Introduction
Concentrated disadvantage, also known as neighborhood disadvantage, or deprivation index in some cases, refers to areas with a high proportion of people with low socioeconomic status. Concentrated disadvantaged areas aggregate groups such as low-income earners, welfare recipients, and single households [
1], and some may also include ethnic minority groups [
2]. These groups face a great number of challenges in socioeconomic development [
2] and health wellbeing [
3]. Due to social and cultural segregation, the disintegration of collective cohesion, limited institutional resources, dirty and disorderly environment, higher disadvantaged areas are associated with higher levels of crime rates [
4] and fear of crime [
5], antisocial behavior [
6], intimate partner violence [
7], violent victimization among youths [
8], alcohol abuse [
9], lower life satisfaction [
10], adult unemployment and earnings [
11], and negative educational outcomes [
12]. Meanwhile, many studies have revealed the significant relationships between concentrated disadvantages and differential forms of health inequalities, such as the increased risk of breast cancer [
13], increased incidence rate of lung cancer [
14], diabetes and cholesterol control [
15], obesity [
16], pediatric obstructive sleep apnea [
17], DNA methylation [
18], adolescent brain cognitive development [
19], depression [
3,
20,
21], and worse mental health status [
22].
Concentrated disadvantaged areas are more likely to suffer disproportionate COVID-19 infection [
23] and deaths [
24]. Some studies have also found a significant link between proxies of concentrated disadvantage (e.g., income) and case fatality rate (use fatality in the following section) [
25]. The residents of concentrated areas are more likely to have poorer socioeconomic status and be essential workers in professions such as grocery delivery, truck drivers, and cleaners [
26,
27]. Most of these jobs are difficult to perform remotely and lack the conditions to maintain social distancing. Meanwhile, disadvantaged populations may use public transportation more frequently, as a study in New York City found that areas with low-income people, essential workers, and non-white populations had more mobility extracted from subway data during the pandemic [
28]. These groups also live in mostly poor house conditions, with many live together and without good post-infection isolation [
29]. These factors of physical status, work environment, commuting patterns, and house conditions may contribute to a higher risk of exposure to COVID-19 and the increased likelihood of COVID-19 infection and fatality in socioeconomically disadvantaged populations.
Place connectivity is another key factor in predicting COVID-19 transmission among concentrated disadvantaged areas. The connectivity of a place can be described as the strength of a connection between a place and one or more places, and this connection is generally manifested in terms of the road, train, air, and social media, among others. Unlike direct population movements, connectivity is more stable, as it is closely related to geographical location, transportation facilities, and other related static factors. Place connectivity affects socioeconomic development and health outcomes of a region. Transportation connectivity (road, Internet, and air travel connectivity) improvements can promote economic growth by increasing market access and connecting intermodal terminals [
30‐
32], as well as improve regional development by allowing different areas within the region to fully collaborate and reap the socio-economic benefits of integration [
33,
34]. Greater accessibility is also associated with greater economic resilience in the region [
35].
In terms of health effects, place connectivity can result in both positive and negative consequences. Transportation connectivity increases access to health care [
32]. High transportation connectivity is also linked to lower levels of mental health distress [
36]. Connectivity, on the other hand, is associated with some negative health outcomes, particularly infectious disease transmissions [
37] (i.e., dengue outbreaks [
38], influenza outbreaks [
39], and HIV transmission [
40]. For example, the intensity of air travel has been shown to be a significant predictor of virus arrival time [
41]. The greater the connectivity between areas, the higher level of population mobility between these areas. Higher connectivity could be associated with a higher risk of exposure, and greater risk of COVID-19 infection. Several studies have found that air connectivity [
37], high-speed train connectivity [
42], road connectivity [
43], and Twitter-based place connectivity [
44] are associated with the initial outbreak of COVID-19. Particularly, Twitter-based connectivity, representing the extent to which a place shares the same users with other places, gives a comprehensive measure of the degree of connectivity in all aspects of transportation in that place, which can be a more direct proxy for population mobility and exposure risk [
44].
There are a few studies with mixed results regarding the association between connectivity and COVID-19 clinical consequences (including fatality). Some studies have shown a significant association between air connectivity index and increased death [
45] and death risk [
46] in early-stage, while another suggested that pedestrian-oriented street connectivity is associated with lower COVID-19 death rates, because residents in this built environment engage in more physical activity and have lower levels of obesity and chronic disease [
47].
Despite the above-mentioned studies, there are still knowledge gaps in investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality. First, while several studies have investigated the effects of concentrated disadvantage and connectivity on COVID-19, most studies have focused on the incidence and mortality, with a paucity of studies linking concentrated disadvantage and COVID-19 fatality. As fatality is more influenced by pre-existing health conditions and the quality of the healthcare system [
48], we hypothesize a significant association between concentrated disadvantage and COVID-19 fatality because concentrated disadvantage will be linked to health infrastructure in the area, access to health services, and pre-existing health conditions of a population on COVID-19 clinical outcomes.
Second, to the best of our knowledge, no study has yet evaluated the moderation effect of connectivity on the association between concentrated disadvantage and COVID-19 fatality. As many studies have confirmed [
26,
49], people living in high concentrated disadvantaged areas may have higher needs to travel because most of them are essential workers and have limited resources to support remote working. In this case, if the area is also highly connected, these people may be more likely to take advantage of the convenient connectivity conditions (e.g., transportation) to go to work. Under the implementation of non-pharmacological interventions (NPIs) like travel restrictions during the pandemic, a high-connectivity place with a concentration of disadvantaged groups may have higher mobility compared to other high connectivity places without a concentration of disadvantaged groups. Higher mobility is associated with higher rates of infection [
37,
42]. For people living in disadvantaged areas, a higher infection rate is usually linked with higher fatality given their poor pre-existing health conditions [
50] and barriers to access to healthcare services [
51]. Therefore, we hypothesize that connectivity may amplify the negative impacts of concentrated disadvantage on COVID-19 fatality.
Third, few studies investigate the associations between concentrated disadvantage, place connectivity, and COVID-19 outcomes across time [
52,
53]. COVID-19 is constantly mutating and spreading, and the non-pharmaceutical COVID-19 prevention policies change over time. Place connectivity may not contribute to population movement in the same way at different periods, so the effect of place connectivity on concentrated disadvantage and COVID-19 fatality may vary. Travel restrictions were much stricter in the early period of the pandemic, resulting in decreased human mobility. In this situation, the impact of concentrated disadvantage on COVID-19 fatality may be less influenced by place connectivity. When life returns to normal and travel restrictions are lifted, such as during the Omicron variant period, the role of connectivity may become increasingly significant. As a result, we hypothesize that the moderation effect of place connectivity on the link of place connectivity – COVID-19 fatality varies along with the period of the pandemic.
In this paper, we use Twitter data to measure place connectivity. Twitter-based place connectivity is a comprehensive connectivity measurement among places, as previous studies have noted that it reflects connectivity not only in terms of transportation, but also in terms of social networks, geography, and socioeconomics [
44]. Meanwhile, given the close association with these relatively static factors, place connectivity is a stable factor across years [
44]. This study uses historical place connectivity to analyze its relationship with current COVID-19 fatality, which will be useful in guiding the role place connectivity may play in future infectious disease prevention and control.
In sum, this paper proposes that place connectivity can intensify the harmful effects of county-level concentrated disadvantage on county-level COVID-19 fatality. If a county with a concentration of disadvantaged populations is also a highly connected county, the disadvantaged group will have higher mobility through place connectivity and a greater probability of exposure to the virus, which may contribute to the deleterious effect of concentrated disadvantage on COVID-19 fatality. Our study will help to address the existing knowledge gaps and advance the understanding of complicated interaction between concentrated disadvantage, place connectivity, and COVID-19 fatality across pandemic periods. Specifically, we present the following hypotheses:
-
H1: Concentrated disadvantage is associated with higher COVID-19 fatality.
-
H2: The association between concentrated disadvantage and COVID-19 fatality is stronger in counties of high Twitter-based place connectivity compared to counties of low place connectivity.
-
H3: The moderation effect of place connectivity may vary along with the period of the pandemic.
Discussion
Leveraging concentrated disadvantage and Twitter-based place connectivity, we examined the relationship between concentrated disadvantage and COVID-19 fatality in the US, and how this association is moderated by place connectivity. In addition to examining the harmful effect of concentrated disadvantage, this study partially explored the mechanism of this effect. The significant interaction between place connectivity and concentrated disadvantage suggests that socioeconomically disadvantaged groups in an area with high levels of place connectivity may be more likely to experience higher mobility, and thus face higher incidence and fatality risk. The results provide new insights into the association between concentrated disadvantage and COVID-19 fatality and may provide some guidance for future infectious disease control policies in socioeconomically disadvantaged areas.
We further found that the moderation effect of place connectivity increased over time, which may be related to increased mobility and the loosening of travel restrictions. At the early stages of the pandemic, COVID-19 fatality was more severe, travel restrictions were higher, and people were also in a precautionary awareness to reduce their outside activities, so the effect of concentrated disadvantage on fatality may be less influenced by place connectivity. In contrast, with widespread vaccination, life returns to normal and daily travel is less restricted, thus the moderation effect of place connectivity on the link between concentrated disadvantage and COVID-19 fatality became increasingly significant.
The significant association between place connectivity and decreased COVID-19 fatality rate is observed in time periods 2 to 4. We found a moderate correlation between population density, ICU percentage, and place connectivity. The high fatality was mostly found in districts with low population density due to poorer health care systems [
58]. Rural areas hold less access to health facilities, but urban areas, which are more likely to encounter large numbers of cases, instead have better health facility preparation and prevention to avoid more deaths [
57]. It implies that highly connected areas are generally areas with higher population density and urbanization, and may have better medical conditions and facilities, leading to a lower fatality. This finding is consistent with associations between urbanization [
59] and population density [
58], and lower COVID-19 fatality. However, the effect of place connectivity on COVID-19 was not significant in period 1, probably due to strict travel restrictions and low travel needs in the early stage, resulting in connectivity not working.
Our findings have public health implications in the practice of responding to infectious disease epidemics/pandemics in terms of disease surveillance and monitoring as well as resource allocation and health equalities. Timely monitoring of outbreaks in concentrated disadvantaged areas with high place connectivity can aid in identifying potential epidemic hotspots and vulnerable areas, which will contribute to evidence-based decision-making in secondary prevention strategies and efforts. In addition, our study results suggest the importance of resource allocation measures favoring the areas with high socioeconomic disadvantage and high place connectivity. For example, financial assistance integrated into the transportation restriction policy can significantly reduce the mobility of concentrated disadvantaged neighborhoods. Vaccination promotion via free supplements and increased vaccine administration sites among the vulnerable population will improve community immunity toward the virus. These strategies reduce disproportionate COVID-19 fatality, interrupt transmission, and improve health equities among concentrated disadvantaged areas.
There are a few limitations to this study. First, place connectivity is measured from Twitter data, while is less used by some groups, such as the elderly and children. Also, data in some counties where Twitter is less used may be underrepresented. Second, low-income populations may be less likely to report their illness when the symptoms are mild. Similarly, disadvantaged populations may be less likely to take COVID-19 testing at all due to the access barriers to relevant healthcare services. These will result in underestimating COVID-19 cases and biasing the fatality. Third, utilizing county-level data to understand the effects of concentrated disadvantage on COVID-19 fatality may ignore the role of neighborhood-level factors. There may exist several neighborhoods with high socioeconomic status even in concentrated disadvantaged counties. Last, this study used spatial-scale variables rather than individual-level COVID-19 data, hence the results can only indicate the associations between geospatial environment and COVID-19 outcomes and cannot be interpreted as individual-level associations or causalities. Future multilevel analyses could be applied, including data on individual characteristics and neighborhood factors, which could yield more robust findings.
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