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Lessons learned to improve COVID-19 response in communities with greatest socio-economic vulnerabilities

Abstract

Background

Vulnerable communities are susceptible to and disproportionately affected by the impacts of the COVID-19 pandemic. Understanding the challenges faced, perceptions, lessons learned, and recommendations of the organizations that provide services in response to COVID-19 to vulnerable communities is critical to improving emergency response and preparedness in these communities.

Methods

This study employed GIS mapping to identify the needs and assets that exist in communities in Baltimore City, where vulnerabilities related to social determinants of health and the burden of the COVID-19 pandemic were greatest. We also conducted an online survey between September 1, 2021, and May 30, 2022, to assess the COVID-19-related services provided by local organizations, challenges faced, perceptions, lessons learned, and recommendations to inform policies, programs, and funding related to improving the COVID-19 response in underserved communities. The survey was disseminated through the online Kobo Toolbox platform to leaders and representatives of organizations in Baltimore City.

Results

Based on GIS mapping analysis, we identified three communities as the most vulnerable and 522 organizations involved in the COVID-19 response across Baltimore City. 247 surveys were disseminated, and 50 survey responses were received (20.24% response rate). Out of these organizations, nearly 80% provided services in response to COVID-19 to the identified vulnerable communities. Challenges experienced ranged from funding (29%), and outreach/recruitment (26%), to not having access to updated and accurate information from local officials (32%).

Conclusions

This research highlights critical insights gained related to the experiences of vulnerable populations and suggests ways forward to address challenges faced during the emergency response by providing recommendations for policy and program changes. Furthermore, the findings will help better prepare vulnerable communities for public health emergencies and build more community resilience.

Peer Review reports

Background

The outbreak and rapid spread of the novel coronavirus (COVID-19) pandemic became a global health problem due to its high transmission rate and swift development worldwide [1]. Within the first few months of its spread, beginning in December 2019 in Wuhan, China, the number of cases drastically increased and reached nearly 80 million cases within one year in December 2020, with over 1.7 million deaths globally [2][3]. With more than 93 million confirmed COVID-19 cases and over one million associated deaths as of August 2022, the United States is one of the most impacted countries (Centers for Disease Control and Prevention, 2022). Although the pandemic may have affected most people across the United States in some capacity, disease surveillance reports documented COVID-19 disproportionately impacting individuals and families in vulnerable communities, specifically racial and ethnic groups such as African American, Spanish speaking and immigrant populations residing in low-income communities [4, 5]. Vulnerability is a term that describes communities facing pre-existing disparities in social determinants of health including health and quality health care, neighborhood environment, food access, education, transportation, and economic stability due to inequalities that challenge a community’s ability to cope with disastrous outbreaks such as the COVID-19 pandemic [6,7,8]. Numerous reports suggest that people residing in vulnerable communities disproportionately had greater risks of infection, worse health outcomes, hospitalizations, and death from COVID-19 [9,10,11].

Previous studies highlight pandemic adverse outcomes and challenges. Uncoordinated approaches in vulnerable communities shed a harsh light on the need for better policy solutions to improve integrated COVID-19 prevention services, particularly in vulnerable communities experiencing myriad health inequities, now exacerbated by COVID-19. Despite advances and improvements in health care and disease prevention, with gaps in health coverage, Black communities are left susceptible to environmental factors, leading to poorer outcomes. At the beginning of the pandemic, the lack of transportation to testing sites was another issue [6]. In large cities, testing sites were more prevalent in economically stable communities compared to the poor neighborhoods. Moreover, residents from low-income neighborhoods with COVID-19 symptoms were less likely than well-off individuals with the same symptoms to receive a COVID-19 test [9,10,11]. After this disparity became public knowledge, agencies began to work to protect the most vulnerable populations by concentrating COVID-19 testing sites in areas where these individuals lived and had access, such as in churches and nearby community centers. To ensure a timely response to COVID-19 and other future outbreaks, the availability of testing sites, access to health care systems for treatment, and the interpretation and reporting of findings need to be strengthened in vulnerable communities [12]. While healthcare centers provide vital services, non-health organizations like faith-based groups, schools, and charities also play a critical role in meeting the needs of vulnerable groups through education, financial assistance, and other support services.

As the largest city in Maryland and the 22nd most populous city in the U.S., Baltimore has a population of around 585,708 people - over 60% identifying as Black or African American, and approximately 29% as White. Baltimore’s demographic profile is similar to other urban cities with significant African American populations like Detroit, Philadelphia, and Atlanta. However, the city faces challenges related to poverty, crime, and population decline, which has prompted ongoing revitalization efforts [13]. Because of the strikingly high prevalence of long-standing social, environmental, and financial challenges, communities in Baltimore, specifically ethnic and racial groups, were disproportionately impacted by COVID-19 in terms of higher mortalities and hospitalizations as well as other indirect burdens such as lost income, etc. [14, 15]. Existing challenges in Baltimore present large populations of families residing in low-income economically segregated neighborhoods with limited opportunities for high-quality education and decent employment, and access to fresh food and quality health care [16, 17]. The majority of residents have a great dependence on public transportation to access parts of the region where jobs, healthier food, health care, and better education are located, and the ease in transit accessibility and length of distance to these destinations is limited [14, 17, 18]. In addition, the pandemic and the related shutdown of support services, such as public transportation further exacerbated the struggles for many residing in underserved communities [19]. A recent collaborative study between Johns Hopkins University, the Baltimore Transit Equity Coalition (BTEC), and Baltimore community members examined public transportation and health impacts in communities in Baltimore [20]. Utilizing publicly available data and geographic information systems (GIS) tools, data pinpointed vulnerable communities in greater need of better transportation and highlighted recommendations for highest need areas [21].

This study uses GIS technology to demonstrate community socio-economic vulnerabilities, pandemic response, and input from community stakeholders in Baltimore. Given the impact of COVID-19, the future waves, the rise of deadly variants, and the appalling death tolls, it is important to identify and ensure a timely and equitable administration of medical care, health services, and essential resources for all individuals in affected communities. To overcome these limitations, successful emergency response requires identifying and managing locations or facilities accessible to the entire population to deliver basic needs and health services at these points. Thus, the purpose of this study is: 1) to identify vulnerable communities in Baltimore City where the burden of the COVD-19 Pandemic was the greatest, 2) to identify and engage with organizations involved in COVID-19 response in the identified communities, and 3) to gain valuable insights into the challenges, perceptions, lessons learned, and recommendations to inform policies, programs, and funding related to improving the COVID-19 response in Baltimore City. Our hypothesis is that low-income and underserved minorities are uniquely vulnerable due to the negative impact of social determinants of health, and policy and medical procedures that address their needs can help mitigate these vulnerabilities. We hope this study will enlighten effective ways of reaching, engaging, and supporting vulnerable communities worldwide.

Methods

Study setting and design

As a part of our ongoing work of community-based participatory research (CBPR), this study’s design, recruitment, data collection activities, and analyses took place at Morgan CARES (Community-Aligned REsearch Solutions). Morgan CARES is the Community Engagement Core of the Center for Urban Health Disparities Research and Innovation (RCMI@ Morgan) at Morgan State University and has a presence working in Baltimore City communities. As the mission is to support a network of members from diverse backgrounds, create new partnerships, and enhance their capacity for addressing pressing health problems in Baltimore by providing resources and training services, Morgan CARES has recruited over 380 community and academic members in Baltimore City. For this COVID-19 outreach study, we engaged members and representatives of organizations within these communities. The details of the data analysis are presented in the flow chart in Fig. 1.

Figure 1
figure 1

The methodology scheme of the study.

Study area

We used Community Statistical Areas (CSAs) as the primary geographic unit. CSAs have been developed by the Baltimore City Health Department, which are statistical units formed by aggregating census tracts [22]. The resulting 55 CSAs represent a neighborhood of similar socio-demographic and economic characteristics. Each CSA had a total population ranging from 5,000 to 20,000 persons and allows for collecting and aggregating a wide range of data for relatively stable geography over time (Baltimore City Health Department, 2018).

Data sources

We used data collected through several publicly available databases. The Baltimore City data were obtained from the 2017 Neighborhood Health Profiles. Baltimore City Data compiles a variety of individual and community-level sociodemographic data as well as several health outcomes data from several sources (Baltimore City Health Department, 2017) 23]. COVID-19 data (cases and vaccination rates) were obtained from the Maryland Department of Health beginning in 2021. These datasets are publicly available (Open Maryland Portal) at the zip code level. The sociodemographic data were retrieved from the American Community Survey (ACS) from Census Bureau, which releases new data every year and is publicly available to access with different data tools. Spatial data were retrieved from Open Streets Maps which provides map data such as City boundary, CSA boundary, and the list of organizations used for this study. Geography CrossWalk was also utilized to build relationships and harmonize tract data between different geographic locations for COVID cases retrieved both at the zip code and CSA levels.

To capture the variability of health, social, and economic factors across Baltimore, we developed the COVID-19 Community Vulnerability Index (CCVI). The CCVI is similar to the Social Vulnerability Index, previously developed by the CDC to support disaster management [20], except with additional elements specific to the vulnerable communities impacted by the COVID-19 pandemic. The index combines indicators of vulnerability from four themes (socioeconomic status, accessibility, health, and COVID-19). We defined the following quantities, where \(n\) represents the total number of CSAs, and i represents the value for the selected indicator at the time (t). S represents socioeconomic status, H represents health, A represents accessibility, and C represents COVID-19.

\(CCVI=\underset{i,t}{\overset{n}{\int }}(S+A+H+C )\)

The variables were aggregated at the CSA level. For each CSA, its percentile rank was calculated comparing all CSAs in the city for eleven individual variables, four themes, and its overall position: 1) Socioeconomic status, including “below poverty”, “unemployed”, “high school diploma”, and “non-white population”; 2) Accessibility including “no car household”, “food desert”, and “access to health care”; 3) Health including “heart disease mortality rate” and “lung cancer mortality rate”; and 4) COVID-19 including “positive cases” and “hospitalized.” For each of the four themes, the percentiles were aggregated for the variable comprising each theme. The summed percentiles for each theme illustrate theme-specific percentile rankings. For the overall CSA rankings, the aggregate for each theme was ordered by the CSAs, and then calculated by overall percentile rankings. We further classified the rankings. CSAs in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate “high vulnerability”. Tracts below the 90th percentile are given a value of 0. For a theme, the category value is the number of flags for variables comprising the theme. We calculated the overall flag value for each CSA as the number of all variable flags. We mapped the following using ArcGIS software: COVID-19 cases and vaccination rates, COVID-19 vulnerable zip codes, socioeconomic variables, and overlapped vulnerable communities identified by comparing the two maps.

Data Collection and Analysis

To engage with leaders and representatives from organizations in Baltimore City to assess COVID-19 response in their communities, an online questionnaire survey was prepared using KoboToolbox and disseminated to organizations within our created database, between September 1, 2021**,**May 30, 2022. Out of 522 organizations involved in COVID-19 responses across Baltimore City, 247 were selected based on their role in serving vulnerable communities and being active. These organizations included healthcare centers, faith-based organizations, educational institutions, small businesses, and non-profit organizations engaged in providing testing, vaccination, treatment, information sharing, food distribution, shelter, and other support services. Surveys were distributed to one of the key staff and the leaders of these organizations who could describe their organizations’ roles, share their personal opinions on the challenges faced by affected communities, and provide potential policy recommendations to address these challenges. The questionnaire was intended to gain insight on: 1) the organization’s role in COVID-19 response, 3) to capture any difficulties that the organization experienced in providing services to targeted populations in their community, [3] and to understand their perceptions of the COVID-19 response at the City level. The participants’ perception of the COVID-19 vaccine rollout process in Baltimore City was measured using a five-point scale: 1-Not effective, 2-Slightly effective, 3-Somewhat effective, 4-Very effective, & 5-Extremely effective. The survey was completely voluntary and took no more than ten minutes. All responses were anonymous and kept confidential. Each person that completed the survey received a $15 incentive for their participation. Due to COVID-19 restrictions, we could not reach many of the organizations we originally planned to reach. Data analysis was performed using Stata version 15.1 statistical software to calculate the total number and percentage.

Results

We examined the neighborhood health profile and the determinants of susceptibility based on social demographics to determine a community’s vulnerability (Supplementary Fig. 1). In further analysis, we captured the variability of health, social, and economic factors across Baltimore utilizing the COVID-19 Community Vulnerability Index. Based on these analyses, 3 CSAs were found to have high vulnerability to COVID-19 risk, 13 had a medium-high vulnerability, 22 were observed to have low-medium, and the remainder of the communities were considered to have less vulnerability during the COVID-19 pandemic (Fig. 2).

Figure 2
figure 2

GIS map of Baltimore communities identified as having high or low vulnerabilities based on the community covid vulnerability index.

We identified 522 organizations involved in the COVID-19 response across Baltimore City. To evaluate the organizational response in vulnerable communities, we compared the level of vulnerability with the number of organizations involved. The distribution of organizations was normalized while mapping using ArcGIS software. To perform this, the number of total organizations was standardized against the population data in each of the respective CSAs. Five categories of organization distribution were created including: Lowest (0.0189-0.0465%), Low (0.0466-0.0814%), Medium (0.0815-0.1312%), High (0.1313-0.2154%), Highest (0.2155-0.5581%). Based on these categories, over 200 organizations were involved in the COVID-19 response in low-medium and medium-high vulnerable communities, whereas 32 and 41 were involved in low or high vulnerable communities, respectively (Fig. 3; Table 1).

Figure 3
figure 3

GIS Mapping of organizations involved in COVID-19 Response in Baltimore City.

Table 1 Distribution of organizations by community COVID vulnerability

We engaged with several leaders and representatives from organizations that have been involved in the COVID-19 response in the identified communities in Baltimore City. Surveys (n = 247) were disseminated, and 50 survey responses were received (Table 2). In response to the COVID-19 pandemic, 88% of respondents reported working with community members, serving various populations including children, older adults, persons with disabilities, and the LGBT community. 76% provided COVID-19 related services such as healthcare provider connections, food distribution, and temporary shelter. 30% facilitated access to COVID-related information, 16% provided COVID-19 testing, and 20% served as vaccination centers. 60% of organizations provided both in-person and virtual services, 30% provided in-person only, and 8% provided virtual services only. Half of the organizations formed new partnerships with others during the pandemic. Access to funding (58%) and PPE (24%) were among the challenges organizations faced. Furthermore, only 50% of respondents found the COVID-19 vaccine rollout process to be somewhat effective, with few finding it extremely effective or slightly effective.

Table 2 Descriptive statistics of the study sample

To improve preparedness, the participants recommended better coordination between government organizations, healthcare providers, and non-profit organizations, access to funding, and more consistent and better-executed policies and program plans (Table 3). According to the survey, the top three recommendations for improving preparedness differed slightly based on the type of organization. While a detailed sub-analysis was not possible due to the small sample size, respondents from faith-based and non-profit organizations suggested the need for more funding and resources, as well as better coordination between different organizations. On the other hand, healthcare centers and clinics suggested the need for better-executed and consistent policies, as well as standardized pandemic management protocols. Participants were given the option to report their recommendations in their own wording.

Table 3 Recommendations for better preparedness

Discussion

Our research findings contribute to the existing body of knowledge by highlighting the unequal distribution of social determinants of health in Baltimore through the use of geographic and survey data [16, 2325]. Our results demonstrate that these disparities create varying levels of vulnerabilities and structural disadvantages among residents in different neighborhoods [16, 2325]. Here, we focused primarily on the impact of the COVID-19 pandemic, the experiences, and the challenges faced in communities where vulnerabilities exist, in terms of social determinants of health, and where the burden of COVID-19 is the greatest. The use of COVID-19 Vulnerability index provided us with a simple measure to better understand the demographics, socioeconomic status, and health outcomes that are associated with vulnerable communities identified in this study. The analysis of geographic data highlighted the significant impact of social determinants of health on the health status, healthcare access, and COVID-19 vulnerability of Baltimore residents. These factors resulted in unique challenges reported by the participating organizations serving these communities, such as limited resources, lack of coordination, high levels of mistrust in the system, and misinformation.

Several studies described the incidence of communities with relatively high vulnerabilities that have significantly increased since the COVID-19 pandemic in several areas across the world [26,27,28,29,30]. Similarly, vulnerable communities identified in Baltimore City remained at greater risk during the pandemic. Our findings reveal substantial challenges faced by both vulnerable communities and the organizations that purposed to serve these communities during COVID-19. While nearly 80% of these organizations (surveyed) were actively providing services, funding was the leading challenge for most organizations. Without funding, this resulted in several limitations in properly serving vulnerable communities. For example, the populations served, reported experiencing several challenges during this pandemic including having access to essential resources. These findings were not surprising, since in many cases, the identified communities faced pre-existing challenges. For this reason, organizations played a significant role in providing necessities such as food and transportation; however, without this support of critical resources, communities were left at greater risk. Because of these limitations, a timely allocation and management of resources to vulnerable communities during the pandemic is critical to building some form of community resilience and lessening morbidity and mortality rates [31,32,33]. Previous studies show that having resources available eases the impact of major emergencies, aids in developing neighborhood resilience, and play a substantial role in better health outcomes [31, 34,35,36,37,38].

The lack of equal access to health care in vulnerable communities also contributed to increasing existing health inequalities [24, 37, 39]. With pre-existing health conditions, individuals in vulnerable communities were uniquely susceptible and disproportionately affected by the impacts of the COVID-19 pandemic. As of the date, Baltimore City communities were faced with the most cases, and the highest death rates in Maryland (Baltimore City Health Department, 2022). Populations served in this study experienced challenges in accessing COVID-19-related healthcare such as testing and treatment sites and healthcare insurance which are potential key factors leading to high death rates in Baltimore City. It is documented that at the beginning of the pandemic, the availability of COVID-19 testing sites did not always correspond with the vulnerability of the neighborhood, making them a hard access point for vulnerable populations, while they were more abundant near communities that were less vulnerable [40]. In many areas across the world, accessing health care, in general, during COVID-19 had its challenges due to health care systems being overburdened and under-sourced, though it was critical for communities with high-risk vulnerabilities [37, 39, 41].

During any crisis, the role of the government in communications, as well as the provision of accessible and accurate information, becomes critical more than ever [9, 34, 42]. For organizations that facilitated access to COVID-19-related information, having the access to accurate and up-to-date information themselves was quite challenging. Individuals’ need for information has increased significantly in time of big events such as the COVID-19 pandemic. Accessing updated and accurate information such as testing guidelines and eligibility, managing COVID-19, locating testing sites, transportation to testing sites, and where to find resources were key challenges for organizations providing services to vulnerable communities. In the early phase of the pandemic, government agencies and public health experts responsible for communicating information focused on engaging the public primarily to promote adherence to isolation and other protocols in an effort to stop the spread of the virus and fell short of providing additional pertinent information (Coronavirus Resource, 2020). In the later phases, public healthcare organizations begin partnering with community-based organizations (schools and churches) and healthcare centers to increase access to COVID-related information and resources. Having this pertinent information and access to health care is critical to developing community resilience in the face of disaster and in terms of rebounding afterward [36, 43]. Vulnerable communities, such as those identified in this study, would have more easily and positively navigated this emergency if there were better preparedness in terms of accessing and providing essential services.

Contributions to the increasing health inequalities and hardship seen in underserved vulnerable communities are primarily due to policy gaps and the lack of an equity perspective in the design and implementation of these practices [39, 44]. Increasing the awareness that the current policies in place are not effective and have failed to such support programs, can be strengthened, and tailored to aiding underserved communities [45]. This study highlights the unfavorable effects of policy programming related to living and working environments, particularly among vulnerable communities that previously suffer from food, housing, and job insecurities. Numerous challenges and insecurities in full effect prior to the pandemic coupled with the difficulties in accessing health services and other basic needs have exposed these individuals to the adverse effects of the pandemic [46,47,48,49]. Results from this study provide essential information for mandatory action for measures to improve preparedness and control of COVID-19 and other public health emergencies in vulnerable communities in the future. These findings can also be used to inform change to both health programming and policy. This information could potentially be utilized to improve and adapt new strategies that will help prepare local governments, community-based organizations, and community residents living in vulnerable areas for future public health disasters.

Recommendations

Based on the challenges faced, organizations made the following recommendations for policy, practice, and programming for better preparedness. Nearly 65% of the organizations surveyed recommended having better coordination between all government organizations, health care, non-profit organizations, and others. The majority of these organizations served people who also reported having misinformation (disinformation around COVID-19) and faced challenges in terms of access to food, transportation, and COVID-related health care. Our research findings offer a valuable resource for developing targeted recommendations for public health strategies during public emergencies to improve health outcomes, particularly for vulnerable communities and specific types of organizations. These recommendations could include the development of better risk communication guides and plans, criteria for the allocation of funding and resources in high-risk areas, and increased involvement of community and organizational leaders in the decision-making process. Non-profit and faith-based organizations serving vulnerable communities require special attention and support, including specific criteria for resource allocation and funding. By implementing these strategies, we can work towards improving health equity and addressing the disparities exacerbated by social determinants of health. This approach will help to ensure that resources are effectively distributed and utilized to support those who need it most.

Strengths and limitations

A strength of this study is that our questionnaire survey responses supported the knowledge and findings of historical challenges faced by vulnerable communities in Baltimore City. Another strength of this study is that we collected this information while people were still in the middle of the pandemic. Therefore, we obtained real-time insight into their experiences, which future studies will not be able to do. Our study also has a few limitations. One is that these findings are from a limited number of organizations in Baltimore City that provided services in response to COVID-19. Although we believe that our sample size of surveyed organizations is significant to inform city officials of the challenges faced by vulnerable communities and to provide insights for better preparedness, we also believe that there is a greater possibility that if all the organizations within these communities had surveyed, then responses may have been different. In addition, during COVID-19, we had to adhere to local COVID-19 guidelines and restrictions, and thus were not able to carry out in-person conversations and found it difficult to reach out to certain organizations who did not readily respond to our outreach methods. Given the nature of the chaotic response in the city, peoples’ time and attention were mostly absorbed by their role in the response, and we believe that this survey was not of priority to them. These limitations bring about restricted insights and approaches that have the potential to help better these community outcomes. With this in mind, we consider conducting a follow-up when things have calmed down, to try to capture the thoughts of some individuals who may have wanted to respond but were unable to because of these constraints.

Conclusions

In a city that has been significantly affected by the COVID-19 pandemic, we have indicated needed ways to improve community health outcomes. This study suggests that in the case of disaster, vulnerable communities require access to essential resources and information to prepare in order to reduce their susceptibility to worse outcomes. The perspectives from several organizations involved in serving vulnerable populations demonstrated effectiveness in providing suggested recommendations that will help vulnerable communities with disaster planning (prepare and respond to) and will aid in building community resilience. We believe that developing partnerships across diverse disciplines and among health researchers, health care providers, community leaders, communities, community leaders and planners, and policymakers will be key to implementing these changes.

Data Availability

The datasets analyzed during the current study were derived from the following public domain resources including Baltimore’s CSA data for the 2017 Neighborhood Health Profiles (https://health.baltimorecity.gov/neighborhoods/neighborhood-health-profile-reports); COVID-19 data (https://coronavirus.maryland.gov/); Zip code level data (https://opendata.maryland.gov/); Sociodemographic data (https://www.census.gov/programs-surveys/acs/data.html), and Spatial data (https://www.openstreetmap.org).

Abbreviations

BTEC:

Baltimore Transit Equity Coalition

GIS:

geographic information systems

CBPR:

community-based participatory research

CARES:

Community-Aligned REsearch Solutions

CSAs:

Community Statistical Areas

ACS:

American Community Survey

CCVI:

COVID-19 Community Vulnerability Index.

References

  1. Jasim IA, Fileeh MK, Ebrahhem MA, Al-Maliki LA, Al-Mamoori SK, Al-Ansari N. Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading.Environ Sci Pollut Res Int. 2022.

  2. Kim SY, Yeniova A. Global, regional, and national incidence and mortality of COVID-19 in 237 countries and territories, January 2022: A systematic analysis for World Health Organization COVID-19 Dashboard.Life Cycle. 2022;/.

  3. Organization WH. COVID-19 Weekly Epidemiological Update. 2020:17.

  4. Marrett CB. Racial disparities and COVID-19: the Social Context. J Racial Ethn Health Disparities. 2021;8(3):794–7.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sevelius JM, Gutierrez-Mock L, Zamudio-Haas S, McCree B, Ngo A, Jackson A, et al. Research with marginalized Communities: Challenges to Continuity during the COVID-19 pandemic. AIDS Behav. 2020;24(7):2009–12.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Dasgupta S, Bowen VB, Leidner A, Fletcher K, Musial T, Rose C, et al. Association between Social Vulnerability and a County’s risk for becoming a COVID-19 hotspot - United States, June 1-July 25, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(42):1535–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tang S, Horter L, Bosh K, Kassem AM, Kahn EB, Ricaldi JN, et al. Change in unemployment by social vulnerability among United States counties with rapid increases in COVID-19 incidence-July 1-October 31, 2020. PLoS ONE. 2022;17(4):e0265888.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Flanagan B, Gregory E, Hallisey E, Heitgerd J, Lewis B. A social vulnerability index for disaster management.Journal of Homeland Security and Emergency Management2011;8(1).

  9. Mullin S, Wang S, Morozova I, Berenson J, Asase N, Rodney DJ et al. Testing Communication Concepts on COVID-19 Contact Tracing Among Black and Latinx/Hispanic People in the United States.J Racial Ethn Health Disparities. 2022.

  10. Gold JAW, Rossen LM, Ahmad FB, Sutton P, Li Z, Salvatore PP, et al. Race, ethnicity, and Age Trends in persons who died from COVID-19 - United States, May-August 2020. MMWR Morb Mortal Wkly Rep. 2020;69(42):1517–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Killerby ME, Link-Gelles R, Haight SC, Schrodt CA, England L, Gomes DJ, et al. Characteristics Associated with hospitalization among patients with COVID-19 - Metropolitan Atlanta, Georgia, March-April 2020. MMWR Morb Mortal Wkly Rep. 2020;69(25):790–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Worsley-Tonks KEL, Bender JB, Deem SL, Ferguson AW, Fevre EM, Martins DJ, et al. Strengthening global health security by improving disease surveillance in remote rural areas of low-income and middle-income countries. Lancet Glob Health. 2022;10(4):e579–e84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bureau USC, QuickFacts M. 2021 [Available from: https://www.census.gov/quickfacts/baltimorecitymaryland.

  14. Badger E. The long, painful and repetitive history of how Baltimore became Baltimore. The Washington Post, WP Company; 2019.

  15. Cashin S, Opinion. How Larry Hogan Kept Blacks in Baltimore Segregated and Poor. POLITICO. 2020.

  16. Wen LS. Healthy Baltimore 2020: a blueprint for Health. Baltimore City Health Department; 2017.

  17. Chavis C, Bhuyan IA. Data-Driven Food Desert Metric to Understand Access to Grocery Stores using Chi-Square Automatic Interaction detector decision Tree Analysis. Transp Res Rec. 2022;2676(5):571–89.

    Article  Google Scholar 

  18. Chavis C, Jones A. Understanding Access to Grocery Stores in Food Deserts in Baltimore City. 2020.

  19. Operario D, King EJ, Gamarel KE. Prioritizing Community Partners and Community HIV Workers in the COVID-19 pandemic. AIDS Behav. 2020;24(10):2748–50.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jordan S, Latshaw MW, Boyapalli S, Corrigan AE, Bhuyan IA, Mohebbi M, Transit, Equity. & Environmental Health in Baltimore City 2021 [Available from: https://americanhealth.jhu.edu/sites/default/files/2021-09/JHU-016%20Transit%20Equity%20Report-FINAL_0.pdf.

  21. Banerjee S, Bhuyan I. Correlation of Crime Rate with Transit Connectivity and Transit Demand at Census Block Group Level. International Conference on Transportation and Development 2019. 2019.

  22. Galiatsatos P, Kineza C, Hwang S, Pietri J, Brigham E, Putcha N, et al. Neighbourhood characteristics and health outcomes: evaluating the association between socioeconomic status, tobacco store density and health outcomes in Baltimore City. Tob Control. 2018;27(e1):e19–e24.

    Article  PubMed  Google Scholar 

  23. Department BCH. Neighborhood health profile Reports 2017 [Available from: https://health.baltimorecity.gov/neighborhoods/neighborhood-health-profile-reports.

  24. Maza C. Black West Baltimore Is Still Waiting for Equity: Talk Poverty; 2020 [Available from: https://talkpoverty.org/2020/01/14/black-west-baltimore-still-waiting-equity/#:~:text=In%20Baltimore%E2%80%99s%20Greenmount%20East%20neighborhood%2 C%20the%20average%20life,of%20the%20world%E2%80%99s%20most%20and%20least%20developed%20countries.

  25. Department BCH. State of Health in Baltimore: Summary of Key Issues, Services, and Policies 2017 [Available from: https://health.baltimorecity.gov/sites/default/files/BCHD%202017%20White%20Paper_0.pdf.

  26. Ameyaw EK, Ahinkorah BO, Seidu AA, Njue C. Impact of COVID-19 on maternal healthcare in Africa and the way forward. Arch Public Health. 2021;79(1):223.

    Article  PubMed  PubMed Central  Google Scholar 

  27. D’Amico EJ, Palimaru AI, Dickerson DL, Dong L, Brown RA, Johnson CL, et al. Risk and resilience factors in urban american indian and Alaska native youth during the Coronavirus Pandemic. Am Indian Cult Res J. 2020;44(2):21–48.

    PubMed  Google Scholar 

  28. Malmusi D, Pasarin MI, Mari-Dell’Olmo M, Artazcoz L, Diez E, Tolosa S, et al. Multi-level policy responses to tackle socioeconomic inequalities in the incidence of COVID-19 in a european urban area. Int J Equity Health. 2022;21(1):28.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Santana CLA, Manfrinato CV, Souza PRP, Marino A, Conde VF, Stedefeldt E, et al. Psychological distress, low-income, and socio-economic vulnerability in the COVID-19 pandemic. Public Health. 2021;199:42–5.

    Article  CAS  PubMed  Google Scholar 

  30. Veiga Goncalves N, De Melo Neto JS, Kazumi da Trindade Noguchi S, Silva Machado A, Da Silva Martins Junior A, Da Silva Peixoto MC, et al. COVID-19 in socially vulnerable quilombola populations in Salvaterra, para, Eastern Amazon, Brazil. J Infect Dev Ctries. 2021;15(8):1066–73.

    Article  PubMed  Google Scholar 

  31. Diez Roux AV. Neighborhoods and health: where are we and were do we go from here? Rev Epidemiol Sante Publique. 2007;55(1):13–21.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Owens S. The Relationship between Neighborhood Disadvantage Trajectories and Health Outcomes in a Large Urban US City. Graduate Theses, Dissertations, and Problem Reports. 2016;3636.

  33. Protecting public health. & vulnerable populations in a pandemic. AMA Journal of Ethics; 2020. [.

  34. Demiris G, Chung J, Turner AM. Using Informatics to Engage vulnerable populations in Research: Lessons learned from the COVID-19 pandemic. Stud Health Technol Inform. 2022;290:858–61.

    PubMed  PubMed Central  Google Scholar 

  35. Ghosh-Jerath S, Kapoor R, Dhasmana A, Singh A, Downs S, Ahmed S. Effect of COVID-19 pandemic on Food Systems and Determinants of Resilience in Indigenous Communities of Jharkhand State, India: a serial cross-sectional study. Front Sustain Food Syst. 2022;6:724321.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wells KB, Tang J, Lizaola E, Jones F, Brown A, Stayton A, et al. Applying community engagement to disaster planning: developing the vision and design for the Los Angeles County Community Disaster Resilience initiative. Am J Public Health. 2013;103(7):1172–80.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Fauci AJ, Latina R, Iacorossi L, Coclite D, D’Angelo D, Napoletano A, et al. Allocation of scarce critical care resources during public health emergencies: which ethical principles support decision making. Clin Ter. 2022;173(4):384–95.

    CAS  PubMed  Google Scholar 

  38. O’Grady N, Shaw D, Parzniewski S. People in a pandemic: rethinking the role of ‘Community’ in community resilience practices. Geoforum. 2022;132:32–41.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Urtaran-Laresgoiti M, Gonzalez-Rabago Y, Martin U, Rivadeneyra-Sicilia A, Morteruel M. Health and health inequalities impact assessment for non-clinical measures to control COVID-19 in the Basque Country and Navarre (Spain). J Public Health Res. 2022;11(3):22799036221110020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Thompson CN, Baumgartner J, Pichardo C, Toro B, Li L, Arciuolo R, et al. COVID-19 outbreak - New York City, February 29-June 1, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(46):1725–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sparling A, Walls M, Mayfield CA, Priem JS, Durham J, Hetherington T, et al. Racial/Ethnic disparities in Health Care setting choice for adults seeking severe Acute Respiratory Syndrome Coronavirus 2 testing. Med Care. 2022;60(1):3–12.

    Article  PubMed  Google Scholar 

  42. Gisondi MA, Chambers D, La TM, Ryan A, Shankar A, Xue A et al. A Stanford Conference on Social Media, Ethics, and COVID-19 Misinformation (INFODEMIC): Qualitative Thematic Analysis. J Med Internet Res. 2022;24(2):e35707.

  43. Favas C, Ansbro E, Eweka E, Agarwal G, Lazo Porras M, Tsiligianni I, et al. Factors influencing the implementation of remote delivery strategies for non-communicable Disease Care in Low- and Middle-Income Countries: a narrative review. Public Health Rev. 2022;43:1604583.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Iceland J, Sakamoto A. The Prevalence of Hardship by Race and Ethnicity in the USA, 1992–2019.Popul Res Policy Rev. 2022:1–36.

  45. Hudson B, Hunter D, Peckham S. Policy failure and the policy-implementation gap: can policy support programs help? Policy Des Pract. 2019;2(1):1–14.

    Google Scholar 

  46. Ibe CA, McNair OS. Advancing and sustaining the Community Health Worker Workforce in Baltimore City: a call to action for key stakeholders. Baltimore, MD: Abell Foundation Report; 2021.

    Google Scholar 

  47. Thornton RL, Glover CM, Cene CW, Glik DC, Henderson JA, Williams DR. Evaluating strategies for reducing Health Disparities by addressing the Social Determinants of Health. Health Aff (Millwood). 2016;35(8):1416–23.

    Article  PubMed  Google Scholar 

  48. COVID-19. : Protecting people and societies 2020 [Available from: https://www.oecd.org/coronavirus/policy-responses/covid19-protecting-people-and-societies-e5c9de1a/.

  49. Woodfine L, Green L, Evans L et al. Exploring the health and well-being impact of COVID-19 on housing and housing insecurity. Public Health Wales NHS Trust, Cardiff, (2021, accessed 11 July 2022). 2021.

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Acknowledgments

We would like to thank Kristen Walker and Toni-Ann Lee who helped with the development of the organizational databases and outreach. We would also like to thank all organizations for their participation in our online surveys in providing invaluable information involved with providing COVID-19 response in vulnerable communities.

Funding

Open Access funding enabled and organized by Projekt DEAL

This study was funded by the National Science Foundation NSF 2029441 and has been supported by the Community Engagement Core of the Center for Health Disparities Research and Innovation at Morgan State University (NIMHD U54MD013376).

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Authors and Affiliations

Authors

Contributions

PS designed the study. PS and ES supervised and provided suggestions for the study. RB and ES created the organizations’ databases. RB and ES assisted in reaching out to the organizations. PS, RB, and ES designed the questionnaire. RB collected and analyzed the data. IB developed the Covid-19 Community Vulnerability Index. IB and RB analyzed publicly available data sources and created the GIS maps. BE contributed to the original draft of the paper. BE, ES, RB, PS, and IB reviewed and edited the manuscript. All authors read and approved the final version.

Corresponding author

Correspondence to Payam Sheikhattari.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This study was approved by Morgan State University Institutional Review Board (IRB #21/07-0123). All methods were conducted in accordance with the relevant guidelines and regulations. Informed consent was obtained from all subjects involved in the study.

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Not applicable.

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12889_2023_15479_MOESM1_ESM.docx

Supplementary Fig. 1. GIS Mapping of study sites in Baltimore City based on COVID-19 Community Vulnerability (CCVI) index. Sites are categorized by indicators (A) poverty rate, (B) unemployment rate percentile, (C) no high school diploma percentile, (D) non-White percentile, (E) lung cancer mortality rate, (F) heart disease mortality rate, G) sociodemographic vulnerability, and health vulnerability.

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Sheikhattari, P., Barsha, R.A.A., Shaffer, E. et al. Lessons learned to improve COVID-19 response in communities with greatest socio-economic vulnerabilities. BMC Public Health 23, 659 (2023). https://doi.org/10.1186/s12889-023-15479-0

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