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Health information technology (HIT) is utilized by people with different chronic conditions such as diabetes and hypertension. However, there has been no comparison of HIT use between persons without a chronic condition, with one chronic condition, and multiple (≥2) chronic conditions (MCCs).
The aim of the study was to assess the difference in HIT use between persons without a chronic condition, with one chronic condition, and with MCCs, to describe the characteristics of HIT use among those with chronic conditions and to identify the predictors of HIT use of the persons with one chronic condition and MCCs.
A secondary data analysis was conducted in spring 2017 using the National Health Interview Survey (NHIS) 2012 Family Core and Sample Adult Core datasets that yielded 34,525 respondents aged 18 years and older. Measures included overall HIT use (ie, any use of the following five HIT on the Internet: seeking health information, ordering prescription, making appointment, emailing health provider, and using health chat groups), as well as sociodemographic and health-related characteristics. Sociodemographic and health characteristics were compared between HIT users and nonusers among those who reported having at least one chronic condition using chi-square tests. Independent predictors of HIT use were identified using multiple logistic regression analyses for those with one chronic condition, with MCCs, and without a chronic condition. Analyses were weighted and performed at significance level of .005.
In 2012, adults with one health chronic condition (raw count 4147/8551, weighted percentage 48.54%) was significantly higher than among those with MCCs (3816/9637, 39.55%) and those with none of chronic condition (7254/16,337, 44.40%,
This study provides a snapshot of HIT use among those with chronic conditions and potential factors related to such use. Clinical care and public health communication efforts attempting to leverage more HIT use should acknowledge differential HIT usage as identified in this study to better address communication inequalities and persistent disparities in socioeconomic status.
According to the 2012 update of National Health Interview Survey (NHIS) data [
The use of health information technology (HIT) can include a wide range of activities, from searching general health information to using individual computerized modules or Web portals. HIT has been utilized by people with different specific chronic conditions such as diabetes [
To address this research gap, we analyzed NHIS 2012 data to (1) assess whether patterns of HIT use differ for persons without a chronic condition, with one chronic condition, and with MCCs; (2) describe the characteristics of HIT use among those with chronic conditions; and (3) identify predictors of HIT use among individuals with one chronic condition and MCCs. The aim of this study was to provide health professionals with a better understanding of HIT use among patients with one or more chronic conditions to facilitate better clinical care and patient education.
This paper reports a secondary analysis of data from the NHIS, a cross-sectional household interview survey targeting the noninstitutionalized civilian population of the United States conducted by the Centers for Disease Control and Prevention’s (CDC) National Center for Health Statistics (NCHS) periodically. This study utilized the 2012 NHIS Family Core and Sample Adult Core. The NHIS Family Core questionnaire contained information on the participant’s sociodemographic characteristics and health status. Data on chronic conditions and computer use were collected via the Sample Adult Core questionnaire. Details of the NHIS sampling are reported elsewhere [
Participants were asked whether they have ever used computers in the past 12 months for any of the following tasks: (1) to look up health information on the Internet (referred as seeking Web-based health information in the text below), (2) to fill a prescription (referred as ordering a Web-based prescription in the text below), (3) to schedule a Web-based appointment with a health care provider, (4) to communicate with a health care provider by email, or (5) to use online chat groups to learn about health topics (referred as using Web-based chat group in the text below). If an individual indicated use for any of these five purposes, they were considered to have used HIT in the past 12 months.
The chronic conditions included in this study were 10 most frequently reported physical health conditions from a list of 20 conditions identified by the US Department of Health and Human Services (DHHS) to foster a more consistent and standardized approach to measuring the occurrence of chronic conditions in the United States [
HIT use has been found to vary by age [
Previous research suggests that after controlling for sociodemographic characteristics, self-rated health status may not be significantly associated with HIT use [
Analyses were performed using the Statistical Package for Social Sciences (SPSS) software (IBM SPSS Statistics for Windows, release 24.0. Armonk, NY: IBM Corp). Because NHIS is a complex survey using a multistage probability complex sampling design that incorporates stratification, clustering, and oversampling of some subpopulations (eg, black, Hispanic, and Asian), sampling weights must be used to produce representative estimates and standard errors. We utilized SPSS Complex Samples to compute statistics and standard errors from complex sample designs by incorporating sample designs into survey analysis. HIT use by respondents with and without chronic conditions as well as characteristics of HIT users and nonusers were compared among those who reported having at least one chronic condition, using chi-square tests. Independent predictors of HIT use were identified using multiple logistic regression analyses for those with one chronic condition, with MCCs, and without a chronic condition. All variables were included in the logistic regression analyses without forward or backward procedures. Due to the large sample size, a statistical significance level of .005 was chosen, and the 99.5% CI were calculated.
In 2012, an estimated 98.5 million US adults (42%) sought Web-based health information, 15.8 million (6.7%) ordered a Web-based prescription, 10.8 million (4.6%) made Web-based appointments with their health care provider, 13.5 million (5.7%) emailed their health care provider, and 6.8 million (2.9%) used Web-based health chat groups. Approximately half (116.7 million, 49.7%) of US adults reported having at least one chronic condition, and 57.3 million (24.4%), 32.7 million (13.9%), and 26.9 million (11.4%) reported having one, two, and three or more chronic conditions, respectively. The prevalence of each condition varies from the most frequently reported hypertension (50.5 million, 21.5%) to the least reported weak or failing kidneys (3.9 million, 1.7%).
A comparison of HIT use by respondents with and without chronic conditions is shown in
Weighted percentage of persons who had used health information technology by chronic condition groups.
Health information technology use variables | All, % (Nb=34,525) | No chronic condition, % (Nb=16,337) | One condition, % (Nb=8551) | MCCsa, % (Nb=9637) | Chi-square | |
Any health information technology use | 44.2 | 44.4 | 48.5 | 39.6 | 141.3 | <.001 |
Looked up health information | 42.0 | 42.4 | 45.9 | 37.2 | 133.5 | <.001 |
Ordered prescription | 6.7 | 4.7 | 8.4 | 9.0 | 218.8 | <.001 |
Made appointment | 4.6 | 4.4 | 5.4 | 4.3 | 15.5 | .02 |
Emailed health provider | 5.7 | 5.3 | 6.3 | 6.4 | 15.0 | .02 |
Used health chat groups | 2.9 | 3.0 | 3.2 | 2.5 | 8.5 | .07 |
aMCCs: multiple chronic conditions.
bN: raw count.
Comparison of characteristic between health information technology (HIT) users and nonusers among those who had at least one chronic condition in the past 12 months: weighted percentage and 99.5% CI.
Sociodemographic and health characteristics | All, % |
Did not use health information technology, % (99.5% CI) |
Used health information technology, % (99.5% CI) |
|
18 to 29 | 8.6 | 7.4 (6.6-8.2) | 10.2 (9.2-11.3) | |
30 to 39 | 9.9 | 7.4 (6.8-8.2) | 13.1 (12.2-14.0) | |
40 to 49 | 16.0 | 13.6 (12.8-14.5) | 19.1 (18.0-20.2) | |
50 to 64 | 34.8 | 32.1 (30.9-33.4) | 38.2 (36.7-39.7) | |
65 to 74 | 16.9 | 19.2 (18.3-20.2) | 14.0 (13.1-14.9) | |
75+ | 13.7 | 20.2 (19.2-21.3) | 5.4 (4.8-6.0) | |
Male | 46.2 | 49.0 (47.8-50.2) | 42.6 (41.1-44.1) | |
Female | 53.8 | 51.0 (59.8-52.2) | 57.4 (55.9-58.9) | |
Hispanic | 10.5 | 13.1 (12.3-14.0) | 7.1 (6.3-7.9) | |
Non-Hispanic white | 72.2 | 66.6 (65.3-67.9) | 79.3 (78.1-80.5) | |
Non-Hispanic black | 12.6 | 15.4 (14.5-16.4) | 9.1 (8.3-10.0) | |
Non-Hispanic Asian | 3.8 | 3.9 (3.4-4.4) | 3.7 (3.2-4.3) | |
Non-Hispanic all other race | 0.9 | 1.0 (0.7-1.4) | 0.8 (0.6-1.1) | |
Less than high school | 15.2 | 23.7 (22.6-24.8) | 4.4 (3.8-5.0) | |
High school graduate and some college | 59.4 | 61.8 (60.6-63.0) | 56.4 (54.8-57.9) | |
Bachelor’s degree | 15.6 | 9.3 (8.5-10.1) | 23.6 (22.4-24.9) | |
Master’s degree or higher | 9.8 | 5.2 (4.6-5.8) | 15.7 (14.6-16.8) | |
Not employed | 44.9 | 54.5 (53.1-55.8) | 31.5 (30.1-33.0) | |
Employed | 55.1 | 45.5 (44.2-46.9) | 68.5 (67.0-69.9) | |
Up to 14,999 | 22.8 | 26.8 (24.8-28.9) | 19.7 (18.3-21.1) | |
15,000 to 34,999 | 24.2 | 32.3 (30.4-34.3) | 24.2 (22.6-25.8) | |
35,000 to 54,999 | 21.8 | 21.4 (19.7-23.2) | 21.8 (20.2-23.4) | |
55,000 to 74,999 | 14.0 | 9.3 (8.2-10.6) | 14.0 (12.8-15.3) | |
75,000 and higher | 20.4 | 10.1 (8.7-11.7) | 20.4 (18.7-22.1) | |
Not in relationship | 38.3 | 43.0 (41.7-44.4) | 32.3 (31.0-33.7) | |
In relationship | 61.7 | 57.0 (55.6-58.3) | 67.7 (66.3-69.0) | |
Up to 18.49 | 1.2 | 1.6 (1.3-2.0) | 0.7 (0.5-1.0) | |
18.5 to 24.9 | 25.6 | 25.0 (23.9-26.1) | 26.3 (25.0-27.7) | |
25-29.9 | 34.4 | 33.7 (32.5-34.9) | 35.3 (34.0-36.6) | |
30 and more | 38.8 | 39.6 (38.5-40.8) | 37.7 (36.3-39.0) | |
Very good to excellent | 44.8 | 38.2 (37.1-39.3) | 53.2 (51.7-54.7) | |
good | 33.4 | 34.6 (33.4-35.8) | 31.8 (30.5-33.2) | |
poor to fair | 21.8 | 27.2 (26.1-28.4) | 15.0 (14.0-16.1) |
The characteristics related to HIT use among adults with at least one chronic condition are presented in
When adding the chronic condition status as an independent variable in the logistic regression model, the finding shows that higher prevalent HIT use is more likely to be reported by adults with one chronic condition (odds ratio, OR 1.55, 99.5% CI 1.44-1.68,
Factors associated to health information technology (HIT) use among respondents with none, one chronic condition, and multiple chronic conditions (MCCs): weighted logistic regression model results.
Independent variable | With one chronic condition, adjusted ORb (99.5% CI) |
With MCCsa, |
With no chronic conditions, |
|
18 to 29 | 1.00 | 1.00 | 1.00 | |
30 to 39 | 0.95 (0.61-1.49) | 0.61 (0.27-1.34) | 0.77 (0.62-0.95) | |
40 to 49 | 0.66 (0.42-1.04) | 0.46 (0.22-0.98) | 0.68 (0.55-0.85) | |
50 to 64 | 0.51 (0.33-0.80) | 0.40 (0.19-0.85) | 0.54 (0.42-0.69) | |
65 to 74 | 0.37 (0.21-0.65) | 0.25 (0.12-0.55) | 0.44 (0.26-0.76) |
|
75+ | 0.12 (0.03-0.45) | 0.11 (0.04-0.32) | 0.19 (0.04-0.91) |
|
Male | 1.00 | 1.00 | 1.00 | |
Female | 1.92 (1.49-2.46) | 2.21 (1.63-3.00) | 2.25 (1.62-2.63) | |
Hispanic | 1.00 | 1.00 | 1.00 | |
Non-Hispanic white | 1.93 (1.36-2.73) | 1.95 (1.24-3.06) | 1.58 (1.28-1.96) | |
Non-Hispanic black | 1.18 (0.76-1.83) | 1.09 (0.62-1.91) | 0.90 (0.68-1.20) | |
Non-Hispanic Asian | 0.93 (0.52-1.66) | 1.04 (0.47-2.30) | 1.20 (0.84-1.71) | |
Non-Hispanic all other | 1.39 (0.37-5.18) | 2.00 (0.57-6.98) | 1.01 (0.44-2.33) | |
Less than high school | 1.00 | 1.00 | 1.00 | |
High school graduate and some college | 3.02 (1.71-5.35) | 4.28 (2.38-7.68) | 2.79 (2.07-3.787) |
|
Bachelor’s degree | 6.88 (3.73-12.72) | 12.66 (6.56-24.44) | 5.89 (4.21-8.25) | |
Master’s degree or higher | 9.89 (5.00-19.57) |
13.18 (6.55-26.51) | 7.57 (5.11-11.20) |
|
Not employed | 1.00 | 1.00 | 1.00 | |
Employed | 0.88 (0.47-1.64) | 1.00 (0.56-1.78) | 1.02 (0.65-1.61) | |
Up to 14,999 | 1.00 | 1.00 | 1.00 | |
15,000 to 34,999 | 1.05 (0.73-1.50) | 0.88 (0.59-1.31) | 1.05 (0.85-1.29) | |
35,000 to 54,999 | 1.30 (0.90-1.87) | 1.08 (0.71-1.64) | 1.28 (1.01-1.61) | |
55,000 to 74,999 | 1.46 (0.89-2.40) | 2.02 (1.24-3.31) | 1.35 (1.01-1.82) | |
75,000 and higher | 2.13 (1.34-3.37) | 1.86 (1.09-3.18) | 1.89 (1.39-2.56) | |
Not in relationship | 1.00 | 1.00 | 1.00 | |
In relationship | 1.21 (0.94-1.55) | 1.28 (0.95-1.73) | 1.07 (0.91-1.25) | |
Up to 18.49 | 0.90 (0.29-2.79) | 0.34 (0.07-1.74) | 0.87 (0.45-1.70) | |
18.5 to 24.9 | 1.00 | 1.00 | 1.00 | |
25-29.9 | 1.10 (0.80-1.52) | 1.12 (0.73-1.73) | 0.87 (0.73-1.03) | |
30 and more | 1.01 (0.76-1.35) | 0.94 (0.62-1.41) | 0.90 (0.74-1.11) | |
Very good to excellent | 1.00 | 1.00 | 1.00 | |
Good | 0.95 (0.72-1.25) | 0.99 (0.72-1.36) | 1.06 (0.86-1.31) | |
Poor to fair | 1.11 (0.73-1.70) | 0.93 (0.61-1.4) | 1.31 (0.87-1.98) |
aMCCs: multiple chronic conditions.
bOR: odds ratio.
Our findings show that HIT use is relatively common among people with chronic conditions, ranging from about 40% of those with MCCs, to 49% of those with one chronic condition. The number of HIT users is expected be even higher nowadays with the increasing adoption of electronic health record (EHR) systems since the passage of the Health Information Technology for Economic and Clinical Health (HITECH) provisions of the American Recovery and Reinvestment Act (ARRA) of 2009 [
We found that overall HIT use significantly differed among adults with or without chronic conditions, those with one chronic condition being the most active HIT users, those with MCCs the least, and those with none of the 10 chronic conditions falling in between. Our findings based on the multivariate regression models suggest that socioeconomic factors may have more influence on HIT use than health-related characteristics because the same sociodemographic factors were predictive of HIT use across all three of our study groups (adults with no chronic conditions, one chronic condition, and with MCCs). Specifically, consistent with the findings of previous studies on digital divide [
The lower use of HIT among adults with MCCs than those with one or no chronic condition may be explained by differences in the sociodemographic profile of each group. Whereas prevalence of MCCs varies by age, gender, and race or ethnicity, older age might be the key factor related to the lower use of HIT by adults with MCCs. First of all, for both genders, adults with MCCs are more likely to be older (aged ≥65 years) than those with only one or no chronic conditions [
This study has a number of strengths, including using a dataset with a good response rate and a large sample drawn from a representative nationwide survey. Nonetheless, this study was subject to a few limitations. First, NHIS information was collected via self-report and the questions relating to health conditions and HIT use examined the participant’s experience in the previous 12 months; hence, the study findings are potentially subject to recall bias and social desirability bias. Second, because of the nature of the cross-sectional study design, it is not possible to draw conclusions about probable causal pathways between the two explored variables (eg, chronic conditions and computer use), and therefore, the study findings should be interpreted with caution. These limitations should be balanced against the strengths of the study, including the large sample size and representativeness of the US population.
Our study provides a snapshot of HIT use among those with chronic conditions and potential factors related to such use. Our study suggests that HIT may serve as an alternative to more traditional methods of obtaining health information or communicating directly with health care providers, which in turn may help those with chronic conditions to better manage their illness over the long term. However, clinical care and public health communication efforts attempting to leverage more HIT use should acknowledge differential HIT usage as identified in this study to better address communication inequalities and persistent disparities in socioeconomic status.
body mass index
Centers for Disease Control and Prevention
chronic obstructive pulmonary disease
Department of Health and Human Services
electronic health record
health information technology
health information technology for economic and clinical health
multiple chronic conditions
National Health Interview Survey
National Center for Health Statistics
odds ratio
Statistical Package for Social Sciences
Conception of the work was done by YZ, DS, and JA. Data analysis was done by RL, YZ, and DS. Introduction was written by YZ, BO, and JA. Methods and Results were written by RL, YZ, and DS. Discussion was written by YZ, BO, RC, and JA.
None declared.