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Research Article

Health communication, information technology and the public’s attitude toward periodic general health examinations

[version 1; peer review: 2 approved]
Quan-Hoang Vuong
https://orcid.org/0000-0003-0790-1576
Quan-Hoang Vuong
https://orcid.org/0000-0003-0790-1576
PUBLISHED 30 Dec 2016
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

Abstract

Background: Periodic general health examinations (GHEs) are gradually becoming more popular as they employ subclinical screenings, as a means of early detection. This study considers the effect of information technology (IT), health communications and the public’s attitude towards GHEs in Vietnam. Methods: A total of 2,068 valid observations were obtained from a survey in Hanoi and its surrounding areas. Results: In total, 42.12% of participants stated that they were willing to use IT applications to recognise illness symptoms, and nearly 2/3 of them rated the healthcare quality at average level or below. Discussion: The data, which was processed by the BCL model, showed that IT applications (apps) reduce hesitation toward GHEs; however, older people seem to have less confidence in using these apps. Health communications and government’s subsidy also increased the likelihood of people attending periodic GHEs. The probability of early check-ups where there is a cash subsidy could reach approximately 80%.

Keywords

general health examination, subclinical screenings, information and communication technology, healthcare subsidy

Corresponding author: Quan-Hoang Vuong
Competing interests: No competing interests were disclosed.
Grant information: The author(s) declared that no grants were involved in supporting this work.
Copyright:  © 2016 Vuong QH. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
How to cite: Vuong QH. Health communication, information technology and the public’s attitude toward periodic general health examinations [version 1; peer review: 2 approved]. F1000Research 2016, 5:2935 (https://doi.org/10.12688/f1000research.10508.1) First published: 30 Dec 2016, 5:2935 (https://doi.org/10.12688/f1000research.10508.1) Latest published: 30 Dec 2016, 5:2935 (https://doi.org/10.12688/f1000research.10508.1)

Introduction

Nowadays, people tend to avoid taking clinical treatments, instead, they prefer having subclinical tests and screenings as preventive medicine1–4. Using mobile applications (apps) in medical care is now becoming more popular thanks to the proliferation of information technology (IT)5–8 (http://www.mobihealthnews.com/4740/physician-smartphone-adoption-rate-to-reach-81-in-2012). As of 2012, there were 114 countries all over the world using mobile technology in medical care9, and a total of 165,000 mobile health apps were on the market in 2015 (http://www.imedicalapps.com/2015/09/ims-health-apps-report/), which were used in various different specialities from orthopaedics to cardiology10,11. West (2012) indicated that mobile technology was helping with chronic disease management, empowering the elderly and expectant mothers, reminding people to take medication at the proper time, extending services to underserved areas, and improving health outcomes and medical system efficiency9. In the same vein, some other studies also underscored the effectiveness of these apps in remote treatment in developing countries12–14. This efficiency was allegedly because they assisted faster decision making, transmitting messages more quickly and therefore saving money9,15. However, Buijink et al argued that almost all these mobile apps lacked authenticity or professional involvement, which could result in a wrong diagnosis, which may cause harm to the users10,18.

Due to the above limitations, many people still prefer to have direct clinical check-ups with doctors for prevention and early detection through periodic general health examinations (GHEs). However, this usually costs a substantial amount of money for clinical treatment, subclinical screenings or preventive services that we use19–21. People are more worried about increasing healthcare costs than being unemployed or terrorism22, since the financial burden could push them into poverty or even destitution23. Yet, the quality of medical services is still not compatible with what the patient’s pay for, as the majority of patients have low satisfaction with doctors and nursing care, especially with waiting time24,25. Responsiveness is usually the top factor that patients expect26,27, but the reality still falls far short of their expectations24,25,28,29. Those who have a high education background are more likely to demand higher standards on medical quality30,31. Conversely, the elderly tend to be more easily satisfied, with evidence from different countries in the world32,33.

Health communications, usually delivering case information, social consequences and policy messages, also have a certain influence on peoples’ behaviours and attitudes toward medical services33. Vivid, fearful and credible messages are apparently more persuasive22,33–35. Younger people prefer social consequence communications, whereas older people are more influenced by physical consequences33. Furthermore, women respond to emotional messages with social consequences for oneself or health consequences to near and dear ones, whereas men are more influenced by unemotional messages that emphasise personal physical health consequences33.

The majority of Vietnamese households still take advice from relatives or friends rather than from professionals on making clinical treatment-related decisions36. Families are the primary units for health education across most countries, whatever the level of economic development, and help establish culturally engrained beliefs about health and illness37. Family members and friends are huge sources of health information that can affect prevention, control and care activities38. Moreover, the social networks surrounding each health consumer also have powerful influences on their health beliefs and behaviours39. The quality of information and professional credibility are critical factors that help patients choose a healthcare provider40. However, it is not productive to encourage people to seek early detection, diagnosis and treatment when they have limited access to care, which is a reality in many developing countries41.

In this study, four models are employed to find out the influences of factors, including health communications, IT apps, age, education backgrounds, willingness/hesitations toward periodic GHE and government subsidies, on peoples’ attitude and behaviours toward preventive, subclinical or GHE decisions.

Methods

Survey characteristics

A survey was conducted by the research team from the office of Vuong & Associates (http://www.vuongassociates.com/home), who directly interviewed people in the areas of Hanoi and Hung Yen (Vietnam) in the period between September and October 2016. The study was performed under a license granted by the joint Ethics Board of Hospital 125 Thai Thinh, Hanoi, and Vuong & Associates Research Board (V&A/07/2016; 15 September 2016). Written informed consent was obtained from the participants prior to starting the survey. The questions selected were fairly simple and easy to understand, which when coupled with the enthusiasm of the participants, led to straightforward interviews. The subjects of the survey were chosen completely randomly and there was no exclusion criteria. The obtained dataset contained 2,068 observations (Dataset 142).

Regarding the data collecting process, since the data sample is random, no specific criteria for selecting some groups of people, like gender or age or job, were imposed. The survey team targeted places where most people are willing to spend time to take part in the survey. The interviewing places were public and private hospitals, junior high and high schools and business offices around Hanoi. Each respondent was given 10 to 20 minutes for each questionnaire, and the survey took place after the participant had understood the research ethics, content of the survey and ways of responding to the questions. The full questionnaire was delivered in Vietnamese, with a clear statement of research ethics standards, and is provided in Supplementary File 1 (an English translation can be found in Supplementary File 2).

Apart from the basic descriptive statistics, the present study employed statistical methods of categorical data analysis for modelling baseline category logits (i.e., BCL models), with the existence of continuous variables, as provided in Table 2. The practical estimations of categorical data following BCL models follow23.

Data modelling

The data were entered into Microsoft Office Excel 2007, then processed by R (3.3.1). The estimates in the study were made using BCL logistic regression models23 to predict the likelihood of a category of response variable Y in various conditions of predictor variable x.

The general equation of the baseline-categorical logit model is:

                                                              ln(πj(x)/πJ(x)) = αj+βj’x,       j=1,…, J-1.

in which x is the independent variable; and πj(x)=P(Y=j/x) is its probability. Thus πj=P(Yij=1), with Y being the dependent variable.

In the logit model in consideration, the probability of an event is calculated as:

                                                              πj(x) = exp(αj+βj’x)/[1+ J-1∑(h-1)exp(αj+βj’x)]

with ∑jπj(x) =1; αJ = 0 and βJ = 0; n is the number of observations in the sample, j is the categorical values of an observation i and h is a row in basic matrix Xi, see 23. In the analysis, z-value and p-value are the bases to conclude the statistical significance of predictor variables in the models, with P < 0.05 being the conventional level of statistical significance required for a positive result.

Results

Sample characteristics

The sample totalled 2,068 participants, of which 1,510 had an educational level of university or above (73.02%). A total of 1,073 participants expressed hesitation toward attending GHEs because they do not think it is not urgent or important (Table 1).

When seeing clinical signs, many respondents choose clinics as the first priority (43.04%), while 29.45% seek relatives or friends’ advice and 27.51% prefer to self-study. Furthermore, the majority (86.32%) are ready to pay for healthcare if the cost of a periodic GHE is less than VND 2 million.

Of the participants, 42.12% were willing to use mobile health apps if they are supposedly credible. If the apps reveal some health problems, 78.96% of participants may or will certainly go to the clinic to receive a check-up. Regarding the quality of medical services, most of the respondents expressed poor experiences; 1,291 participants scored the quality of medical services medium, while 60 scored it low.

Regarding peoples’ assessments of GHE quality, a scale of 5 (1 is lowest, 5 is highest) was used. “Respon” is the element that was assessed lowest among five elements (Response, Tangibility, Reliability, Assurance and Empathy) with 3.38 points (Tangibility 3.61 points; Reliability 3.57 points; Assurance 3.69 points; and Empathy 3.47 points) and is 0.17 points lower than the composite point (3.55). On the contrary, when it comes to health communications, ‘sufficiency of information’ achieved 3.01 points (95% CI: 2.96 - 3.06), which is the highest among the four components constituting the factor of health communications, apart from ‘the efficiency of health communications’, which is 0.18 points higher than the average at 2.83 (the two other components are: the attractiveness (2.69 points) and emphasis of information (2.82 points)).

Table 1. Descriptive statistics concerning education background, motivation for attending GHEs, income and use of IT apps in survey participants.

CharacteristicsN%
Education background (“Edu”)
Secondary or high school (“Hi”)
University or higher (“Uni”)
558
1,510
26.98
73.02
Hesitation due to non-urgency and unimportance (“NotImp”)
Yes
No
1,073
995
51.89
48.11
Readiness due to community subsidy (“ComSubsidy”)
Yes
No
1,061
1,007
51.31
48.69
Usage of subsidy (“UseMon”)
Spending all soon (“allsoon”)
Spending part and saving the rest (“partly”)
Taking the money and using it later (“later”)
1,286
311
471
62.19
15.04
22.77
First choices as having illness symptoms (“StChoice”)
Clinic (“clinic”)
Asking relatives or friends (“askrel”)
Self-study (“selfstudy”)
890
609
569
43.04
29.45
27.51
Affordable GHE costs
Less than VND 1 million (“low”)
VND 1–2 million (“med”)
Above VND 2 million (“hi”)		876
909
283		42.36
43.96
13.68
Ready to use IT apps (“UseIT”)
Yes
Maybe
No
871
721
476
42.12
34.86
23.02
Take GHE if IT apps show health problems (“AfterIT”)
Yes
Maybe
No
815
900
353
39.41
43.52
17.07
Assessments toward GHE’s quality (“QualExam”)
From 1 to < 2 points (“low”)
From 2 to < 4 points (“med”)
From 4 to 5 points (“hi”)
60
1,291
717
2.90
62.43
34.67

*Note: Codes of variables used in R estimations in brackets

Propensities toward periodic GHE

Propensities toward the first choice when experiencing disease symptoms. Employing logistic regression estimations with the dependent variable “StChoice” against four independent variables “Edu”, “Age”, “Respon” and “PopularInfo”, introduced in Table 2, the results reported in Table 3 show that there are relationships between the choice people prioritise when they recognise their symptoms with age, educational background, physicians’ responsiveness and the sufficiency of health information.

Table 2. Descriptive statistics for continuous variables used in subsequent estimations.

CharacteristicsAverageSDCI
Age, years29.1710.0928.74-29.60
Assessments of responsiveness (“Respon”)3.381.2603.33-3.43
Assessments of efficiency of health communications (“PopularInfo”)2.801.1802.75-2.85
Assessments of information sufficiency (“SuffInfo”)3.011.1702.96-3.06

*Note: Variables “Respon”, “PopularInfo” and “SuffInfo” have the lowest value of 1 and highest 5.

Table 3. Estimation results with response variable “StChoice” and predictors “Edu”, “Age”, “Respon” and “PopularInfo”.

Intercept“Edu”“Age”“Respon”“PopularInfo”
“Hi”
β0 β1 β2 β3 β4
logit(askrel|selfstudy)1.004***
[3.636]		0.712***
[4.844]		-0.025***
[-3.438]		-0.225***
[-4.709]		0.123*
[2.398]
logit(clinic|selfstudy)-0.673**
[-2.656]		0.578***
[4.227]		0.026***
[4.372]		-0.067
[-1.502]		0.159***
[3.354]

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; z-value in square brackets; baseline category for: “Edu”=“Uni”. Residual deviance: 4304.03 on 4126 degrees of freedom.

(Eq.1) and (Eq.2) are established based on Table 3 as follows:

        ln(πaskrel/πselfstudy) = 1.004 + 0.712×Hi.Edu – 0.025×Age – 0.225×Respon + 0.123×PopularInfo                (Eq.1)

        ln(πclinic/πselfstudy) = –0.673 + 0.578×Hi.Edu + 0.026×Age – 0.067×Respon + 0.158×PopularInfo                (Eq.2)

From the two above formulas, the probability of a person aged 30, giving 3.38 points for doctors’ responsiveness and 2.08 points for the efficiency of health communications (average points), choosing to go to clinic as the first choice is:

πclinic= e-0.673+0.578+0.026×30-0.067×3.38+0.158×2.8/[1+ e-0.673+0.578+0.026×30-0.067×3.38+0.158×2.8 + e(1.004+0.712-0.025×30-0.225×3.38+0.123×2.8)] = 0.474

In the same manner, the probability calculated in the case that this person has a university or higher education background is 42.74%.

Decision to attend periodic GHE after using IT apps. The results of logistic regression with the independent variables “Age”, “UseIT”, “PopularInfo” and the dependent variable “AfterIT” has shown the effect of age, the efficiency of health communications and the readiness to use IT health apps on the decision to attend GHE if the apps identify health problems.

From that, in ln(πmaybe/πyes), the intercept β0=1.624 (P<0.001, z=6.833), the coefficient of “Age” β1=0.001 (P<1, z=0.165); the coefficient of “UseIT” at “no” is β2 =-1.744 (P<0.001, z=-9.816) and at “yes” is β3=-2.558 (P<0.001, z=-19.870). The coefficient of “PopularInfo” β4=-0.008 (P<1, z=-0.169).

In ln(πno/πyes), the intercept β0=-1.290 (P<0.001, z=-3.785), the coefficient of “Age” β1=0.026 (P<0.001, z=3.470); the coefficient of “UseIT” at “no” is β2=2.022 (P<0.001, z=9.095) and at “yes” β3=-1.774 (P<0.001, z=-6.859). For the coefficient “PopularInfo”, β4=-0.210 (P<0.01, z=-3.094).

The two formulas below describe the relationships between the factors:

        ln(πmaybe/πyes) = 1.624 + 0.001×Age – 1.744×no.UseIT – 2.558×yes.UseIT – 0.008×PopularInfo                (Eq.3)

        ln(πno/πyes) = –1.290 + 0.026×Age + 2.022×no.UseIT – 1.774×yes.UseIT – 0.210×PopularInfo                (Eq.4)

Based on (Eq.3) and (Eq.4), we can calculate the probabilities of a patient taking GHE after IT apps reveal health problems with “Age”=30, “PopularInfo”=2.80 and “UseIT”=“yes” is 68.84%. In case “UseIT” = “no”, πyes=22.66%.

Assessments of healthcare services’ quality associated with health communications

Employing a logistic regression model with the response “QualExam” and two continuous dependent variables “SuffInfo” and “PopularInfo”, the results are described as follows. In ln(πhi/πmed), the intercept β0=-1.525 (P<0.001, z=-10.317), the coefficients of “SuffInfo” and “PopularInfo” are β1=0.114 (P<0.05, z=2.298) and β2=0.204 (P<0.001, z=4.169), respectively. In addition, for ln(πlow/πmed), intercept β0=-1.454 (P<0.001, z=-4.235), the coefficients of “SuffInfo” and “PopularInfo” are β1=-0.635(P<0.001, z=-4.080) and β2=-0.005 (P<1, z=-0.035), respectively.

The two regression equations:

        ln(πhi/πmed) = –1.525 + 0.114 × SuffInfo + 0.204 × PopularInfo                (Eq.5)

        ln(πlow/πmed) = –1.454 – 0.635 × SuffInfo – 0.005 × PopularInfo                (Eq.6)

Propensities of attending GHEs with availability of healthcare subsidy

The correlation between the hesitation toward GHE, due to perceived non-urgency and unimportance, the readiness due to community subsidy, affordable costs and the usage of subsidy is confirmed with the results as follows: In ln(πallsoon/πpartly), the intercept β0=1.868 (P<0.001, z=12.763), the coefficient of “NotImp” at “yes” is β1=-0.350 (P<0.01, z=-2.706), the coefficient of “ComSubsidy” at “yes” is β2=0.097 (P<1, z=0.751), the coefficient of “AffCost” at “hi” is β3=0.699 (P<0.05, z=2.477) and at “low” is β4=-0.752 (P<0.001, z=-5.490).

Likewise, in ln(πlater/πpartly), the intercept β0=0.910 (P<0.001, z=5.464), the coefficient of “NotImp” at “yes” is β1=0.303 (P<0.05, z=1.989), the coefficient of “ComSubsidy” at “yes” is β2=-0.672 (P<0.001, z=-4.459), and “AffCost” at “hi” is β3=0.790 (P<0.01, z=2.622) and at “low” is β4=-0.916 (P<0.001, z=-5.714).

Regression equations (Eq.7) and (Eq.8) are built based on the above results:

        ln(πallsoon/πpartly) = 0.910 + 0.303×yes.NotImp – 0.672×yes.ComSubsidy + 0.790 × hi.AffCost – 0.916×low.AffCost                (Eq.7)

        ln(πlater/πpartly) = 1.868 – 0.350×yes.NotImp + 0.097×yes.ComSubsidy + 0.699 × hi.AffCost – 0.752×low.AffCost                (Eq.8)

From (Eq.7) and (Eq.8), the probability of a person using all of a subsidy soon being ready to participate in GHE, having no hesitation and willing to pay at high cost is calculated as follows:

        πallsoon = e1.868+0.097+0.699/[1+ e1.868+0.097+0.699 + e0.910-0.672+0.790]=0.791

The same procedure could be used to compute other likelihoods (Supplementary File 3).

iddateAgesexEduNotImpComSubsidyUseMonTangiblesReliabilityResponAssuranceEmpathyAverageQualQualExamStChoiceAffCostUseITAfterITSuffInfoPopularInfo
m000012016091919femaleUniyesyespartly 222211.8lowselfstudylowyesyes44
m000022016091920femaleUninonoallsoon231211.8lowcliniclownoyes12
m000032016091924femaleUninonoallsoon114232.2medaskrellownono25
m000042016091920femaleUniyesyespartly 332322.6medselfstudylowyesyes41
m000052016091923maleUniyesnopartly 333333medselfstudymedyesyes32
m000062016091919femaleUninoyeslater111211.2lowaskrellowyesyes53
m000072016091920maleUniyesnolater333222.6medaskrellowmaybeyes44
m000082016091922femaleUniyesyespartly 322332.6medselfstudylowmaybemaybe32
m000092016091921femaleUniyesyesallsoon544554.6highaskrelhinono23
m000102016091948femaleHinonoallsoon121211.4lowclinichinono54
m000112016091930femaleHiyesyesallsoon341422.8medselfstudyhiyesyes44
m000122016091933maleHiyesnolater443443.8medcliniclowyesmaybe44
m000132016091931femaleHiyesyeslater433423.2medcliniclownono33
m000142016091920femaleUniyesyesallsoon444544.2highcliniclowyesyes44
m000152016091925femaleUninoyesallsoon333433.2medaskrelmedyesyes33
m000162016091928maleHinoyesallsoon532343.4medaskrelmedmaybeyes35
m000172016091928maleUniyesyesallsoon332422.8medselfstudymednoyes44
m000182016091929femaleUniyesyesallsoon554554.8highclinicmedmaybemaybe55
m000192016091948femaleHiyesnolater454454.4highclinichiyesno45
m000202016091929femaleUninoyesallsoon224343medselfstudyhiyesyes33
m000212016091942maleHinonoallsoon554544.6highclinichiyesyes31
m000222016091930femaleUninonoallsoon555555highaskrelmedyesyes53
m000232016091926maleUniyesyesallsoon545554.8highcliniclowyesno22
m000242016091933femaleUninonoallsoon331422.6medclinicmedyesyes33
m000252016091921femaleHiyesyesallsoon344544highaskrelmedyesyes45
m000262016091929femaleUninoyesallsoon434343.6medaskrelhiyesyes32
m000272016091937femaleUninoyesallsoon554554.8highcliniclownoyes33
m000282016091923femaleUniyesnoallsoon554554.8highclinichinoyes55
m000292016091926femaleHiyesyesallsoon435554.4highcliniclownono11
m000302016091924femaleUniyesnoallsoon344454highcliniclowyesmaybe55
m000312016091924maleUniyesyesallsoon321422.4medselfstudymedyesyes33
m000322016091931femaleUniyesyesallsoon454554.6highselfstudymedmaybeyes22
m000332016091930maleUniyesyeslater444454.2highselfstudylowmaybeyes32
m000342016091930maleUniyesyesallsoon344443.8medcliniclownono21
m000352016091921femaleUniyesnolater542443.8medselfstudylowyesyes33
m000362016091921femaleUninoyesallsoon444354highclinichiyesyes43
m000372016091926femaleHiyesyeslater454454.4highclinicmedyesyes33
m000382016092028maleUniyesnolater345444highclinicmednoyes42
m000392016092024femaleUniyesnoallsoon455454.6highselfstudymedmaybeyes12
m000402016092025femaleUniyesyesallsoon555555highclinichiyesyes43
m000412016092018femaleHinonoallsoon252433.2medaskrelmedyesyes44
m000422016092058femaleHinoyesallsoon455554.8highaskrelhinono34
m000432016092045maleHiyesnolater554554.8highclinichinono32
m000442016092037maleHiyesnoallsoon345444highaskrelmednoyes45
m000452016092025femaleUniyesyeslater544434highaskrelhimaybemaybe43
m000462016092028maleUninoyesallsoon443544highselfstudymednoyes33
m000472016092024maleUniyesyespartly 345343.8medaskrelmedyesyes24
m000482016092031maleUniyesnolater332332.8medselfstudylownoyes43
m000492016092024femaleUniyesyesallsoon433343.4medaskrelmedmaybeyes24
m000502016092024femaleHiyesnolater213312medclinichinoyes11
m000512016092032femaleHiyesnolater434443.8medselfstudyhinoyes24
m000522016092027femaleUniyesyesallsoon332433medselfstudyhiyesyes22
m000532016092024femaleUniyesnoallsoon354554.4highselfstudymednomaybe11
m000542016092030maleUninoyesallsoon555555highselfstudyhiyesyes33
m000552016092027maleUniyesnoallsoon344443.8medclinicmednoyes11
m000562016092029femaleHiyesnolater234423medcliniclownono32
m000572016092046femaleHiyesyesallsoon555555highaskrelhinomaybe55
m000582016092036femaleHiyesnolater455554.8highclinichinoyes55
m000592016092027femaleUniyesnoallsoon143422.8medaskrelmedyesyes53
m000602016092025maleUniyesnolater555555highclinicmedyesyes55
m000612016092026femaleUninonoallsoon353443.8medaskrellownomaybe32
m000622016092023femaleUninoyesallsoon355444.2highselfstudymedyesyes41
m000632016092025femaleUniyesyesallsoon355444.2highselfstudymedyesyes43
m000642016092023femaleUniyesyespartly 333333medaskrelhimaybemaybe33
m000652016092023femaleUniyesnoallsoon223312.2medcliniclowyesyes22
m000662016092022femaleUniyesnoallsoon341433medclinicmedyesmaybe12
m000672016092026maleUninoyesallsoon321342.6medaskrelmedyesyes31
m000682016092028femaleUniyesyesallsoon444333.6medclinicmedmaybemaybe11
m000692016092026femaleUniyesyesallsoon321422.4medaskrellowyesyes23
m000702016092026femaleUniyesyesallsoon123232.2medaskrelhimaybemaybe23
m000712016092028femaleUniyesyesallsoon111111lowselfstudylowmaybemaybe11
m000722016092027femaleUninonoallsoon212131.8lowselfstudylowyesyes11
m000732016092025maleUninonolater555555highcliniclownoyes55
m000742016092026maleUniyesnolater111111lowselfstudymedyesyes15
m000752016092027femaleUninoyesallsoon434333.4medclinicmedyesmaybe33
m000762016092028femaleUninoyesallsoon532554highclinicmednoyes33
m000772016092022femaleHinoyesallsoon445444.2highclinicmedyesyes43
m000782016092025maleHinoyesallsoon343433.4medselfstudymedyesyes44
m000792016092031femaleUniyesnolater543433.8medcliniclownomaybe22
m000802016092020femaleHiyesnolater555555highselfstudymedyesyes12
m000812016092021femaleHiyesnoallsoon343443.6medselfstudylowmaybeyes23
m000822016092026femaleUninoyesallsoon235343.4medclinichinoyes32
m000832016092022femaleUniyesnolater445554.6highaskrelmedyesyes55
m000842016092022femaleUninonolater342322.8medselfstudylowyesyes23
m000852016092123femaleUninonolater344554.2highcliniclowmaybeyes32
m000862016092130femaleUninoyesallsoon443443.8medselfstudymedmaybemaybe22
m000872016092133femaleUninoyesallsoon445354.2highselfstudylownoyes35
m000882016092136femaleUninonoallsoon555555highcliniclowmaybeyes33
m000892016092132femaleUninonoallsoon554554.8highselfstudylowyesyes52
m000902016092127femaleUninonoallsoon342443.4medaskrelhiyesno51
m000912016092124femaleUninoyesallsoon344433.6medselfstudymednoyes43
m000922016092136maleUninonoallsoon244333.2medaskrelhiyesmaybe41
m000932016092138femaleHinoyesallsoon131311.8lowaskrellownomaybe11
m000942016092132maleUniyesyesallsoon323232.6medselfstudyhimaybeyes32
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m001092016092131femaleUniyesyesallsoon342423medcliniclowmaybeyes32
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m001142016092158femaleHinoyesallsoon453544.2highclinicmednoyes54
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m001192016092127femaleUninonoallsoon545454.6highselfstudyhiyesyes31
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m001212016092119femaleUniyesnolater321432.6medaskrellowyesmaybe22
m001222016092124femaleUniyesyeslater455454.6highselfstudyhinomaybe32
m001232016092118femaleHiyesyesallsoon455554.8highaskrellowmaybemaybe35
m001242016092134femaleUniyesyesallsoon341523medselfstudylownoyes33
m001252016092243femaleUninonoallsoon342433.2medclinicmednono32
m001262016092222femaleHinonoallsoon332353.2medclinicmedyesmaybe23
m001272016092220femaleUniyesnoallsoon455554.8highclinicmednono12
m001282016092226femaleUniyesnolater555555highselfstudylownoyes21
m001292016092234maleHinonolater452523.6medselfstudylownoyes52
m001302016092227femaleUninoyesallsoon555555highselfstudyhinoyes43
m001312016092223maleUniyesnoallsoon355544.4highaskrellowmaybeyes34
m001322016092230maleUniyesnolater34232.52.9medselfstudymednomaybe33
m001332016092222maleHinonoallsoon555555highselfstudymednoyes42
m001342016092246femaleUninonolater344544highselfstudymednoyes43
m001352016092229maleUniyesnopartly 455454.6highselfstudylowyesyes54
m001362016092224femaleHinonolater433443.6medaskrellownomaybe44
m001372016092223femaleUniyesyesallsoon544344highselfstudylownoyes33
m001382016092222femaleUninonoallsoon432443.4medselfstudylowyesmaybe23
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m001582016092742maleUninoyesallsoon344443.8medselfstudyhiyesyes12
m001592016092725femaleHiyesyeslater332332.8medaskrelhiyesyes43
m001602016092741femaleUninonoallsoon222332.4medselfstudyhimaybeno22
m001612016092720femaleUniyesyesallsoon455554.8highaskrelmedyesno22
m001622016092726femaleUninoyesallsoon345454.2highselfstudyhinoyes55
m001632016092728femaleUniyesnolater222422.4medselfstudyhinono22
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m001652016092723femaleUninoyesallsoon555555highaskrelmedmaybeno52
m001662016092725femaleUniyesnolater222222medselfstudyhinono12
m001672016092730femaleUniyesnolater455544.6highselfstudylownono11
m001682016092731maleHiyesyesallsoon455554.8highselfstudymedyesyes51
m001692016092724femaleUninonolater555555highclinichinono24
m001702016092736femaleUninonolater335343.6medaskrellowmaybeyes34
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m001722016092732maleHinonoallsoon553554.6highcliniclownoyes43
m001732016092720femaleHiyesyesallsoon555555highclinicmednoyes55
m001742016092725femaleHinonoallsoon553554.6highselfstudyhinomaybe22
m001752016092727maleUniyesnoallsoon343523.4medselfstudymedyesyes23
m001762016092729femaleUniyesnolater454554.6highselfstudylowyesmaybe32
m001772016092630maleHiyesyesallsoon555555highselfstudylownoyes44
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m001792016092619femaleUniyesnoallsoon335443.8medclinicmedmaybemaybe33
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m001812016093024femaleUninoyesallsoon454544.4highselfstudylowyesyes43
m001822016093032femaleHinoyespartly 444444highselfstudymedyesyes44
m001832016093023femaleHiyesnolater555555highcliniclownono11
m001842016093035femaleUniyesyesallsoon344554.2highclinichiyesyes11
m001852016093034femaleUninoyesallsoon555555highclinichinono55
m001862016092630maleHinonolater555555highclinichiyesyes55
m001872016092631maleUniyesnoallsoon333333medaskrelhiyesno11
m001882016092625femaleUninonoallsoon555555highselfstudyhiyesyes33
m001892016092630maleUniyesnoallsoon232232.4medclinicmednoyes55
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m001912016092622maleUninoyeslater554554.8highselfstudylowmaybeyes43
m001922016092626femaleUninoyesallsoon341332.8medselfstudymedyesmaybe12
m001932016092626femaleUniyesnopartly 545554.8highselfstudylowyesno33
m001942016092653femaleHinonolater445554.6highclinicmednomaybe45
m001952016092618femaleUninoyesallsoon111111lowclinicmedmaybeyes54
m001962016092622maleUniyesnoallsoon223232.4medaskrelhinono32
m001972016092630femaleUninoyesallsoon435343.8medclinicmednono23
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m001992016092628femaleUniyesnolater452533.8medselfstudymednono45
This is a portion of the data; to view all the data, please download the file.
Dataset 1.Raw data gathered from the survey.
The data table used for providing descriptive statistics and preparing data subsets for statistical analysis (see also Supplementary Table 1).

Discussion

Comparing πclinic=47.4% at the “Edu”=“Hi” with πclinic=42.74%=“Edu”=“Uni”, it can be concluded that people with lower levels of education (high school or less) are more likely to go to clinics than those with a higher education (university or above). Also, a change of πclinic from 43.7% to 51.6% when “PopularInfo” runs from 1 to 5 points proves that effective communication will increase the likelihood of people going to clinics when finding illness symptoms. Similarly, πclinic also increases if physicians’ responsiveness is rated at a high level. Moreover, it can be seen that the older people are, the higher the probability they prioritise visiting clinics (Table 4a).

Table 4. Distribution of conditional probabilities.

Probabilities of “Clinic” vary according to “Age”,
“PopularInfo” and “Respon” (4a)
Condition“Edu”=“Hi”, “Age”=30, “PopularInfo”=2.8
“Respon”12345
πclinic0.4220.4450.4670.4850.501
Condition“Edu”=“Hi”, “Age”=30, “Respon”=3.38
“PopularInfo”12345
πclinic0.4370.4580.4780.4970.516
Conditions“Edu”=“Hi”, “PopularInfo”=2.8,
“Respon”=3.38
“Age”1030507090
πclinic 0.2750.4740.6690.8100.894
Probabilities of “AfterIT”=“yes” vary according to “Age”
and “PopularInfo” (4b)
Condition“UseIT”=“yes”, “PopularInfo”=2.8
“Age”1030507090
πyes 0.7030.6880.6670.6350.591
Condition“UseIT”=“yes”, “Age”=30
“PopularInfo”12345
πyes 0.6740.6820.6900.6960.702
Probabilities of “QualExam” vary according to “SuffInfo”
and “PopularInfo” (4c)
Condition“PopularInfo”=2.8
“SuffInfo”12345
πhi 0.2780.3120.3440.3740.403
πlow 0.0790.0420.0220.0110.006
Condition“SuffInfo”=3.01
“PopularInfo”12345
πhi 0.2670.3080.3540.4020.451
πlow 0.0240.0230.0210.0200.018

From the two equations (Eq.3) and (Eq.4), it can be observed that the absolute value of the coefficient corresponding to the variable “UseIT” is the largest, with β3=-2.558 (P < 0.001) at (Eq.3) and β2=2.022 (P < 0.001) at (Eq.4). It means that the increase or decrease of the probability of attending GHE after using IT apps will bear the greatest impact from the readiness or hesitation toward using IT health apps. In addition, Table 4b shows that the likelihood of attending GHE after using IT apps decreases as age increases. In contrast, this figure increases when health communication becomes increasingly popular.

Regarding assessment of the quality of healthcare services, the probability of a high score is larger than a low score in all conditions, especially when the efficiency of communication and the sufficiency of information reach the highest point (5 points), the probability that healthcare quality is assessed highly is largest (πhi > 40%). Therefore, it can be stated that the more widely and adequately information is disseminated, the more probable people will feel positive about healthcare quality (Table 4c).

It can be seen that the regression coefficient β1 of variable “NotImp” in (Eq.7) is negative and is positive in (Eq.8). Therefore, those who are hesitant, due to considering GHEs as not urgent and important, are less likely to make use of the total subsidy in the near future. The influence of “ComSubsidy” and “AffCost” are clarified through the analyses of Figure 1.

fbbded27-d4ec-4aaf-8af4-b4bd854fb702_figure1.gif

Figure 1. Probability of using a cash subsidy for GHE of a person expressing hesitation, due to its non-urgency and unimportance.

The figure represents trends of changing probabilities using funds available for GHEs, which control for the provision of community cash support. With community subsidies, respondents showed a stronger propensity to quickly use up the funds for GHEs.

Firstly, it can be seen that the probability line of “using all the money soon” (“allsoon”) in both the charts in Figure 1 have downward trends when moving from point “hi” to point “low” of “AffCost”, whereas the opposite trend occurs for the “later_partly” line. This means that the probability of using all the money soon reduces when people are willing to pay a high cost for a GHE. Moreover, (Eq.7) and (Eq.8) also imply that acceptable costs have the strongest impact on the use of provided money for GHEs.

Furthermore, the probability line of “allsoon” ranges from over 55% to nearly 70% in Figure 1 (left panel) and from over 47% to nearly 53% on the right panel. Therefore, participants tend to take all the money for an early GHE if they receive a subsidy from the community or government.

Finally, the two probability lines in Figure 1 (left panel) lie separately, while those in the right panel intersect with one another. This proves that when a person demonstrates a willingness toward GHEs, due to a community subsidy, then they tend to give priority to GHEs.

Conclusion

The analyses in the present study helps to provide some valuable conclusions as follows:

IT apps increase the likelihood of GHE participation, as 83% of participants said they might or would definitely visit a doctor if the apps reveal health problems or illness symptoms. The remainder expressed doubts on the reliability of the apps. This usually occurred in older people; nearly ¾ of people aged above 50 years did not completely trust the quality of these mobile apps.

Educational attainment is also a strong influence on the decision of GHE participation (with β2=0.712 (P<0.001) at (Eq.1) and β2=0.578 (P<0.001), following (Eq.2)). The preventive medicine or subclinical tests applied in GHE require inquiry and a certain amount of knowledge, which is limited for the people with a lower level of education. In this case, the clinical methods appear more effective. These people are eager to get direct advice from relatives, friends or doctors, while only about 18% of participants preferred self-study.

By contrast, effective health communications helped participants to have enough information and a thus formed a more trustworthy base, forming standards of comparison instead of purely emotional and personal conclusions, so that the evaluation tends to be improved and more objective. The proof is that nearly 70% of respondents rated the quality of healthcare services highly if they rated the sufficiency and coverage of information highly. Moreover, ITs also reduce the expensiveness of information36. However, health communications in Vietnam are still defective, especially as they are less widespread (assessment of efficacy: 2.8 out of 5 points; Table 2). Therefore, people expect a better coverage of health information.

Apart from ICTs, the community/government subsidy is also one measure that promotes GHEs. People tend to attend early GHEs when they receive cash subsidies (58.4 – 79.1%). However, about 52% of participants do not appreciate the importance of regular check-ups (Table 1). This may be due to limited finance (accounting for 60.8%), but might also be because they feel GHEs are not really necessary; therefore, they could use the subsidy for other improper purposes (accounting for 37.81%). For that reason, the authorities/communities need support in a reasonable manner in order to further promote the public’s readiness toward GHEs for their family and themselves.

Also, it cannot be denied that the quality of healthcare services in clinics and hospitals, particularly the responsiveness of nurses and doctors, remains low. With an average of 3.38 out of 5 points, responsiveness is rated lowest among the five elements included, whereas the empirical average score for quality of medical services is only at a medium level (3.55 out of 5 points). This somewhat reduces peoples’ desire to go to hospitals to check their health. Therefore, it is definitely necessary to improve the quality of medical services in Vietnam, especially public hospitals, since people tend to be more satisfied with private hospitals31.

Data availability

Dataset 1: Raw data gathered from the survey, doi, 10.5256/f1000research.10508.d14754842. The data table used for providing descriptive statistics and preparing data subsets for statistical analysis (see also Supplementary Table 1).

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Supplementary material

Supplementary File 1: The survey questionnaire is provided in full (in Vietnamese).

Click here to access the data.

Supplementary File 2: The survey questionnaire is provided in full (in English).

Click here to access the data.

Supplementary File 3: Estimations in R. These data files are available for verification and re-confirmation of the results found by the present study.

Click here to access the data.

Supplementary Table 1: Contingency table for estimations. Counts for relevant factors involved in statistical analysis.

Click here to access the data.

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VERSION 1 PUBLISHED 30 Dec 2016
  • Reader Comment 22 Dec 2022
    Hoàng Nguyễn, Ritsumeikan Asia Pacific University, Beppu, Japan
    22 Dec 2022
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    Technology is a great help in the healthcare sector nowadays, but it also leads to new problems that need solving. More studies like this research should be conducted, especially to ... Continue reading
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  • Reader Comment 04 Jan 2017
    N.K. Napier, Boise State University, USA
    04 Jan 2017
    Reader Comment
    Good to see this kind of needed and valuable research being done.
    Competing Interests: No competing interests were disclosed.
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Vuong QH. Health communication, information technology and the public’s attitude toward periodic general health examinations [version 1; peer review: 2 approved] F1000Research 2016, 5:2935 (https://doi.org/10.12688/f1000research.10508.1)
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Reviewer Report 13 Jan 2017
Bach Xuan Tran, Institute of Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam 
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https://doi.org/10.5256/f1000research.11326.r18859
The study findings enrich the literature on factors that influence health behaviors and health care services seeking in Vietnam. The analysis was sufficiently robust and the study has enough scientific merit for indexing.

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Tran B. Reviewer Report For: Health communication, information technology and the public’s attitude toward periodic general health examinations [version 1; peer review: 2 approved]. F1000Research 2016, 5:2935 (https://doi.org/10.5256/f1000research.11326.r18859)
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https://f1000research.com/articles/5-2935/v1#referee-response-18859
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  • Author Response 13 Jan 2017
    Quan-Hoang Vuong, Western University Hanoi - Centre for Interdisciplinary Social Research, Vietnam
    13 Jan 2017
    Author Response
    I would like to thank Professor Bach Xuan Tran for the review report and related comment. With respect to Prof Tran's suggestion on further description of the sample and the ... Continue reading
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COMMENTS ON THIS REPORT
  • Author Response 13 Jan 2017
    Quan-Hoang Vuong, Western University Hanoi - Centre for Interdisciplinary Social Research, Vietnam
    13 Jan 2017
    Author Response
    I would like to thank Professor Bach Xuan Tran for the review report and related comment. With respect to Prof Tran's suggestion on further description of the sample and the ... Continue reading
    Report a concern
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Reviewer Report 03 Jan 2017
Cuong Viet Nguyen, Institute of Public Policy and Management, National Economics University, Hanoi, Vietnam 
Approved
VIEWS 19
https://doi.org/10.5256/f1000research.11326.r18862
Thank you for giving me a chance to review the paper ‘Health communication, information technology and the public’s attitude toward periodic general health examinations’. I find the paper is interesting and important for health care. In Vietnam as well as ... Continue reading
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HOW TO CITE THIS REPORT
Viet Nguyen C. Reviewer Report For: Health communication, information technology and the public’s attitude toward periodic general health examinations [version 1; peer review: 2 approved]. F1000Research 2016, 5:2935 (https://doi.org/10.5256/f1000research.11326.r18862)
The direct URL for this report is:
https://f1000research.com/articles/5-2935/v1#referee-response-18862
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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  • Author Response 03 Jan 2017
    Quan-Hoang Vuong, Western University Hanoi - Centre for Interdisciplinary Social Research, Vietnam
    03 Jan 2017
    Author Response
    I would like to thank Professor Cuong V. Nguyen of the National Economics University (Vietnam) for your comment and especially a valid point on the utilizing of the IT devices ... Continue reading
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  • Respond or Comment
COMMENTS ON THIS REPORT
  • Author Response 03 Jan 2017
    Quan-Hoang Vuong, Western University Hanoi - Centre for Interdisciplinary Social Research, Vietnam
    03 Jan 2017
    Author Response
    I would like to thank Professor Cuong V. Nguyen of the National Economics University (Vietnam) for your comment and especially a valid point on the utilizing of the IT devices ... Continue reading
    Report a concern

Comments on this article Comments (2)

Version 1
VERSION 1 PUBLISHED 30 Dec 2016
  • Reader Comment 22 Dec 2022
    Hoàng Nguyễn, Ritsumeikan Asia Pacific University, Beppu, Japan
    22 Dec 2022
    Reader Comment
    Technology is a great help in the healthcare sector nowadays, but it also leads to new problems that need solving. More studies like this research should be conducted, especially to ... Continue reading
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  • Reader Comment 04 Jan 2017
    N.K. Napier, Boise State University, USA
    04 Jan 2017
    Reader Comment
    Good to see this kind of needed and valuable research being done.
    Competing Interests: No competing interests were disclosed.
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Open Peer Review

Reviewer Status

Alongside their report, reviewers assign a status to the article:

Approved
The paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved
Fundamental flaws in the paper seriously undermine the findings and conclusions

Reviewer Reports

Invited Reviewers
1 2
Version 1
30 Dec 16 		read read
  1. Cuong Viet Nguyen, National Economics University, Hanoi, Vietnam
  2. Bach Xuan Tran, Hanoi Medical University, Hanoi, Vietnam

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    Alongside their report, reviewers assign a status to the article:
    Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
    Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
    Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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