Predictors of Health Information-Seeking Behaviors in Hispanics

Abstract

The objective of this study was to examine factors predicting use of the Internet to seek health information among Hispanics in the Washington Heights and Inwood areas of New York City. Data were collected by community health workers through the Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER) community survey and a random sample of 100 surveys was selected for analysis. Binary logistic regression (N=100) was used to examine predictors of online health information-seeking behaviors (HISBs) of respondent and household members (dependent variables). Younger age, better health status, and higher education level significantly predicted respondents’ HISBs. Respondents’ health status and education level also significantly predicted household members’ HISBs.

Introduction

Patients have become active consumers of health information, and their health information seeking behaviors (HISBs) are considered as an important strategic issue in the health field 1, 2. The emergence of the Internet increased access to health information and helps people to enhance their self-management skills and to participate in medical decision making actively 3. The Pew Internet and American Life Project estimated that 63% of Americans have sought health information via the Internet3. Further, the Internet plays an important role in assisting marginalized groups to access health resources and social support 4.

However, disparities exist between people who have access to the Internet and those who do not 4, 5. To increase the percentage of the Internet use for health information seeking, several studies suggested that it is important to identify the types of individuals who are seeking health information via the Internet, and understand issues and situational circumstances 6, 7.

Researchers at Columbia University and their collaborators are creating the Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER). Residents of Washington Heights and Inwood areas are primarily (71%) Hispanic 8. Hispanics lag behind others in health information-seeking behaviors (HISBs) and also disproportionately experience health disparities 5. Few studies have examined the predictors of HISBs in Hispanics 5. As part of WICER, the research team is collecting community surveys on 8,000 community residents.

Objective

The objective of this study was to examine factors predicting HISBs among Hispanics in Washington Heights and Inwood areas through WICER community survey.

Methods

3.1. Sample/Setting

The sample comprised 100 randomly selected respondents from among 1,000 Washington Heights/Inwood residents completing the WICER survey.

3.2. Questionnaire

Questionnaire items included demographics, health behaviors, physical activity, social relations, mental health, and Internet health information-seeking behaviors (HISBs) among others. Four questions related to HISBs: 1) respondent participation in an online support group for people with similar health or medical issues, 2) respondent use of email or the Internet to communicate with a doctor or doctor’s office, 3) respondent use of the Internet to look up health or medical information, and 4) respondents’ household use the Internet to look up health or medical information.

3.3. Data Analysis

Data were analyzed using Statistical Package for the Social Science (SPSS) Version 18.0 software. First, differences in categorical variables according to Internet uses were tested by chi-square tests. Second, based on the descriptive data, two binary logistic regressions were conducted to examine predictors of online HISBs of respondent and household members (dependent variables).

Results

4.1. Sample

Only two respondents responded positively to questions related to online support groups or communication with doctor or doctor’s office. A relatively small percentage (14.5%) of the sample reported HISBs and most respondents were female (Table 1). Additionally, 22.9% of respondents’ household members had sought health-related information using the Internet.

Table 1.

Descriptive Characteristics of study sample (N=100)

Variables	Use of Internet to seek health or medical information (respondent) %		Use of Internet to seek health or medical information (household member) %
%	yes	no	Yes	No
Sex
Men	2.1	17.5	2.1	16.7
Women	12.4	68.0	20.8	60.4
Age
18–24	3.0	2.0	2.0	3.0
25–34	3.0	7.0	4.0	6.1
35–44	2.0	10.0	4.0	8.1
45–54	3.0	13.0	4.0	12.1
55–64	4.0	24.0	7.1	20.2
65+	0.0	29.0	3.0	26.3
Education
<High School	4.1	61.2	10.3	54.6
=> High school graduate	11.2	23.5	13.4	21.6
Employment status
Yes	7.0	17.0	7.1	17.2
No	8.0	68.0	17.2	58.6
Race
White non-Hispanic	0.0	0.0	0.0	0.0
Black non-Hispanic	1.0	0.0	1.0	0.0
Hispanic	14.0	85.0	23.2	75.8
Marriage (%)
Married/living as family	4.1	11.2	10.3	25.8
Otherwise	31.6	53.1	14.4	49.5
Hypertension
Yes	2.0	52.0	8.1	45.5
No	13.0	33.0	16.2	30.3
General health status
Excellent	0.0	28.6	1.0	27.8
Good	11.2	23.5	12.4	22.7
Poor	4.1	32.7	10.3	25.8

4.2. Binary logistic regression

Significant variables in the bivariate analyses were gender, age, education level, health status and current employment status. In the binary logistic regressions, younger age, higher education level, and better general health status increased respondents’ odds of HISBs. Better health status and higher education level increased odds of household members’ HISBs.

Discussion

Studies of general US populations have shown that being female, being younger and having more education are positively associated with health-related internet use9. Due to the small number of positive responses, we did not make gender comparisons. Furthermore, previous studies have found that being in worse health was associated with HISBs9. However, our findings showed the opposite - better health was associated with HISBs. A recent study found that HISB is purposive, but is not driven by an immediate health problem 11. One distinguishing characteristic of the Washington Heights/Inwood community is the high percentage of immigrants which may influence HISBs and their relationship with health status.

It is worth noting that, in this study, a participant’s better health and higher education level predicted household members’ HISBs. Familism, an important Hispanic cultural value with implications for the involvement of extended family in the health care of a patient, is a possible rationale for this finding5. To improve HISBs in the Hispanic community, further investigations need to consider the role of the Internet as a useful tool to assist Hispanics in caring for family members5.

The National Assessment of Adult Literacy (NAAL) reported that the health literacy level of Hispanic is lower than other races or ethnicities5. Although the Newest Vital Signs measure10 of health literacy is included in the WICER survey, we did not have sufficient data in our random sample of 100 to analyze the relationship between health literacy and HISBs, but we will do so in future analyses. Moreover, the relatively small sample and low frequencies of use of the Internet for HISB thus limited the types of analyses that could be conducted.

Conclusion

This study confirmed several predictors for Hispanic HISBs that had been identified in the literature. Our finding related to health status contrasts with prior research. Further study with larger samples is recommended.

Table 2.

Binary Logistic Regression (No=0, Yes=1): Predictors of HISBs (Respondent) (n=100)

Predictors of variables	B (Unstandardized) coefficient	Odds ratio (95% Confidence Interval)
Gender (0=male, 1=female)	− 0.422	0.656 (0.096–4.477)
Age **	− 0.658	0.518 (0.317–0.846)
General health status *	1.220	3.386 (1.194–9.601)
Employment status	1.214	3.368 (0.730–15.548)
Education level *	1.80	6.048 (1.278–28.615)
Nagelkerke R2	0.399
χ2***	24.194

^*p< 0.05

^**p<0.01

^***p<0.001

Table 3.

Binary Logistic Regression (No=0, Yes=1): Respondent Predictors of Household Members’ HISBs (n=100)

Explanatory variables	B (Unstandardized) coefficient	Odds ratio (95% Confidence Interval)
Gender (0=male, 1=female)	0.495	1.641 (0.315–8.558)
Age	− 0.342	0.710 (0.487–1.035)
General health status *	1.032	2.807 (1.280–6.156)
Employment status	− 0.384	0.681 (0.168–2.766)
Education level *	1.234	3.437 (1.008–11.712)
Nagelkerke R2	0.239
χ2**	15.877

^*p< 0.05

^**p<0.01

^***p<0.001