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. 2023 Jun 26;18(6):e0287598. doi: 10.1371/journal.pone.0287598

Telehealth use among pediatric Alabama Medicaid enrollees, March-December 2020: Variations by race/ethnicity & place of residence

Bisakha Sen 1,‡,*, Md Jillur Rahim 1,‡, Julie McDougal 1, Pradeep Sharma 1, Nianlan Yang 2, Anne Brisendine 1, Ye Liu 1, Van Nghiem 1, David Becker 1
Editor: Kevin Lu3
PMCID: PMC10292692  PMID: 37363881

Abstract

During the early days and months of the COVID-19 pandemic, healthcare facilities experienced a slump in non-COVID-related visits, and there was an increasing interest in telehealth to deliver healthcare services for adult and pediatric patients. The study investigated telehealth use variation by race/ethnicity and place of residence for the pediatric enrollees of the Alabama Medicaid program. This retrospective observational study examined Alabama Medicaid claims data from March to December 2020 for enrollees less than 19 years. There were 637,792 pediatric enrollees in the Alabama Medicaid program during the study period, and 16.9% of them had used telehealth to meet healthcare needs. This study employed a multivariate Poisson mixed-effects model with robust error variance to obtain differences in telehealth utilization and found that Non-Hispanic Black children were 80% as likely, Hispanic children were 55% as likely, and Asian Children were 46% as likely to have used telehealth compared to Non-Hispanic White children. Pediatric enrollees in large rural areas and isolated areas were significantly less likely (IRR: 0.90 for both, p<0.05) to use telehealth than those in urban areas. This study’s findings suggest that attention needs to be paid to addressing race/ethnicity disparities in accessing telehealth services.

Introduction

It is well-known that telehealth became a crucial vehicle for healthcare delivery to children and adults alike during the COVID-19 pandemic. In the U.S., under the Public Health Emergency (PHE) declaration, the Centers for Medicare and Medicaid Services (CMS) extended coverage eligibility for telehealth services and eased regulatory requirements for telehealth, and states and private health insurers followed suit. The PHE has currently been extended through November 2022 [1]. However, the U.S. House of Representatives has already voted with bipartisan support to extend Medicare telehealth benefits through 2024 [2], and as of July 2022, 21 states had implemented policies at the state-level requiring payment parity between services delivered via telehealth and in-person, and five more states had implemented payment parity with caveats [3]. Hence, telehealth is on track to remain a major vehicle for healthcare delivery post-PHE. Thus, questions about disparities in telehealth access and use, and the extent to which it benefits vulnerable populations, are of paramount interest.

Findings on disparities in telehealth use among adults are mixed. Surveys of adult respondents indicate that 17%-23% of adults have used telehealth for healthcare needs [4, 5], with higher use reported among respondents of color and lower-income households. At the same time, reports from several healthcare systems suggest that patients of color, rural patients, and publicly insured patients are less likely to use telehealth. [6–8] Prior studies also find that the association of patient characteristic like education with accessing routine healthcare vary by race/ethnicity [9]. For pediatric patients, there is evidence of telehealth visits increasing across a range of outpatient specialties and cautious optimism that telehealth will help disadvantaged pediatric populations by reducing “parent burden, decreasing both times off from work and distances traveled for health care” [10]. However, there is a paucity of population-level research on pediatric telehealth use, which informs on inequities by race/ethnicity and place of residence. This study helps address that gap by examining telehealth use among the pediatric enrollee population in Alabama’s Medicaid Program. Like other Deep South states, Alabama is characterized by high poverty, large African-American populations, a substantial rural population, and poor performance on health indicators [11–13]. Thus, this study can inform on telehealth use among a particularly vulnerable sub-population—low-income children in one of the most disadvantaged regions of the U.S.

Materials and methods

This retrospective observational study used claims data from Alabama Medicaid for enrollees aged 0–18 years who were enrolled for any period of time from March to December 2020. Telehealth claims were identified using the place of service code of 02 on the medical claims data.

Enrollee characteristics like race/ethnicity, age, self-reported gender, and county and zip code of residence were extracted from Medicaid enrollment files. Telehealth use from March to December 2020 was examined for the demographic characteristics of enrollees. Race/ethnicity was categorized into non-Hispanic white (NHW), non-Hispanic Black (NHB), Hispanic, Native American, Asian, and Other (which includes unknown). [14, 15] Rural-Urban Commuting Area (RUCA) designation, a standardized classification system to identify rural and urban areas, was divided into four categories: urban, large rural, small rural, and isolated areas. [16] The latest version of RUCA codes, revised on July 2019, was obtained from the United States Department of Agriculture. [17] To measure the socio-economic status of the area where the enrollee resided, quartiles of zip-code level poverty were constructed (Pov_Q1 through Pov_Q4), following precedence in the literature. [18, 19] To construct this, the percentage of households living below the federal poverty line at each zip code level was collected from the US census bureau, and divided into quartiles [20]. Note that lower quartiles indicate a smaller share of households in poverty. A binary indicator indicating whether the zip code was in the highest decile nationwide for lack of broadband access versus all other deciles. In the highest decile, a minimum of 40.2% and an average of 53.7% of zip-code residents reported no broadband or data access, compared to an average of 19.5% reporting no broadband access in the other deciles pooled.

The outcome of interest was a binary indicator for whether an enrollee used any telehealth services from March to December 2020. Descriptive statistics were computed on the rate of telehealth use by the above enrollee characteristics. Given the possibility of correlation between RUCA, zip-code level poverty, and lack of broadband access, multicollinearity between variables was tested by using a linear probability model and computing and estimating variance inflation factors.

Since one goal of this study is to investigate the association between neighborhood characteristics and telehealth utilization, a Poisson mixed effects model with robust error variance was estimated with zip code level as the nested structure. The Poisson mixed effects model incorporates random effect for each level of hierarchy to account for correlation among observations at each level and accounts for overdispersion, a situation when variance is greater than the mean, that could lead to a biased estimate. [21, 22] Due to the relatively large sample size of Medicaid enrollees, even minuscule differences by enrollee characteristic may appear to be statistically significant; hence meaningful effect size of differences in telehealth use was of interest rather than just whether the differences were statistically significant. To this end, given the well-known challenges of obtaining effect sizes from odds ratios from logistic regressions and the documented distortions of scientific findings when odds ratios have been misinterpreted as risk ratios [23, 24], the Poisson models were preferred, since the incidence rate ratios from such models can be interpreted as relative risk ratios [25]. Additional controls for potential confounders included in the multivariate Poisson mixed effects model were self-reported gender, age group (0–3 years, 3–6 years, 6–12 years, and 12–18 years), pediatricians or family medicine practitioners per 1000 population and hospital beds per 1000 population in the county of residence. The actual number of months an individual enrolled in Medicaid from March to December 2020 was used as exposure. Stratified multivariate analyses by gender and by age-group (<12 years and 12-<19 years) were also conducted. A priori level of significance was set at p<0.05, and all statistical analyses were performed using STATA version 17/SE. The study was approved by the University of Alabama at Birmingham Institutional Review Board.

Results

Of 637,792 pediatric Medicaid enrollees during March-December 2020, approximately 16.9% had any telehealth use based on the place of service in claims data (Table 1). The proportion with any telehealth use was approximately 19.6% among NHW children, 17.5% for Native American children, 16.0% for NHB children, 10.5% for Hispanic children, 8.8% for Asian children, and 16.33% for ‘Other/Unknown’ children. The proportion of telehealth use among enrollees in different RUCA designations was similar, ranging from 16.1%-17.1%. Enrollees in Pov_Q1 through Pov_Q3 zip codes had similar rates of telehealth use (17.5%-17.7%), while among those in the highest poverty quartile zip codes, the rate was 15.6%. Among enrollees residing in zip codes in the highest decile of lack of broadband, 15.5% reported any telehealth use compared to 17.1% of enrollees in other zip codes. The pre-covid or “baseline” telehealth use by enrollees for comparable months in 2018 & 2019 (excluded January and February data for both years), and variations in telehealth utilization by race-ethnicity and the other characteristics, is provided in the S1 Appendix.

Table 1. Variations in any telehealth use by race/ethnicity, RUCA, zip-code level poverty & broadband access among Alabama pediatric Medicaid enrollees.

Sample Size Poisson Mixed Effect Modela
n (%) Incidence Rate Ratio 95% Confidence Interval
Full Sample 647,930 (16.9)
Race/Ethnicity
 Non-Hispanic White 225,130 (19.6) Reference
 Non-Hispanic Black 240,560 (16.0) 0.80** [0.78, 0.81]
 Hispanic 41,091 (10.5) 0.55** [0.54, 0.57]
 Native American 1,779 (17.5) 0.97 [0.86, 1.08]
 Asian 5,003 (8.8) 0.46** [0.42, 0.51]
 Other 134,367 (16.3) 0.84** [0.82, 0.85]
RUCA Categoryb
 Urban 484,999 (17.1) Reference
 Large rural 89,835 (16.1) 0.90** [0.83, 0.97]
 Small rural 48,209 (16.9) 0.95 [0.86, 1.05]
 Isolated 24,887 (16.3) 0.90** [0.82, 0.98]
Zip-code Poverty Quartile
 Poverty Quartile 1 105,621 (17.6) Reference
 Poverty Quartile 2 165,187 (17.5) 1.03 [0.96, 1.11]
 Poverty Quartile 3 180,246 (17.7) 1.04 [0.97, 1.12]
 Poverty Quartile 4 196,747 (15.6) 0.98 [0.91, 1.07]
No Broadband Connectivity Decile
 First-ninth decile 579,446 (17.1) Reference
 Highest decile 68,484 (15.5) 0.95 [0.88, 1.02]
Open in a new tab

a Poisson mixed-effects model was estimated at the zip-code level with robust error variance. The exposure variable was months enrolled in Medicaid from March to December 2020. Additional control variables included in the model were self-reported gender, age group (0–3 years, 3–6 years, 6–12 years, 12–18 years), pediatricians/family medicine practitioners per 1000 population, and hospital beds per 1000 population in the county of residence.

b RUCA: Rural-urban commuting area

c Zip-Code level no broadband connectivity decile

**:P<0.05

Results from the multivariate Poisson mixed-effect model showed that, compared to the reference group of NHW children, NHB children 81% as likely, Hispanic children 55% as likely, and Asian children 46% as likely to have any telehealth use (p<0.05 in all cases). Compared to the reference group of urban enrollees, the large rural area enrollees and isolated area enrollees were significantly less likely to use any telehealth (IRR: 0.90 for both areas; P<0.05,), while enrollees in small rural areas had statistically similar rates (IRR: 1.01, p>0.05). The results regarding the zip-code poverty quartile were not statistically significant; when compared to the reference group of Pov-Q1, those in Pov_Q2 & Pov_Q3 had statistically higher rates of any telehealth use (IRR: 1.03 and 1.04, respectively, p>0.05), and those in Pov_Q4 had statistically lower rates of any telehealth use (IRR: 0.98, p>0.05). Those residing in zip-codes in the highest decile of lack of broadband were not significantly associated with telehealth utilization as those living in all other zip-codes (IRR: 0.95, p>0.05). Notably, variance inflation factor analysis did not find evidence of a high degree of multicollinearity between RUCA designation, poverty quartiles, or lack of broadband access. Analyses stratified by gender (Table 2) yielded very similar results for males and females with the exception that those residing in zip-codes in the highest decile of lack of broadband were significantly associated and slightly less likely to use any telehealth as those living in all other zip-codes (IRR:0.91 and IRR:0.93, respectively for females and males; p<0.05). Analyses by age-group (Table 3) also find largely similar results for enrollees under 12 years and 12-<19 years though, once again, enrollees living in the highest decile of lack of broadband were less likely to use any telehealth as those living in all other zip-codes (IRR:0.92 for enrollees under 12 years and 12-<19; p<0.05).

Table 2. Variations in any telehealth use by race/ethnicity, RUCA, zip-code level poverty & broadband access among pediatric Medicaid enrollees, stratification by gender.

Poisson Mixed Effects Model (Female)a Poisson Mixed Effects Model (Male)a
Incidence Rate Ratio 95% Confidence Interval Incidence Rate Ratio 95% Confidence Interval
Full Sample
Race/Ethnicity
 Non-Hispanic White Reference Reference
 Non-Hispanic Black 0.79** [0.78, 0.80] 0.80** [0.79, 0.82]
 Hispanic 0.57** [0.56, 0.59] 0.58** [0.56, 0.60]
 Native American 0.93 [0.83, 1.04] 0.95 [0.84, 1.06]
 Asian 0.47** [0.43, 0.52] 0.46** [0.42, 0.51]
 Other 0.88** [0.86, 0.89] 0.89** [0.87, 0.90]
RUCA Categoryb
 Urban Reference Reference
 Large rural 0.89** [0.82, 0.96] 0.89** [0.82, 0.96]
 Small rural 0.95 [0.86, 1.05] 0.94 [0.85, 1.04]
 Isolated 0.89** [0.82, 0.98] 0.90** [0.82, 0.98]
Zip-code Poverty Quartile
 Poverty Quartile 1 Reference Reference
 Poverty Quartile 2 1.03 [0.96, 1.12] 1.02 [0.95, 1.10]
 Poverty Quartile 3 1.03 [0.96, 1.11] 1.02 [0.95, 1.10]
 Poverty Quartile 4 0.97 [0.90, 1.05] 0.97 [0.89, 1.05]
No Broadband Connectivity Decile
 First-ninth decile Reference Reference
 Highest decile 0.91** [0.85, 0.98] 0.93** [0.86, 1.00]
Open in a new tab

a Poisson mixed-effects model was estimated at the zip-code level with robust error variance. The exposure variable was months enrolled in Medicaid from March to December 2020. Additional control variables included in the model were age group (0–3 years, 3–6 years, and 6–12 years), pediatricians/family medicine practitioners per 1000 population, and hospital beds per 1000 population in the county of residence.

b RUCA: Rural-urban commuting area

c Zip-Code level no broadband connectivity decile

**:P<0.05

Table 3. Variations in any telehealth use by race/ethnicity, RUCA, zip-code level poverty & broadband access among pediatric Medicaid enrollees, stratification by age.

Poisson Mixed Effects Model (Age 12–18 years)a Poisson Mixed Effects Model (Age less than 12 years)a
Incidence Rate Ratio 95% Confidence Interval Incidence Rate Ratio 95% Confidence Interval
Full Sample
Race/Ethnicity
 Non-Hispanic White Reference Reference
 Non-Hispanic Black 0.78** [0.77, 0.80] 0.81** [0.79, 0.82]
 Hispanic 0.55** [0.53, 0.57] 0.59** [0.57, 0.61]
 Native American 0.90 [0.80, 1.01] 0.98 [0.87, 1.09]
 Asian 0.46** [0.42, 0.51] 0.47** [0.43, 0.51]
 Other 0.83** [0.82, 0.85] 0.91** [0.90, 0.93]
RUCA Categoryb
 Urban Reference Reference
 Large rural 0.89** [0.82, 0.96] 0.89** [0.82, 0.96]
 Small rural 0.94 [0.85, 1.05] 0.94 [0.85, 1.04]
 Isolated 0.90** [0.82, 0.99] 0.89** [0.82, 0.98]
Zip-code Poverty Quartile
 Poverty Quartile 1 Reference Reference
 Poverty Quartile 2 1.03 [0.96, 1.11] 1.02 [0.95, 1.10]
 Poverty Quartile 3 1.03 [0.96, 1.11] 1.02 [0.95, 1.10]
 Poverty Quartile 4 0.98 [0.90, 1.06] 0.96 [0.89, 1.04]
No Broadband Connectivity Decile
 First-ninth decile Reference Reference
 Highest decile 0.92** [0.86, 0.99] 0.92** [0.86, 0.99]
Open in a new tab

a Poisson mixed-effects model was estimated at the zip-code level with robust error variance. The exposure variable was months enrolled in Medicaid from March to December 2020. Additional control variables included in the model were self-reported gender, pediatricians/family medicine practitioners per 1000 population, and hospital beds per 1000 population in the county of residence.

b RUCA: Rural-urban commuting area

c Zip-Code level no broadband connectivity decile

**:P<0.05

Discussion

During the COVID-19 pandemic, telehealth became a major vehicle for delivering healthcare services. The COVID-19 PHE declaration, under which CMS made provisions to expand telehealth services, is set to expire soon. However, there is a robust discussion on how telehealth can continue to be a vital part of healthcare delivery and help improve health equity [26]. Thus, it is imperative to understand existing disparities in telehealth use, including among pediatric patients. This is one of the first assessments of telehealth use in a publicly insured pediatric population during the COVID-19 pandemic and explores differences in any telehealth use by race/ethnicity and place of residence. This study focuses on Alabama, a state that struggles with poor performance on several health metrics and saw increase in telehealth utilization for overall population but declines in overall healthcare use and in Emergency Department visits for publicly insured children during the COVID-19 pandemic [14, 27].

Overall, approximately 16.9% of pediatric Alabama Medicaid enrollees used telehealth at least once from March to December 2020 which represents a substantial increase from the 0.16% telehealth utilization rate during comparable periods in 2018 and 2019. The rate of telehealth utilization among Medicaid enrollees during first 10 months of the pandemic is also higher than the 13.4% use of any telehealth in Alabama’s stand-alone Children’s Health Insurance Program (which covers children 146% to 317% of the Federal Poverty Level) during the same period [15]. This concurs with findings in the literature that low-income populations are more likely to use telehealth than their higher-income counterparts [4, 5]. At the same time, this is lower than the 22–24% of pediatric telehealth use reported in the Household Pulse Survey [28], though it also reported substantial variation across states.

One of the key disparities in telehealth use that emerged from this study’s results is by race/ethnicity. While children of all other races/ethnicities had lower telehealth use rates than NHW children, the differences were especially stark for Hispanic and Asian children, who were only about half as likely to use telehealth as NHW children. In contrast, NHB children 80% as likely to use any telehealth compared to NHW children. This strongly suggests that language or communication barriers may have impeded telehealth services. Prior research has indicated that Spanish-speaking patients have greater difficulty with telehealth than English-speaking patients [29], A recent roundtable report from the National Committee for Quality Assurance underlined that using a patient’s preferred language was a key element in delivering culturally competent care and that one area that providers had struggled with was how to include translators in telehealth visits [30]. This study’s findings reinforce the possibility that language and communication are barriers to telehealth use among publicly insured children and highlight the need to devote resources to overcoming this barrier to reduce race/ethnicity disparities in telehealth utilization.

Interestingly, this study found relatively few differences in rates of any telehealth use by place of residence. Enrollees residing in small rural areas showed no significant differences in telehealth use than enrollees in urban areas, and enrollees in large rural areas and isolated areas were only slightly less likely (90%) to use telehealth than urban residents. It is noted that one California study found that telehealth improved access for publicly insured pediatric patients who lived long distances away from hospitals [31]. Finally, and perhaps surprisingly, lack of broadband access had no significant association with telehealth use.

The research team acknowledges several limitations. Since the analyses are based on claims data, for enrollees with no telehealth use, it cannot be determined whether this was because of a lack of need for health services, language barriers, technology barriers, healthcare provider inability to provide telehealth care, or perceptions about telehealth among the patient or provider. However, one survey of parents and guardians of pediatric patients in Alabama conducted in the fall of 2020 suggests that primary barriers included not being given the option of telehealth by the provider, not clearly understanding what telehealth is, and concerns about the usefulness of telehealth [32]. Also, this study focuses on the role of race and place on pediatric telehealth use, but it does not explore potentially complex interplays between enrollee race and community-level factors, which have been shown to exist for other health services like ED use [33]; this is an area that future research on telehealth use should explore. Further, this study’s analysis focused on any versus no telehealth use but did not investigate how extensively patients used telehealth or for what health services telehealth was most frequently used. There was no information on whether telehealth encounters were audio only or audio-visual or whether this impacted patient experience and the quality of care. Also, the data only extended to the end of 2020 and cannot inform on trends in 2021 –though preliminary findings from CMS research indicate that rates of telehealth use among pediatric Medicaid patients nationwide remained relatively steady from June 2020 through June 2021 [34]. Finally, this study is based on one state’s Medicaid program. However, the research team believes the findings are particularly relevant for other Deep South states which share many of Alabama’s socio-economic and demographic characteristics.

In conclusion, if telehealth continues to be a major vehicle for the delivery of healthcare services for Alabama Medicaid, then particular attention must be paid to race/ethnicity disparities–particularly understanding and addressing the barriers that Hispanic and Asian pediatric patients face in accessing telehealth.

Supporting information

S1 Appendix

(DOCX)

Click here for additional data file. (16.3KB, docx)

Data Availability

This research was conducted using administrative claims data from Alabama Medicaid that includes PHI, which we accessed through a research contract with Alabama Medicaid. We are prohibited from publicly making this data available. However, interested researchers can contact us for the data after obtaining written permission from the Alabama Medicaid Agency to access the data (https://medicaid.alabama.gov/).

Funding Statement

This study was funded in part by the Alabama Medicaid Agency (https://medicaid.alabama.gov/), contract number: C200629944. BS is the principal investigator of the award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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  • 29.Samuels-Kalow ME, Chary AN, Ciccolo G, Apro A, Danaher F, Lambert R, et al. Barriers and facilitators to pediatric telehealth use in English- and Spanish-speaking families: A qualitative study. Journal of Telemedicine and Telecare. 2022;0(0):1357633X211070725. doi: 10.1177/1357633X211070725 [DOI] [PubMed] [Google Scholar]
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Decision Letter 0

Kevin Lu

17 Jan 2023

PONE-D-22-31503Telehealth Use Among Pediatric Alabama Medicaid Enrollees, March-December 2020: Variations by Race/Ethnicity & Place of ResidencePLOS ONE

Dear Dr. Rahim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Kevin Lu, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a nice paper with new results. It has a large sample and robust methods. Writing is also good.

Here are areas for improvement (Most of them about nuances):

1- Genders differ in all processes related to health care use. In this paper, however, gender differences in correlations are not shown. Only main effects of gender are shown. This is like gender is a confounder or a covariate with only main effects. Some of the effects of gender are indirect through changing pathways.

2- Age groups differ in correlates of health care use in part because differences in their health literacy and chronic disease. Thus, show us if older people and older-old people differ in these paths.

3- Race and ethnic groups differ in correlates of health care use. This means race and ethnicity are not just confounders but also moderators of the SES effects.

Based on minorities' diminished returns, SES has stronger effects on health and health care use of Whites than men. That is, due to racism and social stratification, SES loses some of its effect for marginalized people particularly Black people.

Some examples are here:

https://pubmed.ncbi.nlm.nih.gov/32783022/

https://pubmed.ncbi.nlm.nih.gov/32457934/

https://pubmed.ncbi.nlm.nih.gov/32190811/

4- This paper only focuses on patients data. Neighborhood, health care system, health care provider, and geographic distribution of resources are not even considered.

After these issues are addressed, I will be happy to recommend acceptance.

**********

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Reviewer #1: No

**********

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PLoS One. 2023 Jun 26;18(6):e0287598. doi: 10.1371/journal.pone.0287598.r002

Author response to Decision Letter 0

10 Feb 2023

Here are areas for improvement (Most of them about nuances):

1- Genders differ in all processes related to health care use. In this paper, however, gender differences in correlations are not shown. Only main effects of gender are shown. This is like gender is a confounder or a covariate with only main effects. Some of the effects of gender are indirect through changing pathways.

Thank you for your positive comments on your paper and for your helpful suggestions. We agree that gender may change pathways of associations between race and place with telehealth use; hence, we now also present results that are stratified by gender in table 2.

2- Age groups differ in correlates of health care use in part because differences in their health literacy and chronic disease. Thus, show us if older people and older-old people differ in these paths.

Thank you for your comment. We agree that age may change pathways of associations between race and place with telehealth use. Since we focus on the pediatric population (<19 years), the distinction between older and older-old is not directly relevant, but we believe differences may exist between pre-teen and teen children since the latter may have more of a “say” in their own health utilization decisions. Hence, we stratify results by <12 years and 12-<19 years.

3- Race and ethnic groups differ in correlates of health care use. This means race and ethnicity are not just confounders but also moderators of the SES effects.

Based on minorities' diminished returns, SES has stronger effects on health and health care use of Whites than men. That is, due to racism and social stratification, SES loses some of its effect for marginalized people particularly Black people.

Some examples are here:

https://pubmed.ncbi.nlm.nih.gov/32783022/

https://pubmed.ncbi.nlm.nih.gov/32457934/

https://pubmed.ncbi.nlm.nih.gov/32190811/

Thank you for your comment. To be clear, we do not treat race and ethnicity as confounders. Rather, they are the main exposure variables of interest, and we are interested in exploring racial and ethnic disparities (as well as disparities by place of residence) in telehealth utilization. That said, future research should explore the interplay of race/ethnicity and place in various telehealth service utilization, and we now mention this in the Limitations and state that this is an area for future research.

Also, thank you for making suggestions regarding additional citations. Since they pertained to the broader adult population while our study focuses on the publicly-insured pediatric population, not all of them were very directly relevant. However, we did cite https://pubmed.ncbi.nlm.nih.gov/32457934/ in the introduction as one example of disparities by race across education levels in the existing literature.

4- This paper only focuses on patient data. Neighborhood, health care system, health care provider, and geographic distribution of resources are not even considered.

We agree that neighborhood, health care system, health care provider, and geographic distribution of resources are important, and indeed we accounted for several of those in our model. Specifically, we included zip-code level poverty quartiles, zip-code level broadband access (especially relevant for telehealth), and family medicine or pediatric physicians per 1000 population in the county. Further, in response to the reviewer’s comment about healthcare systems, we also included hospital beds per 1000 in the models. These additional variables are listed clearly in the notes below in Tables 1, 2, and 3 as model control variables.

After these issues are addressed, I will be happy to recommend acceptance.

Thank you. We believe we have been able to address the issues that you raised and thank you for your thoughtful comments.

Attachment

Submitted filename: Response_to_Reviewer_Comments.docx

Click here for additional data file. (25KB, docx)

Decision Letter 1

Kevin Lu

20 Mar 2023

PONE-D-22-31503R1Telehealth Use Among Pediatric Alabama Medicaid Enrollees, March-December 2020: Variations by Race/Ethnicity & Place of ResidencePLOS ONE

Dear Dr. Sen,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 04 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Kevin Lu, PhD

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: This is an interesting cross-sectional study aimed to investigate telehealth usage variation by race/ethnicity and place of residence for the pediatric enrollees of the Alabama Medicaid plan. The study found racial disparities as well as the rural-urban disparities in using telehealth among the pediatric enrollees of the Alabama Medicaid plan. The language of the manuscript is also well-written. However, I have some concerns and comments, and I have listed them below:

Major concerns:

1. The measurements of the variables are not clearly described. The authors only talked about what the variables were included, but did not explain how they were categorized or applied.

a. For example, the patients mentioned the RUCA codes was used to categorize the residence areas but did not elaborate how the specific codes were applied. They also did not mention the version of the RUCA codes, and there is no reference for the RUCA code, which could be confusing for the readers who are not familiar with this field.

b. Similarly, what’s the rationale of using quartiles of zip-code level poverty. Is there any reference?

2. There is no data and baseline information on the patients, especially the information on confounders is missing.

3. Is it possible to conduct a Poisson mixed effects model, as there might be fixed differences between residence areas but homogeneity within residence areas?

4. Is it possible to consider the impact of the time, as the usage of telehealth in March and in December could be different?

Minor comments:

1. Is there a specific reason why the study period was only in 2020?

2. What was the telehealth use rate before the pandemic? Is there any published literature about this?

3. Enrollees in Pov_Q3 were more likely to use telehealth, while those in Pov_Q4 were less likely. What’s the potential reason for this difference?

4. Tables: Does “Other” of Race/Ethnicity means both other and unknown?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

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PLoS One. 2023 Jun 26;18(6):e0287598. doi: 10.1371/journal.pone.0287598.r004

Author response to Decision Letter 1

25 Apr 2023

We have uploaded a document listing our response to the reviewer comments. We are also copying and pasting them here.

____________________________________________________________________________________________________

We are grateful to the reviewer for the detailed and valuable feedback provided. We are also grateful that the reviewer stated that we adequately addressed all points raised in the prior review.

Below, we outline specific changes we made, with reviewer comments in plain text followed by our response in bold text.

Major concerns:

1. The measurements of the variables are not clearly described. The authors only talked about what the variables were included, but did not explain how they were categorized or applied.

a. For example, the patients mentioned the RUCA codes was used to categorize the residence areas but did not elaborate how the specific codes were applied. They also did not mention the version of the RUCA codes, and there is no reference for the RUCA code, which could be confusing for the readers who are not familiar with this field.

Thank you for this comment. We have used the latest RUCA codes, revised and released in July 2019, and provided details about RUCA in the methods section, including the citation for the most recent version of RUCA codes (‘Materials and Methods’ subsection, para 2).

b. Similarly, what’s the rationale of using quartiles of zip-code level poverty. Is there any reference?

We have included citations that support using zip-code level poverty quartiles as a covariate in our model (‘Materials and Methods’ subsection, para 2). Examples include:

https://www.sciencedirect.com/science/article/pii/S0022480415003510

https://acsjournals.onlinelibrary.wiley.com/doi/10.1002/cncr.21732

https://journals.sagepub.com/doi/10.1177/107327480901600210

2. There is no data and baseline information on the patients, especially the information on confounders is missing.

We agree that this would be useful information for the reader. We have included the baseline information on variation in telehealth utilization by the enrollee characteristics during comparable periods in 2018 & 2019 in the Appendix. Note that this also helps address the reviewer’s later question about telehealth use before the pandemic.

3. Is it possible to conduct a Poisson mixed effects model, as there might be fixed differences between residence areas but homogeneity within residence areas?

In response to this suggestion, we have changed our model to Poisson mixed effects model.

4. Is it possible to consider the impact of the time, as the usage of telehealth in March and in December could be different?

The number of months an enrollee was in the program is used as an exposure variable in the model. Since our research question is not focused on monthly trends per se, we did not report on monthly rates of telehealth use in this paper, but we have cited a CMS source that reports monthly trends in Medicaid use in telehealth for the country as a whole (https://www.medicaid.gov/state-resource-center/downloads/covid19-data-snapshot-11122021.pdf)

Minor comments:

1. Is there a specific reason why the study period was only in 2020?

We selected this study period to understand the initial impact of the pandemic on telehealth utilization among pediatric enrollees of the Alabama Medicaid program. There is a substantial lag in time before we receive the completed Alabama Medicaid claims data for any calendar year; hence using data for the latter part of the pandemic was not feasible for this manuscript, though we hope to investigate that in future research.

2. What was the telehealth use rate before the pandemic? Is there any published literature about this?

As mentioned earlier, we have now reported telehealth use among Alabama’s pediatric Medicaid population in 2018 and 2019 in the appendix. To the best of our knowledge, there is no published literature about the telehealth utilization rate of pediatric enrollees in the Alabama Medicaid program. However, we have provided citations that provide information on the telehealth utilization trends among all Medicaid enrollees.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290332/

3. Enrollees in Pov_Q3 were more likely to use telehealth, while those in Pov_Q4 were less likely. What’s the potential reason for this difference?

In the updated mixed effects models, we do not find any statistically significant association between poverty quartiles and telehealth utilization. Thus, we have updated the Results and Discussion sections to reflect this.

4. Tables: Does “Other” of Race/Ethnicity means both other and unknown?

Thank you for this question. We have now clarified that” Other” includes both “other and unknown” and the method section of the manuscript.

Attachment

Submitted filename: Response to Reviewer_Plos_One_april23.docx

Click here for additional data file. (17.4KB, docx)

Decision Letter 2

Kevin Lu

8 Jun 2023

Telehealth Use Among Pediatric Alabama Medicaid Enrollees, March-December 2020: Variations by Race/Ethnicity & Place of Residence

PONE-D-22-31503R2

Dear Dr. Sen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Kevin Lu, PhD

Academic Editor

PLOS ONE

Acceptance letter

Kevin Lu

15 Jun 2023

PONE-D-22-31503R2

Telehealth Use Among Pediatric Alabama Medicaid Enrollees, March-December 2020: Variations by Race/Ethnicity & Place of Residence

Dear Dr. Sen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Kevin Lu

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Appendix

    (DOCX)

    Click here for additional data file. (16.3KB, docx)
    Attachment

    Submitted filename: Response_to_Reviewer_Comments.docx

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    Attachment

    Submitted filename: Response to Reviewer_Plos_One_april23.docx

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    Data Availability Statement

    This research was conducted using administrative claims data from Alabama Medicaid that includes PHI, which we accessed through a research contract with Alabama Medicaid. We are prohibited from publicly making this data available. However, interested researchers can contact us for the data after obtaining written permission from the Alabama Medicaid Agency to access the data (https://medicaid.alabama.gov/).

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