandemic in the US.

Authors:  G Caleb Alexander; Matthew Tajanlangit; James Heyward; Omar Mansour; Dima M Qato; Randall S Stafford
Journal:  JAMA Netw Open       Date:  2020-10-01

10.  Association Between Social Vulnerability and a County's Risk for Becoming a COVID-19 Hotspot - United States, June 1-July 25, 2020.

Authors:  Sharoda Dasgupta; Virginia B Bowen; Andrew Leidner; Kelly Fletcher; Trieste Musial; Charles Rose; Amy Cha; Gloria Kang; Emilio Dirlikov; Eric Pevzner; Dale Rose; Matthew D Ritchey; Julie Villanueva; Celeste Philip; Leandris Liburd; Alexandra M Oster
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2020-10-23       Impact factor: 17.586

View more
  2 in total

1.  Influenza vaccination coverage among an urban pediatric asthma population: Implications for population health.

Authors:  Sarah J Parker; Amy M DeLaroche; Alex B Hill; Rajan Arora; Julie Gleason-Comstock
Journal:  PLoS One       Date:  2022-10-21       Impact factor: 3.752

2.  Utilizing Mobile Health Units for Mass Hypertension Screening in Socially Vulnerable Communities Across Detroit.

Authors:  Robert D Brook; Katee Dawood; Bethany Foster; Randi M Foust; Catherine Gaughan; Paul Kurian; Brian Reed; Andrea L Jones; Barbara Vernon; Phillip D Levy
Journal:  Hypertension       Date:  2022-03-16       Impact factor: 10.190

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.