Internet Access, Social Risk Factors, and Web-Based Social Support Seeking Behavior: Assessing Correlates of the “Digital Divide” Across Neighborhoods in The State of Maryland (Preprint)
Authors: Hatef Elham , Ma Xiaomeng , Shaikh Yahya , Kharrazi Hadi , Weiner Jonathan , Gaskin Darrell 
Published: February 2020

BACKGROUND

Digital technologies are now key to a wide range of social interactions, including those related to the receipt of health and social services. But the lack of affordable internet connectivity and the underlying “digital divide” has led to significant disparities across the U.S. in terms of access to such technology. Recently, within the U.S. medical care context, there is increased appreciation that Social Determinants of Health (SDOH), both at the community and personal level, represent key factors that must be considered when assessing and addressing the health of patients and populations. Some suggest that the lack of adequacy of digital access should be considered as a new type of SDOH risk factor. But before this approach might be widely adopted, we need to gain empirical evidence regarding the association and interplay between the availability of internet connectivity services, standard SDOH measures, and social service agency availability and access at the neighborhood and regional level.

OBJECTIVE

To empirically measure the degree to which there is a “digital divide” in terms of access to the internet at the small-area community level within the State of Maryland and the City of Baltimore and to assess the relationship and association of this divide with community-level SDOH risk factors, community-based social service agency location, and web-mediated support service seeking behavior.

METHODS

To assess the socio-economic characteristics of the neighborhoods across the state, we calculated the Area Deprivation Index (ADI) using the U.S. Census, American Community Survey (5-year estimates) of 2017. ADI allowed for ranking of neighborhoods by the socioeconomic disadvantage with a higher percentile representing disadvantaged neighborhoods. To assess the digital divide, at the community level, we used the Federal Communications Commission (FCC) data on the number of residential fixed Internet access service connections. We assessed the availability of and web-based access to community-based social service agencies using data provided by the “Aunt Bertha” information platform, which is very widely used across the nation to connect community-based health and social services to those in need. We performed community and regional level descriptive and special analyses for ADI social risk factors, connectivity, and both the availability of and web-based searches for community-based social services. To help assess potential neighborhood linked factors associated with the rates of web-based social services searches by individuals in need, we applied logistic regression using generalized estimating equation modeling.

RESULTS

Baltimore City contained more disadvantaged neighborhoods compared to other areas in Maryland. In Baltimore City, 20.3% of neighborhoods (defined by census block groups) were disadvantaged with ADI at the 90th percentile while only 6.6% of block groups across Maryland were in this disadvantaged category. Across the State, more than half of all census tracts had 801-1000 households (per 1000 households) with internet subscription. In contrast, in Baltimore City about half of all census tracts had only 401-600 of the households (per 1000 households) with internet subscriptions. Most block groups in Maryland and Baltimore City lacked access to social services facilities (61% of block groups at the 90th percentile of disadvantage in Maryland and 61.3% of block groups at the 90th percentile of disadvantage in Baltimore City). After adjusting for other variables, a 1% increase in the ADI measure of social disadvantage, resulting in a 1.7% increase in the number of individuals seeking social services. Also, a 1 unit increase in the social services density (number of social services facilities in each ZIP code) resulted in an 8.9% increase in the number of individuals seeking those services.

CONCLUSIONS

Our results present the evidence of the underlying digital divide, challenges related to SDOH, and their potential impact on accessing available community-based social services among vulnerable populations in one state and one large city. While more work is needed, our findings support the premise that the digital divide is closely associated with other SDOH factors, as well as the need and use of community-based social support. Thus, the digital divide should be considered part-and-parcel as part of programs seeking to improve the health and wellbeing of communities.

CLINICALTRIAL