job satisfaction on subjectivo oral health australian workers

8/10/2019 Job Satisfaction on Subjectivo Oral Health Australian Workers

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2004 VOL. 28 NO. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 259

Job characteristics and the subjective oral health

of Australian workers

Anne E. Sanders and A. John Spencer

Australian Research Centre for Population Oral Health, Dental School,

University of Adelaide, South Australia

Abstract

The way in which work is structured and

organised is associated with the health and

well-being of workers.

Objectives:To examine the associations

between hours worked, job security, skill

maintenance and work and home

interference and subjective oral health; and

to compare findings for different

occupational groups.

Methods:Data were collected in 1999 froma random stratified sample of households in

all Australian States and Territories using a

telephone interview and a questionnaire

survey. Subjective oral health was

evaluated with the short form Oral Health

Impact Profile (OHIP-14), which assesses

the adverse impact of oral conditions on

quality of life.

Results:Data were obtained for 2,347

dentate adults in the workforce. In the

12 months preceding the survey, 51.9%

had experienced oral pain and 31.0%reported psychological discomfort from

dental problems. Males, young adults,

Australian-born workers, and those in

upper-white collar occupations reported

lower mean OHIP-14 scores (ANOVA

p40 hours a week was associated

with higher OHIP-14 scores for other

workers. Conclusions:Aspects of the work

environment are associated with the

subjective oral health of workers. Because

these contexts are subject to only limited

control by individual workers, their influence

is a public health issue.

(Aust N Z J Public Health2004; 28: 259-66)

Submitted:December 2003

Revision requested:March 2004

Accepted:April 2004

Correspondence to:Professor A. John Spencer, Australian Research Centre for Population Oral Health,Dental School, University of Adelaide, South Australia 5005. Fax: (08) 8303 4858;e-mail: [email protected]

Article Eating, Drinking and Oral Health

The restructuring of the labour mar-

ket has altered several features of the

labour force in Australia. Changes

in working hours are one example. The Aus-

tralian Bureau of Statistics monthly labour

force surveys1show that not only has theaverage number of hours worked by full-time

workers increased over two decades, but also

the proportion working long hours has in-

creased. According to the Australian Coun-

cil of Trade Unions, Australia ranks second

behind Korea for average working hours and

has the highest proportion of its labour force

working more than 50 hours per week among

OECD countries.2Yet not all workers are

working longer hours, because the propor-

tion of the labour force working part timehas also increased.3Changes are also appar-

ent in perceived job security. Time series data

show that the proportion of Australian work-

ers who believed their job to be secure de-

clined in the early to mid 1990s.4,5In 1999,

a national poll of Australian workers found

that 74% believed their job to be safe, which

represented a decrease of seven percentage

points since the previous year.6Organisa-

tional downsizing and job creation schemes

have spurred a need for retraining programs

and professional development to maintain a

skilled workforce.

Coinciding with these changes, labour

force participation rates for females in-

creased from 46% in 1985 to 55% in 2001.7

Workers, especially those combining parent-

hood and paid work, require flexibility to

balance work and home demands. Currently,

Australia and the United States remain the

only two OECD countries not to offer a paid

parenting or maternity leave scheme, with

New Zealand introducing a scheme in 2002.

Because these changes were introduced

rapidly, they are likely to have an impact on

the health and well-being of workers. The

negative effect on employee health of organi-sational downsizing has been reported in

several longitudinal studies.8-10A recent US

study found that physical and mental symp-

toms associated with downsizing were not

confined to those directly targeted by struc-

tural changes but were, to a milder extent,

also reported by workers less immediately

affected.11To date, Australian research in this

area is limited and no studies have exam-

ined job characteristics and the subjective

oral health of workers. Unlike objectivelyassessed measures of dental disease, subjec-

tive measures of oral health convey infor-

mation about the impact of oral disease on

quality of life from the individuals perspec-

tive.

The first objective of the study was to ex-

amine the associations of hours worked, per-

ceived job security, perceived risk of skill

obsolescence, and the strain of work and

home interference on the subjective oral

health of workers in Australia. The second

objective was to compare findings for dif-

ferent occupational groups.

Methods

Data were from the 1999 National Dental

Telephone Interview Survey (NDTIS) and a

self-complete questionnaire sent to first per-

son adult interviewees immediately follow-

ing the interview. NDTIS is a periodic


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260 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2004 VOL. 28 NO. 3

Table 1: Socio-demographic characteristics of the

weighted sample.

n %

Occupational group

Upper white-collar 791 38.5

Lower white-collar 1,009 49.1

Blue-collar 256 12.5

Sex

Male 1,301 55.5

Female 1,045 44.5

Age group

18-24 years 392 16.7

25-34 years 571 24.3

35-44 years 700 29.8

45-54 years 477 20.3

55+ years 206 8.8

Country of birth

Australia 1,900 81.8

Other 421 18.2

Education

Tertiary 961 41.2

No tertiary 1,373 58.8Household income

$50,000 1,000 44.8

Sanders and Spencer Article

cross-sectional population survey that monitors the self-reported

oral health of Australian residents aged five years and over and

their use of dental services. In 1999, a random stratified sample

was drawn for all States and Territories from the electronic tele-

phone listings and one randomly selected household member was

interviewed. To maximise participation and response the meth-

ods recommended by Dillman were used.12These included an

information letter sent to all households in advance of telephone

contact and up to four personalised approaches for the question-

naire.

Subjective oral health was evaluated with the 14-item Oral

Health Impact Profile (OHIP-14).13This short form is useful when

space constraints and the risk of respondent burden do not permit

use of the full 49-item scale. This 49-item OHIP14was based on

the international classif ication of impairments, disabilities and

handicaps developed in 1980 by the World Health Organization

and adapted for oral health by Locker.15The OHIP explores seven

dimensions of impact arranged in ascending hierarchical order

from functional limitation, pain and discomfort, psychological

discomfort, through to physical, psychological and social disability

and finally handicap. In the short form OHIP-14 two questions

tap each of the seven dimensions. Participants are asked to report

the frequency with which they experienced impacts over the

12 months preceding the survey. Responses are coded on a five-

point scale of 0=never, 1=hardly ever, 2=occasionally, 3=fairly

often and 4=very often. We used two summary statistics from

this scale: the percentage of persons reporting an impact occa-

sionally or more often, and the mean scale score with higher scores

reflecting more adverse impact.The questionnaire asked about occupation, working hours, per-

ceptions of job security and skill obsolescence, and work-home

interference. Occupational title and main task descriptors were

coded according to the Australian Standard Classification of

Occupations16and then collapsed into three groups: upper white-

collar (manager, administrator, professional), lower white-collar

(paraprofessional, tradesperson, clerk, salesperson, personal serv-

ice work) and blue-collar (plant or machine operator, driver,

labourer or related). Response options for hours worked were up

to 30, 30-40 and >40, representing part time, standard working

week and overtime hours worked. Perceived job security was as-sessed with the question Do you expect that your job will be

secure for the next five years? Response options were yes, prob-

ably, unlikely, and no. The same response options were used

for the question Do you expect that your present job skills will

be obsolete within 10 years? Scoring on this item was reversed

so that an affirmative response reflected a high expectation of

skill maintenance. Finally, work-home interference was evaluated

using an eight-item scale tested by Gutek and colleagues.17Four

items assessed the degree to which work interfered with home

life and the remaining items assessed the level of home-to-work

interference. Responses were recorded on a five-point scale coded

from 0 to 4 with higher scores indicating greater interference. In

an exploratory factor analysis of the items, a two-factor solution

emerged that conceptually supported the scales bi-directional

structure and which was empirically appropriate. Both factors had

eigenvalues greater than one that together accounted for 60.0%

of the total variance. The first factor was labelled work interferes

with home (=0.80) and the second home interferes with work

(=0.72). Continuous scores on the overall scale and two subscales

were categorised into five groups labelled low, low-moderate,

moderate, moderate-high, and high interference, with higher scores

reflecting a higher level of conflict. It was not possible to con-

struct equal-sized quintiles because of the clustering of scores.

Consequently, the five groups approximated quintile ranges as

closely as data permitted.

Data were weighted to account for differing sampling prob-

abilities due to the sampling design to be representative of the

Australian population in its age and sex composition for each

sampling stratum.

Bivariate associations between the explanatory variables and

OHIP-14 scores were examined using one-way ANOVA with the

level of statistical significance set to 5%. All explanatory vari-

ables were retained and were entered into a multiple regression

model to estimate the association between job characteristics and

the social impact of oral conditions. In multivariate analysis, the

ordinal variables of hours worked, job security and skill mainte-

nance were transformed to dummy variables. Blocks of explana-

tory variables were entered in two steps so that the relative

contribution of the job characteristics entered at step two could

be distinguished from the effect of socio-demographic variables

entered in step one. A separate model was constructed for each of

the three occupational groups.

Both unstandardised and standardised beta coefficients are


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Eating, Drinking and Oral Health Job characteristics and subjective oral health

reported. The beta value (B) is a measure of how strongly each

independent variable is associated with mean OHIP-14 scores. It

indicates the change in mean OHIP-14 score that is due to a change

of one unit of each independent variable. To compare the relative

contribution of each independent variable across different mod-

els that is, between different occupational groups these

unstandardised beta coeff icients (B) are reported. However, within

a single model (occupational group), comparisons of beta values

are difficult to interpret as the units in which these variables are

measured differ. To facilitate interpretation of the relative contri-

bution of each independent variable, the standardised beta coef-

ficients (beta) are also reported. These values standardise the

different units to standard deviations that vary from 1 to +1.

Results

Participation in the NDTIS was 56.6% (n=7,829). Of the 6,152

adults who were sent the self-complete questionnaire, 3,973

responded (64.6%). Analysis was limited to dentate adults who

were aged 18 to 65 years and in paid work (n=2,347). Males domi-

nated blue-collar (82.0%) occupations and to a lesser extent up-

per white-collar occupations (57.9%), but contributed less than

half of the lower white-collar occupations (48.6%). Other sample

characteristics are presented in Table 1. Workers in upper white-

collar (UWC) occupations comprised 38.5%, lower white-collar

occupations (LWC) comprised nearly half (49.1%) and blue-col-

lar (BC) workers comprised 12.5%. A sizeable minority (41.2%)

had tertiary education, and 44.8% had household income of more

than $50,000.

One-quarter (25.3%) worked 40 hours is higher than the na-

tional estimate of 37%.19Overtime was twice as common among

UWC workers (61.1%) than among LWC workers (30.5%).

Table 2: Distribution in hours worked, job security, skill maintenance and work-home interference for occupational

groups.

Upper Lower Blue collarc Total

white collara white collarb

n % n % n % n %

Hours worked

Up to 30 hours 119 15.1 344 34.4 54 21.1 517 25.3

31-40 hours 187 23.8 352 35.2 110 43.0 649 31.8

More than 40 hours 481 61.1 305 30.5 92 35.9 878 43.0

Total 787 100.0 1,001 100.0 256 100.0 2,044 100.0

Job securityYes 401 50.8 410 40.8 92 36.4 903 44.1

Probably 281 35.6 424 42.2 102 40.3 807 39.4

Unlikely 67 8.5 114 11.3 30 11.9 211 10.3

No 41 5.2 57 5.7 29 11.5 127 6.2

Total 790 100.0 1,005 100.0 253 100.0 2,048 100.0

Skill maintenance

Yes 369 46.6 391 39.1 97 38.0 857 41.9

Probably 309 39.1 403 40.3 103 40.4 815 39.8

Unlikely 75 9.5 155 15.5 44 17.3 274 13.4

No 38 4.8 52 5.2 11 4.3 101 4.9

Total 791 100.0 1,001 100.0 255 100.0 2,047 100.0

Work interferes with home

Low interference 154 19.7 353 36.2 73 28.8 592 28.1

Low-moderate 172 22.0 235 24.1 58 22.9 490 23.2

Moderate 154 19.7 170 17.4 38 15.0 379 18.0

Moderate-high 138 17.7 127 13.1 30 11.8 308 14.6

High interference 163 20.9 89 9.1 54 21.4 340 16.1

Total 782 100.0 976 100.0 252 100.0 2,109 100.0

Home interferes with work

Low interference 223 28.5 286 29.3 53 20.8 578 27.4

Low-moderate 110 14.1 119 12.2 42 16.5 283 13.4

Moderate 246 31.5 318 32.6 71 27.9 669 31.7

Moderate-high 64 8.2 72 7.4 49 19.2 190 9.0

High interference 139 17.7 181 18.5 39 15.5 389 18.5

Total 782 100.0 976 100.0 252 100.0 2,109 100.0Notes:(a) Manager/administrator; professional.(b) Paraprofessional; tradesperson; clerk; sales or personal service worker.(c) Plant or machine operator or driver; labourer or related worker.


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Table 3: Mean (se) OHIP-14 scores for socio-

demographic characteristics.

Mean (se) OHIP-14

Occupational groupc

Upper white collar 0.46 (0.02)

Lower white collar 0.58 (0.02)

Blue collar 0.56 (0.04)

Sexa

Male 0.50 (0.01)

Female 0.56 (0.02)

Age groupb

18-24 years 0.45 (0.02)

25-34 years 0.52 (0.02)

35-44 years 0.57 (0.02)

45-54 years 0.53 (0.02)

55+ years 0.57 (0.04)

Country of birthc

Australia 0.50 (0.01)

Other 0.61 (0.03)

Educationa

Tertiary 0.50 (0.02)

No tertiary 0.55 (0.01)

Household incomeb

$50,000 0.58 (0.02)

Notes:(a) p


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for BC workers, differences failed to reach statistical significance.

For LWC workers, the increasing risk to security was associated

with a stepwise increase in mean OHIP-14 scores. Differences in

skill maintenance expectations were significantly associated with

OHIP-14 scores for each occupational group. For all workers,

those who were certain that their job skills would be maintained

reported lowest mean OHIP-14 scores. For white-collar workers

a monotonic gradient was observed characterised by decreasing

mean OHIP-14 scores with increasing certainty of skill mainte-

nance.

For both upper and lower white-collar workers, highest levels

of work interference and home interference were associated with

highest mean OHIP scores. Although those BC workers who ex-

perienced least interference also reported least social impact, there

was not a clear relationship between level of interference and the

impact of dental problems.

The correlations between the independent variables were ex-

amined for collinearity as high correlation would reduce the pre-

cision of estimates in the multivariate regression analysis.

Although significant, associations were weak, with Spearmans

rank correlation coefficients ranging from 0.05 for the associa-

tion between hours worked and job security, to 0.31 for the asso-

ciation between work-to-home interference and job security.

In multivariate regression analysis, the potential confounding

effect of socio-demographic factors was taken into account by

entering sex, age in years, country of birth (Australia or other),

education (tertiary or not tertiary) and household income

(>$A50,000 or $A50,000) in the models before the explana-

tory variables. The results are presented in Table 6. For UWC

workers, female sex and age were positively associated with mean

OHIP-14 scores but country of birth and socio-economic indica-

tors were not. Compared with those working standard hours, part-

time workers had greater impact scores. Workers with uncertain

job security (but not those whose job was definitely not secure)

reported greater social impact than workers in secure jobs.

Workers in jobs where skill maintenance was unlikely reported

significantly greater social impact than workers whose skill main-

tenance was assured. Both work interference with home and home

Table 5: Mean (se) social impact scores according to work-related characteristics for occupational groups.

Upper Lower Blue collar

white collar white collar

Mean (se) Mean (se) Mean (se)

Hours worked b a d

Up to 30 hours 0.56 (0.04) 0.60 (0.03) 0.59 (0.10)

31-40 hours 0.41 (0.03) 0.58 (0.03) 0.40 (0.05)

More than 40 hours 0.45 (0.02) 0.56 (0.03) 0.74 (0.06)

Total 0.46 (0.02) 0.58 (0.02) 0.56 (0.04)

Job security d d a

Yes 0.35 (0.02) 0.50 (0.02) 0.47 (0.05)

Probably 0.59 (0.04) 0.61 (0.03) 0.65 (0.06)

Unlikely 0.58 (0.06) 0.63 (0.04) 0.43 (0.11)

No 0.45 (0.10) 0.77 (0.09) 0.53 (0.10)

Total 0.46 (0.02) 0.58 (0.02) 0.54 (0.04)

Skill maintenance c d b

Yes 0.41 (0.02) 0.51 (0.02) 0.44 (0.05)

Probably 0.48 (0.03) 0.58 (0.03) 0.71 (0.07)

Unlikely 0.56 (0.07) 0.69 (0.05) 0.50 (0.07)

No 0.65 (0.10) 0.82 (0.11) 0.58 (0.15)

Total 0.46 (0.02) 0.58 (0.02) 0.56 (0.04)Work interferes with home d d c

Low interference 0.32 (0.03) 0.58 (0.03) 0.41 (0.07)

Low-moderate 0.45 (0.04) 0.49 (0.03) 0.52 (0.07)

Moderate 0.35 (0.03) 0.51 (0.03) 0.80 (0.10)

Moderate-high 0.55 (0.04) 0.67 (0.05) 0.48 (0.08)

High interference 0.62 (0.04) 0.92 (0.08) 0.72 (0.09)

Total 0.46 (0.02) 0.59 (0.02) 0.57 (0.04)

Home interferes with work d d d

Low 0.30 (0.02) 0.44 (0.03) 0.32 (0.06)

Low-moderate 0.44 (0.04) 0.64 (0.04) 0.98 (0.12)

Moderate 0.53 (0.03) 0.55 (0.03) 0.40 (0.06)

Moderate-high 0.38 (0.05) 0.51 (0.05) 0.61 (0.06)

High 0.63 (0.05) 0.87 (0.05) 0.72 (0.11)

Total 0.46 (0.02) 0.59 (0.02) 0.57 (0.04)

Notes:(a) p>0.05; (b) p


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Table 6: Multiple regression unstandardised coefficients (se) and standardised coefficients for the social impact of oral

problems for occupational groups.

Upper white collar Lower white collar Blue collar

Ba SE Betab Sig Ba SE Betab Sig Ba SE Betab Sig

1 (Constant) -0.31 (0.09) f 0.29 (0.08) f -0.11 (0.17) c

Sex

Male (ref)

Female 0.09 (0.04) 0.09 d -0.09 (0.04) -0.08 d 0.07 (0.10) 0.05 c

Age in years 0.01 (0.00) 0.14 f 0.00 (0.00) -0.01 c 0.00 (0.00) 0.02 c

Country of birth

Australia (ref)

Overseas 0.08 (0.04) 0.06c

0.05 (0.05) 0.04c

0.26 (0.10) 0.18e

Education

Tertiary (ref)

No tertiary 0.06 (0.04) 0.06 c 0.00 (0.04) 0.00 c -0.04 (0.11) -0.02 c

Household income

>$50,000 (ref)

0.05; (d) p


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explained in the model for BC workers (21.5%) than for UWC

(14.3%) or LWC workers (10.9%). An examination of the change

statistics revealed that in each model, the job characteristics en-

tered in the second step accounted for a substantially greater pro-

portion of the explained variance than did the socio-demographic

factors entered at step one. For UWC and BC workers, the job

characteristics explained about three times more variance than

did the socio-demographic factors.

Discussion

The main finding of this study was a strong association between

job characteristics and the subjective oral health of workers. Com-

parative studies of workers in the oral health literature are very

limited. Marcenes and Sheiham found that work-related mental

demand was related to periodontal disease in male workers aged

35 to 44 years,20and in other research the flexibility of working

hours was associated with dental self-care behaviour in workers

aged 24-44 years.21The general health literature has reported

widely the relationship between work-related psychosocial fac-

tors such as decision latitude, job demands and social support

and workers health. Yet fewer studies have examined the associa-

tions between health and ways that work is structured in terms of

hours worked, job security, continuing education and flexibility

to manage competing work and home commitments. Moreover,

many studies have been limited to white-collar workers, omitting

those in manual occupations.

The OHIP-14 questionnaire and the original 49-item OHIP have

been widely used to evaluate subjective oral health in more than25 studies, including randomised clinical trials and nationally

representative population surveys.22The importance of this studys

findings from a population perspective is the extent to which oral

health problems are experienced. In all, 61.5% of workers reported

impacts occasionally or more often. More than half (51.9%) re-

called that dental problems had caused oral pain and almost one-

third (31.0%) reported feeling self-conscious or tense as a

consequence of dental problems.

Our findings are limited by the cross-sectional design of the

study. It is not possible, for instance, to infer that changing char-

acteristics of the labour force have affected the health of workers.However, f indings from the Whitehall II prospective cohort study

of British civil servants support this argument. Like Australia,

Britain underwent economic reform to improve productivity and

international competitiveness. Whitehall II showed that the threat

of privatisation of the civil service had a greater adverse effect on

the subjective health of employees than the actual change in em-

ployment status that followed.23This finding also supports our

observation among UWC workers that a perceived threat to job

security was associated with greater impact than the knowledge

that the job was not secure.

Because both the OHIP-14 and job characteristics were self-

reported, a second limitation is self-reporting bias. In reviewing

the literature on organisational stress, Zapf and colleagues24de-

scribed this as bias whereby underlying factors such as negative

affect can lead to a tendency to report in one direction potentially

altering the association between perceived stress and subjective

health status. We argue that this is a threat when measuring sub-

jective oral health with the global self-rated health item. Responses

to this global item reflect multiple dimensions of oral health that

are not specified by the researcher, and which are consequently

prone to personality traits of the respondent. Because OHIP items

address specific impacts such as the sense of taste, pain, inter-

ruption to meals, and social irritability, their clearly defined

boundaries minimise the potential impact of subjective interpre-

tation resulting in bias.

In Australia, there is a strong occupational dimension to work-

ing overtime. Overall, managers are most likely to work the long-

est hours, while professionals have the greatest proportion of

workers who routinely work overtime.25We found that although

a greater proportion of UWC workers worked overtime, this fac-

tor was not associated with elevated mean OHIP-14 scores among

these workers. Yet for BC workers, and to a lesser extent for LWC

workers, working overtime was a key risk factor. It is likely that

the long hours worked by managers and professionals are chosen

rather than obligated by financial need or employer demand.

Clearly, substantial variation exists in levels and types of stressors

experienced by different occupational groups in the Australian

labour force. Implicit in this finding is the implication that differ-

ent interventions are required for different groups to optimise the

health of workers.

Two job characteristics were associated with the social impact

of oral conditions for all three occupational groups. One was the

perception that the maintenance of job skills was unlikely and theother was the interference of work demands on home obligations.

For white-collar workers the interference of home obligations on

work demands was also associated with greater impact of oral

conditions on daily living.

Conclusion

Job characteristics in the Australian labour force are associated

with subjective oral health. This is one of an increasing number

of health outcomes that have been linked to conditions in the

workplace. Our study underscores the importance of recognising

that people are kept healthy or become ill in the environments in

which they live and work. Because job characteristics that shape

the work environment are subject to only limited control by the

individual, their influence is a public health issue.

Acknowledgement

The research on which this paper is based was supported by the

Australian Dental Research Foundation.

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