Leveraging technology in public-private partnerships: a model to address public health inequities.

Long-standing inequities in healthcare access and outcomes exist for underserved populations. Public-private partnerships (PPPs) are where the government and a private entity jointly invest in the provision of public services. Using examples from the Health Equity Consortium (HEC), we describe how technology was used to facilitate collaborations between public and private entities to address health misinformation, reduce vaccine hesitancy, and increase access to primary care services across various underserved communities during the COVID-19 pandemic. We call out four enablers of effective collaboration within the HEC-led PPP model, including: 1. Establishing trust in the population to be served 2. Enabling bidirectional flow of data and information 3. Mutual value creation and 4. Applying analytics and AI to help solve complex problems. Continued evaluation and improvements to the HEC-led PPP model are needed to address post-COVID-19 sustainability.

The BLV App Arcade: a new curated repository and evaluation rubric for mobile applications supporting blindness and low vision.

PURPOSE: Visual impairment-related disabilities have become increasingly pervasive. Current reports estimate a total of 36 million persons with blindness and 217 million persons with moderate to severe visual impairment worldwide. Assistive technologies (AT), including text-to-speech software, navigational/spatial guides, and object recognition tools have the capacity to improve the lives of people with blindness and low vision. However, access to such AT is constrained by high costs and implementation barriers. More recently, expansive growth in mobile computing has enabled many technologies to be translated into mobile applications. As a result, a marketplace of accessibility apps has become available, yet no framework exists to facilitate navigation of this voluminous space. MATERIALS AND METHODS: We developed the BLV (Blind and Low Vision) App Arcade: a fun, engaging, and searchable curated repository of app AT broken down into 11 categories spanning a wide variety of themes from entertainment to navigation. Additionally, a standardized evaluation metric was formalized to assess each app in five key dimensions: reputability, privacy, data sharing, effectiveness, and ease of use/accessibility. In this paper, we describe the methodological approaches, considerations, and metrics used to find, store and score mobile applications. CONCLUSION: The development of a comprehensive and standardized database of apps with a scoring rubric has the potential to increase access to reputable tools for the visually impaired community, especially for those in low- and middle-income demographics, who may have access to mobile devices but otherwise have limited access to more expensive technologies or services.

A wide array of assistive mobile applications now serve as low cost, convenient, and effective alternatives to standard tools in the rehabilitation domain.Given an extensive (and growing) marketplace of assistive apps, we highlight the importance of developing standardized evaluation frameworks that serve to assess the merit, functionality, and accessibility of tools in respective rehabilitation fields.To provide an introduction to a novel resource accessible to the public to exhibit verified and reliable assistive apps for the visually impaired community, especially for those in low- and middle-income demographics who may not have access to common technologies and services.

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US.

Importance: While research has identified racial and ethnic disparities in access to autism services, the size, extent, and specific locations of these access gaps have not yet been characterized on a national scale. Mapping comprehensive national listings of autism health care services together with the prevalence of autistic children of various races and ethnicities and evaluating geographic regions defined by localized commuting patterns may help to identify areas within the US where families who belong to minoritized racial and ethnic groups have disproportionally lower access to services. Objective: To evaluate differences in access to autism health care services among autistic children of various races and ethnicities within precisely defined geographic regions encompassing all serviceable areas within the US. Design, Setting, and Participants: This population-based cross-sectional study was conducted from October 5, 2021, to June 3, 2022, and involved 530 965 autistic children in kindergarten through grade 12. Core-based statistical areas (CBSAs; defined as areas containing a city and its surrounding commuter region), the Civil Rights Data Collection (CRDC) data set, and 51 071 autism resources (collected from October 1, 2015, to December 18, 2022) geographically distributed into 912 CBSAs were combined and analyzed to understand variation in access to autism health care services among autistic children of different races and ethnicities. Six racial and ethnic categories (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and White) assigned by the US Department of Education were included in the analysis. Main Outcomes and Measures: A regularized least-squares regression analysis was used to measure differences in nationwide resource allocation between racial and ethnic groups. The number of autism resources allocated per autistic child was estimated based on the child's racial and ethnic group. To evaluate how the CBSA population size may have altered the results, the least-squares regression analysis was run on CBSAs divided into metropolitan (>50 000 inhabitants) and micropolitan (10 000-50 000 inhabitants) groups. A Mann-Whitney U test was used to compare the model estimated ratio of autism resources to autistic children among specific racial and ethnic groups comprising the proportions of autistic children in each CBSA. Results: Among 530 965 autistic children aged 5 to 18 years, 83.9% were male and 16.1% were female; 0.7% of children were American Indian or Alaska Native, 5.9% were Asian, 14.3% were Black or African American, 22.9% were Hispanic or Latino, 0.2% were Native Hawaiian or other Pacific Islander, 51.7% were White, and 4.2% were of 2 or more races and/or ethnicities. At a national scale, American Indian or Alaska Native autistic children (ß = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (ß = 0.02; 95% CI, 0-0.06; P = .02) had significant disparities in access to autism resources in comparison with White autistic children. When evaluating the proportion of autistic children in each racial and ethnic group, areas in which Black autistic children (>50% of the population: ß = 0.05; <50% of the population: ß = 0.07; P = .002) or Hispanic autistic children (>50% of the population: ß = 0.04; <50% of the population: ß = 0.07; P < .001) comprised greater than 50% of the total population of autistic children had significantly fewer resources than areas in which Black or Hispanic autistic children comprised less than 50% of the total population. Comparing metropolitan vs micropolitan CBSAs revealed that in micropolitan CBSAs, Black autistic children (ß = 0; 95% CI, 0-0; P < .001) and Hispanic autistic children (ß = 0; 95% CI, 0-0.02; P < .001) had the greatest disparities in access to autism resources compared with White autistic children. In metropolitan CBSAs, American Indian or Alaska Native autistic children (ß = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (ß = 0.01; 95% CI, 0-0.06; P = .02) had the greatest disparities compared with White autistic children. Conclusions and Relevance: In this study, autistic children from several minoritized racial and ethnic groups, including Black and Hispanic autistic children, had access to significantly fewer autism resources than White autistic children in the US. This study pinpointed the specific geographic regions with the greatest disparities, where increases in the number and types of treatment options are warranted. These findings suggest that a prioritized response strategy to address these racial and ethnic disparities is needed.