What is the nature of the digital health divide in the UK?

8/13/2019 What is the nature of the digital health divide in the UK?

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SIR 023 Quantitative Research Methods Student 1014662

So far I have discussed the evaluations of digital health projects which seem to show that this

digital health divide still exists. Several population studies have also been published exploring who is

online and seeking health information. These include studies from France (Renahy, Parizot et al.

2008), Italy (Siliquini, Ceruti et al. 2011), seven European countries (Andreassen, Bujnowska-Fedak et

al. 2007), and the United States (Rice 2006,Lustria, Smith et al. 2011). In general higher levels of

education , income and younger age is associated with increased likelihood of Internet access(Dutton, Blank et al. 2013,White and Selwyn 2013). In a population study in Paris it was found that

after adjusting for these factors having a current health problem made Internet access less

likely(Renahy, Parizot et al. 2008).

For those who are online, the strongest predictors of seeking health information are being

female (Rice 2006,Andreassen, Bujnowska-Fedak et al. 2007,Renahy, Parizot et al. 2008,Atkinson,

Saperstein et al. 2009, Lustria, Smith et al. 2011,Siliquini, Ceruti et al. 2011), higher levels of

education (Rice 2006,Andreassen, Bujnowska-Fedak et al. 2007,Renahy, Parizot et al. 2008,Lustria,

Smith et al. 2011,Siliquini, Ceruti et al. 2011), and having a chronic condition or current health

condition (Rice 2006,Andreassen, Bujnowska-Fedak et al. 2007,Renahy, Parizot et al. 2008, Siliquini,

Ceruti et al. 2011).

Although general Internet access in the UK is well studied through the biennial Oxford Internet

Study (Dutton, Blank et al. 2013)in particular, I could not identify any UK population study which

looked at accessing online health information. A web-based study of the users of NHS Choices ( a

general health information website) found that they were younger, more likely to be female and

have higher levels of education that the UK population in general (Powell, Inglis et al. 2011)

In the absence of a published population study addressing them I would like to use an existing

UK dataset to answer the following two research questions.

1. What is the association between socio-economic and demographic factors (age, sex,education, and household income), health status and using the Internet?

2. For those who have used the Internet in the last 3 months what is the association between

socio-economic and demographic factors (age, sex, education, and household income),

health status and using the Internet to seek health-related information?

In their Parisian study Renahy, Parizot et al. (2008)describe a double divide in health information

seeking on the Internet whereby the socio-economic factors which predict Internet access, are also

associated with accessing health information when online. This paper will explore whether parts of

the UK population also face a double whammy digital divide.


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Methods

Choice of Data Set

To explore internet use in the UK and particularly engagement with online health information

several datasets are available. TheOpinions and Lifestyle Survey, formerly known asONS Opinions

SurveyorOmnibus, is a monthly survey carried out across Great Britain of those ages 16 and

older(UK Data Service 2013). In the months of January, February and March it includes a module on

internet access. Since 2008 this has included a question about whether or not in the past 3 months

the respondent has used the internet to obtain health information. In total 2925 interviews were

conducted in 20143 with response rates of 57%, 53% and 51% in January, February and March

respectively.

The Oxford Internet Survey is a biennial survey carried out by the Oxford Internet Institute since

2003. All but the most recent dataset is available to researchers who wish to access it. At the time

this essay was commenced only the 2009 dataset was available. This interviewed 2013 respondents

with a 62% response rate (Dutton and Blank 2011).

I decided to use the ONS Opinions and Lifestyle Survey as it was the most up-to-date dataset

which was available to address the research question. As this is an individual level analysis weighting(Wta) has been applied.

Outcome variables

Two outcome variables have been explored. The first is internet use within the last 3 months.

The original survey questions asked When did you last use the Internet?. Possible responses were

within the last 3 months, between 3 months and a year ago,more than a year ago and never. A

preliminary analysis established that less than 3% of responses were between 3 months and a year

ago or more than a year ago. Responses were then recoded in to a new dichotomous variable

Have you used the Internet in the last 3 months?

The second outcome variable constructed was the dichotomous variable In the last 3 months

have you used the Internet to see health-related information? This was recoded from a multiple

response variable asking about purposes for which the Internet had been used in the last 3 months,

including seeking health-related information.

Independent Variables

Socio-economic and demographic

The following variables were used:

Age : In the descriptive analysis, the relationship of age and smoking will be examined by using

grouped age bands . In the multivariate analysis it will be assessed as a continuous variable.

Sex

Education Highest education qualification has been grouped in to degree or equivalent, below

degree level, other qualification (including foreign qualification below degree level and non (no

formal qualifications).

Income Gross household income was originally categorised in to 38 bands which were not

evenly divided. These bands were then recoded into a new variable with 8 categories. Bands 1-5


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were the 1st

category, bands 6-10 the 2nd

, and so on, with bands 36-38 forming the 8th

category. This

new variable was used in the analyses.

Health status

The ONS Lifestyle and Opinions survey asks two questions relating to self-reported health status.The first How is your health in general? There are 5 possible responses very good, good, fair,

bad and very bad. These were not recoded. This question has been asked in surveys since the

1950s and poor self-reported health status has been shown to correlate with mortality(Guimares,

Chor et al. 2012), although the mechanism is not understood (Jylh 2009). It is also used to calculate

healthy life expectancy(Smith, Evans et al. 2012), a measure which is increasingly being used

globally (Stiefel, Perla et al. 2010).

The second variable concerned the presence of a chronic condition. Have you any long-standing

illness, disability or infirmity?is again widely in surveys in part to calculate disability free life

expectancy (Smith, Olatunde et al. 2010).

Analysis

Descriptive

Unadjusted odds ratios are given for each of the socio-demographic variables- age group, sex,

education and income, and the two health status variables with the outcome variables. Confidence

intervals were calculated for odds ratios with SPSS v20, by using the enter procedure with individual

logistic regressions.

Multivariate

For the two outcome variable, having used the Internet in the past 3 months or not, and having

used the Internet in the past 3 months to see health information or not, stepwise logisticregressions using the forward conditional mode of variable selection in SPSS v20 were performed.

The models included the three categorical socio-economic variables (sex, income and education, the

two categorical health status variables and age as continuous variable. 95% confidence intervals for

the odds ratios are given.

.


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Results

Use of Internet in Last 3 months

Descriptive Statistics

Overall prevalence of internet use in the last 3 months

When asked when they had last used the Internet, 83% responded that they had done so in the

last 3 months. This compares to 78% who described themselves as current Internet users in the

2013 Oxford Internet Study (Dutton, Blank et al. 2013)of the UK population, and 85% of US adults

this year (Zickuhr 2013).

Frequency Valid Percent

Valid

No 608 17.2

Yes 2305 82.8

Total 2913 100.0

Missing System 7

Total 2920

Source: Opinions and Lifestyle Survey, Internet Access Module, January, February and March, 2013

*Weighted % and unweighted sample N are presented.

Table 1 Have you used the Internet in the last 3 months?


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Age

Figure 1 shows that the likelihood of using the Internet in the last 3 months is strongly inversely

correlated with increasing age. This is in keeping with previously reported studies in the UK (Dutton,

Blank et al. 2013).

Figure 1: Proportion who have used the Internet in the last 3 months by age group

Table 2 gives frequencies and unadjusted odds ratios for using the Internet in the last 3 months ,

and shows that the strong linear relationship between age and online access. Those over 75 haveless than 1/50 the odds of being an Internet user compared to the reference group of 16-24 year

olds. The largest differences are between those of working age and those who have retired. This is

possibly in part related to having access to the Internet at work, or having to use it as part of routine

work.

Have you used the Internet in

the last 3 months? Total Exp(B)

95%

C.I.for

EXP(B)

No Yes % Yes Lower Upper

Grouped age 16 to 24 2 216 99 218 1.00

25 to 44 33 852 97 885 0.25 0.07 0.90

45 to 54 47 422 91 469 0.09 0.02 0.31

55 to 64 82 412 84 494 0.05 0.01 0.16

65 to 74 185 286 64 471 0.01 0.00 0.05

75 and over 259 117 34 376 0.00 0.00 0.02


*Weighted % and unweighted sample N are presented

Table 2 Frequencies and unadjusted odds ratios for internet use in the last 3 months by age group

0

10

2030

40

50

60

70

80

90

100

16 to 24 25 to 44 45 to 54 55 to 64 65 to 74 75 and over

Grouped age

%


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Sex

Figure 2 Proportion who have used the Internet in the last 3 months by sex

Figure 2 and Table 3 show that there is no significant difference in Internet use in the past 3

months between males and females.



95%

C.I.for

EXP(B)


Sex of Respondent Male 253 1035 84 1288 1.00

Female 355 1270 82 1625 0.88 0.72 1.07



Table 3 Frequencies and unadjusted odds ratios for internet use in the last 3 months by sex

0

10

20

30

40

50

60

7080

90

100

Male Female

Sex of Respondent

%


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Education

Figure 3 shows a linear relationship between highest educational qualification and Internet use

within the last 3 months. Those with no qualifications have 1/30th

the odds of those with a degree

level education of being an Internet user (Table 4). As the Oxford Internet Survey this year found this

year, it is only those with no educational qualifications at all *who+ tend to be left out (Dutton,Blank et al. 2013;21)

Figure 3 Proportion who have used the Internet in the past 3 months by highest level of qualification

010

20

30

40

50

60

70

80

90

100

Degree or

equivalent

Below degree

level

Other

qualifications (inc.

foreign quals

below degree

level)

None (no formal

qualifications)

What is the highest level of qualification?

%


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95%

C.I.for

EXP(B)


What is the highestlevel of

qualification?

Degree or

equivalent 23 654 97 677 1.00

Below degree

level 139 1108 91 1247 0.28 0.17 0.46

Other

qualifications

(inc. foreign

quals below

degree level) 100 287 77 387 0.09 0.05 0.16

None (no

formal

qualifications) 346 256 48 602 0.03 0.02 0.04



Table 4 Frequencies and unadjusted odds ratios for internet use in the last 3 months by highest level of qualification


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Income

The relationship between income and Internet use is not straightforward. Those with lowest

income had high levels of internet usage. This is in part because they are disproportionately younger

than the higher income groups. The highest income band had significantly lower odds of using the

Internet than the reference group which was a very low income of less than 50/week. This is not in

keeping with the Oxford Internet Study which found a linear relationship with increasing Internet

use with higher income (Dutton, Blank et al. 2013). They found that 99% of those with a household

income over 40000 were Internet users compared to only 86% of those with a household icome of

over 46799 here.

Figure 4 Proportion who have used the Internet in the past 3 months by gross annual income



95%

C.I.for

EXP(B)


Gross annual

income 0- 2599 5 72 96 77 1.00

2600 -

5199 61 166 75 227 0.14 0.05 0.39

5200 -

10399 230 354 66 584 0.09 0.03 0.2410400 -

15599 135 374 78 509 0.16 0.06 0.44

15600 -

20799 46 288 90 334 0.41 0.15 1.14

20800 -

33799 33 434 93 467 0.66 0.23 1.84

33800 -

46799 2 196 99 198 4.32 0.81 22.94

46800 - 96 421 86 517 0.28 0.10 0.74



Table 5 Frequencies and unadjusted odds ratios for internet use in the last 3 months by gross annual income

0

10

20

30

40

50

60

70

80

90

100

0- 2599 2600 -

5199

5200 -

10399

10400 -

15599

15600 -

20799

20800 -

33799

33800 -

46799

46800 -

Gross annual income

%


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Health Status

Figure 5 Proportion who have used the Internet in the past 3 months by presence of long-standing illness, disability

or infirmity

Those with chronic health conditions or disabilities are significantly less likely to have used the

Internet in the previous three months than those who have not (Figure 5 and Table 6). This is in

keeping with the findings of the Oxford Internet Study which found that 51% of those who said they

were disabled compared to 84% of non-disabled were Internet users (Dutton, Blank et al. 2013). A

Pew Study in the US (Fox 2011)found that 64% of those who had at least one chronic condition had

Internet access compared to 81% of those who had none. Again this may be explained by the well

established relationships between chronic conditions and lower socio-economic status(Mackenbach, Looman et al. 1996,Smith 1999).



95%

C.I.for

EXP(B)


Have any long-

standing illness,

disability or

infirmity? Yes 408 776 70 1184 1.00

No 199 1517 90 1716 4.02 3.28 4.92



Table 6 Frequencies and unadjusted odds ratios for internet use in the last 3 months by presence of long-standing

illness, disease or infirmity

0

10

20

30

40

50

60

70

80

90

100

Yes No

Have any long-standing illness, disability or infirmity?

%


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Figure 6 Proportion who have used the Internet in the past 3 months by general health status

Those who rate their health as very good have significantly more likely to be using the Internet

than those who say their health is good(Table 7), and those who say it is fair/bad or very bad are

significantly lower again. This may be because self-reported health is known to be positively

correlated with higher levels of education (Subramanian, Huijtsb et al. 2010)and inversely

correlated with age (Eriksson, Undn et al. 2001).

Have you used the Internet inthe last 3 months? Total Exp(B)

95%

C.I.forEXP(B)


How is your health

in general? Very good 88 904 92 992 1.00

Good 208 975 86 1183 0.49 0.37 0.65

Fair 212 311 63 523 0.14 0.11 0.19

Bad 77 83 55 160 0.10 0.07 0.15

Very bad 22 20 57 42 0.11 0.05 0.21



Table 7 Frequencies and unadjusted odds ratios for internet use in the last 3 months by general health status

0

10

20

30

40

50

60

70

80

90

100

Very good Good Fair Bad Very bad

How is your health in general?

%


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Multivariate Logistic Regression

Six variables were entered into the model using forward conditional mode of variable entry. Five of

these are individual categorical socio-economic and health status variables (sex, education level,

household income, presence of longstanding disability, illness or infirmity and general health status)

and with age entered as a continuous variable.

The results are shown in Table 8. Only four of the variables were found to be significant. Sex was not

significantly associated with Internet usage in the univariate analysis and this is not changed by

adjusting for the other factors. In contrast the presence of long-standing illness, disability or infirmity

had also been found to be negatively associated with Internet usage, but after adjusting for the

other factors this relationship is not present.

Age remains strongly predictive of the likelihood of using the Internet. For each additional year of

age, the odds of being online decreases by 7%.

The relationship between education and Internet access is only slightly attenuated by adjusting forthe other factors. After adjusting for age, income and general health status the odds of someone

with no qualifications using the Internet is 1/15th

that of someone with a degree level education.

With regards to general health status only those with self-reported fair or poor health are

significantly less likely to have used the Internet than those who have very good health when the

other factors are adjusted for.

In the univariate analysis income was not strongly related to Internet use, but the highest income

band had significantly reduced odds of using the Internet compared to the lowest income group.

After adjusting for age, education level and health status the highest income band now has nine

times the odds of the lowest income group. The high levels of Internet access in the lowest incomegroup were thought to be due to confounding by age but the relationship is still U-shaped rather

than linear after adjusting for age.


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Variable Exp(B) 95% C.I.for EXP(B)

Lower Upper

Age 0.93 0.92 0.94

What is the highest

level of

qualification? Degree or equivalent 1.00Below degree level 0.29 0.17 0.50

Other qualifications (inc.

foreign quals below

degree level) 0.15 0.08 0.26

None (no formal

qualifications) 0.07 0.04 0.12

How is your health

in general?

Very good 1.00

Good 0.85 0.60 1.19

Fair 0.53 0.37 0.77

Bad 0.34 0.20 0.57

Very bad 0.44 0.19 1.02

Gross annual

income

0- 2599 1.00

5200 - 10399 0.55 0.34 0.89

10400 - 15599 0.61 0.41 0.89

15600 - 20799 0.88 0.58 1.33

20800 - 33799 1.46 0.88 2.44

33800 - 46799 1.28 0.76 2.16

46800 - 9.52 2.17 41.80

Source: Opinions and Lifestyle Survey, Internet Access Module, January, February and March,

2013

Table 8 Logistic regression of using the Internet in the last 3 months (showing odds ratios and 95% CI)


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Using the Internet to seek health-related information in the last 3 months

Descriptive Statistics

Overall prevalence

Of the 82% of those who have used the Internet in the previous three months, just fewer than

50% have used it to see health-related information (Table 9). This is much lower than the level

found in the Oxford Internet Survey (Dutton, Blank et al. 2013). In this 69% of Internet users look

for health information online, but this is not within a specific time-frame, as various categories have

been merged. This is the group who have not said that they never seek online health information. A

recent Pew Survey (Fox and Duggan 2013)found that 72% of US internet users say they looked

online for health information of one kind or another within the past year. A study of 7 European

countries in found 71% of Internet users had accessed online health information (Andreassen,

Bujnowska-Fedak et al. 2007).

As can be seen from these other studies, the level of reported access of health information islower than in other studies. I attempted to validate this result by accessing the 2008 ONS Lifestyle

survey as this was the first to contain the question on seeking health information in the Internet

access module. I constructed a variable for using the Internet to seek health information as in this

current dataset. In the 2008 42% of those who had used the Internet in the previous 3 months had

used it to access health-related information. This is in contrast to the 2007 and 2009 Oxford Internet

surveys which both found 68% of Internet users looking online for health information.

It may be that the framing of the Oxford Internet Survey questions How frequently do you

use the Internet to access health or medical information? daily/weekly/monthly/less than

monthly/never is less likely to receive a never response thanno as a response to Inthe past 3

months have you use the Internet to access health-related informationin the ONS Opinions andLifestyle Survey.

Frequency Valid Percent

Valid

No 1163 50.2

Yes 1149 49.8

Total 2310 100.0

Missing System 608

Total 2920



Table 9 Have you used the Internet in the last 3 months to see health-related information?


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Age

Amongst Internet users only 25-44 year olds are more likely than the reference group to access

health-related information (Figure 7 and Table 10). A recent Pew study found that younger, rather

than older people, were much more likely to look for health information online, but that this may

have been related to compensation for lack of medical insurance (Fox and Duggan 2013). As the UK

has universal healthcare this may explain the different pattern.

Figure 7 Proportion who have used the Internet in the last 3 months to seek health-related information by age

group

In the last 3 months have

you used the Internet toseek health-related

information? Total Exp(B)

95%C.I.for

EXP(B)

No Yes

%

Yes Lower Upper

Grouped age 16 to 24 117 99 46 216

25 to 44 348 506 59 854 1.69 1.30 2.20

45 to 54 245 178 43 423 0.90 0.67 1.20

55 to 64 212 201 49 413 1.15 0.85 1.54

65 to 74 168 120 41 288 0.84 0.60 1.17

75 and over 73 45 41 118 0.82 0.52 1.30



Table 10 Frequencies and unadjusted odds ratios for using the Internet in the last 3 months to seek health-related

information by age group

0

10

20

30

40

50

60

70

80

90

100

16 to 24 25 to 44 45 to 54 55 to 64 65 to 74 75 and over

Grouped age

%


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Sex

Women are more likely to access health-related information. This has been found repeatedly in

studies of online health information seeking in France (Renahy, Parizot et al. 2008), United States

((Rice 2006,Lustria, Smith et al. 2011), and Italy (Siliquini, Ceruti et al. 2011).Figure 8 Proportion who have used the Internet in the last 3 months to seek health-related information by sex


you used the Internet to

seek health-related


95%

C.I.for

EXP(B)

No Yes

%

Yes Lower Upper

Sex Male 587 451 44 1038 1.00

Female 576 698 55 1274 1.55 1.32 1.82




information by sex

0

10

20

30

40

50

60

70

80

90

100

Male Female

Sex of Respondent


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Education

The finding that higher levels of educational qualification are significantly related to increased

likelihood of seeking online health information (Figure 9 and Table 12) is also consistent with other

studies (Rice 2006,Andreassen, Bujnowska-Fedak et al. 2007,Renahy, Parizot et al. 2008,Lustria,

Smith et al. 2011,Siliquini, Ceruti et al. 2011).Figure 9 Proportion who have used the Internet in the last 3 months to seek health-related information by highest

level of qualification



seek health-related


95%

C.I.for

EXP(B)

No Yes

%

Yes Lower Upper

What is the

highest level of

qualification?

Degree or

equivalent 253 403 61 656 1.00

Below degree

level 556 554 50 1110 0.62 0.51 0.76

Other

qualifications

(inc. foreign

quals below

degree level) 165 123 44 288 0.50 0.38 0.66

None (no

formal

qualifications) 189 69 26 258 0.22 0.16 0.31




information by highest level of qualification

0

10

20

30

40

50

60

70

80

90

100

Degree or

equivalent

Below degree

level

Other

qualifications (inc.

foreign quals

below degree

level)

None (no formal

qualifications)

What is the highest level of qualification?

%


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Income

In this univariate analysis there is no discernible relationship between income and likelihood to

seek online health information (Figure 10). None of the income bands has significantly different odds

of using the Internet to access health-related information from the reference category (Table 13).

Figure 10 Proportion who have used the Internet in the last 3 months to seek health-related information by gross

annual income



seek health-related


95%

C.I.for

EXP(B)

No Yes

%

Yes Lower Upper

Gross annual

income 0- 2599 34 38 50 72 1.00

2600 - 5199 93 73 49 166 0.98 0.60 1.62

5200 - 10399 192 164 45 356 0.83 0.53 1.31

10400 -

15599 215 160 43 375 0.75 0.48 1.19

15600 -

20799 154 134 48 288 0.92 0.58 1.47

20800 -

33799 187 248 55 435 1.25 0.80 1.96

33800 -

46799 86 111 59 197 1.43 0.87 2.34

46800 - 202 221 51 423 1.06 0.68 1.65




information

0

10

20

30

40

50

60

70

80

90

100

0- 2599 2600 -

5199

5200 -

10399

10400 -

15599

15600 -

20799

20800 -

33799

33800 -

46799

46800 -

Gross annual income

%


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Health Status

Those with a chronic condition- a long-standing illness, disability of infirmity are significantly

more likely to access health information than those who do not (Table 14). This has also been found

in other studies (Rice 2006,Andreassen, Bujnowska-Fedak et al. 2007,Renahy, Parizot et al. 2008,

Siliquini, Ceruti et al. 2011). With regards to general health status those who state their health is

very good are significantly less likely to use the Internet to seek health-related information than

those who consider their health to be good or fair (Table 15).

.

Figure 11 Proportion who have used the Internet in the last 3 months to seek health information by presence of

long-standing illness, disability or infirmity



seek health-related


95%

C.I.for

EXP(B)

No Yes

%

Yes Lower Upper

Have any long-

standing illness,

disability or

infirmity? Yes 358 419 55 777 1.00No 798 725 47 1523 0.72 0.61 0.86




information by presence of long-standing illness, disability or infirmity

0

10

20

30

40

5060

70

80

90

100

Yes No

Have any long-standing illness, disability or infirmity?

%


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Figure 12 Proportion who have used the Internet in the last 3 months to seek health-related information by general

health status



seek health information? Total Exp(B)

95%

C.I.for

EXP(B)

No Yes

%

Yes Lower Upper

How is your

health in

general? Very good 481 427 46 908

Good 474 503 52 977 1.28 1.07 1.52

Fair 151 160 53 311 1.32 1.02 1.71

Bad 39 44 55 83 1.41 0.88 2.28

Very bad 11 10 55 21 1.36 0.58 3.20




information by general health status

0

10

20

30

40

50

60

70

80

90

100

Very good Good Fair Bad Very bad

How is your health in general?

%


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Multivariate Logistic Regression

Again six variables were entered into the model using forward conditional mode of variable entry.

Five of these are individual categorical socio-economic and health status variables (sex, education

level, household income, presence of longstanding disability, illness or infirmity and general health

status) and with age entered as a continuous variable.

The results are shown in Table 16. All six variables were significant in the model. After adjusting for

all the other variables with each year of increase in age, the odds of seeking online health

information reduces by 1%.

No relationship was found between income and accessing online health information in univariate

analysis. Once the other variables are adjusted for there is the suggestion of a U-shaped curve

relationship. Only one category had significantly decreased odds than the reference range, and

another (the highest income band) was significantly increased. With regards to education those with

no formal qualifications have of the odds of those with degree level education.

The odds of women, compared to men, seeking health information online are increased when othervariables are accounted for.

Both health status variables are significant. Those with chronic conditions are more likely to use the

Internet to see health-related information, and those who state they have good or fair health are

more likely than those who say their health is very good.


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Variable Exp(B) 95% C.I.for EXP(B)

Lower Upper

Age 0.99 0.98 0.99

What is the highest

level of qualification?

Degree or equivalent

Below degree level 0.61 0.50 0.75

Other qualifications (inc.

foreign quals below

degree level) 0.49 0.37 0.67

None (no formal

qualifications) 0.22 0.16 0.32

Have any long-

standing illness,disability or

infirmity?

Yes

No 0.64 0.52 0.80

How is your health in

general?

Very good 1.00

Good 1.37 1.13 1.66

Fair 1.69 1.24 2.31

Bad 1.60 0.94 2.73

Very bad 1.64 0.64 4.18

Sex Male 1.00

Female 1.73 1.45 2.06

Gross annual income

0- 2599 1.00

2600 - 5199 0.83 0.52 1.32

5200 - 10399 0.91 0.64 1.31

10400 - 15599 0.77 0.57 1.03

15600 - 20799 0.67 0.50 0.89

20800 - 33799 0.90 0.66 1.22

33800 - 46799 1.14 0.87 1.50

46800 - 1.43 1.01 2.03

Source: Opinions and Lifestyle Survey, Internet Access Module, January, February and March,

2013Table 16 Logistic regression of using the Internet in the last 3 months to see health-related information (showing

odds ratios and 95% CI)


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Discussion and Conclusion

This study shows that there is evidence of a digital divide in accessing health information in the

UK. Those who are younger, have higher levels of education and higher income, are more likely to

have online access, and to be using it to seek health information. This reflects the discrepancy in

uptake of new digital health technologies such as Renal PatientView, and threatens initiatives such

as Digital First unless ways can be found of addressing this digital divide.

Of note, self-reported health status is negatively associated with the likelihood of being online

after adjusting for income, age and education. This was also found in the Parisian population study

(Renahy, Parizot et al. 2008). It may be because those with poor self-reported health have physical

or psychological problems which make it difficult for them to physically use a computer, or they may

lack motivation. This is an area which would benefit from further exploration.

However it is also clear that the health digital divide is not just about access. Selwyn (2004)is

critical of the narrow focus where one is considered to be on one side of the divide or not depending

on whether or not one has access. This is reflected in government policy aiming to reduce the

digital divide by increasing access to broadband (Wilkinson 2010). Instead Selwyn argues that we

should consider access a staged progress moving from formal access through engagement to

meaningful outcomes. He suggests that the digital divide could be considered in terms of access to

different forms of technological capitaleconomic, cultural and social.

Let us consider educational attainment. Higher qualifications in particular are associated with

higher levels of internet access and higher levels of accessing health information. This quantitative

analysis does not help us to understand why higher levels of education are associated with increased

use of the Internet for health information, and it cannot tell us that higher education causes

increased use of online health information. However, the fact that this relationship has been found

in studies across the world over different periods of time makes it interesting to explore further.

It may represent what Lee (2009)describes as the health-knowledge gap. This suggests that

those with higher levels of education can more quickly acquire knowledge from a given medium

than those with lower levels of education. Or education may be giving access to the various forms of

capital that Selwyn (2004) considers are important. Neter and Brain (2012)consider the health

digital divide in terms of eHealth Literacy. This mayalso be a pathway mediating the relationship

between education and online information seeking. There is still much work to be done in identifying

and addressing these roots of the digital divide.

Even if we had full understanding of what predicted seeking online health information and were

able to find measures which would address the digital divides I have described here, other factorswould still need to be addressed before some of the more ambitious plan for the use of technology

in healthcare which initiatives such as Digital First envisage could be realised. Qualitative research

such as that of Kerns, Krist et al. (2013)which investigated how patients want to engage with a

personal health record shows the complexity of these adoptions. Beyond the motivation and skills of

individual patients, how the technology is embedded in existing practices and the relationships

between staff and patients is key to success.

In the meantime, this analysis of a large UK dataset reminds us that without addressing and

considering some aspects of the digital divide described here, the increased use of online

technologies may increase health inequalities (Jones 2013).


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What is the nature of the digital health divide in the UK?

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