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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: Med Care Res Rev. 2024 Dec 10;82(2):119–136. doi: 10.1177/10775587241298566

Urban and Rural Disparities in COVID-19 Outcomes in the United States: A Systematic Review

Jung A Kang 1, Denise D Quigley 2, Ashley M Chastain 1, Hsin S Ma 3, Jingjing Shang 1, Patricia W Stone 1
PMCID: PMC11871999  NIHMSID: NIHMS2041604  PMID: 39655727

Abstract

This systematic review investigates disparities in COVID-19 outcomes (infections, hospitalizations, and deaths) between urban and rural populations in the U.S. Of the 3,091 articles screened, 55 were selected. Most studies (n=43) conducted national analyses, using 2020 data, with some extending into 2021. Findings show urban areas had higher COVID-19 cases and hospitalizations in 2020, while rural areas saw increased cases in 2021 and mixed hospitalization results. Urban areas also had higher mortality rates in 2020, with rural rates rising in 2021 and 2022. Most studies did not explore reasons for urban/rural differences. The few that did found that vulnerable groups, including racially and ethnically minoritized populations, older adults, those with comorbidities, lower socioeconomic status and vaccination rates, experienced exacerbated disparities in rural regions. COVID-19 outcomes varied over time and by area due to population density, healthcare infrastructure, and socioeconomic factors. Tailored interventions are essential for health equity and effective policies.

Keywords: Rural Health, Urban Health, Urban Rural Disparities, COVID-19, Systematic Review

Introduction

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has presented an unprecedented global challenge with far-reaching health, social, and economic consequences. By October 2023, the United States (U.S.) surpassed more than 103.4 million COVID-19 cases, resulting in approximately 6.5 million hospitalizations and 1.1 million deaths (Centers for Disease Control and Prevention, 2022). While the beginning of the COVID-19 pandemic in the U.S greatly impacted urban residents, rural areas have had higher numbers of COVID-19 cases relative to the population throughout the pandemic (Centers for Disease Control and Prevention, 2021), highlighting the existence of rural health disparities. Health disparities are defined as differences in the burden of disease between communities or socio-demographically defined groups of people (Healthy People 2020, 2016). They can arise as a result of limited access to healthcare or other social net resources, but might also arise from community characteristics (e.g., poverty, violence) or environmental conditions (e.g., poor air quality) (Institute of Medicine Committee on the et al., 2006). Recently, the Centers for Disease Control and Prevention (CDC) announced strategies to reduce COVID-19-related health disparities (Centers for Diesase Control and Prevention, 2021). In this announcement, the CDC identified populations in rural areas as potential groups suffering from disproportionate COVID-19 burden.

Approximately 20% of the U.S. population, which are about 65 million people, live in areas designated as rural by the U.S. Census Bureau (Kozhimannil & Henning-Smith, 2021). Rural residents have multiple risk factors for COVID-19 infection, hospitalization, and death, such as being generally older and having higher rates of multiple chronic diseases compared to urban residents (Lakhani et al., 2020). Furthermore, researchers have identified healthcare access issues among rural Americans including the lack of Intensive Care Unit (ICU) beds, healthcare workers, and available ventilators, which were also revealed as challenging factors associated with higher risk of COVID-19 (Lakhani et al., 2020).

Despite these vulnerable characteristics and the unique COVID-19 challenges in rural populations, disparities in COVID-19 outcomes in the U.S. have not been comprehensively synthesized for urban and rural individuals. To address this knowledge deficit, we conducted a systematic review to examine differences in COVID-19 outcomes, encompassing infection rates, hospitalizations, and mortality, within urban and rural populations across the U.S. This systematic review aims to provide a comprehensive analysis of the evolving landscape of COVID-19 disparities in urban and rural areas, considering a myriad of factors that influence these disparities.

Conceptual Framework

This review was guided by a conceptual framework (Figure 1) adapted from the National Institute on Minority Health and Health Disparities (NIMHD) framework (Alvidrez et al., 2019). This framework depicts social determinants of health across a variety of domains and different levels including individual, interpersonal, and community.17 Technically, urban and rural location and the COVID-19 environment are community level indicators, which impact everything in the model. However, because of the importance of these two social determinants of health in our review, we have drawn them separately. The individual’s cultural identity is operationalized by the vulnerable population variable. All social determinants of health impact the care environment (hospitals, nursing homes, other) and health outcomes; and the care environment also impacts health outcomes. The main health outcomes of interest are COVID-19 cases, hospitalizations and deaths.

Figure 1: Conceptual Model Adapted From the NIMHD Minority Health and Health Disparities Framework.

Figure 1:

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NOTE: NIMHD stands for National Institute on Minority Health and Health Disparities. The thick black arrow represents the main social determinant of health of interest, i.e., the influence of urban and rural location, on the health outcomes of COVID-19 cases, hospitalizations and deaths.

Methods

We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021).

Search Strategy

We applied a structured search strategy to three databases to identify articles that compared COVID-19 outcomes in U.S. urban and rural populations. We searched PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Embase. The search included terms related to the U.S. (e.g., America), rural (e.g., non-metropolitan), urban (e.g., metropolitan), COVID-19 (e.g., SARS-CoV-2), and outcomes including cases, hospitalizations, and deaths (e.g., prevalence, hospital admission, mortality). We developed search terms based on previous systematic reviews related to the urban and rural differences (Aljassim & Ostini, 2020) and COVID-19 outcomes (Mackey et al., 2021). Appendix A presents the search strategies for each database. An ascendency and descendancy search were conducted to identify additional articles. We limited the search to include English-language articles published between January 2020 and April 15, 2024. We applied this date restriction to the search since the COVID-19 pandemic started in December 2019 (World Health Organization, 2020).

Eligibility Criteria

All peer reviewed, English-language, observational studies published between January 2020 and April 15, 2024, were eligible for inclusion. Studies were included if they met the following criteria: 1) focused on adult populations in the U.S.; 2) examined differences between urban and rural residents; and 3) examined COVID-19 outcomes including cases, hospitalizations, and deaths. COVID-19 outcomes were limited to cases, hospitalizations, and deaths to differentiate COVID-19 outcomes from COVID-19 preparedness, risk factors, and COVID-19 behavioral and psychological outcomes. Studies were excluded based on the following exclusion criteria: 1) focused on non-U.S. populations; 2) focused on pediatric population; 3) examined COVID-19 outcomes in urban areas only or in rural areas only; 4) examined different outcome measures related to COVID-19 (e.g., pandemic preparedness); and 5) non-empirical (e.g., editorials), qualitative or non-peer reviewed (e.g., abstracts) studies.

Study Selection

From a team of six researchers (JK, DQ, AC, JS, PS, HM), two researchers were randomly assigned to articles and independently screened all titles and abstracts using the inclusion and exclusion criteria and then a full-text assessment of the remaining articles was conducted independently by at least two researchers using Covidence, a web-based platform for systematic reviews. During the full text review, references were reviewed to identify additional articles. Any conflicts were resolved through team discussion.

Data Extraction

Two researchers from the team of six (JK, DQ, AC, JS, PS, HM) were randomly assigned to articles and independently extracted data from included studies using a form developed a priori including: author, publication year, study purpose, data sources, timeframe, study sample and setting, study design, urban-rural measures, COVID-19 outcome measures, study results and limitations. Data abstraction was reviewed and adjudicated through team review and discussion.

Quality Appraisal

A different set of two researchers on the team (JK, DQ, AC, JS, PS, HM) were assigned to independently assess the quality of included studies using the Newcastle-Ottawa Quality Assessment Scale (NOS) (Wells et al., 2014). The NOS was developed to assess the quality of non-randomized studies based on three perspectives with a score range from 0 to 9: the selection of the study groups (maximum 4 points); the comparability of the groups (maximum 2 points); and the ascertainment of either the exposure or outcome of interest (maximum 3 points) (Wells et al., 2014). Studies were scored with the Agency for Healthcare Research and Quality (AHRQ) scoring system: ‘good’ (3 or 4 points for selection, 1 or 2 points for comparability, and 2 or 3 points for outcome/exposure); ‘fair’ (2 points for selection, 1 or 2 points for comparability, and 2 or 3 points or outcome/exposure); and ‘poor’ (0 or 1 point for selection, 0 point for comparability, and 0 or 1 point for outcome/exposure). Because the NOS was originally developed to assess cohort and case-control studies, we also used the modified NOS developed to assess cross-sectional and ecological studies (Mackey et al., 2021; Moskalewicz & Oremus, 2020; Yuan et al., 2019). Appendix B includes the modified NOS questions and AHRQ thresholds.

Data Synthesis

We synthesized data narratively by identifying common themes or areas across the studies (Popay et al., 2006); meta-analyses were not performed due to heterogeneity of the included studies (e.g., different covariates included in adjusted models). We present quality appraisal and data extraction followed by a narrative synthesis. The waves of the COVID-19 pandemic waves are defined as wave 1 (March 1, 2020, to May 31, 2020), wave 2 (June 1, 2020, to September 30, 2020), wave 3 (October 1, 2020, to June 19, 2021), wave 4-Delta (June 20, 2021, to December 18, 2021), wave 5-Omicron (December 19, 2021, to February 28, 2022), and Wave 6 (after March 2022–March 2023).

Results

Search Results

Figure 2 shows the PRISMA flow diagram. The search strategy identified 3,091 unique articles. The title and abstract screening eliminated 2,836 studies that did not meet the inclusion criteria. We conducted a full text review of the remaining 255 articles, which yielded 58 studies that were deemed eligible for quality appraisal. No additional studies were identified for inclusion through ascendency and descendancy searches. The primary reason for exclusion of studies was not having an urban and rural comparison (n = 83; e.g., rural description only) followed by not having COVID-19 infections, hospitalizations or deaths as an outcome (n = 49; e.g., severity of COVID-19 symptoms), not having an observational study design (n = 23; e.g., qualitative design), not being peer reviewed (n = 20; e.g., conference abstracts), and studying a non-U.S. population (n = 19).

Figure 2. PRISMA 2020 Flow Diagram.

Figure 2

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Note. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71

Quality Appraisal

Of the 58 studies assessed for quality, we excluded three studies based on low-quality, yielding a total of 55 included studies. The reason these three studies received a poor-quality rating was that the analyses did not control for confounding factors. Among the 55 included studies, nine were cohort (Anzalone et al., 2023; Dixon et al., 2021; Giannouchos et al., 2023; Hatfield et al., 2023; Ioannou et al., 2021; Jiang et al., 2022; Kennedy et al., 2022; McCoy et al., 2022; Uschner et al., 2022), 17 were cross-sectional (Abrams et al., 2020; Chang et al., 2021, 2022; Curtin & Heron, 2022; Epané et al., 2023; Fan et al., 2021; Ferguson et al., 2021; Greenhouse et al., 2023; Grome et al., 2022; Jones et al., 2023; Li et al., 2022; Lundberg et al., 2023; MacCallum-Bridges et al., 2024; Schold et al., 2021; Travers et al., 2021; Tsai et al., 2021; Yang et al., 2021), and 29 were ecological studies (Ahmed et al., 2020; Andrews et al., 2021; Brandt et al., 2021; Choi & Yang, 2021; Fielding-Miller et al., 2020; Finch et al., 2021; Huang et al., 2021; Iyanda et al., 2022; Jackson et al., 2021; Karim & Chen, 2021; Khan et al., 2022; Kitchen et al., 2021; Li et al., 2021; Lin et al., 2022; Lundberg et al., 2022; McLaughlin et al., 2021; Millett et al., 2020; Mourad et al., 2021; Paglino et al., 2024; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Paul et al., 2020; Qeadan et al., 2021; Rafiq et al., 2022; Rifat & Liu, 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Tang et al., 2023; Wang et al., 2021).

All cohort and cross-sectional studies were scored as ‘good’ quality. Five ecological studies (Jackson et al., 2021; Lundberg et al., 2022; McLaughlin et al., 2021; Paglino et al., 2024; Tang et al., 2023) received ‘good’ quality and the remaining (Ahmed et al., 2020; Andrews et al., 2021; Brandt et al., 2021; Choi & Yang, 2021; Fielding-Miller et al., 2020; Finch et al., 2021; Huang et al., 2021; Iyanda et al., 2022; Jackson et al., 2021; Karim & Chen, 2021; Khan et al., 2022; Kitchen et al., 2021; Li et al., 2021; Lin et al., 2022; McLaughlin et al., 2021; Millett et al., 2020; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Paul et al., 2020; Qeadan et al., 2021; Rafiq et al., 2022; Rifat & Liu, 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Wang et al., 2021) were scored as ‘fair’ quality (n = 24). Reasons for receiving ‘fair’ quality included not having a description of missing data and/or not including a comparison of missing data versus included data. Appendix Table 1 presents the quality appraisal for the 55 studies.

Study Characteristics

Study Population.

Seventy-eight percent of the studies (n = 43) conducted a national-level analysis and the remaining 12 studies (Brandt et al., 2021; Dixon et al., 2021; Giannouchos et al., 2023; Grome et al., 2022; Huang et al., 2021; Kennedy et al., 2022; MacCallum-Bridges et al., 2024; McCoy et al., 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Uschner et al., 2022) focused on one state. Most of the studies (n = 46, 84%) used COVID-19 data from wave 1, 70% (n = 39) from wave 2, 62% (n = 34) from wave 3, 22% (n = 12) from wave fourth-Delta, 16 % (n = 9) from wave five-Omicron, and the remaining 4 studies (n = 4, 7%) used data from wave six (See Table 2). We also examined how many studies included COVID-19 data covering the full time period of a COVID-19 wave and found that 42% (n = 23) of all included studies examined all of wave 1, 24% (n = 13) examined all of wave 2, 27% (n = 15) examined all of wave 3, 24% (n = 13) examined all of wave 4, 13% (n = 7) examined all of wave 5, and no studies included all of wave 6. Also, only two studies examined data across the first, second and third waves of the COVID-19 (Anzalone et al., 2023; MacCallum-Bridges et al., 2024).

Table 2.

Summary of Urban versus Rural Results by Outcome and Sorted By Significance and Across Time

Results Author, Year COVID data time frame Urban Rural
2020 2021 2022
Wave (W) 1 W2 W3 W4 W5 W6
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3
COVID-19 Cases
Higher Urban (HU) Fan 2021 pX X ↑
Paul 2020 X ↓
Rafiq 2022 X ↑0/1
Abrams 2020 X Xp ↑
Wang 2021 X Xp ↑1–3vs4–7,8–9
Choi & Yang 2021 X X ↑0/1
Karim & Chen 2021 X X ↑ metropolitan vs micropolitan, rural
Huang 2021 pX X X ↓rural, high rural vs urban
Kitchen 2021 X X Xp ↓0/1
McLaughlin 2021 X X Xp ↑
Ferguson 2021 pX X X X ↓rural, high rural vs urban
Li 2021 X X X X ↓2–4,5–9 RUCCvs1
Rifat 2022 X X X X Xp ↑1–3vs4–9
Yang 2021 pX X X Xp ↓
Andrews 2021 X X X X X Xp ↓% rural, ↔% rural in 2021
Sattenspiel 2023 pX X X X X X X X X ↑ urban vs semirural, rural
Higher Rural (HR) Brandt 2021 X Xp ↓ ↑rural, black residents
Jiang 2022 X X ↑0/1
Travers 2021 X X Xp ↑rural, black residents
Dixon 2021 pX X X X ↑
Jackson 2021 X X X X ↑0/1
Khan 2022 X X X ↑
Li 2022 X X X X ↑0/1
Uschner 2022 Xp X X ↑ rural, suburban vs urban
Jones 2023 pX X X X X ↑9 vs 1
MacCallum-Bridges2024 X X X X X X X X X Xp ↑ rural, micropolitan vs metropolitan
Mixed (M) Mourad 2021 X ↑2 vs 1, 3 vs 1 ↓4 vs 1
Millett 2020 X Xp ↔1–6 ↑black residents
Ioannou 2021 pX X X X ↑ prior to April 2020 ↑ after to September 2020
Schnake-Mahl 2022 pX X X X X X X Xp ↑ NOLA proper prior to July 2020 ↑ Other urban, suburban, rural after July 2020
Hatfield X X X X X X X X ↔+
NS Kennedy 2022 pX X ↔
Tang 2023 X X ↔
Results Author, Year COVID data time frame Urban Rural
2020 2021 2022
Wave (W) 1 W2 W3 W4 W5 W6
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3
COVID-19 Hospitalizations
HU Chang 2022 X X X ↑urban/suburban/rural
Greenhouse 2023 pX X X X X X X Xp ↑0/1
HR Dixon 2021 pX X X X ↑
Tsai 2021 pX X X X ↓0/1
Chang 2021 X X X X ↑0/1
Anzalone 2023 X X X X X X ↑ urban adjacent & non-urban-adjacent rural vs urban
NS Giannouchos 2023 X X X X Xp ↔ 0/1
COVID-19 Deaths
HU Karim & Chen 2021 pX X ↓ rural, micropolitan vs metropolitan
Qeadan 2021 X X ↓ a continuous measure of % rural
Fielding-Miller 2020 pX ↑
Huang 2021 pX X X ↑1–3vs4–9 RUCC
Kitchen 2021 X X Xp ↓0/1
McLaughlin 2021 X X Xp ↑
Paul 2021a X X Xp ↑
Paul 2021b X X Xp ↑
Li 2021 X X X X ↓2–4,5–9 RUCC vs 1
Rifat 2022 X X X X Xp ↑1–3vs4–9
Yang 2021 pX X X Xp ↓
Finch 2021 X X X X Xp ↓0/1, rural and least R/E diverse counties
Epane 2023 pX X X X X X X X Xp ↑ urban vs rural
Sattenspiel 2023 pX X X X X X X X X ↑ urban vs semirural, rural
HR Jiang 2022 X X ↑0/1
Dixon 2021 pX X X X ↑
Iyanda 2022 * X X X X ↑* rural, most rural vs most urban
Jackson 2021 * X X X X ↑ * 0/1
McCoy 2022 X X X X ↑0/1
Grome 2022 pX X X X Xp ↑ 0/1
Khan 2022 X X Xp ↑
Giannouchos 2023 X X X X Xp ↑ 0/1, inpatient and outpatient
Lundberg 2022 pX X X X X X X X Xp ↑ rural for Blacks, AIAN, Hispanics vs Whites
Lundberg 2023 pX X X X X X X X Xp ↑ rural ↑ rural for Blacks, AIAN, Hispanics vs Whites
Paglino 2024 pX X X X X X X X X X Xp ↑# rural vs large, medium/small metropolitan
Jones 2023 pX X X X X ↑9 vs 1
M Millett 2020 Xp ↔1–6 ↑black residents
Ahmed 2020* X X ↑1vs7–9 RUCC ↔1vs2–6 RUCC ↑*9vs1–4,6,7 RUCC ↔ 8vs9 RUCC
Travers 2021 X X pX ↑rural and high % black residents
Schold 2021 Xp X X X ↑ most urban vs urban ↑ most rural vs urban
Curtin 2022 X X X X ↑ most urban vs medium large-fringe urban, rural ↑ rural vs medium large-fringe urban
Ioannou 2021 Xp Xp Xp X X ↑ prior to April 2020 ↑ after to September 2020
Schnake-Mahl 2022 pX X X X X X X Xp ↑ Urban Jan-June 2020,↓ Urban July 2020-March 2021 ↑ Suburban, rural after July 2020 – June 2021
NS Lin 2022 X X X X Xp ↔RUCA; rural, suburban vs urban
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NOTE: A p proceeding or after an X (i.e. Xp or pX) denotes that the data timeframe is partially included for that quarter. We annotate significance on COVID-19 outcomes using ↑ to indicate a positive statistically significant difference (p<0.05), ↓ to indicate a negative statistically significant difference and ↔ no statistically significant difference (null findings). Bold indicates statistically significant findings where urban is higher/rural is lower. Italics indicates statistically significant findings where rural is higher/urban is lower. Normal text indicates mixed or null findings (i.e. non-significant differences for urban-rual measure). NS indicates no significant differences found for urban-rural measure.

+

indicates hospital-onset COVID-19 cases (i.e., testing positive for COVID-19 after admission and while hospitalized).

*

indicates case fatality as an outcome.

#

indicates excess natural-cause deaths (i.e., unrecognized COVID-19 deaths). AIAN = American Indian/Alaska Native. Across all of the urban/rural measures, 1 indicates the most urban.

The majority of studies used sampling frames of the U.S. population at the county-level (n = 32, 58%) (Ahmed et al., 2020; Andrews et al., 2021; Choi & Yang, 2021; Epané et al., 2023; Grome et al., 2022; Huang et al., 2021; Jiang et al., 2022; Jones et al., 2023; Karim & Chen, 2021; Kitchen et al., 2021; Li et al., 2021; Lin et al., 2022; Lundberg et al., 2022; McLaughlin et al., 2021; Millett et al., 2020; Mourad et al., 2021; Paglino et al., 2024; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Paul et al., 2020; Qeadan et al., 2021; Rafiq et al., 2022; Rifat & Liu, 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Uschner et al., 2022; Wang et al., 2021), followed by state-level population (n = 5) (Brandt et al., 2021; Dixon et al., 2021; MacCallum-Bridges et al., 2024; McCoy et al., 2022; Tang et al., 2023), Veterans (n = 4) (Fan et al., 2021; Ferguson et al., 2021; Greenhouse et al., 2023; Ioannou et al., 2021), nursing home-level (n = 3) (Abrams et al., 2020; Travers et al., 2021; Yang et al., 2021), hospital-level (n = 3) (Anzalone et al., 2023; Giannouchos et al., 2023; Hatfield et al., 2023), Medicare Fee-for-Service beneficiaries (n = 3) (Chang et al., 2021, 2022; Tsai et al., 2021), U.S. National Population (n = 2) (Curtin & Heron, 2022; Lundberg et al., 2023) transplant recipients (n = 1) (Schold et al., 2021), blood donors (n = 1) (Li et al., 2022), and single state central laboratory for outpatients (n = 1) (Kennedy et al., 2022). Because of the heterogeneity in descriptive statistics and the nature of the county-level and facility-level analysis, sample characteristics were described in various manners. Appendix Table 2 presents the detailed study characteristics of the included studies.

COVID-19 Outcomes.

Table 1 lists the main outcomes by type of urban-rural measure as well as identifying the COVID-19 data source. Thirty three articles reported on the number of COVID-19 cases (Abrams et al., 2020; Andrews et al., 2021; Brandt et al., 2021; Choi & Yang, 2021; Dixon et al., 2021; Fan et al., 2021; Ferguson et al., 2021; Hatfield et al., 2023; Huang et al., 2021; Ioannou et al., 2021; Jackson et al., 2021; Jiang et al., 2022; Jones et al., 2023; Karim & Chen, 2021; Kennedy et al., 2022; Khan et al., 2022; Kitchen et al., 2021; Li et al., 2021; Li et al., 2022; MacCallum-Bridges et al., 2024; McLaughlin et al., 2021; Millett et al., 2020; Mourad et al., 2021; Paul et al., 2020; Rafiq et al., 2022; Rifat & Liu, 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Tang et al., 2023; Travers et al., 2021; Uschner et al., 2022; Wang et al., 2021; Yang et al., 2021), 33 reported on COVID-19 deaths (Ahmed et al., 2020; Dixon et al., 2021; Epané et al., 2023; Fielding-Miller et al., 2020; Finch et al., 2021; Giannouchos et al., 2023; Grome et al., 2022; Huang et al., 2021; Ioannou et al., 2021; Iyanda et al., 2022; Jackson et al., 2021; Jiang et al., 2022; Jones et al., 2023; Karim & Chen, 2021; Khan et al., 2022; Kitchen et al., 2021; Li et al., 2021; Lin et al., 2022; Lundberg et al., 2022; Lundberg et al., 2023; McCoy et al., 2022; McLaughlin et al., 2021; Millett et al., 2020; Paglino et al., 2024; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Qeadan et al., 2021; Rifat & Liu, 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Schold et al., 2021; Travers et al., 2021; Yang et al., 2021), and six reported on COVID-19 hospitalizations (Anzalone et al., 2023; Chang et al., 2021, 2022; Dixon et al., 2021; Greenhouse et al., 2023; Tsai et al., 2021). Four studies also reported COVID-19 case fatality rates (Ahmed et al., 2020; Curtin & Heron, 2022; Iyanda et al., 2022; Jackson et al., 2021).

Table 1:

Main Outcomes Studied By Type of Urban Rural Measure Grouped By COVID-19 Data Source

Outcome Urban vs Rural
(0/1) Measure
(N=32)		 Non-Binary
Rural Urban Continuum Codes (RUCC) Index
(N=7)		 Non-Binary
National Center for Health Statistics
(NCHS) Urban Rural Classification Scheme
(N=8)		 Non-Binary
Other Urban and Rural Measures
(N=8)
COVID-19 Cases (N=33) Arkansas Department of Health lab test data:
Kennedy 2022
Blood Collection Organization Data:
Li 2022
CDC NH Module:
Travers 2021
Yang 2021
Missouri GISAID data:
Tang 2023
JHU CSSE:
Kitchen 2021
McLaughlin 2021
Paul 2020
Rafiq 2022
Michigan CReSS:
MacCallum-Bridges 2024
New York Times:
Huang 2021
Jackson 2021
PINC AI Healthcare Database:
Hatfield 2023+
State Department of Health Data:
Abrams 202030 states
Brandt 2021NC
Dixon 2021IN
USA Facts:
Choi & Yang 2021
Jiang 2022
Rifat 2022
VA Data:
Fan 2021Corporate Warehouse
Ioannou 2021EHR data 		Louisiana Department of Health:
Schnake-Mahl 20224
New York Times:
Khan 20229
USA Facts:
Li 20213
Jones 20239
Wang 20213 		JHU CSSE:
Mourad 20214
USA Facts:
Millett 20206 		North Carolina COVID-19 Community Research Partnership:
Uschner 2022–merged on US Census data for a three-category urban/suburban/rural measure
Missouri Department of Health and Senior Services:
Sattenspiel 2023–merged on US Census data for a three-category rural/semirural/urban measure
USA Facts:
 Andrews 2021 –merged on US Census Population data for a continuous measure of percent rural
 Karim & Chen 2021–merged on AHRF data for a three-category metropolitan/micropolitan/rural measure
VA Corporate Data Warehouse:
 Ferguson 2021–merged on US Census data for a three-category urban/rural/high rural measure
Hospitalizations (N=7) Medicare Fee-for-Service Claims Data:
Chang 2021
Tsai 2021
South Carolina Department of Health and
Environmental Control:
Giannouchos 2023
State Department of Health Data:
Dixon 2021IN
The COVID-19 Shared Data Resource:
Greenhouse 2023		 N3C Data Enclave:
Anzalone 20233 		Medicare Fee-for-Service Claims Data:
Chang 20223
COVID-19 Deaths (N=34) CDC NH Module:
Travers 2021
Yang 2021
JHU CSSE:
Kitchen 2021
McLaughlin 2021
Paul 2021a
Paul 2021b
Minnesota Death Certificate Data:
McCoy 2022
New York Times:
Fielding-Miller 2020
Finch 2021
Huang 2021
Jackson 2021*
South Carolina Department of Health and
Environmental Control:
Giannouchos 2023
State Department of Health Data:
Dixon 2021IN
Tennessee National Electronic Disease Surveillance System Base System:
Grome 2022
USA Facts:
Epane 2023
Jiang 2022
Rifat 2022
VA Data:
Ioannou 2021EHR data 		JHU CSSE:
Schnake-Mahl 20224
New York Times:
Khan 20229
USA Facts:
Ahmed 2020*9
Jones 20239
Li 20213
Scientific Registry of Transplant Recipients:
Schold 20219

 National Vital Statistics System:
Curtin 20226
USA Facts:
Millett 20206
WONDER online database:
Lundberg 20223
Lundberg 20233
Paglino 2024# 3 		CDC:
Lin 2022 – RUCA data for a three-category urban/suburban/rural
JHU CSSE:
 Qeadan 2021 – merged on County Health Rankings database for a continuous measure of percent rural
Missouri Department of Health and Senior Services:
Sattenspiel 2023–merged on US Census data for a three-category rural/semirural/urban measure
New York Times:
Iyanda 2022*-merged on US Census Population data for a three-category most/urban/most rural/complete rural
USA Facts:
 Karim & Chen 2021–merged on AHRF data for a three-category metropolitan/micropolitan/rural measure
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NOTE: AHRF =Area Health Resource File; CDC =Centers for Disease Control and Prevention; EHR = Electronic Health Record; GISAID = Global Initiative of Sharing Avin Influenza data; HHS =Health and Human Services; IN = Indiana Department of Health; JHU CSSE indicates county-level COVID-19 Data Repository by Johns Hopkins University Center for Systems Science and Engineering; MI CReSS = Michigan COVID-19 Recovery Surveillance Study; VA =Veterans Administration; NH indicates nursing home.

+

indicates hospital-onset COVID-19 cases (i.e., testing positive for COVID-19 after admission and while hospitalized).

#

indicates excess natural-cause deaths (i.e., unrecognized COVID-19 deaths).

*

indicates case fatality.

The most frequently used data source for the COVID-19 outcomes were the USAFacts (n = 11, 20%) (Ahmed et al., 2020; Andrews et al., 2021; Choi & Yang, 2021; Epané et al., 2023; Jiang et al., 2022; Jones et al., 2023; Karim & Chen, 2021; Li et al., 2021; Millett et al., 2020; Rifat & Liu, 2022; Wang et al., 2021) followed by State Department of Health data (n = 10, 18%) (Abrams et al., 2020; Brandt et al., 2021; Dixon et al., 2021; Giannouchos et al., 2023; Grome et al., 2022; Kennedy et al., 2022; McCoy et al., 2022; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022; Tang et al., 2023), COVID-19 Data Repository by the Johns Hopkins University (n = 8, 15%) (Kitchen et al., 2021; McLaughlin et al., 2021; Mourad et al., 2021; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Paul et al., 2020; Qeadan et al., 2021; Rafiq et al., 2022), and the New York Times (n = 6, 17%) (Fielding-Miller et al., 2020; Finch et al., 2021; Huang et al., 2021; Iyanda et al., 2022; Jackson et al., 2021; Khan et al., 2022). CDC data was used in five studies (Curtin & Heron, 2022; Fan et al., 2021; Ferguson et al., 2021; Ioannou et al., 2021; Lin et al., 2022; Lundberg et al., 2022; Lundberg et al., 2023; Paglino et al., 2024).VA data was used in four studies (Fan et al., 2021; Ferguson et al., 2021; Greenhouse et al., 2023; Ioannou et al., 2021).Medicare Fee-for-Service administrative claims data was used in three studies (Chang et al., 2021, 2022; Tsai et al., 2021), Two studies used the CDC nursing home module (Travers et al., 2021; Yang et al., 2021), and the remaining studies used blood collection organization data (n = 1) (Li et al., 2022), N3C Data Enclave (n = 1) (Anzalone et al., 2023), scientific registry of transplant recipients (n = 1) (Schold et al., 2021), PINC AI Healthcare Database (n = 1) (Hatfield et al., 2023), adult survey participants in the North Carolina CCRP (n = 1) (Uschner et al., 2022), and Michigan COVID-19 Recovery Surveillance Study (MI CReSS) (n = 1) (MacCallum-Bridges et al., 2024).

Urban/Rural Measures.

Urban and rural status was determined using several different methods in the 55 included studies. The most commonly used measures were from the Rural-Urban Continuum Codes (RUC codes; n = 14, 25%) (Ahmed et al., 2020; Anzalone et al., 2023; Greenhouse et al., 2023; Huang et al., 2021; Jiang et al., 2022; Jones et al., 2023; Khan et al., 2022; Li et al., 2021; MacCallum-Bridges et al., 2024; Rifat & Liu, 2022; Schnake-Mahl & Bilal, 2022; Schold et al., 2021; Tang et al., 2023; Wang et al., 2021), and National Centers for Health Statistics (NCHS; n = 13, 24%) (Chang et al., 2021, 2022; Curtin & Heron, 2022; Finch et al., 2021; Grome et al., 2022; Jackson et al., 2021; Kitchen et al., 2021; Li et al., 2022; Millett et al., 2020; Mourad et al., 2021; Paglino et al., 2024; Rafiq et al., 2022; Tsai et al., 2021), the remaining 26 studies used various sources to determine urban and rural location (e.g., Area Health Resources File, US Census Bureau). Two studies (Hatfield et al., 2023; McLaughlin et al., 2021) did not describe how they defined urban and rural.

Among these 55 studies, most used a binary variable for urban versus rural classification (n = 32, 58%) (Abrams et al., 2020; Brandt et al., 2021; Chang et al., 2021; Choi & Yang, 2021; Dixon et al., 2021; Epané et al., 2023; Fan et al., 2021; Fielding-Miller et al., 2020; Finch et al., 2021; Giannouchos et al., 2023; Greenhouse et al., 2023; Grome et al., 2022; Hatfield et al., 2023; Huang et al., 2021; Ioannou et al., 2021; Jackson et al., 2021; Jiang et al., 2022; Kennedy et al., 2022; Kitchen et al., 2021; Li et al., 2022; MacCallum-Bridges et al., 2024; McCoy et al., 2022; McLaughlin et al., 2021; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Paul et al., 2020; Rafiq et al., 2022; Rifat & Liu, 2022; Tang et al., 2023; Travers et al., 2021; Tsai et al., 2021; Yang et al., 2021), and the remaining studies used three (n = 13) (Anzalone et al., 2023; Chang et al., 2022; Ferguson et al., 2021; Iyanda et al., 2022; Karim & Chen, 2021; Li et al., 2021; Lin et al., 2022; Lundberg et al., 2022; Lundberg et al., 2023; Paglino et al., 2024; Sattenspiel et al., 2023; Uschner et al., 2022; Wang et al., 2021), four (n = 2) (Mourad et al., 2021; Schnake-Mahl & Bilal, 2022), six (n = 2) (Curtin & Heron, 2022; Millett et al., 2020), or nine (n = 5) (Ahmed et al., 2020; Jones et al., 2023; Khan et al., 2022; Li et al., 2021; Schold et al., 2021) categories for rurality variables. Six studies used a continuous variable indicating the percentage of rural population by county (Andrews et al., 2021; Grome et al., 2022; Jiang et al., 2022; Qeadan et al., 2021; Sattenspiel et al., 2023; Schnake-Mahl & Bilal, 2022). Seven studies described 20%–30% of their sample to be from rural areas (Chang et al., 2021; Epané et al., 2023; Grome et al., 2022; McCoy et al., 2022; Travers et al., 2021; Tsai et al., 2021; Yang et al., 2021). However, when using different urban and rural classification methods, a few studies noted that 60% to 70% of their sample were from rural areas (Giannouchos et al., 2023; Kitchen et al., 2021; McLaughlin et al., 2021; Rifat & Liu, 2022).

Differences in Urban and Rural COVID-19 Outcomes

Table 2 provides a summary of the findings, organized by significance, and displayed across study timeframes. We found that the impact of urban-rural location on COVID-19 outcomes varies not only across outcomes but also over time. While some general trends emerged, such as the U.S. having higher COVID-19 case rates in urban areas (n = 16) (Abrams et al., 2020; Andrews et al., 2021; Choi & Yang, 2021; Fan et al., 2021; Ferguson et al., 2021; Huang et al., 2021; Karim & Chen, 2021; Kitchen et al., 2021; Li et al., 2021; McLaughlin et al., 2021; Paul et al., 2020; Rafiq et al., 2022; Rifat & Liu, 2022; Sattenspiel et al., 2023; Wang et al., 2021; Yang et al., 2021), the rural-urban effects were not consistent across all studies. Factors like the timing of data collection and different data sources played important roles in these discrepancies. Appendix Table 3 provides a detailed summary of results. We review here the findings for each outcome.

COVID-19 Cases.

Across the 33 studies, there were mixed results when looking cross-sectionally at the differences in urban versus rural COVID-19 cases; however, there were urban-rural differences in COVID-19 case trends over time. In 2020, urban areas generally exhibited higher case rates compared to rural areas across all quarters (Abrams et al., 2020; Choi & Yang, 2021; Fan et al., 2021; Ferguson et al., 2021; Huang et al., 2021; Karim & Chen, 2021; Kitchen et al., 2021; Li et al., 2021; McLaughlin et al., 2021; Paul et al., 2020; Rafiq et al., 2022; Rifat & Liu, 2022; Sattenspiel et al., 2023; Wang et al., 2021), with some fluctuations (Dixon et al., 2021; Jackson et al., 2021). However, in 2021, five studies reported an increase in rural cases in the latter part of the year (n = 5) (Andrews et al., 2021; Ioannou et al., 2021; Khan et al., 2022; Li et al., 2022; Schnake-Mahl & Bilal, 2022). In two studies (Brandt et al., 2021; Travers et al., 2021), researchers observed higher case rates in rural areas especially when there was a higher proportion of black residents. In two studies (Andrews et al., 2021; Paul et al., 2020), researchers examined temporal trends and spatial patterns in COVID-19 cases over different time periods highlighting the dynamic nature of the pandemic and the changing prevalence of cases in urban and rural areas. In the early phase of the pandemic, rural counties experienced a smaller growth in COVID-19 infection rates compared to urban counties (Paul et al., 2020); however, as the pandemic progressed, rural counties had increasingly prevalent high COVID-19 cluster areas (Andrews et al., 2021).

COVID-19 Hospitalizations.

One study found that COVID-19 hospitalization rates in urban areas were higher from early 2020 to end of 2021 than in rural areas (Greenhouse et al., 2023); another study found this same pattern in early to mid-2020 (Chang et al., 2022). However, in another study (Tsai et al., 2021) researchers found a decrease in urban hospitalizations over time in 2020. Four studies found that rural hospitalization rates increased over time, i.e., later in 2020 (n = 3) (Chang et al., 2021; Dixon et al., 2021; Tsai et al., 2021) and until the end of June 2021 (n = 1) (Anzalone et al., 2023).

COVID-19 Deaths.

Urban and rural differences in COVID-19 mortality rates also exhibited considerable heterogeneity across studies. In 14 studies, researchers observed lower death rates in rural areas (Epané et al., 2023; Fielding-Miller et al., 2020; Finch et al., 2021; Huang et al., 2021; Karim & Chen, 2021; Kitchen et al., 2021; Li et al., 2021; McLaughlin et al., 2021; Paul, Adeyemi, et al., 2021; Paul, Arif, et al., 2021; Qeadan et al., 2021; Rifat & Liu, 2022; Sattenspiel et al., 2023; Yang et al., 2021). In contrast, 11 other studies reported higher rural death rates (n=11) (Dixon et al., 2021; Giannouchos et al., 2023; Grome et al., 2022; Iyanda et al., 2022; Jackson et al., 2021; Jiang et al., 2022; Jones et al., 2023; Khan et al., 2022; Lundberg et al., 2022; Lundberg et al., 2023; McCoy et al., 2022; Paglino et al., 2024). In seven studies, researchers found mixed results (Ahmed et al., 2020; Curtin & Heron, 2022; Ioannou et al., 2021; Millett et al., 2020; Schnake-Mahl & Bilal, 2022; Schold et al., 2021; Travers et al., 2021). Additionally, three studies examined case fatality as an outcome and consistently reported higher case fatality in rural areas (Ahmed et al., 2020; Iyanda et al., 2022; Jackson et al., 2021).

Vulnerable Populations and Disparities Exacerbated by Urban and Rural Location

Research has extensively explored the impact of COVID-19 on vulnerable populations, including racial and ethnic minorities, older adults, and individuals with comorbidities, with a specific focus on distinguishing disparities between urban and rural settings. In the context of U.S. nursing homes, several studies (n = 3) (Abrams et al., 2020; Travers et al., 2021; Yang et al., 2021) have found that rural nursing homes were significantly less likely to have COVID-19 cases and deaths compared to their urban counterparts. However, one study (Travers et al., 2021) researchers highlighted that higher incidence and mortality rates of COVID-19 were more pronounced in rural nursing homes with a greater concentration of Black residents, underscoring the significant disparities faced by Black individuals in rural areas (Travers et al., 2021).

Furthermore, six studies (Chang et al., 2021; Choi & Yang, 2021; Fielding-Miller et al., 2020; Kitchen et al., 2021; Millett et al., 2020; Travers et al., 2021) have sought to identify the contributory factors behind the disparities in COVID-19 outcomes between urban and rural regions. These determinants encompass population density, socioeconomic status, racial and ethnic composition, healthcare accessibility, and healthcare utilization. Particularly, race and ethnicity, with a focus on Black and Hispanic populations, emerged as crucial factors in understanding the urban-rural divide in COVID-19 outcomes. Black rural residents had a significantly higher risk of COVID-19 outcomes including COVID-19 cases (Choi & Yang, 2021; Travers et al., 2021), hospitalizations (Chang et al., 2021; Greenhouse et al., 2023), and deaths (Lundberg et al., 2022; Lundberg et al., 2023; Millett et al., 2020; Travers et al., 2021) compared to Black urban residents. Also, Hispanic residents had a significantly higher risk of COVID-19 cases compared to White residents, and those disparities were more significant if residing in rural areas (Choi & Yang, 2021). The rural disadvantage in mortality was observed across most racial and ethnic groups by the second year of the pandemic. Lower vaccination rates in rural areas may contribute to higher mortality (Jones et al., 2023; Lundberg et al., 2023).

Moreover, three studies (Fielding-Miller et al., 2020; Kitchen et al., 2021; Rifat & Liu, 2022) delved into the influence of social determinants of health on COVID-19 outcomes within both urban and rural contexts. Notably, urban counties had higher COVID-19 death rates, with contributing factors including a higher percentage of farm workers, uninsured residents, and advanced age (Fielding-Miller et al., 2020). Additionally, a higher area deprivation index score, reflecting socioeconomic disparities such as poverty, income, and education (with higher scores indicating greater deprivation), and higher social vulnerability were both associated with elevated COVID-19 cases and deaths. These associations were particularly pronounced in rural counties compared to urban ones, emphasizing the crucial role of social determinants of health in shaping disparities in COVID-19 outcomes (Jones et al., 2023; Kitchen et al., 2021; Rifat & Liu, 2022).

Discussion

Our systematic review of 55 articles illuminated the complexity of factors contributing to COVID-19 disparities between urban and rural areas. While general trends emerged, such as urban areas generally had higher case rates and rural areas had lower mortality rates, it is crucial to recognize the multifactorial nature of these urban and rural COVID-19 differences. Additionally, some of the variation in findings across studies may stem from differences in data sources and timing of data collection (i.e., timeframe considered).

Overall, the data show that the course of the COVID-19 pandemic and its outcomes -- case rates, hospitalizations, and deaths -- in urban and rural areas evolved over time. While urban areas initially experienced higher case rates and mortality, the situation became more balanced across urban and rural areas in 2021, with some rural areas seeing increases (and clustering) in cases and hospitalizations. These trends highlight the importance of ongoing monitoring and the flexibility and adaptation of public health strategies to address changing dynamics across time and across urban and rural settings.

Considering the transmission dynamics of COVID-19 (World Health Organization, 2020), the elevated COVID-19 case rates observed in urban areas were likely influenced by higher population densities, which facilitates rapid transmission of the virus. Urban regions often had more crowded living conditions and increased interpersonal interactions, contributing to heightened disease spread. Due to their lower population density and limited travel, rural populations initially had less exposure to COVID-19 (World Health Organization, 2020), which may have resulted in differences in the perception of COVID-19 risk among rural residents (Matthews et al., 2021). Several early studies found that rural populations had lower pandemic preparedness (Lakhani et al., 2020), higher vaccine hesitancy (Khubchandani et al., 2021), lower vaccination rates (Jones et al., 2023; Lundberg et al., 2023) and lower preventive behaviors (Callaghan et al., 2021) that might have impacted the eventual upward trend of COVID-19 burden in rural areas. In the future, public health education should address the unique challenges faced by urban and rural populations. This includes promoting health literacy, vaccine education, and adherence to preventive measures.

Indeed, as the pandemic continued, there were higher COVID-19 hospitalization and case fatality rates in rural areas. Differences in hospitalization rates between urban and rural areas may be attributed to disparities in healthcare infrastructure (Berland & Hughes, 2021; Lakhani et al., 2020). Urban areas typically have more extensive healthcare facilities and resources, which may have resulted in lower hospitalization rates per case. Conversely, rural areas have fewer healthcare resources (e.g., telehealth, primary care providers) resulting in residents often having to travel long distances to receive medical care. For rural patients, difficulties in receiving timely care likely contributed to higher COVID-19 hospitalization rates among those with severe symptoms. Increased case fatality rates in rural areas may be attributed to limited access to healthcare services, particularly specialized intensive care. Also, lower vaccination rates in rural areas may have contributed to higher mortality (Jones et al., 2023; Lundberg et al., 2023).

Even before the pandemic, rural Americans had unfavorable health outcomes compared to their urban counterparts (Kozhimannil & Henning-Smith, 2021). For example, rural residents had significantly lower life expectancy compared to the urban residents (Garcia et al., 2019). Rural residents also have an increased risk for multiple comorbidities (Kozhimannil & Henning-Smith, 2021), which can increase their risk for severe COVID-19 illness (Razzaghi et al., 2020). Social determinants of health, such as access to education, employment opportunities, and housing conditions, are critical drivers of health disparities. A combination of these factors likely contributed to the increase in COVID-19 hospitalizations and case fatality rates among rural residents.

Most studies in our review, however, did not aim to explain why urban/rural differences exist in COVID-19 outcomes. The few studies that did found that vulnerable groups, including racially and ethnically minoritized populations, older adults, those with comorbidities lower socioeconomic status and lower vaccination rates, experienced exacerbated disparities in rural regions.

Vulnerable populations, like racially and ethnically minoritized populations, have been disproportionately affected by the COVID-19 pandemic (Mackey et al., 2021). Findings of our systematic review demonstrated that being a rural resident exacerbates racial and ethnic disparities in COVID-19 outcomes. Specifically, we found U.S. rural counties with higher proportions of Black residents were more likely to have higher COVID-19 burden. Although the majority of rural residents are White individuals (Kozhimannil & Henning-Smith, 2021), studies have found that there has been a growing number of racial and ethnic minority residents moving into rural areas thus increasing the diversity of rural populations (Jensen et al., 2020). Our review also identified that nursing home residents are vulnerable to COVID-19 outcomes (Abrams et al., 2020; Travers et al., 2021; Yang et al., 2021). Similar to demographics in rural areas, nursing home residents have more advanced age and multiple underlying diseases, which make them vulnerable to COVID-19 cases, hospitalizations, and deaths (Centers for Diesase Control and Prevention, 2006). Multiple studies reported rural nursing homes had limited healthcare resources (Gracner et al., 2021; Stone et al., 2020). However, these studies were conducted with COVID-19 outcome data from early 2021.

Research examining underlying factors associated with the disparities in COVID-19 outcomes should use more recent and comprehensive data sources to provide clearer insights. Future studies should investigate actual vaccination rates and hesitancy, the effects of different treatments, and the influence of patient characteristics, such as ethnicity, gender, age, and geographic location (urban versus rural). Understanding these factors is crucial to explaining the observed heterogeneity in mortality rates across different settings. For example, variations in healthcare infrastructure, public health response, and demographic composition could significantly impact COVID-19 outcomes, suggesting that factors like local healthcare capacity, the prevalence of comorbidities, and social determinants of health may play a role.

Efforts to address social determinants of health, such as income support, housing assistance, and educational initiatives, could help mitigate COVID-19 disparities by improving overall health and well-being. Moreover, targeted policy and practice strategies are needed to address health disparities among rural, racially, and ethnically minoritized groups, who may face unique challenges in accessing care and resources. Healthcare resource allocation should also be tailored to local needs: urban areas might benefit from investments in critical care capacity and public health infrastructure, while rural regions could require enhanced primary care access, telemedicine services, and community-based interventions to reduce hospitalization rates and improve outcomes.

Further research should also explore the impact of these tailored strategies over time, examining how changes in resource allocation, public health measures, and community-based support affect COVID-19 outcomes in both urban and rural areas.

Limitations

Our review has several potential limitations. First, variation in study methodologies and data sources may have contributed to the complexity and differences in identifying consistent patterns of results. The dynamic nature of the COVID-19 pandemic, evolving public health interventions, and regional variations further contributed to the complexity of these urban and rural disparities and their evolution over time. Second, we were also not able to fully examine state or county-level COVID-19 mitigation policies and vaccination rates which may contribute to regional variations in cases, hospitalizations, and deaths. Lastly, future research should include more representative samples with the consideration of adequate factors related to COVID-19 burden.

Conclusion

COVID-19 outcomes differed over time and across urban and rural areas due to the complex interplay of population density, health care infrastructure, and socioeconomic determinants. While urban areas generally exhibited higher case rates, the impact of urban-rural status varied across studies and outcomes. However, most of the current evidence did not aim to explain why urban/rural differences exist; the few studies that did found that vulnerable groups, including racially and ethnically minoritized populations, older adults, those with comorbidities lower socioeconomic status and lower vaccination rates, experienced exacerbated disparities in rural regions. Recognizing the multifaceted nature of COVID-19 disparities in urban and rural areas is essential for making informed policy and intervention design. Tailored approaches that address the unique challenges and strengths of each setting are crucial for effectively combating the ongoing pandemic and future public health crises. Further research is warranted to explore these disparities in greater detail and refine strategies to reduce the impact of COVID-19 on diverse communities.

Supplementary Material

Appendix

Acknowledgements:

We acknowledge the support of Yuji Mizushima at RAND who provided support during article review and data abstraction.

Funding:

This study was funded by the National Institute of Nursing Research of the National Institutes of Health (R01NR016865); and the National Institute on Aging, National Institute of Allergy and Infectious Diseases, and National Institute on Minority Health and Health Disparities (R01AG074492). J. A. K. was supported by the Columbia University School of Nursing Comparative and Cost-Effectiveness Research Training for Nurse Scientists Program (CER2; T32NR014205) and the National Institute of Nursing Research of the National Institutes of Health (F31 NR020566); and is funded by the Patient-Centered Outcomes Research Institute (PCORI), Award HS-2019C2-17373.

SPONSOR’S ROLE

All content is the sole responsibility of the authors and does not necessarily represent the official views of the study sponsors.

Footnotes

CONFLICT OF INTEREST

The authors have no conflict of interest.

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