Background Patient-to-provider teledermatology relies on a patient’s access to technology to ensure a successful visit. However, access to broadband internet and technology varies across populations in the United States—leading to the digital divide. While teledermatology has been recognized as a model to improve access, little is known about how often demographic data associated with digital inequity are captured in studies. Objective Given the expansion of teledermatology over the past decade, we sought to determine how often demographic data associated with digital inequity are reported in patient-to-provider teledermatology studies. Methods A scoping literature review search was conducted using the search term teledermatology in the following databases: PubMed, Embase, and the Cochrane Database of Systematic Reviews. All studies published between December 31, 2011, and December 31, 2021, that evaluated patient-to-provider teledermatology were eligible. Results In total, 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (28/46, 61%). However, only 41% (19/46) of studies were representative of race or ethnicity, defined as including >20% nonwhite participants. Studies rarely reported the number of participants greater than 65 years of age (14/46, 30%), preferred language (9/46, 20%), income (6/46, 13%), highest level of education (5/46, 11%), or access to a device (4/46, 9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language (9/25, 36% vs 0%; P=.002) and appeared more likely to report other demographic data associated with digital inequity, without reaching statistical significance (P>.05). Conclusions Demographic data associated with digital inequity are rarely reported in patient-to-provider teledermatology studies to date. These studies frequently lack adequate representation of racial and ethnic minorities. With increased calls for equitable representation in dermatology studies, future teledermatology studies can improve the reporting of race and ethnicity and consider demographic data associated with digital inequity as an important criterion in research design.

BACKGROUND Patient-to-provider teledermatology relies on a patient’s access to technology to ensure a successful visit. However, access to broadband internet and technology varies between populations in the United States – leading to the digital divide. While teledermatology has been recognized as a model to improve access, little is known about how often demographic data associated with digital inequity is captured in studies. OBJECTIVE To characterize how often demographic data associated with digital inequity is reported in patient-to-provider teledermatology studies. METHODS A scoping literature review search was conducted using search term teledermatology for the following databases: PubMed, Embase, and Cochrane Database of Systematic Reviews. All studies published between December 31, 2011 and December 31, 2021 that evaluated patient-to-provider teledermatology were eligible. RESULTS 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (61%). However, only 41% of studies were representative of race or ethnicity, defined as including > 20% non-white participants. Studies rarely reported participants greater than 65 years old (30%), preferred language (20%), income (13%), highest level of education (11%), or access to a device (9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language and appeared more likely to report other demographic data associated with digital inequity without reaching statistical significance. CONCLUSIONS Demographic data associated with digital inequity is rarely reported in patient-to-provider teledermatology studies to-date. These studies frequently lack adequate representation of racial and ethnic minorities. Demographic data associated with digital inequity should be reported in teledermatology studies to advance inclusive research methodologies and ensure generalizable conclusions.

Introduction The rapid increase in opioid-related deaths since the early 2000s is a major US public health concern. This crisis has transitioned from pharmaceuticals to illicit synthetic opioids and street mixtures. This epidemic has significantly impacted the Appalachian region. This study investigated opioid-related death rates among the Appalachian states, focusing on death rates among urban, suburban, and rural counties. Methods Opioid-related death data from 2018-2021 for the 13 states that make up the Appalachian region were collected using the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database. Opioid analgesic overdose deaths were defined using ICD-10 codes X40-X44, X60-X64, and Y10-Y14, where an opioid analgesic was also coded (T40.2-T40.4). US census data was used to calculate opioid-related death rates by population. Counties were classified as urban, suburban, and rural using the 2013 Rural-Urban Continuum Codes from the US Department of Agriculture. The data were descriptively broken down and reported as either percentages or means. Results Of the opioid-related deaths between 2018 and 2021, 498 counties were identified in the 13 Appalachian states as having reported at least 10 opioid-related deaths per year. Among these counties, 337 (67.7%) were classified as urban/metropolitan, 138 (27.7%) as suburban, and 23 (4.62%) as rural. Overall, mean opioid-related deaths by populations per 1000 among all counties were 0.24 in 2018, 0.24 in 2019, 0.33 in 2020, and 0.38 in 2021. For urban/metropolitan counties, opioid-related deaths per 1000 gradually increased from 0.23 in 2018 to 0.35 in 2021. For suburban counties, the mean opioid-related deaths per 1000 increased from 0.25 in 2018 to 0.43 in 2021. For rural counties, the mean opioid-related deaths per 1000 increased from 0.43 in 2018 to 0.62 in 2021. Conclusion Opioid-related deaths, on average and by population, have risen steadily in the Appalachian region from 2018-2021 across all geographic areas (urban/metropolitan, suburban, rural). Rural counties consistently showed the highest opioid-related deaths per population compared to urban/metropolitan and suburban areas. Addressing social determinants of health such as income level, education level, healthcare access, and community-based interventions is crucial in combating this issue. Community and health system interventions must be implemented to combat the disproportionately high rate of opioid prescribing in the Appalachian region.

BACKGROUND Patient-to-provider teledermatology relies on a patient’s access to technology to ensure a successful visit. However, access to broadband internet and technology varies between populations in the United States – leading to the digital divide. While teledermatology has been recognized as a model to improve access, little is known about how often demographic data associated with digital inequity is captured in studies. OBJECTIVE To characterize how often demographic data associated with digital inequity is reported in patient-to-provider teledermatology studies. METHODS A systematic literature search was conducted using search term teledermatology for the following databases: PubMed, Embase, and Cochrane Database of Systematic Reviews. All studies published between December 31, 2011 and December 31, 2021 that evaluated patient-to-provider teledermatology were eligible. RESULTS 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (61%). However, only 41% of studies were representative of race or ethnicity, defined as including > 20% non-white participants. Studies rarely reported participants greater than 65 years old (30%), preferred language (20%), income (13%), highest level of education (11%), or access to a device (9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language and appeared more likely to report other demographic data associated with digital inequity without reaching statistical significance. CONCLUSIONS Demographic data associated with digital inequity is rarely reported in patient-to-provider teledermatology studies to-date. These studies frequently lack adequate representation of racial and ethnic minorities. Demographic data associated with digital inequity should be reported in teledermatology studies to advance inclusive research methodologies and ensure generalizable conclusions.