Web-Based Consumer Health Information: Public Access, Digital Division, and Remainders

Abstract

Public access Internet portals and decreasing costs of personal computers have created a growing consensus that unequal access to information, or a “digital divide,” has largely disappeared for US consumers. A series of technology initiatives in the late 1990s were believed to have largely eliminated the divide. For healthcare patients, access to information is an essential part of the consumer-centric framework outlined in the recently proposed national health information initiative. Data from a recent study of health information-seeking behaviors on the Internet suggest that a “digitally underserved group” persists, effectively limiting the planned national health information infrastructure to wealthier Americans.

Readers are encouraged to respond to George Lundberg, MD, Editor of MedGenMed, for the editor's eye only or for possible publication via email: glundberg@medscape.net

Introduction

In the digital age, health information is a resource that is necessary for “staying well, preventing and managing disease, and making other decisions related to health and health care.[1]” It provides the rationale for guiding appropriate health behaviors, treatments, and decisions. As such, growing numbers of consumers are connecting to the Internet to seek health information. Although the diversity of online health information seekers is high and continuously increasing, estimates of both number and diversity of such groups vary widely.[2–4] Several studies have demonstrated that the use of the Internet in healthcare offers a number of potential benefits, including improved equity in access to health information, effective dissemination of new information, enhanced communication, and shared decision making between patients and physicians.[5–9] Given this increasing diversity of online health information seekers and the great potential of the Internet as an effective health communication channel and information resource, it still remains unclear, however, whether all groups are afforded the opportunity to participate.

Eliminating the Divide

1999 proved to be a watershed year for efforts aimed at reducing the digital divide. The Universal Service program, for example, was funded in an effort to make basic telecommunications services accessible to the public at reasonable and affordable costs. Congress had previously expanded the goal of this program to cover advances in telecommunications and information technology, and required “reasonable comparability” of services and rates between rural and urban areas. The Federal Communications Commission's (FCC's) Universal Service Fund, a key component of the program and funded at $1.7 billion for 1999, was promoted as helping telecommunications carriers mitigate the high costs of providing service to consumers in rural and insular parts of the country. Totaling $500 million, the FCC's low-income support program was implemented through 2 basic forms: the Lifeline Assistance program, designed to help low-income households pay monthly service bills, and the Link-Up America program, designed to help low-income subscribers pay the installation costs required to initiate service. Almost every state participated in at least 1 of the 2 programs. These efforts were designed to achieve higher telephone penetration rates among all US households. The Clinton Administration also sought to enact legislation that extended universal service to schools, libraries, and rural healthcare providers, enabling them to access the Internet more easily. This was accomplished, in part, through the eRate program, which required telecommunications carriers to provide, upon request by an eligible school or library, commercially available telecommunications services at a discounted rate. Discounts ranged from 20% to 90%, with the highest discounts accruing to the most economically or geographically disadvantaged schools and libraries. Total expenditures for the program were capped at $2.25 billion. The FCC supported full funding of the program beginning July 1, 1999, with the stated goal of helping connect more than 80,000 schools and libraries, and assisting numerous children and adults in learning how to use new technologies through new points of access to the Internet. In addition, during the same time period the US Agriculture Department's Rural Utilities Service initiated a targeted lending strategy and technical advice to help establish advanced telecommunications infrastructures in rural communities. The Administration also launched a coordinated public/private sector initiative to provide Internet functionality at community access centers, such as schools, libraries, and other public access facilities. The US Commerce Department's National Telecommunications and Information Administration (NTIA) was likewise one of the first programs to fund Community Access Centers (CAC) through its Telecommunications Information Infrastructure Assistance Program (TIIAP). A parallel program at the US Department of Education, the Community Technology Centers (CTC) program was initiated to enable the funding of CACs in economically distressed communities on a broader scale. Private corporations were also solicited to donate computers and software to neighborhood centers in support of these access efforts.[10]

Prior Research

Despite the ambitious technology advancement efforts of the late 1990s, little has been done to examine the availability of Web resources across underserved groups, and no standardized mechanism exists to ensure equal access to the valuable benefits mentioned above. Little evidence is available to even identify an underserved consumer population that may lack equal access to health information. To address the digital divide, however, healthcare managers and policymakers will need to develop, under the National Health Information Infrastructure (NHII) plan, new strategies or interventions to help the underserved groups gain access to information that is relevant to their healthcare.[11]

Several randomized studies on Internet users have been conducted to identify the characteristics of the digitally underserved population. Generally, such studies reported that people in higher age groups and with lower socioeconomic status and ethnic minority were less likely to connect to the Internet and search for relevant health information on the Web.[12–15] In particular, annual income has been suggested as a key indicator of access to the Internet and online health information, because lower income impedes consumers to gain access to the Internet as well as computers. Consequently, lower income people have fewer opportunities to explore online health information.[16,17]

Regardless of the low-cost public access to computer technology, no studies have shown whether low-income populations have a differentiated pattern of access to and use of the Internet, and if so, why and to what extent such patterns exist. The purpose of this study is to examine the extent to which annual income levels have influenced distribution patterns and diffusion trends in access to computers, the Internet, and online health information. In particular, 2 research questions were proposed: First, were low-income populations significantly underserved in regard to computer access, Internet access, and information seeking of health information in 2002? Second, despite telecommunications and technology advances, did differences between the low- and high-income population in access to computers, Internet services, and online health information narrow, remain constant, or widen from March 2000 to December 2002 following the creation of divide-reducing programs?

Methods

Sample and Definition

The December 2002 tracking survey study of the Pew Internet & American Life Project served as the source data for this study. The survey contained an extensive set of questions about the demographic profile of the participants and the use of the Internet for healthcare information. For online health information seekers, the survey additionally questioned consumers about the topics of health information, the frequency of successful online health information retrieval, and information-seeking behaviors. For general (not health-related) information seekers, the survey assessed the reasons why they did not search information on the Web. Comparative data collected in March 2000 by the Pew Internet & American Life Project (N = 2721) were also used to investigate whether the disparity in the use of computers, the Internet, and online health information narrowed, remained constant, or widened between March 2000 and December 2002.

A randomly selected group of 2463 US adults aged 18 years or older participated in the Pew Internet & American Life Project telephone survey in December 2002. Of the 2463 survey respondents, 1950 reported a valid annual income level. The data of these 1950 participants were used in this study. The annual incomes of respondents were classified into 3 levels: (1) less than $30,000, (2) from $30,000 to $75,000, and (3) over $75,000. The 3 levels are subsequently referred to as low-, medium-, and high-income groups, respectively. Resulting group sizes were 33.33% (N = 650), 42.56% (N = 830), and 24.10% (N = 470) of participants, respectively. Most participants were healthy (86.06%), middle-aged (42.08%), and white (75.64%). The resulting group of 1950 respondents was stratified into “computer user,” “Internet user,” and “online health information seeker” (Figure). The computer users were defined as those who occasionally used computers; 73.95% (N = 1442) of participants belonged to the computer user group. The computer user was classified into “Internet user” and “not-Internet user.” The Internet users were defined as the people reporting that they went online to access the Internet. All others were classified as not-Internet users. The 84.33% (N = 1216) and 15.67% (N = 228) of computer users were referred to as “Internet user” and “not-Internet user,” respectively. The Internet users were again divided into “online health information seeker” and “not-online health information seeker.” The information seeker was defined as an Internet user who searched any kind of health information on the Web; 67.68% (N = 823) and 32.32% (N = 393) of 1216 Internet users were classified into online health information seekers and not-health information seekers, respectively (see Figure 1).

Figure 1.

Stratification of participants.

Data Analysis

An initial exploratory analysis used Pearson's chi-square test and Mantel-Haenszel chi-square tests to examine the association of the level of household annual income with key variables and the homogeneity of probability of each cell in a contingency table. The key variables included the place where the users connected to the Internet, the frequency of the Internet access, the types of online activities, the topics of online health information, the attitude to the online health information, and the reasons why Internet users do not explore health information on the Web.

In addition, binary logistic regression analyses were conducted to estimate odds ratios with a 95% confidence interval (CI), as well as the probability of each income group reporting a specific event, such as the use of a computer, the Internet, or online health information. The odds ratios illustrate how much the odds increased or decreased by changing the level of income. In the estimation of odds ratios, the high-income group (over $75,000) was used as a benchmark reference point for comparison purposes. All statistical analyses for this study were performed at the .05 level of significance and generated with SAS software, Version 8.02 of the SAS system for Windows.

Results

Main factors motivate/impede Internet users to seek health information. With stepwise logistic regression, our model identified that gender (P < .0001), age (.0023), Internet experience (.0126), frequency of Internet access (.0053), and the health conditions of the interviewees (.0054) were marginally associated with the binary response (yes/no) of health information-seeking activity, at the .05 level. The logit regression model is summarized by the equation:

The estimated coefficients in the above regression equation illustrate that the health condition of the respondent was the most crucial factor motivating health information seeking, and a lack of Internet experience (less than 1 year) was the strongest predictor of impeded health information seeking (Table 1). The overall model displayed a Wald statistic equal to 58.7412, significant at the .05 level (P < .0001), and the Max-rescaled R2 was .0868. Other key maximum likelihood estimates are summarized in Table 1.

Table 1.

Analysis of Maximum Likelihood Estimates

Variable	Class Level	Degrees of Freedom	Estimate	Standard Error	Wald Chi-Square Test	Probability
Intercept		1	0.5730	0.3002	3.6435	0.0563
Gender	Male	1	−0.7051	0.1472	22.9333	< 0.0001
Age	18–29	1	−0.3964	0.1938	4.1828	0.0408
	30–49	1	0.2181	0.1706	1.6345	0.2011
Experience	≤ 1 year	1	−0.7774	0.2753	7.9734	0.0047
	2–3 years	1	−0.2526	0.1826	1.9135	0.1666
How often	Daily	1	0.8639	0.2730	10.0163	0.0016
	Weekly	1	0.6336	0.2882	4.8355	0.0279
Health condition	Yes	1	0.8005	0.2876	7.7451	0.0054

The logit regression analysis provided the estimated odds ratio for each predictor. The odds ratio illustrated how much the odds increased or decreased per unit change of the associated predictor, with all other predictors held constant. The odds ratio for the gender coefficient (male vs female) was 0.4941, with (95% CI, 0.3702 and 0.6593). This suggests that male Internet users were almost one half as likely to seek health information on the Web as female Internet users. The odds ratios for age demonstrated that younger (18–29 years) users were 0.6728 times less likely to seek health information than those age 50 years and older (P < .05). The odds ratios of middle-aged users (30–50 years) and older users (over age 50) were observed to be 0.8903 and 1.7373, respectively (at P < .05). There was little difference in health information-seeking activity between the 2 age groups. When examining Internet use experiences, users who had less Internet experience (less than 1 year) rarely sought health information (monthly or less); those without any disability, handicap, or chronic disease were also less likely to seek health information on the Web (P < .05), as seen in Table 2.

Table 2.

Estimated Odds Ratios in Logistic Regression Model

Effect	Comparison	Odds Ratio Estimates	Lower Confidence Level	Upper Confidence Level
Gender	Male vs female	0.4941	0.3702	0.6593
Age	18–29 vs over 50	0.6728	0.4601	0.9836
	30–49 vs over 50	1.2437	0.8903	1.7373
Experience	≤ 1 vs > 3 years	0.4596	0.2679	0.7884
	2–3 vs > 3 years	0.7768	0.5431	1.1110
Frequency of Internet access	Daily vs monthly	2.3724	1.3894	4.0508
	Weekly vs monthly	1.8845	1.0713	3.3148
Health condition	Yes vs no	2.2267	1.2671	3.9129

The Impact of Annual Income on the use of Computer, Internet, and Online Health Information and Comparison of the Access Rates Between March 2000 and December 2002

The Use of Computers

Table 3 shows that the use of computers was strongly associated with the level of income both in 2000 and 2002. Adults with lower incomes had fewer opportunities to use computers than those with higher incomes. Only 51.8% (48.0%) of the low-income group reported that they occasionally used computers, whereas 80.4% (74.8%) and 93.2% (89.1%) of the medium- and high-income groups, respectively, responded that they used computers in 2002 (2000). These rates indicate that computer utilization rates of the low-, medium-, and high-income populations increased 3.8%, 5.6%, and 4.1% (respectively) between 2000 and 2002.

Table 3.

The Association of the Level of Income With the Use of Computers and the Odds Ratios

Test	Year	DF	Value	P Value
Chi square	2002	2	273.3118	< .0001
	2000	2	307.0758	< .0001
Mantel-Haenszel chi square	2002	1	258.1189	< .0001
	2000	1	295.5776	< .0001
Odds Ratio	Year	Point	95% Confidence Interval
Low vs high	2002	0.079	0.053	0.116
	2000	0.113	0.083	0.154
Medium vs high	2002	0.299	0.201	0.445
	2000	0.363	0.266	0.494

Note: To measure effects of level of income, multivariate analysis (adjusting for demographic covariates) was employed with logistic regression. This procedure is used when the dependent variable is dichotomous and nominal. The independent variables may be continuous, discrete, categorical, or a mix. The advantage of logistic regression is that it provides the probability of a discrete outcome for a given dependent variable, rather than predicting the effects of several continuous independent variables on a single dependent variable, as is done in multiple regression. The discrete outcome provided for our analysis is the probability of participation in a particular health information behavior for each case analyzed. Logistic regression does not produce negative predicted probabilities. Rather, it predicts the probabilities of participation in a specific behavior, even though the practice itself may be either positive/empowering (eg, health information searching) or negative/disabling (eg, don't use computers). The statistics presented are the odds ratios, which express the direction and magnitude of the relationship between an independent and dependent variable. The 95% confidence intervals associated with the odds ratios are also reported.

The different increases of computer use rates in each income group highlight the disparity between low- and medium-income groups and low- and high-income groups. This condition was not improved over time, although the disparity between the medium- and high-income groups was improved somewhat. The data were also examined with the odds ratios for the use of computers among 3 income groups (Table 3). The odds ratios of low- to high-income groups were 0.113 (95% CI, 0.083 and 0.154) and 0.079 (95% CI, 0.053 and 0.116) in 2000 and 2002, respectively. That is, the probability of low-income groups to use computers was 0.113 and 0.079 times less than that of the high-income groups in 2000 and 2002, respectively. Because the two 95% CIs between 2000 and 2002 partially overlap, the 2 probabilities of low-income people to use computers between 2000 and 2002 are not significantly different at the 0.05 CI. However, the probability that low-income people will use computers is still significantly lower than that of high-income people, and this probability did not increase during 2000–2002. With the same application, the probability of medium-income people to use computers was also significantly lower than that of high-income people in both in 2000 and 2002, and this probability was not changed during the same time period. (The odds ratios of the medium- to high-income populations were 0.363 [CI, 0.266 and 0.494] in 2000 and 0.299 [CI, 0.201 and 0.445] in 2002).

We find, then, that overall computer usage rates increased in the high-, medium-, and even low-income populations between 2000 and 2002. The disparities in the use of computers between the lower and higher income populations, however, did not improve during this period.

Access to the Internet by Computer Users

Table 4 shows that Internet access was significantly associated with the level of income, both in 2000 and 2002. Although 74.8% and 84.7% of the low- and medium-income computer users, respectively, gained access to the Internet, 91.1% of the high-income computer users connected to the Internet in 2002. Compared with the Internet access rates in 2000, the rates in 2002 were increased 8.9%, 7.8%, and 3.4% for low-, medium-, and high-income computer users, respectively. It is notable that low-income computer users showed the higher increase in access to the Internet, compared with medium- and high-income computer users during the same time period. The gap in access to the Internet between low- and high-income computer users, however, was not reduced over time (Table 4). In regard to access to the Internet, the odds ratios of low- to high-income computer users were 0.268 (CI, 0.190 and 0.378) and 0.290 (CI, 0.192 and 0.437) in 2000 and 2002, respectively. The odds ratios of medium- to high-income computer users were 0.463 (CI, 0.335 and 0.639) in 2000 and 0.541 (CI, 0.366 and 0.800) in 2002. These overlapped 95% CIs between 2000 and 2002 show that the gaps in access to the Internet among low-, medium-, and high-income computer users remained at the .05 level.

Table 4.

The Association of the Level of Income With the Use of Internet and the Odds Ratios

Test	Year	Degrees of Freedom	Value	P Value
Chi square	2002	2	38.5108	< .0001
	2000	2	273.3118	< .0001
Mantel-Haenszel chi square	2002	1	37.6407	< .0001
	2000	1	61.4714	< .0001
Odds Ratio	Year	Point	95% Confidence Interval
Low vs high	2002	0.290	0.192	0.437
	2000	0.268	0.190	0.378
Medium vs high	2002	0.541	0.366	0.800
	2000	0.463	0.335	0.639

The overall rates of Internet use and computer use increased in the high-, medium-, and low-income populations between 2000 and 2002. There was, however, no reduction in the disparities seen in the use of computers and the Internet among the low-, medium-, and high-income populations during the same time period.

Use of Online Health Information of Internet Users

Once people gain access to the Internet, the probability that Internet users will explore online health information is relatively consistent across income groups (Table 5). The 62.3% (55.3%), 69.7% (54.1%), and 68.2% (54.5%) of low-, medium-, and high-income Internet users reported that they explored relevant health information on the Web in 2002 (2000). The odds ratios for access to online health information demonstrate that there was no significant difference in the odds ratios between the low- and high-income Internet users (0.772 [CI, 0.554 and 1.074]) as well as between the medium- and high-income Internet users (1.076 [CI, 0.816 and 1.419]) in 2002. The odds ratios for 2000 are similar to the ratios of 2002.

Table 5.

The Association of the Level of Income With the Use of Online Health Information by Internet Users

Test	Year	Degrees of Freedom	Value	P Value
Chi square	2002	2	4.4668	0.1072
	2000	2	0.1051	0.9488
Mantel-Haenszel chi square	2002	1	1.7351	0.1878
	2000	1	0.0237	0.8777
Odds Ratio	Year	Point	95% Confidence Interval
Low vs high	2002	0.772	0.554	1.074
	2000	1.027	0.761	1.386
Medium vs high	2002	1.076	0.816	1.419
	2000	0.983	0.767	1.260

Use of Online Health Information of the Overall Population

Examination of the proportions of health seekers over total participants, however, clearly illustrates the strong association of access to online health information with the income levels of information seekers both in 2000 and 2002 (Table 6). Overall, 24.12% (17.48%), 47.47% (31.13%), and 57.87% (42.66%) of the low-, medium-, and high-income participants reported that they explored online health information in 2002 (2000). During the 2000–2002 period, although low-income populations increased 6.64% in their access to online health information, medium- and high-income populations increased 16.34% and 15.21%, respectively. These different increases in access to online health information reduced the odds ratios between the low- and high-income populations from 0.285 (CI, 0.224 and 0.363) in 2000 to 0.225 (CI, 0.174 and 0.290) in 2002. At the same time, the cross-income disparity increased the odds ratios between the medium- and high-income populations from 0.608 (CI, 0.490 and 0.753) to 0.658 (CI, 0.524 and 0.827). The comparisons of these odds ratios between 2000 and 2002 show the trend in which the digital divide between the low- and high-income populations increased, whereas the digital divide between the medium- and high-income populations decreased.

Table 6.

The Association of the Level of Income With the Use of Online Health Information for the Total Respondents

Test	Year	Degrees of Freedom	Value	P Value
Chi square	2002	2	151.8451	< .0001
	2000	2	112.1508	< .0001
Mantel-Haenszel chi square	2002	1	142.4846	< .0001
	2000	1	111.7547	< .0001
Odds Ratio	Year	Point	95% Confidence Interval
Low vs high	2002	0.225	0.174	0.290
	2000	0.285	0.224	0.363
Medium vs high	2002	0.658	0.524	0.827
	2000	0.608	0.490	0.753

At the user level, there is little difference for accessing online health information among the low-, medium-, and high-income populations. Across the population as a whole, however, the differences are large enough to demonstrate a persistent “digital divide” between the low- and high-income populations.

Discussion

The results of this study demonstrate that low-income groups are still underserved in their access to the Internet as well their use of computers. However, once the low-income population gained access to the Internet, their behavior patterns on the Web were similar to those of high-income populations.

Few studies have investigated in-depth whether the low-income population had a differentiated pattern of access to and use of the Internet, and if so, why and to what extent. This study examined the degree to which annual income level influences distribution patterns and diffusion trends in access to computers, the Internet, and online health information. In particular, 2 research questions were employed: First, were low-income populations significantly underserved among those with computer access, those with Internet access, and those seeking online health information in 2002? Second, have differences between low- and high-income populations in access to computers, Internet service, and online health information narrowed, remained constant, or widened over recent years? Use of computers here was strongly associated with the level of income in 2000 and 2002. Adults with lower incomes had fewer opportunities to use computers than those with higher incomes. Only 51.8% (48.0%) of the low-income group reported that they occasionally used computers, whereas 80.4% (74.8%) and 93.2% (89.1%) of the medium- and high-income groups, respectively, responded that they used computers in 2002 (2000). These rates indicate that the computer utilization rates of low-, medium-, and high-income populations, respectively, increased 3.8%, 5.6%, and 4.1% between 2000 and 2002. The different increases of computer usage rates in each income group imply that the disparity between the low- and medium- as well as high-income groups was not improved, although the disparity between the medium- and high-income groups was improved somewhat.

Furthermore, the probability of searching for health information on the Web was comparable among the low-, medium-, and high-income Internet users. This suggests that efforts by the government, industry, or another entity to raise the number of low-income people using computers and the Internet would pay off. As shown here, increasing the number of computer and Internet users increases the number of online health information seekers in the low-income population. As a result, healthcare consumers were shown to achieve a variety of information benefits associated with greater use of the Internet.

The digital divide between low- and high-income populations still exists, and has not been improved, although the overall availability of computers and Internet access in the United States has increased somewhat. A possible explanation of the persistence of the digital divide is the relatively lower growth rate of the use of computers for the low-income adult population. The small growth rates suggest that national initiatives aimed at reducing the digital divide have had little effect in providing low-income adult populations with opportunities to use computers. Further study about the effectiveness of computer training or community-based computer centers for adult populations overall is needed.

The low-income Internet users were less likely to actively and frequently participate in online activities than higher income Internet users. The inactivity can be explained with the relatively short Internet experience of the low-income Internet users (4.64 years), suggesting that the Internet users who have little Internet experience are not as active as the Internet users who have greater Internet experience. The inactivity of low-income Internet users is not surprising. It is impressive, however, that medium-income Internet users have similar lengths of Internet experience (5.08 years) as low-income users, and show comparable information-seeking behaviors to high-income Internet users.

Predicting Use of Online Health Information

Medium-income populations are expected to be the main consumers of online health information in the near future. Medium-income populations had higher growth rates in the use of computers, the Internet, and online health information than high-income populations. The higher growth rates have decreased somewhat the gaps relative to high-income populations in the use of online health information as well as Internet and computer use. Increasing gaps with low-income populations, however, remain a problem, because low-income groups face limited access to computers and the Internet. The relatively small demand for online health information of high-income populations, and the limited access to computers and the Internet of low-income populations, will likely serve to make medium-income populations the main consumers of online health information for the foreseeable future.

Resolution for the Digital Divide

To realize the benefits that the Internet can improve the equity of the access to health information, healthcare policymakers and managers need to develop low-income, population-targeted approaches focused on (1) the improvement of the availability of computers and the Internet and (2) education for online information search skill or knowledge. This study revealed that as long as lower income people could occasionally use computers and the Internet, the possibility of people searching for health information on the Web is not so different from that of higher income people. Therefore, the first approach should be to provide lower income populations with enough opportunities to use computers and connect to the Internet. Then, for Internet users, healthcare needs to offer reliable and trusted guidelines about online information search, information-retrieval skills, and easy-to-access health Web pages. In regard to the reasons why Internet users gave up exploring health information on the Web, more Internet users in lower income levels reported that the lack of information retrieval (ie, search) skills impedes their ability to look for online health information.

Limitations

This study has several limitations. The first is that the annual income level was not adjusted by the regions where the participants lived, ignoring the real value of annual income across populations and geographic regions. For instance, the income of $100,000 for a metropolitan respondent could have similar real value of income of $70,000 in a rural area. To minimize the impact of misclassification, we categorized income levels into 3 collapsed groups: low (less than $30,000), medium (from $30,000 to $75,000), and high (over $75,000). It is possible that unadjusted annual income levels still result in some degree of misclassification, however. The second limitation is the restricted options in questionnaires used. This survey elicited data with semistructured questionnaires, which naturally restrict answer options and consequently prevent respondents from including their own unique thoughts or qualifying statements. If the questionnaires had listed different options or allowed more flexible responses for questions asking about online activities, or reasons why the nonhealth seekers did not explore online health information, the findings of this study might have been different. The third limitation is the assumption that a computer is the only device for connecting to the Internet. Advanced electronic communication devices, such as cell phones or personal digital assistants, can also provide Internet access service and health information. If this study had included the use of Internet and health information through all advanced electronic devices, the disparity between the low- and high-income groups would likely have been higher.

Conclusion

For consumers of health information, the technology initiatives of the late 1990s appear to have had little effect in eliminating the digital divide. As the NHII begins to take shape, access to information will be an essential part of the consumer-centric, shared decision-making framework outlined in the NHII strategic plan.

Funding Information

This project was funded in part by grant number 046902 from The Robert Wood Johnson Foundation with additional support from the Office of Disease Prevention and Health Promotion, US Department of Health and Human Services. Consumer WebWatch (CWW), a project of Consumers Union was a collaborator on the project.