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The digital divide refers to technological disparities based on demographic characteristics (eg, race and ethnicity). Lack of physical access to the internet inhibits online health information seeking (OHIS) and exacerbates health disparities. Research on the digital divide examines where and how people access the internet, whereas research on OHIS investigates how intersectional identities influence OHIS. We combine these perspectives to explicate how unique context–device access pairings operate differently across intersectional identities—particularly racial and ethnic groups—in the domain of OHIS.
This study aims to examine how different types of internet access relate to OHIS for different racial and ethnic groups. We investigate relationships among predisposing characteristics (ie, age, sex, education, and income), internet access (home computer, public computer, work computer, and mobile), health needs, and OHIS.
Analysis was conducted using data from the 2019 Health Information National Trends Survey. Our theoretical model of OHIS explicates the roles of internet access and health needs for racial and ethnic minority groups’ OHIS. Participant responses were analyzed using structural equation modeling. Three separate group structural equation modeling models were specified based on Black, Latine, and White self-categorizations.
Overall, predisposing characteristics (ie, age, sex, education, and income) were associated with internet access, health needs, and OHIS; internet access was associated with OHIS; and health needs were associated with OHIS. Home computer and mobile access were most consistently associated with OHIS. Several notable linkages between predisposing characteristics and internet access differed for Black and Latine individuals. Older racial and ethnic minorities tended to access the internet on home and public computers less frequently; home computer access was a stronger predictor of OHIS for White individuals, and mobile access was a stronger predictor of OHIS for non-White individuals.
Our findings necessitate a deeper unpacking of how physical internet access, the foundational and multifaceted level of the digital divide, affects specific racial and ethnic groups and their OHIS. We not only find support for prior work on the digital divide but also surface new insights, including distinct impacts of context–device access pairings for OHIS and several relationships that differ between racial and ethnic groups. As such, we propose interventions with an intersectional approach to access to ameliorate the impact of the digital divide.
The benefits of eHealth, or the use of the internet to facilitate health behaviors (eg, online health information seeking [OHIS]) [
However, internet access (hereafter
The internet has become one of the most common ways of accessing health information [
The digital divide first highlighted that certain groups of people (eg, racial and ethnic minorities and individuals of low socioeconomic status) lagged in adopting new technologies. This gradual diffusion represents the first-level factor of the digital divide, which has been situated in issues related to ownership, availability, and affordability of the technology [
Notably, some scholars have applied this multifaceted conceptualization of access to predict the likelihood of web-based activities (including OHIS). Hassani [
Furthermore, scholarship in this area has seldom disaggregated these connections by racial and ethnic groups. Studies that include race and ethnicity self-categorization as predictors of web-based activities [
Previous OHIS theorizing [
Initial online health information seeking model.
First, we posit associations between OHIS and predisposing characteristics. Younger individuals are more likely than older individuals to be able to navigate web-based platforms to seek out web-based health information [
Hypothesis 1 (H1): Demographic variables—age, sex, education, and income—will be associated with OHIS.
Expanding our understanding of OHIS, we extend prior conceptualizations of access [
First,
Second,
Third,
Finally,
Overall, we expect that access will be related to predisposing characteristics and OHIS. Older individuals are less likely to access the internet [
Hypothesis 2 (H2): Predisposing characteristics (ie, age, sex, education, and income) will be associated with access.
Hypothesis 3 (H3): Access will be positively associated with OHIS.
However, it is unclear how our nuanced conceptualization of access (ie, 4 discrete context–device pairings) may differentially affect OHIS. Thus, we pose the following research question (RQ):
RQ1: Which access pairings have the most consistent associations with OHIS across racial and ethnic groups?
We conceptualize health needs as the extent to which individuals perceive that they require current or chronic medical attention. The likelihood that one may endure chronic illness is linked to group identities along the lines of age, gender, education level, and income [
Hypothesis 4 (H4): Predisposing characteristics (ie, age, sex, education, and income) will be associated with health needs.
Hypothesis 5 (H5): Health needs will be positively associated with OHIS.
This study holds that existing racial and ethnic disparities exacerbate the impact of the digital divide on health disparities [
In addition, race and ethnicity may interact with access (H3) and health need (H5) to influence OHIS. Regarding access, even when Black and Latine individuals access the internet at similar rates as White individuals, such access is often marked by greater insecurity [
RQ2: How will the relationships between predisposing characteristics, access, health needs, and OHIS differ across different racial and ethnic groups?
To test our model, we used data from the 2019 HINTS, also known as HINTS 5, Cycle 3 [
An institutional review board approval was not requested because the analysis for this study was conducted using secondary data. All HINTS data sets, including the one used for analysis in this study, have been approved through expedited review by the Westat Institutional Review Board, and subsequently deemed exempt by the U.S. National Institutes of Health Office of Human Subjects Research Protections [
Demographic data were used to assess predisposing factors. Participants were aged 56.58 (SD 16.88) years on average. Approximately 56.62% (2971/5247) of the participants self-categorized as female, and 41.16% (2160/5247) self-categorized as male. Race and ethnicity were operationalized in comparison with those who did not self-categorize as the respective racial or ethnic group as individuals who self-categorize ethnically as Latine may still self-categorize racially as White or Black. Of the 5247 individual, 3727 (71.03%) self-categorized as White, and 1100 (20.96%) did not; 847 (16.14%) self-categorized as Black and 3980 (75.85%) did not; and 716 (13.64%) participants self-categorized as Latine and 4044 (77.07%) did not. The remaining individuals did not disclose their sex, race, or ethnicity. Participants’ level of education was measured on a 5-point scale from
Participant demographics.
Demographics | OHISa,b | ||||
|
No, n (%) | Yes, n (%) | |||
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18-24 | 20 (0.39) | 132 (2.59) | ||
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25-35 | 62 (1.22) | 535 (10.51) | ||
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36-44 | 66 (1.30) | 492 (9.67) | ||
|
45-54 | 172 (3.38) | 627 (12.32) | ||
|
55-64 | 316 (6.21) | 827 (16.25) | ||
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≥65 | 790 (15.52) | 1051 (20.65) | ||
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Male | 652 (12.76) | 1501 (29.37) | ||
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Female | 798 (15.62) | 2159 (42.25) | ||
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No | 344 (7.16) | 746 (15.53) | ||
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Yes | 977 (20.33) | 2738 (56.98) | ||
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No | 1034 (21.52) | 2934 (61.06) | ||
|
Yes | 287 (5.97) | 550 (11.45) | ||
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No | 1015 (21.39) | 3016 (63.56) | ||
|
Yes | 237 (4.99) | 477 (11.45) | ||
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Less than high school | 200 (3.93) | 108 (2.12) | ||
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High school graduate | 445 (8.75) | 448 (8.81) | ||
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Some college | 441 (8.67) | 1093 (21.49) | ||
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Received a bachelor’s degree | 230 (4.52) | 1130 (22.21) | ||
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Received a postbaccalaureate degree | 117 (2.30) | 875 (17.2) | ||
|
|||||
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0-19,999 | 411 (8.86) | 441 (9.51) | ||
|
20,000-34,999 | 213 (4.59) | 380 (8.19) | ||
|
35,000-49,999 | 173 (3.73) | 433 (9.34) | ||
|
50,000-74,999 | 182 (3.92) | 639 (13.78) | ||
|
75,000-99,999 | 116 (2.5) | 461 (9.94) | ||
|
100,000-199,999 | 106 (2.29) | 764 (16.48) | ||
|
>200,000 | 36 (0.78) | 282 (6.08) |
aOHIS: online health information seeking.
bPercentages reflect those who responded to the OHIS item.
Correlations between all variables are displayed in
Descriptive statistics and correlations between study variables (age, sex, and White).
Predictors | Values, mean (SD) | Age | Sex | White | |||
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Age (years) | 56.58 (16.88) | 1 | —a | — | — | — | — |
Sexb | 0.42 (0.49) | 0.04 | .01 | 1 | — | — | — |
Whitec | 0.77 (0.42) | 0.04 | .005 | 0.07 | <.001 | 1 | — |
Blackd | 0.18 (0.38) | –0.01 | .64 | –0.09 | <.001 | –0.79 | <.001 |
Latinee | 0.15 (0.36) | –0.10 | <.001 | 0.01 | .68 | 0.10 | <.001 |
Education | 3.36 (1.16) | –0.17 | <.001 | 0.03 | .07 | 0.08 | <.001 |
Income | 3.76 (1.93) | –0.17 | <.001 | 0.12 | <.001 | 0.14 | <.001 |
Home computer | 1.15 (0.84) | –0.17 | <.001 | 0.10 | <.001 | 0.15 | <.001 |
Work computer | 0.70 (0.90) | –0.45 | <.001 | 0.03 | .06 | 0.06 | <.001 |
Public computer | 0.16 (0.39) | –0.20 | <.001 | 0.01 | .69 | –0.07 | <.001 |
Mobile | 1.27 (0.85) | –0.51 | <.001 | –0.04 | .004 | 0.08 | <.001 |
Health needs | 2.58 (0.94) | 0.16 | <.001 | –0.02 | .29 | –0.09 | <.001 |
Online health information seeking (OHIS) | 0.71 (0.45) | –0.31 | <.001 | –0.04 | .01 | 0.05 | .001 |
aNot applicable.
bCoded as female=0 and male=1.
cCoded as non-White=0 and White=1.
dCoded as non-Black=0 and Black=1.
eCoded as non-Latine=0 and Latine=1.
Descriptive statistics and correlations between study variables (Black, Latine, education, and income).
Predictors | Black | Latine | Education | Income | ||||
|
|
|
|
|
||||
Age (years) | —a | — | — | — | — | — | — | — |
Sexb | — | — | — | — | — | — | — | — |
Whitec | — | — | — | — | — | — | — | — |
Blackd | 1 | — | — | — | — | — | — | — |
Latinee | –0.10 | <.001 | 1 | — | — | — | — | — |
Education | –0.12 | <.001 | –0.17 | <.001 | 1 | — | — | — |
Income | –0.20 | <.001 | –0.12 | <.001 | 0.47 | <.001 | 1 | — |
Home computer | –0.15 | <.001 | –0.16 | <.001 | 0.41 | <.001 | 0.38 | <.001 |
Work computer | –0.09 | <.001 | –0.08 | <.001 | 0.40 | <.001 | 0.46 | <.001 |
Public computer | 0.07 | <.001 | –0.01 | .76 | 0.11 | <.001 | –0.03 | .03 |
Mobile | –0.08 | <.001 | –0.04 | .005 | 0.33 | <.001 | 0.37 | <.001 |
Health need | 0.10 | <.001 | 0.07 | <.001 | –0.25 | <.001 | –0.31 | <.001 |
Online health information seeking (OHIS) | –0.07 | <.001 | –0.07 | <.001 | 0.34 | <.001 | 0.28 | <.001 |
aNot applicable.
bCoded as female=0 and male=1.
cCoded as non-White=0 and White=1.
dCoded as non-Black=0 and Black=1.
eCoded as non-Latine=0 and Latine=1.
Descriptive statistics and correlations between study variables (home computer, work computer, public computer, mobile, and health need).
Predictors | Home computer | Work computer | Public computer | Mobile | Health need | |||||||||
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|||||||||
Age (years) | —a | — | — | — | — | — | — | — | — | — | ||||
Sexb | — | — | — | — | — | — | — | — | — | — | ||||
Whitec | — | — | — | — | — | — | — | — | — | — | ||||
Blackd | — | — | — | — | — | — | — | — | — | — | ||||
Latinee | — | — | — | — | — | — | — | — | — | — | ||||
Education | — | — | — | — | — | — | — | — | — | — | ||||
Income | — | — | — | — | — | — | — | — | — | — | ||||
Home computer | 1 | — | — | — | — | — | — | — | — | — | ||||
Work computer | 0.38 | <.001 | 1 | — | — | — | — | — | — | — | ||||
Public computer | 0.15 | <.001 | 0.13 | <.001 | 1 | — | — | — | — | — | ||||
Mobile | 0.48 | <.001 | 0.48 | <.001 | 0.21 | <.001 | 1 | — | — | — | ||||
Health need | –0.18 | <.001 | –0.23 | <.001 | –0.02 | .14 | –0.20 | <.001 | 1 | — | ||||
Online health information seeking (OHIS) | 0.41 | <.001 | 0.30 | <.001 | 0.14 | <.001 | 0.46 | <.001 | –0.10 | <.001 |
aNot applicable.
bCoded as female=0 and male=1.
cCoded as non-White=0 and White=1.
dCoded as non-Black=0 and Black=1.
eCoded as non-Latine=0 and Latine=1.
Participants reported how often they access the internet on a computer at home, at work, in a public place, and on a mobile device. A single item was used to measure each mode of access. Items were measured on 3-point scales, including
Health needs were operationalized as perceived general health [
Participants reported using a single item, whether they used a computer, smartphone, or other electronic means to look for health or medical information for themselves in the past 12 months. Responses were
The initial demographic data were cleaned and analyzed using SPSS Statistics (version 27, IBM Corporation;
Our proposed models grouped by Black, Latin, and White self-categorization displayed poor fit statistics [
Final online health information seeking model.
First, we examined whether predisposing characteristics were associated with OHIS (H1). Age was negatively associated with OHIS, and education was positively associated with OHIS across all models and groups. Income was positively associated with OHIS for individuals who self-categorized as White (OR 1.122, 95% CI 1.048-1.202;
Next, we investigated whether predisposing characteristics were associated with access (H2) and whether access was associated with OHIS (H3). Age was negatively associated with all forms of access for all the models and groups. Education was positively associated with all forms of access for all models and groups, except public computer access for Latine individuals (
Turning to OHIS, mobile and home access were positively associated with OHIS across all models and groups. Public computer access was positively associated with OHIS for non-White (OR 1.307, 95% CI 0.706-2.418;
Then, we examined whether predisposing characteristics were associated with health needs (H4) and whether health needs were associated with OHIS (H5). Age was positively associated with health needs across all models and groups. Education and income were negatively associated with health needs across all models and groups, except for the relationship between education and health needs for non-White (
Finally, we investigated whether significant differences in H1 to H5 emerged for different groups (RQ2). We found that certain relationships between predisposing characteristics and access differed for each type of access; all reported relationships were significant at
Other relationships also differed across racial and ethnic groups. For predisposing characteristics and OHIS, sex had a stronger negative association with OHIS for non-Black and White individuals, such that the gap between females and males engaging in OHIS was greater for these groups. For access and OHIS, home computer access had a significantly stronger positive association with OHIS for White, non-Black, and non-Latine individuals. Mobile access had a significantly stronger positive association with OHIS for non-White individuals. There were no significant differences in other dimensions of access or health needs.
Standardized coefficients and odds ratios for theorized OHISa models (for Black and non-Black individuals)b.
Path | Group | ||||||
|
Black | Non-Black | |||||
|
Standardized coefficientc or odds ratiod (95% CI) | Standardized coefficient or odds ratio (95% CI) | |||||
|
|||||||
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Age (years) | 0.982f (0.967-0.996) | .003 | 0.976f (0.970 -0.982) | <.001 | ||
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Sex | 1.134f (0.721-1.783) | .74 | 0.591g (0.530-0.660) | <.001 | ||
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Income | 1.081f (0.939-1.245) | .15 | 1.124f (1.052-1.201) | <.001 | ||
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Education | 1.499f (1.126-1.995) | <.001 | 1.439f (1.259-1.644) | <.001 | ||
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Age (years) | –0.146f | <.001 | –0.056g | <.001 | |
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Sex | –0.036f | .44 | 0.074g | <.001 | |
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Income | 0.329f | <.001 | 0.170g | <.001 | |
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Education | 0.213f | <.001 | 0.292f | <.001 | |
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||||||
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Age (years) | –0.294f | <.001 | –0.368f | <.001 | |
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Sex | –0.097f | .003 | 0.018g | .20 | |
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Income | 0.374f | <.001 | 0.283f | <.001 | |
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Education | 0.177f | <.001 | 0.186f | <.001 | |
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Age (years) | –0.255f | <.001 | –0.180g | <.001 | |
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Sex | 0.033f | .39 | 0.035f | .04 | |
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Income | –0.122f | .003 | –0.115f | <.001 | |
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Education | 0.104f | .01 | 0.135f | <.001 | |
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Age (years) | –0.446f | <.001 | –0.444f | <.001 | |
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Sex | –0.105f | <.001 | –0.060f | <.001 | |
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Income | 0.226f | <.001 | 0.220f | <.001 | |
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Education | 0.167f | <.001 | 0.131f | <.001 | |
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Home computer | 1.491f (0.990-2.246) | .001 | 1.981g (1.554-2.526) | <.001 | ||
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Work computer | 0.877f (0.663-1.161) | .55 | 0.939f (0.822-1.073) | .92 | ||
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Public computer | 1.368f (0.692-2.706) | .06 | 1.118f (0.801-1.560) | .16 | ||
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Mobile | 2.158f (1.175-3.962) | <.001 | 1.833f (1.440-2.333) | <.001 | ||
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Age (years) | 0.098f | .005 | 0.102f | <.001 | ||
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Sex | 0.001f | .98 | 0.022f | .16 | ||
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Income | –0.202f | <.001 | –0.245f | <.001 | ||
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Education | –0.072f | .06 | –0.117f | <.001 | ||
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1.235f (0.970-1.572) | .03 | 1.222f (1.078-1.385) | .004 |
aOHIS: online health information seeking.
bComparisons were made for each model per row.
cStandardized coefficients are displayed for paths predicting nondichotomous outcomes; negative relationships are indicated by negative signs. For standardized coefficients, 95% CI values are not available.
dOdds ratios are presented for paths predicting dichotomous outcomes (ie, OHIS) and were generated using Monte Carlo integration because of model complexity; negative relationships are indicated by values <1.
eSignificance values were based on the primary models (ie, without Monte Carlo integration).
fCoefficients or odds ratios differ significantly from those denoted by footnote
gCoefficients or odds ratios differ significantly from those denoted by footnote
Standardized coefficients and odds ratios for theorized OHISa models (for Latine and non-Latine individuals)b.
Path | Group | |||||||||
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Latine | Non-Latine | ||||||||
|
Standardized coefficientc or odds ratiod (95% CI) | Standardized coefficient or odds ratio (95% CI) | ||||||||
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Age (years) | 0.979f (0.966-0.993) | .002 | 0.977f (0.971-0.983) | <.001 | |||||
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Sex | 0.601f (0.474-0.762) | .009 | 0.656f (0.581-0.741) | <.001 | |||||
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Income | 1.058f (0.930-1.204) | .42 | 1.123f (1.051-1.200) | <.001 | |||||
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Education | 1.393f (1.075-1.804) | <.001 | 1.481f (1.291-1.699) | <.001 | |||||
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Age (years) | –0.185f | <.001 | –0.054g | <.001 | ||||
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Sex | 0.060f | .08 | 0.063f | <.001 | ||||
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Income | 0.181f | <.001 | 0.203f | <.001 | ||||
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Education | 0.324f | <.001 | 0.254f | <.001 | ||||
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Age (years) | –0.249f | <.001 | –0.374g | <.001 | ||||
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Sex | 0.019f | .57 | –0.006f | .68 | ||||
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Income | 0.265f | <.001 | 0.301f | <.001 | ||||
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Education | 0.259f | <.001 | 0.171f | <.001 | ||||
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|||||||||
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Age (years) | –0.257f | <.001 | –0.188f | <.001 | ||||
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Sex | –0.024f | .57 | 0.033f | .048 | ||||
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Income | –0.152f | .002 | –0.142f | <.001 | ||||
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Education | 0.087f | .10 | 0.128f | <.001 | ||||
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|||||||||
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Age (years) | –0.462f | <.001 | –0.439f | <.001 | ||||
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Sex | –0.069f | .03 | –0.072f | <.001 | ||||
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Income | 0.139f | <.001 | 0.232f | <.001 | ||||
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Education | 0.206f | <.001 | 0.114g | <.001 | ||||
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Home computer | 1.294f (0.861-1.945) | .04 | 1.950g (1.541-2.467) | <.001 | |||||
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Work computer | 1.121f (0.772-1.627) | .19 | 0.917f (0.806-1.044) | .49 | |||||
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Public computer | 1.500f (0.574-3.919) | .08 | 1.117f (0.824-1.513) | .15 | |||||
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Mobile | 1.739f (1.057-2.861) | <.001 | 1.912f (1.491-2.452) | <.001 | |||||
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Age (years) | 0.140f | <.001 | 0.093f | <.001 | |||||
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Sex | –0.039f | .30 | 0.028f | .07 | |||||
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Income | –0.178f | <.001 | –0.243f | <.001 | |||||
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Education | –0.147f | <.001 | –0.113f | <.001 | |||||
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1.091f (0.869-1.369) | .58 | 1.242f (1.093-1.411) | .001 |
aOHIS: online health information seeking.
bComparisons were made for each model per row.
cStandardized coefficients are displayed for paths predicting nondichotomous outcomes; negative relationships are indicated by negative signs. For standardized coefficients, 95% CI values are not available.
dOdds ratios are presented for paths predicting dichotomous outcomes (ie, OHIS) and were generated using Monte Carlo integration because of model complexity; negative relationships are indicated by values <1.
eSignificance values were based on the primary models (ie, without Monte Carlo integration).
fCoefficients or odds ratios differ significantly from those denoted by footnote
gCoefficients or odds ratios differ significantly from those denoted by footnote
Standardized coefficients and odds ratios for theorized OHISa models (for White and non-White individuals)b.
Path | Group | ||||||
|
White | Non-White | |||||
|
Standardized coefficientc or odds ratiod (95% CI) | Standardized coefficient or odds ratio (95% CI) | |||||
|
|||||||
|
Age (years) | 0.975f (0.967-0.983) | <.001 | 0.982f (0.971-0.994) | <.001 | ||
|
Sex | 0.572f (0.513-0.638) | <.001 | 1.123g (0.757-1.665) | .84 | ||
|
Income | 1.122f (1.048-1.202) | <.001 | 1.121f (0.989-1.271) | .02 | ||
|
Education | 1.456f (1.267-1.673) | <.001 | 1.427f (1.119-1.819) | <.001 | ||
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Age (years) | –0.047f | .003 | –0.158g | <.001 | |
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Sex | 0.071f | <.001 | 0.014f | .62 | |
|
|
Income | 0.176f | <.001 | 0.265g | <.001 | |
|
|
Education | 0.282f | <.001 | 0.280f | <.001 | |
|
|
||||||
|
|
Age (years) | –0.370f | <.001 | –0.298f | <.001 | |
|
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Sex | –0.001f | .95 | 0.010f | .71 | |
|
|
Income | 0.289f | <.001 | 0.329f | <.001 | |
|
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Education | 0.182f | <.001 | 0.205f | <.001 | |
|
|
||||||
|
|
Age (years) | –0.178f | <.001 | –0.242g | <.001 | |
|
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Sex | 0.033f | .06 | 0.038f | .26 | |
|
|
Income | –0.122f | <.001 | –0.129f | <.001 | |
|
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Education | 0.131f | <.001 | 0.126f | .001 | |
|
|
||||||
|
|
Age (years) | –0.450f | <.001 | –0.432f | <.001 | |
|
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Sex | –0.069f | <.001 | –0.065f | .01 | |
|
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Income | 0.223f | <.001 | 0.202f | <.001 | |
|
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Education | 0.121f | <.001 | 0.199g | <.001 | |
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Home computer | 2.002f (1.558-2.573) | <.001 | 1.458g (1.005-2.116) | <.001 | ||
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Work computer | 0.921f (0.806-1.052) | .63 | 0.931f (0.712-1.218) | .96 | ||
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Public computer | 1.098f (0.787-1.532) | .25 | 1.307f (0.706-2.418) | .037 | ||
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Mobile | 1.776f (1.398-2.256) | <.001 | 2.379g (1.281-4.419) | <.001 | ||
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Age (years) | 0.097f | <.001 | 0.123f | <.001 | ||
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Sex | 0.027f | .09 | –0.015f | .64 | ||
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Income | –0.246f | <.001 | –0.226f | <.001 | ||
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Education | –0.124f | <.001 | –0.061f | .08 | ||
|
1.237f (1.085-1.411) | .002 | 1.182f (0.958-1.458) | .07 |
aOHIS: online health information seeking.
bComparisons were made for each model per row.
cStandardized coefficients are displayed for paths predicting nondichotomous outcomes; negative relationships are indicated by negative signs. For standardized coefficients, 95% CI values are not available.
dOdds ratios are presented for paths predicting dichotomous outcomes (ie, OHIS) and were generated using Monte Carlo integration because of model complexity; negative relationships are indicated by values <1.
eSignificance values were based on the primary models (ie, without Monte Carlo integration).
fCoefficients or odds ratios differ significantly from those denoted by footnote
gCoefficients or odds ratios differ significantly from those denoted by footnote
This study applied a nuanced conceptualization of access to theoretical models of OHIS and identified how relationships with OHIS differed between racial and ethnic groups (ie, Black, Latine, and White individuals). We found partial support for all hypotheses, and results regarding the RQs provided deeper insight into the predicted relationships. By examining access as 4 unique context–device pairings, we found that home computer and mobile access were most consistently associated with OHIS. In addition, disaggregating models by racial and ethnic self-categorization identified different patterns between predisposing characteristics and access for different groups, highlighting how the digital divide affects intersectional groups.
Our findings suggest that predisposing characteristics are associated with OHIS for different racial and ethnic groups (H1). Education was positively associated with OHIS, and age was negatively associated with OHIS. These findings align with previous research, such that those with more education and younger individuals are more likely to possess the skills to navigate web-based platforms [
Our findings also suggest that some predisposing characteristics are associated with access for some racial and ethnic groups (H2). Age was negatively associated with all forms of access. Older individuals used all 4 context–device pairings less frequently than younger individuals, which may indicate their use of nondigital means (eg, print media and interpersonal) to obtain health information [
Our findings generally confirm that access is associated with OHIS (H3). As suggested by previous research [
Predisposing characteristics were also associated with health needs (H4), such that older individuals and individuals with less education and income were more likely to describe their health as poor. Older individuals and individuals with less education and income often face barriers to quality health options [
Furthermore, those with greater health needs were more likely to partake in OHIS, apart from non-White and Latine individuals (H5). Past research has found that greater health needs are associated with increased OHIS among Latine individuals [
Finally, our exploratory analyses provide insight into RQ2; however, additional research may be required to fully explicate certain patterns in our model in which stronger relationships were detected for specific racial and ethnic groups. In terms of access, several relationships were stronger for Black individuals. Greater income was associated with more frequent home computer access across all groups; however, this relationship was stronger for Black (vs non-Black) individuals. Income inequality among Black individuals appears to be a stark determinant of home computer access [
In addition to access, the relationship between sex and OHIS differed, such that non-Black (vs Black) females demonstrated a stronger association with OHIS. The extent to which females relieve the burden of family health knowledge [
Our first contribution—applying a multidimensional conceptualization of access to theoretical models of OHIS—revealed that different context–device pairings offer distinct OHIS profiles. Mobile and home computer access were more consistently associated with OHIS than work computer and public computer access. This implies that privacy is important when assessing the digital divide, as home computers and mobile devices can be used in more private contexts [
Our second contribution was to unpack the digital divide using an intersectional approach, as it is crucial to understand which groups have limited access to the internet. We found discrepancies in access for specific groups. Older individuals who self-categorized as a racial or ethnic minority engaged in less frequent home and public computer access. Older (vs younger) individuals and racial and ethnic minorities (vs majorities) tend to access the internet less frequently [
This study supports the criticism that the digital divide is not a dichotomy between access and lack thereof [
These 2 courses of action can also apply to future interventions aimed at addressing the digital divide and OHIS among specific groups. For racial and ethnic minorities, we found weaker positive relationships between home computer access and OHIS and stronger positive relationships between mobile access and OHIS. Interventions can strengthen the established relationship between mobile devices and OHIS or bolster the weaker link for home computer access. Although home computer access is considered a more easily navigable interface [
Some limitations should be considered when interpreting these findings. This study used secondary cross-sectional data. Thus, potentially relevant variables (eg, mobile use at home vs at work vs in public) were not measured, and causality or directionality cannot be determined. Future research could measure additional constructs and use longitudinal designs. In addition, this analysis used self-reported data. Future work could use log and GPS data in tandem to paint a more accurate picture of OHIS. Furthermore, our primary outcome variable (OHIS) was dichotomous, and other variables (eg, access) were trichotomous or single-item measures. Future research should use continuous variables for OHIS and access to better capture the temporal variety of digital media use. Finally, we did not examine second-level digital divide variables (eg, experience, perceived utility, beliefs, and skills) [
This study holds that a nuanced conceptualization of access is necessary to understand how the digital divide differentially affects racial and ethnic groups. Our theoretical model identified variables that predict OHIS while distinguishing the type (ie, device) and location (ie, context) of access, testing these associations for different racial and ethnic groups and examining intersectional characteristics among these groups (ie, age, sex, education, and income). By interlacing a thorough understanding of the first-level digital divide with an awareness of the unique impacts of the digital divide for specific groups, we further theorize on OHIS and suggest important considerations for more targeted interventions. As we continue to understand the complexities of the digital divide and its relationship with health, racial, and ethnic disparities, our perspective highlights how web-based health resources may not be accessed by those who need them the most.
Data and syntax.
comparative fit index
Health Information National Trends Survey
online health information seeking
odds ratio
root mean square error of approximation
research question
standardized root mean square residual
The authors would like to thank Dr. David DeAndrea and Dr. Kelly Garrett for assisting them in the publication process, and Dr. Kim for teaching them structural equation modeling (SEM) and giving them the space to grow on their own.
None declared.