As advances in computer access continue to be made, there is a need to better understand the challenges of increasing access for racial/ethnic minorities, particularly among those with lower incomes. Larger social contextual factors, such as social networks and neighborhood factors, may influence computer ownership and the number of places where individuals have access to computers.
We examined the associations of sociodemographic and social contextual factors with computer ownership and frequency of use among 1554 adults living in urban public housing.
Bivariate associations between dependent variables (computer ownership and regular computer use) and independent variables were used to build multivariable logistic models adjusted for age and site clusters.
Participants (N = total weighted size of 2270) were on average 51.0 (± 21.4) years old, primarily African American or Hispanic, and earned less than US $20000 per year. More than half owned a computer, and 42% were regular computer users. Reporting computer ownership was more likely if participants lived above the poverty level (OR = 1.78, 95% CI = 1.39-2.29), completed high school (OR = 2.46, 95% CI = 1.70-3.55), were in financial hardship (OR = 1.38, 95% CI = 1.06-1.81), were employed and supervised others (OR = 1.94, 95% CI = 1.08-3.46), and had multiple role responsibilities (OR = 2.18, 95% CI = 1.31-3.61). Regular computer use was more likely if participants were non-Hispanic (OR = 1.94, 95% CI = 1.30-2.91), lived above the poverty level (OR = 2.84, 95% CI = 1.90-4.24), completed high school (OR = 4.43, 95% CI = 3.04-6.46), were employed and supervised others (OR = 2.41, 95% CI = 1.37-4.22), felt safe in their neighborhood (OR = 1.57, 95% CI = 1.08-2.30), and had greater social network ties (OR = 3.09, 95% CI = 1.26-7.59).
Disparities in computer ownership and use are narrowing, even among those with very low incomes; however, identifying factors that contribute to disparities in access for these groups will be necessary to ensure the efficacy of future technology-based interventions. A unique finding of our study is that it may be equally as important to consider specific social contextual factors when trying to increase access and use among low-income minorities, such as social network ties, household responsibilities, and neighborhood safety.
There has been a growing emphasis on technology-based strategies to increase reach, efficacy, sustainability, and cost-effectiveness of preventive health interventions. Communication strategies, many of which utilize computers and the Internet, are being recognized as potential modalities for reducing health disparities via the dissemination of culturally appropriate health information to racial/ethnic minorities and low-income populations [
Certainly, disparities in health outcomes can be attributed to cultural and societal factors, such as access to health care [
As advances in computer access continue to be made, there is a need to better understand the challenges of increasing access for racial/ethnic minorities, particularly among those with lower incomes. It is well known that access to communication technologies is differentially associated with social class. For example, income, education, and employment are positively associated with subscriptions to Internet services and newspapers [
Social contextual factors are those that shape an individual’s day-to-day experience, such as one’s neighborhood or work environment as well as social norms of health and behavior [
There have been very few studies of the association between social contextual factors and computer access and use. This is an important omission because we posit that attempts to reduce communication disparities may fail if focused solely on sociodemographic factors. Therefore, this study examines the combination of sociodemographic and social contextual factors and their influence on computer ownership and frequency of use among adults living in urban public housing.
This study uses baseline data from an ongoing randomized controlled trial of a colorectal cancer prevention intervention, “Open Doors to Health,” conducted in 12 urban subsidized housing complexes in Boston, MA, United States.
The housing site is the unit of randomization and intervention. Unequal probability sampling was used because of the varying size of housing sites. In the sites that had a population of less than 300, all adult residents were sampled. In the remaining sites, with a population greater than 300 adult residents, researchers obtained a 35% sample, with a minimum of 250 participants per site. Sites were matched for randomization to intervention condition based on population size, ethnicity ratio, and age group ratio (≤ 50 years, > 50 years) when possible.
Figure 1 depicts a conceptual framework that explicates the role of the social context in health behavior change [
Social contextual factors, in turn, may influence health behaviors directly or indirectly through individual psychosocial factors. Social cognitive theory [
Conceptual model
One’s social context and day-to-day realities are shaped by sociodemographic characteristics, which may influence a range of interrelated health behaviors. For example, socioeconomic position, race and ethnicity, nativity, gender, and age are important correlates of health outcomes. Identifying disparities in health behaviors across populations with these characteristics can inform priority setting and guide policy decisions. In addition, culture, that is the learned and shared knowledge and beliefs used to interpret experiences, cuts across all domains in this model [
Recruitment for Open Doors to Health began in 2004. Participants provided informed consent and completed an interviewer-administered survey in either English or Spanish. Participants received US $25 compensation. Eligibility criteria for the study survey included (1) living in the housing community, (2) being at least 18 years old, (3) being fluent in English or Spanish, and (4) not having cancer. An initial sample of 3688 subjects was drawn. Of them, 747 (20%) were deemed ineligible, leaving 2941 eligible individuals. Of these, 828 (28%) refused participation, and 559 (19%) could not be reached, leaving 1554 residents who completed the baseline survey. This yielded an overall 53% response rate, with a range of 34% to 92% across the housing sites. The study protocol was approved by the Human Subjects Committee at the Harvard School of Public Health.
Sociodemographic variables collected included gender, date of birth, race/ethnicity (categorized as black, white, Hispanic, and other), and highest level of education completed. We also assessed poverty status and financial situation with two measures. Yearly household income (six response options ranging from less than US $10000 to at least US $50000) and the number of people supported by this income were used to measure poverty status (dichotomized as being above or below the poverty level based on the 2005 federal poverty guidelines on income and household size) [
We assessed employment status in several ways. Participants were asked if they were working, and, if so, (1) whether they worked full-time or part-time and (2) the number of hours worked in a week, including overtime or extra hours. Hours worked were categorized as 0, less than 20, 20 to < 37, and 37+ hours per week. Participants were also asked about the number of jobs (beyond their main job) they worked (0, 1, or more than 1) and whether they supervised employees.
Lastly, we assessed immigrant status by asking participants their birthplace, the number of years they lived in the United States, and their first or native language.
Each participant was asked about several social contextual factors. Neighborhood safety was assessed by asking whether participants felt safe walking alone in their neighborhood during the day and at night [
To assess social cohesion in the housing community, we asked respondents to report their agreement with five statements: (1) people around here are willing to help their neighbors; (2) this is a close-knit neighborhood; (3) people in this neighborhood can be trusted; (4) people in this neighborhood generally do not get along with each other; and (5) people in this neighborhood do not share the same values. Item responses were reversed for the first three statements and then responses to the five items were averaged. The summary score ranged from 1 to 4, with a higher score indicating higher social cohesion [
Marital status, number of close friends, number of close family members, and active membership in organizations (religious, professional, community, civic, etc) were combined to form a continuous measure of the number of social network ties ranging from 0 to 4, with a higher score indicating a greater social network [
Participants were asked about their various family roles, which included “earning money to support the family,” “taking care of children,” and “taking care of another household.” The measure of multiple roles was computed as the number of family roles for which the participant was mostly or fully responsible (0 to 3). To determine role conflicts, participants were asked whether their daily activities made conflicting demands on them (ie, role conflict) [
Health status was captured by asking participants whether health problems make it difficult for them to exercise (yes/no).
Participants were also asked to report the number of hours per day (during the week and weekend) that they watched television [
We assessed computer ownership and frequency of use (daily, weekly, monthly, less than monthly, and never). Use was recoded as regular (daily and weekly), intermittent (monthly and less than monthly), and never. For multivariable modeling purposes, this variable was further dichotomized as regular versus intermittent or no use. Participants were also asked where they most often use a computer: home, work, housing site, library, friend’s house, community center, or other. The latter five response options were coded as “other” for the purpose of these analyses.
On the basis of the cluster design, data for all analyses were weighted up to the population size within each housing site (with a total weighted size of 2270). Frequency distributions and estimates of means and standard deviations were assessed for distributional assumptions and outliers. Bivariate associations between the dependent variables, computer ownership and use of a computer, and independent variables were assessed, and variables found to be significant at the
Sociodemographic characteristics and social contextual factors by computer ownership, frequency of use, and location of use are shown in
The majority of the study participants were female (74%), not working or disabled (63%), and earned less than US $20000 per year (74%). The mean age of the participants was 51.0 ± 21.4 years. Almost half of the participants were black (43%), and an equal number were Hispanic (43%); 52% of participants were born in the United States. A slight majority of participants lived above the poverty level (51%); however, 43% considered themselves to be under financial hardship.
More than half (51%) of participants owned a computer, and 42% reported regular computer use (
Computer use weighted frequencies
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1079 (51.23) | 1027 (48.76) | 1021 (48.14) | 217 (10.23) | 882 (41.58) | 550 (50.00) | 270 (24.55) | 280 (25.45) | |
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Gender | |||||||||
Male | 227 (42.47) | 307 (57.53) | 291 (53.76) | 35 (6.55) | 215 (39.67) | 146 (57.30) | 37 (14.52) | 72 (28.18) | |
Female | 852 (54.21) | 720 (45.79) | 730 (46.24) | 181 (11.49) | 667 (42.27) | 404 (47.85) | 233 (27.54) | 208 (24.60) | |
Age (years) | |||||||||
< 35 | 341 (70.73) | 141 (29.27) | 58 (12.14) | 58 (12.04) | 365 (75.82) | 213 (50.42) | 88 (20.81) | 122 (28.77) | |
35-49 | 383 (72.44) | 146 (27.55) | 173 (32.69) | 82 (15.55) | 273 (51.77) | 182 (50.82) | 110 (30.87) | 65 (18.31) | |
50-64 | 279 (41.31) | 396 (58.69) | 416 (61.39) | 61 (9.03) | 201 (29.58) | 123 (47.27) | 68 (26.03) | 70 (26.70) | |
65+ | 77 (18.28) | 345 (81.72) | 374 (86.42) | 16 (3.60) | 43 (9.98) | 32 (54.86) | 3 (5.95) | 23 (39.19) | |
Poverty level | |||||||||
Below poverty level | 431 (42.61) | 580 (57.39) | 591 (58.14) | 138 (13.59) | 288 (28.27) | 230 (53.69) | 43 (10.06) | 155 (36.24) | |
Above poverty level | 527 (59.35) | 361 (40.65) | 342 (38.18) | 64 (7.11) | 490 (54.71) | 254 (46.07) | 205 (37.04) | 93 (16.89) | |
Financial status | |||||||||
Comfortable/enough | 589 (50.63) | 574 (49.37) | 549 (46.72) | 105 (8.92) | 521 (44.36) | 299 (47.94) | 177 (28.41) | 148 (23.66) | |
Have to cut back/can’t make ends meet | 466 (52.79) | 417 (47.21) | 428 (48.57) | 108 (12.30) | 345 (39.13) | 238 (52.10) | 93 (20.26) | 126 (27.64) | |
Education | |||||||||
≤ 8th grade | 92 (20.05) | 366 (79.95) | 423 (90.94) | 18 (3.79) | 24 (5.26) | 20 (47.16) | 4 (10.03) | 18 (42.81) | |
Some high school | 164 (42.64) | 221 (57.36) | 248 (64.05) | 39 (10.21) | 100 (25.75) | 83 (58.85) | 18 (12.50) | 40 (28.66) | |
Completed high school/vocational | 338 (58.93) | 235 (41.07) | 237 (41.02) | 77 (13.29) | 264 (46.67) | 165 (48.19) | 90 (26.31) | 87 (25.50) | |
At least some college | 483 (70.59) | 201 (29.41) | 111 (16.22) | 82 (11.97) | 492 (71.81) | 281 (49.23) | 158 (27.70) | 132 (23.07) | |
Immigrant | |||||||||
No | 623 (55.40) | 501 (44.60) | 407 (35.91) | 140 (12.35) | 586 (51.74) | 348 (48.09) | 178 (24.64) | 197 (27.27) | |
Yes | 455 (46.47) | 524 (53.53) | 613 (62.23) | 77 (7.82) | 295 (29.94) | 201 (53.61) | 92 (24.40) | 82 (21.98) | |
English 1st language | |||||||||
No | 471 (48.13) | 507 (51.86) | 588 (59.81) | 78 (7.98) | 317 (32.21) | 216 (54.37) | 100 (25.13) | 81 (20.50) | |
Yes | 607 (53.93) | 519 (46.07) | 432 (38.06) | 138 (12.21) | 564 (49.74) | 333 (47.48) | 170 (24.24) | 198 (28.28) | |
Race/ethnicity | |||||||||
Hispanic | 425 (46.03) | 499 (53.97) | 570 (61.19) | 74 (7.97) | 287 (30.84) | 192 (52.63) | 90 (24.58) | 83 (22.79) | |
Black | 496 (54.41) | 415 (45.59) | 343 (37.54) | 123 (13.42) | 448 (49.04) | 263 (46.43) | 136 (24.07) | 167 (29.50) | |
White | 41 (50.91) | 39 (49.09) | 38 (44.93) | 4 (4.59) | 42 (50.48) | 30 (61.98) | 12 (23.92) | 7 (14.10) | |
Other | 111 (61.78) | 69 (38.22) | 68 (37.65) | 16 (8.97) | 96 (53.39) | 59 (52.82) | 31 (27.84) | 22 (19.33) | |
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Work status | |||||||||
Employed | 556 (71.46) | 222 (28.54) | 201 (25.84) | 76 (9.79) | 501 (64.37) | 243 (42.34) | 268 (46.59) | 64 (11.08) | |
Unemployed | 158 (57.08) | 119 (42.92) | 72 (26.22) | 51 (18.45) | 152 (55.33) | 104 (50.63) | 1 (0.50) | 100 (48.86) | |
Not working | 366 (34.82) | 684 (65.18) | 746 (70.01) | 90 (8.45) | 231 (21.54) | 203 (63.50) | 1 (0.32) | 116 (36.18) | |
Hours worked (hours/week) | |||||||||
0 | 521 (39.46) | 800 (60.54) | 817 (61.18) | 139 (10.40) | 380 (28.43) | 306 (58.72) | 1 (0.20) | 214 (41.09) | |
< 20 | 69 (67.91) | 33 (32.09) | 38 (37.63) | 14 (13.66) | 49 (48.70) | 42 (66.89) | 8 (12.85) | 13 (20.26) | |
20 to < 37 | 198 (71.50) | 79 (28.50) | 73 (26.28) | 25 (9.02) | 180 (65.70) | 94 (46.52) | 77 (37.86) | 32 (15.62) | |
37+ | 290 (71.54) | 116 (28.45) | 93 (22.94) | 39 (9.65) | 274 (67.41) | 51 (34.43) | 69 (58.83) | 21 (6.74) | |
Supervisor | |||||||||
Unemployed/not working | 523 (39.46) | 803 (60.54) | 818 (61.03) | 141 (10.50) | 382 (28.47) | 307 (58.47) | 2 (0.39) | 216 (41.14) | |
Employed and did not supervise employees | 435 (69.25) | 193 (30.74) | 186 (29.65) | 63 (10.10) | 379 (60.25) | 191 (43.37) | 198 (45.07) | 51 (11.56) | |
Employed and supervised employees | 119 (81.19) | 28 (18.81) | 14 (9.19) | 13 (8.61) | 121 (82.20) | 51 (38.52) | 69 (51.93) | 13 (9.55) | |
Number of jobs | |||||||||
No jobs | 523 (39.46) | 803 (60.54) | 818 (61.03) | 141 (10.50) | 382 (28.47) | 307 (58.47) | 2 (0.39) | 216 (41.14) | |
One job | 494 (70.59) | 206 (29.41) | 192 (27.38) | 70 (9.99) | 438 (62.63) | 216 (42.70) | 231 (45.70) | 59 (11.59) | |
More than one job | 62 (79.19) | 16 (20.81) | 9 (12.05) | 6 (8.03) | 63 (79.92) | 27 (39.64) | 37 (53.07) | 5 (7.29) | |
Employment status | |||||||||
Full-time | 341 (71.49) | 136 (28.51) | 111 (23.20) | 43 (9.10) | 323 (67.69) | 126 (34.73) | 215 (59.02) | 23 (6.25) | |
Part-time | 738 (45.31) | 891 (54.69) | 910 (55.40) | 173 (10.56) | 559 (34.04) | 424 (57.62) | 55 (7.46) | 257 (34.93) | |
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Neighborhood safety | |||||||||
Unsafe | 141 (47.18) | 158 (52.82) | 178 (59.56) | 30 (10.13) | 91 (30.31) | 66 (54.23) | 25 (20.69) | 31 (25.08) | |
Safe | 897 (53.74) | 772 (46.26) | 740 (44.07) | 182 (10.82) | 757 (45.12) | 458 (48.78) | 240 (25.56) | 241 (25.66) | |
Health problems make it difficult to exercise | |||||||||
Yes | 409 (43.68) | 527 (56.32) | 561 (59.39) | 93 (9.89) | 290 (30.72) | 198 (51.44) | 70 (18.11) | 117 (30.45) | |
No | 670 (57.33) | 499 (42.67) | 461 (39.19) | 124 (10.51) | 591 (50.31) | 353(49.36) | 199 (27.88) | 163 (22.77) | |
Role conflicts (daily activities make conflicting demands) | |||||||||
Yes | 437 (57.83) | 319 (42.17) | 315 (41.63) | 83 (11.02) | 358 (47.35) | 225 (51.12) | 120 (27.23) | 95 (21.64) | |
No | 618 (48.29) | 662 (51.71) | 655 (50.75) | 128 (9.93) | 508 (39.33) | 316 (49.55) | 147 (23.02) | 175 (27.43) | |
TV use (hours/day) | |||||||||
0 | 15 (38.75) | 24 (61.25) | 22 (50.58) | 5 (12.89) | 16 (36.53) | 11 (52.23) | 2 (9.75) | 8 (38.03) | |
> 0 to 2 | 323 (54.61) | 269 (45.39) | 280 (46.99) | 55 (9.17) | 261 (43.84) | 152 (48.32) | 100 (31.83) | 62 (19.85) | |
> 2 to 4 | 409 (53.03) | 363 (46.97) | 360 (46.33) | 83 (10.64) | 335 (43.03) | 198 (47.37) | 107 (25.53) | 113 (27.10) | |
> 4 to 6 | 191 (51.26) | 182 (48.74) | 187 (49.97) | 25 (6.77) | 162 (43.25) | 104 (55.25) | 46 (24.64) | 38 (20.11) | |
> 6 | 138 (42.62) | 186 (57.38) | 167 (51.61) | 49 (15.03) | 108 (33.36) | 85 (54.33) | 15 (9.35) | 57 (36.32) | |
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Social ties/networks (0-4) | 2.72 (0.03) | 2.59 (0.03) | 2.62 (0.03) | 2.63 (0.06) | 2.72 (0.03) | 2.70 (0.04) | 2.81 (0.05) | 2.60 (0.04) | |
Social support (0-5) | 4.47 (0.03) | 4.32 (0.04) | 4.27 (0.04) | 4.36 (0.04) | 4.55 (0.03) | 4.54 (0.04) | 4.51 (0.06) | 4.47 (0.07) | |
Role responsibilities (0-3) | 1.59 (0.02) | 1.22 (0.02) | 1.27 (0.03) | 1.51 (0.03) | 1.53 (0.03) | 1.54 (0.04) | 1.76 (0.04) | 1.30 (0.05) | |
Social cohesion (1-4) | 2.41 (0.03) | 2.57 (0.03) | 2.58 (0.03) | 2.50 (0.05) | 2.40 (0.03) | 2.39 (0.04) | 2.42 (0.06) | 2.48 (0.05) |
Being above poverty (OR = 1.78, 95% CI = 1.39, 2.29), in financial hardship (OR = 1.38, 95% CI = 1.06, 1.81), and having completed high school (OR = 2.46, 95% CI = 1.70, 3.55) were positively associated with computer ownership. Employment and supervisory role (OR=1.94, 95% CI =1.08, 3.46) was also positively associated with computer ownership. Finally, having greater financial and caretaking responsibilities were positively associated with owning a computer (OR=2.18, 95% CI=1.31, 3.61).
Predicting ownership of computer, adjusting for age*
Bivariate Age-Adjusted OR (95% CI) Yes vs No | Multivariable-Adjusted OR (95% CI)†, Yes vs No | ||
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Gender | |||
Male | 1.00 | ||
Female | 1.47 (0.98-2.22) | ||
Poverty level | |||
Below poverty level |
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1.00 | |
Above poverty level | 1.00 |
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Financial status | |||
Comfortable/enough | 1.00 | 1.00 | |
Have to cut back/can’t make ends meet |
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Education | |||
≤ 8th grade | 1.00 | ||
Some high school |
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Completed high school/vocational |
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At least some college |
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Did not complete high school | 1.00 | ||
Completed high school |
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Immigrant | |||
No | 1.00 | 1.00 | |
Yes |
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1.33 (0.98-1.81) | |
English 1st language | |||
No | 1.00 | ||
Yes |
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Race/ethnicity | |||
Hispanic | 1.00 | ||
Black | 1.43 (0.99-2.08) | ||
White |
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Other |
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Hispanic | 1.00 | ||
Non-Hispanic | 1.41 (0.75-2.64) | ||
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Work Status | |||
Employed |
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Unemployed | 1.03 (0.54-1.94) | ||
Not working | 1.00 | ||
Hours worked (hours/week) | |||
0 | 1.00 | ||
< 20 |
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20 to < 37 |
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37+ |
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Supervisor | |||
Unemployed/not working | 1.00 | 1.00 | |
Employed and did not supervise employees |
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Employed and supervised employees |
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Number of jobs | |||
No job | 1.00 | ||
One job |
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More than one job |
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Employment status | |||
Full-time |
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Part-time | 1.00 | ||
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Neighborhood safety | |||
Unsafe | 1.00 | ||
Safe | 1.24 (0.91-1.70) | ||
Health problems make it difficult to exercise | |||
Yes | 1.00 | ||
No | 1.13 (0.86-1.49) | ||
TV use (hours/day) | |||
None | 0.68 (0.31-1.49) | ||
> 0 to 2 | 1.58 (0.98-2.55) | ||
> 2 to 4 |
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> 4 to 6 |
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> 6 | 1.00 | ||
Role conflicts | |||
Yes | 1.00 | ||
No | 0.88 (0.69-1.11) | ||
Social ties/networks | |||
Few (0,1) | 1.00 | ||
Many (2-4) | 1.76 (0.95-3.26) | ||
Social support | |||
Few (0,1) | 1.00 | ||
Many (2-4) | 1.34 (0.64-2.80) | ||
Role responsibilities | |||
0 | 1.00 | ||
1 | 1.20 (0.90-1.62) | ||
2 |
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3 |
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Role responsibilities | |||
0-1 | 1.00 | ||
2-3 |
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Social cohesion (6-24) | 0.99 (0.94-1.04) |
*Boldface indicates statistically significant association.
†Variables found to be significant at the P = .15 level in bivariate analyses were retained for use in multivariable modeling. Multivariable-adjusted models are adjusted for age, poverty level, financial status, education, immigrant status, race/ethnicity, supervisory status, and role responsibilities.
Predicting computer use*
Bivariate Age-Adjusted OR (95% CI) | ||||
Regular vs Never | Intermittent vs Never | |||
Gender | ||||
Male | 1.00 | 1.00 | ||
Female | 1.47 (0.57-1.77) | 1.70 (0.88-3.29) | ||
Poverty level | ||||
Below poverty level | 1.00 | 1.00 | ||
Above poverty level |
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0.95 (0.52-1.75) | ||
Financial status | ||||
Comfortable/enough | 1.00 | 1.00 | ||
Have to cut back/can’t make ends meet | 0.97 (0.76-1.23) |
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Education | ||||
≤ 8th grade | 1.00 | 1.00 | ||
Some high school |
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Completed high school/vocational |
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At least some college |
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Immigrant | ||||
No | 1.09 (0.59-2.01) | 0.91 (0.53-1.55) | ||
Yes | 1.00 | 1.00 | ||
English 1st language | ||||
No | 1.00 | 1.00 | ||
Yes |
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Race/ethnicity | ||||
Hispanic | 1.00 | 1.00 | ||
Black |
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White |
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1.64 (0.63-4.28) | ||
Other |
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Work status | ||||
Employed |
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Unemployed |
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Not working | 1.00 | 1.00 | ||
Hours worked (hours/week) | ||||
0 | 1.00 | 1.00 | ||
< 20 | 1.58 (0.56-4.46) | 1.36 (0.64-2.92) | ||
20 to < 37 |
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1.20 (0.63-2.31) | ||
37+ |
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1.51 (0.72-3.18) | ||
Supervisor | ||||
Unemployed/not working | 1.00 | 1.00 | ||
Employed and did not supervise employees |
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1.23 (0.70-2.16) | ||
Employed and supervised employees |
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Number of jobs | ||||
No job | 1.00 | 1.00 | ||
One job |
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1.33 (0.80-2.20) | ||
More than one job |
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1.89 (0.23-15.58) | ||
Employment status | ||||
Full-time |
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1.35 (0.77-2.38) | ||
Part-time | 1.00 | 1.00 | ||
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Neighborhood safety | ||||
Unsafe | 1.00 | 1.00 | ||
Safe |
|
1.60 (0.87-2.93) | ||
TV use (hours/day) | ||||
0-2 | 1.30 (0.83-2.05) | 0.64 (0.34-1.21) | ||
> 2 to 6 |
|
0.69 (0.39-1.22) | ||
> 6 | 1.00 | 1.00 | ||
Health problems make it difficult to exercise | ||||
Yes | 1.00 | 1.00 | ||
No |
|
1.08 (0.75-1.55) | ||
Role conflicts | ||||
Yes | 1.00 | 1.00 | ||
No | 1.01 (0.67-1.51) | 1.04 (0.64-1.71) | ||
Social ties/networks | ||||
Few (0,1) | 1.00 | 1.00 | ||
Many (2-4) |
|
1.51 (0.74-3.08) | ||
Social support | ||||
Few (0,1) | 1.00 | 1.00 | ||
Many (2-4) | 1.52 (0.67-3.49) | 0.91 (0.26-3.15) | ||
Role responsibilities | ||||
0 | 1.00 | 1.00 | ||
1 | 1.36 (0.76-2.43) | 1.07 (0.54-2.10) | ||
2 | 1.20 (0.62-2.32) | 0.92 (0.50-1.69) | ||
3 | 1.78 (0.69-4.68) | 1.82 (0.77-4.28) | ||
Social cohesion (1-4) | 1.00 (0.85-1.17) | 1.12 (0.88-1.42) |
*Boldface indicates statistically significant association.
Predicting computer use (regular versus intermittent/never use)*
Multivariable Age-Adjusted OR (95% CI)†(N = 1210) | ||
|
||
Poverty level | ||
Below poverty level | 1.00 | |
Above poverty level |
|
|
Education | ||
Did not complete high school | 1.00 | |
Completed high school |
|
|
Race/ethnicity | ||
Hispanic | 1.00 | |
Non-Hispanic |
|
|
|
||
Supervisor | ||
Unemployed/not working | 1.00 | |
Employed and did not supervise employees | 1.38 (0.89-2.13) | |
Employed and supervised employees |
|
|
|
||
Neighborhood safety | ||
Unsafe | 1.00 | |
Safe |
|
|
Social ties/networks | ||
Few (0,1) | 1.00 | |
Many (2-4) |
|
*Boldface indicates statistically significant association.
†Variables found to be significant at the P = .15 level in bivariate analyses were retained for use in multivariable modeling. Multivariable-adjusted models are adjusted for age, poverty level, financial status, education, immigrant status, race/ethnicity, supervisory status, and role responsibilities.
Computers and the Internet show substantial promise for increasing participation in health promotion activities; thus, we might have more difficulty reducing health disparities if access to technology is not actively promoted [
Most of the attention on reducing the digital divide has been focused on improving access for racial/ethnic minorities. However, access is only one piece of the equation. To fully realize the benefits of computers and the Internet, regular computer use, which builds computer literacy and instills confidence, must be achieved. Our study showed that 42% of participants regularly used a computer, which indicates that there is a large group of low-income racial/ethnic minorities that are potentially experienced computer users. Most participants reported that they used a computer more often at home rather than at work or elsewhere. The location where an individual uses the computer often reflects the quality of their computer and/or computer access [
This study also points out that there is still a significant group that does not have access to this technology, with 48% of participants reporting that they had never used a computer. The factors that may impact computer use in this population are not clear. Social contextual factors were not as strongly associated with ownership and use as we hypothesized. In addition to employment, we conjecture that cost is likely an issue, as is lack of interest and relevance. Age did appear to be a key factor, in that the majority of older adults (65+ years) did not own (82%) and had never used (86%) a computer. Although older adults are more likely to report greater barriers (eg, vision problems or other disability) to computer use, [
As expected and consistent with the findings of other reports, [
Interestingly, being non-Hispanic was positively associated with regular computer use, but not computer ownership in multivariable models. Although rates of ownership may be similar among racial/ethnic groups, computer use varies. In our study, more Hispanics (61%) than any other racial/ethnic group never used a computer. We also found that greater education was associated with greater computer ownership and regular computer use; in bivariate analyses, there was a positive dose-response relationship between education and ownership and use. Education is a consistently strong predictor of access to and interest in information services, including the Internet and computers [
Select social contextual factors were also associated with computer ownership and use. For example, feeling safe in one’s neighborhood was associated with a 76% increase in being a regular computer user. This may be particularly salient for those who access computers outside the home, such as a library or neighborhood center. In our study area, there are a number of community computer centers, and this trend of having computers at community centers is growing nationally. We also found that having multiple responsibilities was strongly correlated with computer use. This could be explained by the fact that our low-income study population was largely female (71%) and unmarried (68%) and thus likely to be responsible for childrearing, finances, and taking care of other households (eg, parents). They are also likely to be employed in order to meet these needs. This accounts for our finding that employment increases computer ownership. Moreover, one study found that employed women with caregiving responsibilities were likely to have a “management style” of executing tasks [
Our study focused on access to computers among low-income minority groups. We did not specifically ask about Internet access and use. Information regarding Internet use, type of Internet connection, and reasons for computer use would have further contextualized the communication experience of low-income minority adults. However, government reports show that about two thirds of households with computers also have Internet access [
The racial/ethnic and socioeconomic disparities in access to communication technologies are narrowing, even among very low-income households, making communication technologies for health communication more feasible. However, as the number of technology-based prevention interventions that provide important health information increases, it will be imperative to continue to identify factors that contribute to disparities in access and to connect low-income racial/ethnic minorities to these technologies, particularly computers. In this study, computer ownership among low-income minorities was over 50%, showing noteworthy strides. This suggests that computer-based studies might be reasonable for this population provided that options for nontechnology modalities are also provided. While sociodemographic factors are commonly associated with computer access, a unique finding of our study is that it may be equally as important to consider specific social contextual factors when trying to increase access and use among low-income minorities, such as social network ties, household responsibilities, and neighborhood safety.
This research was supported by grant 5R01CA098864-02 from the National Cancer Institute and support to the Dana-Farber Cancer Institute by Liberty Mutual, National Grid, and the Patterson Fellowship Fund. At the time of this study, Lorna H. McNeill was supported by the Alonzo Yerby Postdoctoral Fellowship at the Harvard School of Public Health. Gary G. Bennett is supported by an award from the Dana-Farber/Harvard Cancer Center and by grant 3R01CA098864-02S1 from the National Cancer Institute.
We gratefully thank the Open Doors to Health research team: Elise Dietrich, Elizabeth Gonzalez-Suarez, Terri Greene, Lucia Leone, Mike Massagli, Vanessa Melamede, Maribel Melendez, Molly Coeling, Tamara Parent, Lina Rincon, Claudia Viega, and Monifa Watson as well as all of the resident helpers and resident service coordinators at our collaborating housing sites. We also thank Martha Zorn for assisting with the analysis of the findings.
None declared.