Abstract
Objectives
To examine demographic, socioeconomic, and regional differences in contraceptive access, differences between telehealth and in-person contraception visits, and telehealth quality in the United States during the COVID-19 pandemic.
Study Design
We surveyed reproductive-age women about contraception visits during the COVID-19 pandemic via social media in July 2020 and January 2021. We used multivariable regression to examine relationships between age, racial/ethnic identity, educational attainment, income, insurance type, region, and COVID-19 related hardship, and ability to obtain a contraceptive appointment, telehealth vs in-person visits, and telehealth quality scores.
Results
Among 2,031 respondents who answered questions about contraception visits, 1,490 (73.4%) reported any visit, of which 530 (35.6%) were telehealth. In adjusted analyses, lower odds of any visit was associated with Hispanic/Latinx and Mixed race/Other identity (aOR 0.59 [0.37-0.94], aOR 0.36 [0.22-0.59], respectively), the South, Midwest, Northeast (aOR 0.63 [0.47-0.85], aOR 0.64 [0.46-0.90], aOR 0.52 [CI 0.36-0.75], respectively), no insurance (aOR 0.63 [0.43-0.91]), greater COVID-19 hardship (aOR 0.52 [0.31-0.87]), and earlier pandemic timing (January 2021 vs July 2020 aOR 2.14 [1.69-2.70]). Respondents from the Midwest and South had lower odds of telehealth vs in-person care (aOR 0.63 [0.44-0.88], aOR 0.54 [0.40-0.72], respectively). Hispanic/Latinx respondents and those in the Midwest had lower odds of high telehealth quality (aOR 0.37 [0.17-0.80], aOR 0.58 [0.35-0.95], respectively).
Conclusions
We found inequities in contraceptive care access, less telehealth use for contraception visits in the South and Midwest, and lower telehealth quality among Hispanic/Latinx people during the COVID-19 pandemic. Future research should focus on telehealth access, quality, and patients’ preferences.
Keywords
1. Introduction
The coronavirus disease 2019 (COVID-19) pandemic has dramatically impacted access to reproductive healthcare, changing how care is delivered and exacerbating structural inequities in access to care. Telehealth use in the United States (US) for reproductive healthcare has drastically increased during the COVID-19 pandemic. Prior to COVID-19, digital technologies were used relatively rarely in the healthcare space, largely due to heavy regulation and payment structures that favored face-to-face care models [
[1]
]. Yet telehealth offers greater convenience for patients and providers and increased access for patients with transportation, childcare, or job security challenges, and for patients in regions where reproductive healthcare is limited or restricted [2
, 3
, 4
, 5
, 6
, 7
]. Contraception visits sharply declined when COVID-19 restrictions took effect and remained low throughout 2020 [- Shin R.J.
- Yao M.
- Akesson C.
- Blazel M.
- Mei L.
- Brant A.R.
An exploratory study comparing the quality of contraceptive counseling provided via telemedicine versus in-person visits.
Contraception. 2022; https://doi.org/10.1016/j.contraception.2022.02.004
[8]
]. However, many studies have demonstrated the rapid increase in the use of telehealth during COVID-19 specifically for contraceptive care, from only 10-20% of family planning providers utilizing telehealth before the pandemic to 80-90% utilizing it during the pandemic [9
, - Rao L.
- Comfort A.B.
- Dojiri S.S.
- Goodman S.
- Yarger J.
- Shah N.
- et al.
Telehealth for Contraceptive Services During the COVID-19 Pandemic: Provider Perspectives.
Womens Health Issues. 2022; https://doi.org/10.1016/j.whi.2022.05.001
10
, 11
].Disparities in telehealth access and uptake before and during the pandemic are well-documented. Non-white and publicly-insured patients were less likely to successfully complete telehealth clinic visits in surgery and oncology settings [
12
, 13
]. And in the family planning setting in states with restricted access, Black/African American and multiracial patients had fewer telehealth visits compared to white patients [[14]
]. Less access to widespread broadband internet, fewer or no devices that can be used to attend telehealth visits, and perceived difficulty in accessing telehealth are possible reasons for the disparity [- Hill B.J.
- Lock L.
- Anderson B.
Racial and ethnic differences in family planning telehealth use during the onset of the COVID-19 response in Arkansas, Kansas, Missouri, and Oklahoma.
Contraception. 2021; https://doi.org/10.1016/j.contraception.2021.05.016
9
, - Rao L.
- Comfort A.B.
- Dojiri S.S.
- Goodman S.
- Yarger J.
- Shah N.
- et al.
Telehealth for Contraceptive Services During the COVID-19 Pandemic: Provider Perspectives.
Womens Health Issues. 2022; https://doi.org/10.1016/j.whi.2022.05.001
15
, 16
]. Concerns have also been raised that telehealth may lead to lower quality care compared to in-person care, specifically with less personal connection and lower confidentiality in virtual contraceptive counseling [- Yarger J.
- Hopkins K.
- Elmes S.
- Rossetto I.
- De La Melena S.
- McCulloch C.E.
- et al.
Perceived Access to Contraception via Telemedicine Among Young Adults: Inequities by Food and Housing Insecurity.
J Gen Intern Med. 2022; https://doi.org/10.1007/s11606-022-07669-0
9
, - Rao L.
- Comfort A.B.
- Dojiri S.S.
- Goodman S.
- Yarger J.
- Shah N.
- et al.
Telehealth for Contraceptive Services During the COVID-19 Pandemic: Provider Perspectives.
Womens Health Issues. 2022; https://doi.org/10.1016/j.whi.2022.05.001
17
, - Hurtado A.C.M.
- Crowley S.M.
- Landry K.M.
- Landry M.S.
Telehealth contraceptive care in 2018: A quality improvement study of barriers to access and patient satisfaction.
Contraception. 2022; https://doi.org/10.1016/j.contraception.2022.02.011
18
]. However, other studies have suggested that counseling via telehealth is similar to in-person, and studies of telehealth abortion have shown that patients find telehealth more private than in-person and prefer telehealth over in-person counseling [7
, - Shin R.J.
- Yao M.
- Akesson C.
- Blazel M.
- Mei L.
- Brant A.R.
An exploratory study comparing the quality of contraceptive counseling provided via telemedicine versus in-person visits.
Contraception. 2022; https://doi.org/10.1016/j.contraception.2022.02.004
19
, 20
, 21
].Though a small number of studies have investigated patterns of telehealth use compared to in-person care for contraception and telehealth quality for contraceptive care during COVID-19, to our knowledge, none have reported on regional trends or differences in telehealth quality on a national scale [
18
, 19
]. As the COVID-19 pandemic continues and restrictions on access to abortion and contraception increase, ongoing investigation into telehealth’s effects on people's access to contraception is essential to understanding how COVID-19 has impacted sexual and reproductive health, and how providers can increase access to family planning care for individuals in regions where in-person care is limited. We sought to examine demographic, socioeconomic, and regional differences in contraceptive access, differences between telehealth and in-person visits, and telehealth quality for contraceptive visits in the US during the COVID-19 pandemic.2. Materials and Methods
In July 2020 and January 2021, we collected data on experiences seeking contraception, prenatal, postnatal, miscarriage, and abortion care during COVID-19 [
22
, 23
, 24
]. We report here on a subset of the data focused on contraception. We recruited a convenience sample of English- or Spanish-speaking individuals through Facebook and Instagram Ads. We recruited in July 2020 for one week and in January 2021 for three weeks because this was part of a parent study looking at trends over time (6 months in the early COVID-19 pandemic). We designed ads that appeared in Facebook and Instagram users’ feeds and included a link to informed consent, in which they were informed of the purpose of the study, the investigators, the survey length, and specifics of data storage, followed by the survey. Eligibility criteria included identifying as a woman aged 18-45 and living in the US. We recruited respondents from across the US, with specific effort made to recruit women of color from the South and Midwest, since these regions and populations face more structural barriers to contraceptive care. The survey was designed by a team of OBGYN clinicians, epidemiologists, and researchers with expertise designing surveys to be deployed over social media. Previously validated measures were used where possible. We piloted the survey for usability on a smartphone using the Qualtrics tools. Respondents in the first round (July 2020) were offered a $10 gift card if they participated in 2 rounds of the survey, and a $5 gift card for one survey in the second round (Jan 2021). All data were recorded in Qualtrics and stored on a secure network. This study was approved by the University of California, San Francisco Institutional Review Board.- Marshall C.
- Gutierrez S.
- Hecht H.
- Logan R.
- Kerns J.
- Diamond-Smith N.
Quality of prenatal and postpartum telehealth visits during COVID-19 and preferences for future care.
AJOG Global Reports. 2023; 100139https://doi.org/10.1016/j.xagr.2022.100139
We asked non-pregnant respondents whether they had had a contraceptive appointment with a healthcare provider in the past three months. If they had not, we asked for reasons why, including not needing contraception or an appointment, or trying to make an appointment but not being able to. For this study’s analysis, we only included respondents who indicated that they desired an appointment for contraception, i.e. respondents who had an appointment and respondents who tried to make an appointment but were unable to. For those indicating that they had had an appointment, we asked about appointment types, including in-person, by phone, video, or online/chat (collectively labeled as “telehealth”, defined as health care provided remotely to a patient using synchronous two-way voice, visual, or online chat communication).
We asked respondents about sociodemographic variables including age, racial/ethnic identity, educational attainment, annual household income, insurance type, and zip code. We assessed whether and how respondents’ lives were affected by COVID-19 by asking whether they experienced hunger, housing changes, or income or job loss due to the pandemic. Among those who reported having a telehealth visit, respondents reported visit quality on a 5-point Likert scale from “strongly disagree” to “strongly agree” in response to the prompts: “it was convenient”, “it was easy”, “it felt personal”, “I could understand what they were trying to tell me/ I got good information”, “it felt safe”, “it felt private”, “I felt cared for”, and “I would do it again if I had the option.” We selected these questions based on a modified list of items from the Person Centered Maternity Care scale, which has been adapted and validated for contraceptive use [
25
, 26
].We cleaned the data first to remove responses that stemmed from the same IP addresses, incomplete surveys, surveys filled out too quickly (potentially bots) and women who were not eligible, including those who reported permanent contraception or were under 18 or over 45. We created a COVID-19 hardship score by summing the 4 questions related to the impact of COVID-19 on reported hunger, income or job loss, or housing changes; scores ranged 0 to 4, with higher scores reflecting greater hardship. We created a telehealth quality score by summing respondents’ answers to the telehealth quality questions, and used the 75th percentile as the cut-off for high telehealth quality. We chose this cut-off because in the histogram of scores, it appeared that the 75th percentile was an appropriate, and we thought theoretically important, marker to set for high vs. low quality experiences. We used the CHERRIES checklist for reporting results of web-based surveys [
[27]
].- Eysenbach G.
Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES).
J Med Internet Res. 2004; 6 (Sep 29) (Erratum in: doi:10.2196/jmir.2042. PMID: 15471760; PMCID: PMC1550605.))e34https://doi.org/10.2196/jmir.6.3.e34
We used Chi-square tests and multivariable logistic regression models to examine relationships between survey period, sociodemographic variables, and COVID-19-related hardship and (1) odds of having any vs no contraception appointment, and (2) odds of having a telehealth vs in-person appointment. Among respondents who reported having a telehealth appointment, we used multivariable logistic regression models to examine relationships between sociodemographic variables and COVID-19-related hardship and odds of rating telehealth quality at or above the 75th percentile. We combined the data from the two cohorts (July 2020 and January 2021) for the purposes of examining contraceptive visits during the pandemic, and to account for variability in the timing of the pandemic, we controlled for the survey round in our adjusted analyses. We developed models based on our conceptual model, theory, and previous literature about factors that could be associated with telehealth quality (age, race/ethnicity, COVID-19 hardship, etc.). We performed an exploratory analysis using a logistic regression to compare odds of high telehealth quality among respondents who answered the survey in Spanish vs English.
3. Results
Out of 5,340 total respondents who were not pregnant, 2,031 (38%) indicated that they had sought contraceptive care (either tried to get an appointment but were unable to or had a contraception appointment during the pandemic) (n=994 July 2020, n=1,037 January 2021). Most respondents seeking care (n=1,491, 73.4%) indicated having had a contraception appointment, 530 (35.6%) of which were via telehealth. The mean age of respondents was 31 years, and over half were white (54.1%), college-educated or more (58.7%), had private insurance (56.7%), and experienced minimal impact on food security, housing, income, or job due to the pandemic (Table 1).
Table 1Demographic factors associated with any vs no appointment for contraception during the COVID-19 pandemic (U.S. 2020, 2021).
| Variable | Totaln | No appointmentn (%) | Appointmentn (%) | p value § | Adjusted odds, any vs no appointment*(95% CI) |
|---|---|---|---|---|---|
| Total ‡ | 2031 | 540 | 1491 | ||
| Survey Period (Jun ‘20 vs Jan ‘21) | |||||
| Round 1 | 993 (48.9) | 310 (57.6) | 683 (45.8) | <0.001 | 1.0 (ref) |
| Round 2 | 1036 (51.1) | 228 (42.4) | 808 (54.2) | 2.14 (1.69-2.70) | |
| Age (years) | |||||
| 18-24 | 397 (19.6) | 114 (21.2) | 283 (19) | 0.015 | 1.0 (ref) |
| 25-34 | 1057 (52.1) | 252 (46.8) | 805 (54) | 1.07 (0.80-1.43) | |
| 35-45 | 574 (28.3) | 172 (32) | 402 (27) | 0.78 (0.57-1.07) | |
| Racial/Ethnic Identity | |||||
| Black/African American | 174 (8.8) | 41 (7.8) | 133 (9.2) | <0.001 | 1.0 (ref) |
| Native American Indian/Alaska Native | 11 (0.6) | 4 (0.8) | 7 (0.5) | 0.31 (0.08-1.19) | |
| Asian/Pacific Islander | 185 (9.4) | 45 (8.6) | 140 (9.7) | 0.69 (0.40-1.18) | |
| Hispanic/Latinx | 329 (16.7) | 101 (19.2) | 229 (15.7) | 0.59 (0.37-0.94) | |
| White (non-Hispanic) | 1069 (54.1) | 247 (47) | 822 (56.7) | 0.87 (0.57-1.33) | |
| Mixed race/Other | 207 (10.5) | 88 (16.7) | 119 (8.2) | 0.36 (0.22-0.59) | |
| Income | |||||
| >$74K | 571 (29) | 137 (26.1) | 434 (30) | 0.197 | 1.0 (ref) |
| $50-74K | 417 (21.1) | 105 (20) | 312 (21.6) | 0.97 (0.71-1.33) | |
| $25-49K | 490 (24.9) | 141 (26.9) | 349 (24.1) | 0.88 (0.64-1.21) | |
| <$25K | 492 (25) | 141 (26.9) | 351 (24.3) | 0.93 (0.65-1.33) | |
| Educational attainment | |||||
| College or more | 963 (48.7) | 234 (44.6) | 729 (50.1) | 0.079 | 1.0 (ref) |
| Some college | 626 (31.6) | 183 (34.9) | 443 (30.5) | 0.82 (0.63-1.07) | |
| High school or less | 390 (19.7) | 108 (20.6) | 282 (19.4) | 0.90 (0.65-1.25) | |
| Insurance | |||||
| Private insurance | 1220 (56.6) | 275 (52.2) | 845 (58.2) | <0.001 | 1.0 (ref) |
| Public insurance | 643 (32.5) | 171 (32.4) | 472 (32.5) | 1.03 (0.78-1.36) | |
| No insurance | 213 (10.8) | 80 (15.2) | 133 (9.2) | 0.63 (0.43-0.91) | |
| Region | |||||
| West | 433 (22.1) | 99 (19.2) | 334 (23.2) | 0.152 | 1.0 (ref) |
| Midwest | 423 (21.6) | 111 (21.5) | 312 (21.7) | 0.64 (0.46-0.90) | |
| South & Southeast | 837 (42.8) | 226 (43.8) | 611 (42.5) | 0.63 (0.47-0.85) | |
| Northeast | 262 (13.4) | 80 (15.5) | 182 (12.6) | 0.52 (0.36-0.75) | |
| Hardship Score | |||||
| 0 | 740 (37.1) | 151 (28.4) | 589 (40.2) | <0.001 | 1.0 (ref) |
| 1 | 529 (26.5) | 148 (27.8) | 381 (26.0) | 0.67 (0.51-0.88) | |
| 2 | 405 (20.3) | 126 (23.7) | 279 (19) | 0.63 (0.47-0.86) | |
| 3 | 221 (11.1) | 73 (13.7) | 149 (10.2) | 0.56 (0.38-0.52) | |
| 4 | 101 (5.1) | 34 (6.4) | 67 (4.6) | 0.52 (0.31-0.87) | |
‡Numbers may not add up to total in each category due to small numbers of missing responses to some questions;
§Unadjusted Chi square test; *All variables listed in the table were included in the multivariable model
In unadjusted analyses comparing a contraceptive appointment vs no appointment, 25-34 year olds reported more visits while Hispanic/Latinx and Mixed race/Other respondents and those with no insurance reported fewer visits. Greater hardship scores were associated with fewer visits (all p<0.05). After adjustment, covariates associated with lower odds of any contraceptive visit were Hispanic/Latinx and Mixed race/Other (aOR 0.59 [0.37-0.94], aOR 0.36 [0.22-0.59], respectively), no insurance (aOR 0.63 [0.43-0.91]), South/Southeast, Midwest, and Northeast (aOR 0.63 [0.47-0.85]; aOR 0.64 [0.46-0.90]; aOR 0.52 [0.36-0.75], respectively), and greater Covid-19 hardship (aOR 0.52 [0.31-0.87]) (Table 1).
In unadjusted analyses comparing telehealth vs in-person contraception appointments, we found differences across all sociodemographic variables other than age (Table 2). After adjustment, we observed higher odds of having a telehealth visit among respondents ages 25-34 (aOR 1.41 [1.02-1.94]) and lower odds among respondents from the Midwest and South (aOR 0.63 [0.44-0.88], aOR 0.54 [0.40-0.72] respectively).
Table 2Demographic factors associated with telehealth vs in-person appointment for contraception during the COVID-19 pandemic (U.S. 2020, 2021).
| Variable | Total n | In-person Appointment n (%) | Telehealth Appointmentn (%) | P value § | Adjusted odds, telehealth vs in-person appointment*(95% CI) |
| Total ‡ | 1491 | 961 | 530 | ||
| Survey Period | |||||
| Round 1 (July 2020) | 683 (45.8) | 463 (48.2) | 220 (41.5) | 0.013 | 1.0 (ref) |
| Round 2 (Jan 2021) | 808 (54.2) | 498 (51.8) | 310 (58.5) | 1.16 (0.24-1.16) | |
| Age (years) | |||||
| 18-24 | 283 (19) | 189 (19.7) | 94 (17.7) | 0.638 | 1.0 (ref) |
| 25-34 | 805 (54) | 516 (53.8) | 289 (54.5) | 1.41 (1.02-1.94) | |
| 35-45 | 402 (27) | 255 (26.6) | 147 (27.7) | 1.37 (0.96-1.96) | |
| Racial/Ethnic Identity | |||||
| Black/African American | 133 (9.2) | 83 (8.9) | 50 (9.7) | 0.005 | 1.0 (ref) |
| Native American Indian/Alaska Native | 7 (0.5) | 3 (0.3) | 4 (0.8) | 1.20 (0.23-6.40) | |
| Asian/Pacific Islander | 140 (9.7) | 71 (7.6) | 69 (13.4) | 1.56 (0.92-2.65) | |
| Hispanic/Latinx | 228 (15.7) | 145 (15.5) | 83 (16.1) | 0.83 (0.51-1.32) | |
| White (non-Hispanic) | 822 (56.7) | 555 (59.5) | 267 (51.7) | 0.87 (0.58-1.31) | |
| Mixed race/Other | 119 (8.2) | 76 (8.1) | 43 (8.3) | 0.85 (0.50-1.46) | |
| Income | |||||
| >$74K | 434 (30) | 290 (31.2) | 144 (27.9) | 0.029 | 1.0 (ref) |
| $50-74K | 312 (21.6) | 199 (21.4) | 113 (21.9) | 1.13 (0.81-1.57) | |
| $25-49K | 349 (24.1) | 237 (25.5) | 112 (21.7) | 0.87 (0.62-1.23) | |
| <$25K | 351 (24.3) | 204 (21.9) | 147 (28.5) | 1.14 (0.78-1.65) | |
| Educational attainment | |||||
| College or more | 729 (50.1) | 497 (53) | 232 (44.9) | 0.007 | 1.0 (ref) |
| Some college | 443 (30.5) | 275 (29.3) | 168 (32.5) | 1.26 (0.95-1.67) | |
| High school or less | 282 (19.4) | 165 (17.6) | 117 (22.6) | 1.26 (0.90-1.78) | |
| Insurance | |||||
| Private insurance | 845 (58.3) | 571 (61.1) | 274 (53.1) | 0.009 | 1.0 (ref) |
| Public insurance | 472 (32.6) | 287 (30.7) | 185 (35.9) | 1.09 (0.82-1.47) | |
| No Insurance | 133 (9.2) | 76 (8.1) | 57 (11) | 1.29 (0.83-2.01) | |
| Region | |||||
| West | 334 (23.2) | 183 (19.7) | 151 (29.6) | <0.001 | 1.0 (ref) |
| Midwest | 312 (21.7) | 217 (23.4) | 95 (18.6) | 0.63 (0.44-0.88) | |
| South & Southeast | 611 (42.5) | 429 (46.2) | 182 (35.7) | 0.54 (0.40-0.72) | |
| Northeast | 182 (12.6) | 100 (10.8) | 82 (16.1) | 1.01 (0.69-1.17) | |
| Hardship Score | |||||
| 0 | 589 (40.2) | 400 (42.3) | 189 (36.4) | <0.001 | 1.0 (ref) |
| 1 | 381 (26) | 262 (27.7) | 119 (22.9) | 0.87 (0.65-1.17) | |
| 2 | 279 (19) | 170 (18) | 109 (21) | 1.15 (0.83-1.60) | |
| 3 | 149 (10.2) | 84 (8.9) | 65 (12.5) | 1.25 (0.83-1.88) | |
| 4 | 67 (4.6) | 30 (3.2) | 37 (7.1) | 1.61 (0.91-2.84) | |
‡Numbers may not add up to total in each category due to small numbers of missing responses to some questions;
§Unadjusted Chi square test; *All variables listed in the table were included in the multivariable model
In adjusted analyses of telehealth quality, Hispanic/Latinx respondents and respondents in the Midwest had significantly lower odds of reporting high telehealth visit quality (aOR 0.37 [0.17-0.80] and aOR 0.58 [0.35-0.95] respectively) (Table 3). We found no differences in quality scores between video vs phone visits. In the unadjusted exploratory analysis assessing telehealth quality among respondents who answered the survey in Spanish, respondents who answered in Spanish (n=34, 6.4% of respondents who had telehealth visits) had significantly lower odds of reporting high telehealth quality (OR 0.59 [0.36 - 0.99]). Due to small numbers, we did not adjust for language (Spanish vs English) in multivariate analyses of telehealth quality.
Table 3Demographic factors and telehealth quality score among respondents who had telehealth visits for contraception during the COVID-19 pandemic (U.S. 2020, 2021).
| Variable | Adjusted odds, telehealth quality score ≥75%ile (95% CI) |
|---|---|
| Age (years) | |
| 18-24 | 1.0 (ref) |
| 25-34 | 1.08 (0.62-1.87) |
| 35-45 | 1.06 (0.57-1.95) |
| Racial/Ethnic Identity | |
| Black/African American | 1.0 (ref) |
| Native American Indian/Alaska Native | 0.27 (0.02-3.68) |
| Asian/Pacific Islander | 1.50 (0.62-3.64) |
| Hispanic/Latinx | 0.37 (0.17-0.80) |
| White (non-Hispanic) | 0.73 (0.37-1.43) |
| Mixed race/Other | 0.42 (0.17-1.02) |
| Income | |
| >$74K | 1.0 (ref) |
| $50-74K | 0.92 (0.53-1.60) |
| $25-49K | 0.69 (0.39-1.21) |
| <$25K | 0.99 (0.54-1.81) |
| Educational attainment | |
| College or more | 1.0 (ref) |
| Some college | 1.47 (0.92-2.35) |
| High school or less | 1.01 (0.58-1.73) |
| Insurance | |
| Private insurance | 1.0 (ref) |
| Public insurance | 0.79 (0.40-1.56) |
| No insurance | 0.78 (0.39-1.58) |
| Region | |
| West | 1.0 (ref) |
| South & Southeast | 0.63 (0.36-1.11) |
| Midwest | 0.58 (0.35-0.95) |
| Northeast | 0.58 (0.32-1.05) |
| Hardship Score | |
| 0 | 1.0 (ref) |
| 1 | 0.98 (0.60-1.60) |
| 2 | 1.37 (0.80-2.37) |
| 3 | 1.25 (0.64-2.43) |
| 4 | 0.78 (0.34-1.82) |
| Survey Round (Jun ‘20 vs Jan ‘21) | 1.83 (0.50-6.69) |
a All variables listed in the table were included in the multivariable model
4. 4. Discussion
Among people seeking contraceptive care during the COVID-19 pandemic, we found significant inequities in contraception access, less telehealth use in the South and Midwest, and lower telehealth quality among Hispanic/Latinx and Spanish-speaking people. Because we only included people actively seeking contraception appointments, the broad disparities in access we observed underscore the prohibitive barriers to care some of our respondents faced. Our findings echo previous data demonstrating that differences in social and health resource distribution overwhelmingly disadvantage people with racialized and historically-marginalized identities [
[28]
]. And consistent with our findings, studies of contraceptive care during the COVID-19 pandemic have similarly found that people identifying as Black, Indigenous, Latinx or a person of color, with greater financial hardship, and facing hunger or income loss due to the pandemic have faced more barriers to care [- Daniel H.
- Bornstein S.S.
- Kane G.C.
Health and Public Policy Committee of the American College of Physicians, Carney JK, Gantzer HE, et al. Addressing Social Determinants to Improve Patient Care and Promote Health Equity: An American College of Physicians Position Paper.
Ann Intern Med. 2018; 168: 577-578
18
, 22
, 29
, 30
].It is unclear whether our finding of the lower rates of telehealth use for contraceptive care in the South and Midwest simply reflect lower use of telehealth in general in these regions, as prior studies have shown, or if respondents in these areas prefer in-person visits. Lower use of telehealth for contraceptive care has been documented in rural vs urban settings in the South, possibly due to less electronic infrastructure [
31
, 32
]. Increasing use of telehealth could considerably reduce barriers to care for people living in the South, Midwest, and rural areas, as people in these areas often must travel long distances for in-person care [- Beatty K.
- Smith M.G.
- Khoury A.J.
- Ventura L.M.
- Ariyo O.
- de Jong J.
- et al.
Contraceptive care service provision via telehealth early in the COVID‐19 pandemic at rural and urban federally qualified health centers in 2 southeastern states.
The Journal of Rural Health. 2022; https://doi.org/10.1111/jrh.12701
[33]
]. Furthermore, making telehealth more accessible would offer some patients care that is better aligned with their preferences [[20]
].Studies are mixed on the association of historically-marginalized identities and telehealth use [
14
, - Hill B.J.
- Lock L.
- Anderson B.
Racial and ethnic differences in family planning telehealth use during the onset of the COVID-19 response in Arkansas, Kansas, Missouri, and Oklahoma.
Contraception. 2021; https://doi.org/10.1016/j.contraception.2021.05.016
16
, - Yarger J.
- Hopkins K.
- Elmes S.
- Rossetto I.
- De La Melena S.
- McCulloch C.E.
- et al.
Perceived Access to Contraception via Telemedicine Among Young Adults: Inequities by Food and Housing Insecurity.
J Gen Intern Med. 2022; https://doi.org/10.1007/s11606-022-07669-0
18
]. We found no association after adjustment. Further research is needed to explore use, and more importantly, preferences for type of visit (telehealth vs in-person), as these identities impact experiences with health care.Finally, our finding that Hispanic/Latinx ethnicity is associated with lower telehealth quality is consistent with one study demonstrating Hispanic/Latinx respondents reported worse patient centeredness compared to their counterparts [
[18]
]. We also found that those answering the survey in Spanish reported lower telehealth quality, but we are unable to parse out whether lower telehealth quality is explained by a language barrier or is related to other factors such as racism (both interpersonal and structural), implicit bias, or logistical factors such as variation in internet access.Among the limitations of our study is the unclear sampling frame. In social media survey studies, the sampling frame consists of users who see the ads and choose to click on them. Users of social media are likely younger and have internet access, potentially biasing our sample towards being more likely to engage in telehealth. While a potential limitation, there is evidence that samples recruited via social media do not differ greatly from those recruited with more traditional approaches [
22
, 34
, 35
]. Additionally, Facebook users are more likely to be women and be younger compared to the general population, which is appropriate for a study on women’s contraceptive use [Goel S. , Obeng A. , Rothschild D. , Research M. Non-representative surveys: Fast, cheap, and mostly accurate. [cited 8 Jan 2023 ]. Available: 〈https://researchdmr.com/FastCheapAccurate.pdf〉
[36]
]. Given the observational nature of our data, we cannot draw causal associations, and while we attempted to adjust for possible confounders, some confounding likely remains. Because we only asked questions about visit quality to respondents who had telehealth visits, we were not able to compare telehealth to in-person visit quality. Despite these limitations, our study adds novel findings to the literature on telehealth for contraceptive care during COVID-19 and provides important information to advocate for improved telehealth infrastructure to ensure equitable access.Ribeiro F.N. , Benevenuto F. , Zagheni E. How Biased is the Population of Facebook Users? Comparing the Demographics of Facebook Users with Census Data to Generate Correction Factors. 12th ACM Conference on Web Science. 2020 . doi:10.1145/3394231.3397923
If implemented equitably, telehealth could improve access to care and thus potentially improve disparate outcomes stemming from systemic racism, implicit bias, and discrimination within our healthcare system. Yet if implemented inequitably, we risk replicating or even exacerbating existing disparities [
[37]
]. To this end, the recently-published Society of Family Planning Clinical recommendation on pandemic contraceptive care explicitly calls for research focusing on access to telehealth among historically excluded populations such as adolescents, people of color, people with low incomes, those with a disability, or people who have a preferred language other than English [[38]
]. Further research should additionally focus on patients’ preferences and desires around telehealth and strategies to foster telehealth access. Given the increasing incorporation of telehealth into routine care, the ongoing COVID-19 pandemic, and legislation restricting access to reproductive healthcare in many regions, it is essential that we better understand how and where telehealth for contraceptive care is being used, and how we can ensure its equitable implementation moving forward.- Stifani B.M.
- Madden T.
- Micks E.
- Moayedi G.
- Tarleton J.
- Benson L.S.
Society of Family Planning Clinical Recommendations: Contraceptive Care in the Context of Pandemic Response.
Contraception. 2022; https://doi.org/10.1016/j.contraception.2022.05.006
Declarations of interest
None
Funding
The Commonwealth Fund (Grant Number: 20213165), Stan and Mary Friedman, and the Department of Epidemiology and Biostatistics at UCSF provided financial support for this study.
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Article info
Publication history
Accepted:
February 27,
2023
Received in revised form:
February 21,
2023
Received:
October 9,
2022
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In Press Accepted ManuscriptIdentification
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© 2023 The Authors. Published by Elsevier Inc.
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