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01.12.2014 | Research article | Ausgabe 1/2014 Open Access

BMC Health Services Research 1/2014

Age, period and cohort analysis of patient dental visits in Australia

Zeitschrift:
BMC Health Services Research > Ausgabe 1/2014
Autoren:
Xiangqun Ju, David S Brennan, A John Spencer
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1472-6963-14-13) contains supplementary material, which is available to authorized users.

Competing interests

The authors have no competing interests to declare.

Authors’ contributions

XJ performed analyses and drafted the manuscript. DSB was involved in interpretation of data and contributing of drafting of the manuscript. AJS was involved in developing the project and revising the manuscript. The authors have read and approved the manuscript.
Abbreviations
HPY
Hours per dentist per year
PPH
Patient visits per dentist per hour
PPY
Patient visits per dentist per year.

Background

In Australia the majority of practising dentists work in the private sector (83%), with dental services being provided mainly by general dental practitioners (85%) [1]. Dental services are generally provided on a fee-for-service basis, paid either directly by the individual or indirectly through private insurance.
The capacity of practising dentists to supply dental services has not only been linked with population demographics and oral health status, but also associated with labour force structure and service-mix provided. To measure practice activity provided by dentists, the time measure of the hours per dentist per year (HPY) and the patient measure of the number of patient visits per dentist per hour (PPH) were used to produce the measure of patient visits per dentist per year (PPY = HPY × PPH) as in previous research [1]. PPY was adopted as a key marker of dentist’s capacity to provide services and has shown a decreasing trend over time in Australia [24].
Age, period and cohort effects are important considerations when explaining trends in patient visits supplied by practising dentists [5]. Age effects are associated with the passage of time, so change in the number of patient visits by practising dentists related to age over time may help to explain the capacity to provide dental service, for instance, if dentists became less productive as they aged. Period effects can affect all ages simultaneously over time. For example, they can mark the occurrence of a particular historical event, such as the availability of modern high-speed electric dental handpieces. Cohort effects involve changes across groups with the same birth year who experience the same event during the same period. There is a linear dependency between age, period and cohort, because age, period and cohort membership is predicted by any two of the three effects. Therefore, it is difficult to estimate the three separate effects. A nested models approach can be used to estimate and assess the fit of different models [6, 7], such as age-period and age-cohort models.
Understanding trends over time in the supply of patient visits and the relationship with labour force age structure and possible cohort effects is important in dental labour force planning, informing current policies and projections of future capacity to supply services. The aim of this study was to identify trends in patients visits supplied by practising dentists in Australia, and estimate age, period and cohort effects in PPY over an observation period spanning 1983 to 2010.

Method

Data collection

The data were from the Longitudinal Study of Dentists’ Practice Activity, which is designed to provide estimates of dentist practices and service provision of Australian private general practising dentists over time. The study was approved by the ethics committee of the Australian Institute of Health and Welfare (AIHW).
Details of the methods have been previously described [1, 810]. Briefly, a random sample of 10% of male and 40% of female dentists was selected from the dental registers for each State or Territory in Australia in 1983–84, 1988–89, 1993–94, 1998–99, 2003–04 and 2009–10 as waves of a longitudinal study. All sampled dentists from previous waves of the study were included again at each successive wave. Sample supplementation of newly registered male and female dentists at each successive wave was also used to add to the sample to ensure representative cross-sectional estimates.
These dentists were surveyed by mailed questionnaire. The practising dentists provided estimates of the number of patients treated per day, and the number of hours per day, days per week and weeks per year spent working. From this information, practice activity measures were calculated as follows:
Hours per dentist per year HPY = hours per day × days per week × weeks per year Patient visits per dentist per hour PPH = patients per day / hours per day
Patient visits per dentist per year PPY = HPY × PPH

Weighting

The data were weighted prior to analysis. The data were weighted using numbers of private general practising dentists at December 1983 and 1988, with age and sex distributions of dentists from the 1981 and 1986 population censuses of Australia, and dental board registration statistics from 1992, 1994, 2000 and 2009 [1, 3, 810]. The weights adjusted the sample to the age-specific population distribution of male and female dentists in the dentist population.

Analysis

All sampled dentists were included and the analysis treated the sample as a synthetic cohort as this maintained representative cross-sectional estimates at each point in time rather than restricting analyses only to longitudinal cases. A standard cohort table was produced to provide an initial description of the effects of age, period and cohort by creating 5-year age groups and corresponding 5-year birth cohorts. Age group formed the rows of the table and time period formed the columns, which provided synthetic 5-year birth cohorts in each diagonal running downwards from left to right across the table [5].
General linear regression was applied to estimate mean number of PPY by age, period and cohort factors using SAS statistical software (SAS 9.2). A set of nested models were examined for goodness-of-fit, and F-tests were applied to determine which models were preferred [11]. The details are described below.
The age model was used as a starting point in analysis and followed by age-drift, age-period, age-cohort and age-period-cohort models [12]. The age model was made up of 14 age groups coded as indicator (dummy) variables. The age-drift model consisted of the 14 variables for age (coded from 1 to 14), plus the six time periods (coded from 1 to 6) entered as a continuous variable to model regular trends not ascribed to either period or cohort influence. This variation is referred to as "drift" [6, 7, 12]. The age-period model consisted of the 14 indicator variables for age, with period entered as indicator variables. The age-cohort model consisted of the 14 indicator variables for age, combined with 17 indicator variables for dentist birth cohort. The age-period-cohort model consisted of the indicator variables for age, period and cohort.
Goodness-of-fit tests were applied for each model and F-tests were used to assess the models. The models with a good fit to data were pursued further to provide a parsimonious explanation of the data.

Results

The response rates were 73%, 75%, 74%, 71%, 76% and 67% in 1883–84, 1988–89, 1993–94, 1998–99, 2003–4 and 2009–10, respectively.
Table 1 is a standard cohort table which presents age distributions of private general practice dentists by time of data collection. Five-year age groups were coded into 14 categories, combined with five-yearly periods (six years interval for 2003 to 2009), which provided synthetic 5-year birth cohorts of practising dentists in each diagonal running downwards from left to right across the table.
Table 1
Number of dentists by age group and time of data collection (unweighted data)
Age group (years)
1983
1988
1993
1998
2003
2009
All
N
(%)
N
(%)
N
(%)
N
(%)
N
(%)
N
(%)
N
(%)
20-24
6
1.7
24
5.2
18
4.2
15
3.3
17
3.3
16
2.6
96
3.4
25-29
62
17.8
77
16.7
62
14.6
69
15.4
60
11.8
98
15.6
428
15.2
30-34
64
18.3
94
20.4
69
16.2
62
13.8
78
15.3
107
17.1
474
16.8
35-39
49
14.0
73
15.8
86
20.2
84
18.7
62
12.2
87
13.9
441
15.6
40-44
43
12.3
49
10.6
55
12.9
80
17.8
98
19.2
64
10.2
389
13.8
45-49
29
8.3
46
10.0
48
11.3
54
12.0
84
16.5
78
12.4
339
12.0
50-54
27
7.7
27
5.9
31
7.3
37
8.2
54
10.6
82
13.1
258
9.1
55-59
40
11.5
26
5.6
19
4.5
24
5.4
29
5.7
45
7.2
183
6.5
60-64
16
4.6
27
5.9
18
4.2
9
2.0
14
2.8
34
5.4
118
4.2
65-69
7
2.0
11
2.4
13
3.1
4
0.9
8
1.6
12
1.9
55
1.9
70-74
4
1.2
5
1.1
3
0.7
10
2.2
2
0.4
3
0.5
27
1.0
75-79
1
0.3
0
0.0
3
0.7
0
0.0
4
0.8
0
0.0
8
0.3
80-84
1
0.3
1
0.2
0
0.0
1
0.2
0
0.0
1
0.2
4
0.1
85-89
0
0.0
1
0.2
0
0.0
0
0.0
0
0.0
0
0.0
1
0.0
Total
349
100.
461
100
425
100
449
100
510
100
627
100.
2821
100
Higher numbers of practicing dentists were observed in the 25–29 to 60–64 year age groups. Cell sizes of less than 10 occurred in the youngest (20–24 years) or 65 years and older groups at some points in time. Dental practice activity for these smaller groups should be interpreted with caution.
Mean number and standard error of PPY by age and year of study are showed in Table 2 and presented graphically in Figures 1 and 2. Figure 1 shows mean number of PPY by age and year of study. In general, mean number of PPY decreased across most age groups over the time of study. PPY was lower in younger age groups (less than 25 years) and older age groups (65 years or older groups), and tended to be higher in middle age groups (30–34 to 60–64 year age groups). Figure 2 shows mean number of PPY by dentist age, year of study and birth cohort. Each line represents a dentist 5-year birth cohort over the times of data collection. Most cohorts are represented by six observation times.
Table 2
Mean number and standard error of PPY by age group and time of data collection (weighted)
Age group (years)
1983
1988
1993
1998
2003
2009
All
Mean
SE
Mean
SE
Mean
SE
Mean
SE
Mean
SE
Mean
SE
Mean
SE
20-24
2103
356
2365
163
2789
236
1550
199
2301
262
2033
186
2231
96
25-29
3005
151
2743
123
2853
146
2375
111
2505
138
2172
89
2617
53
30-34
3902
200
3048
137
2638
155
2662
166
2472
128
2211
113
2772
64
35-39
3807
200
3565
177
2790
132
2435
109
2402
152
2440
128
2845
64
40-44
3506
238
3326
190
3496
162
2772
144
2652
118
2630
228
2979
73
45-49
3425
314
3199
212
2899
173
3033
157
2788
123
2410
128
2859
69
50-54
3267
208
3531
332
3036
269
2884
225
2746
154
2529
135
2889
84
55-59
3533
303
3218
177
2870
241
2660
305
2904
183
2682
233
3005
106
60-64
3529
409
2952
335
2354
171
2805
350
2233
348
2404
156
2603
115
65-69
2424
667
1835
257
1818
292
1499
343
1249
273
2779
492
2000
185
70-74
2030
466
2833
945
888
168
1961
377
1115
425
1821
1043
1891
256
75-79
1000
   
1871
818
  
2518
1028
648
 
2200
602
80-84
  
490
   
320
     
476
103
85-89
  
1000
         
1000
.
Total
3405
82
3097
65
2816
61
2589
58
2550
52
2418
51
2762
25
The fit of a set of age, age-drift, age-period, age-cohort, and age-period-cohort models were assessed for the mean number of PPY. R-squared and P-values from these models are showed in Table 3. A P-value of 0.05 is taken as the significance level, and higher R-squared indicated a better model fit. R-squared increased with more complex models, such as the age drift model’s R-squared was increased over 50% compared to the age model. However, R-squared showed little change from age-drift, age-period or age-cohort models to the age-period-cohort model.
Table 3
R-squared and P-values from goodness-of-fit tests from general linear regression analyses
Models
Number
Degrees of freedom
R squared
F value
P > F
Age
2776
13
0.039
8.6
<0.0001
Age-drift
2776
14
0.099
21.6
<0.0001
Age-period
2776
18
0.102
17.4
<0.0001
Age-cohort
2776
29
0.111
11.8
<0.0001
Age-period-cohort
2776
33
0.115
10.8
<0.0001
F-tests were applied to test age, period and cohort effects in the nested models, which compared the age versus age-drift, then age-drift versus age-period and age-cohort, then age-period and age-cohort versus age-period-cohort models. F and P values from F- tests are presented in Table 4.
Table 4
F and P values from F-test and general linear regression analyses
 
Degrees of freedom
F value
P-value
Models
k
m
n-k-m-1
  
Age & age drift
13
13
2761
9.66
<0.005
Age drift & age-period
14
14
2756
8.88
<0.005
Age drift & age-cohort
14
14
2744
13.96
<0.005
Age-period-Cohort & age-Cohort
29
29
2741
5.64
<0.005
Age-period-Cohort & age-Period
18
18
2740
11.52
<0.005
Notes: n was number of observations (see Table 3); k was degrees of freedom in the general linear regression model; m was additional variables, such as drift, age, period and cohort or their combination.
While the age-period-cohort model provided the best fit to the data, the age, period and cohort effects are not completely independent. The age-period and age-cohort models were therefore examined in order to interpret the effects of age, period and cohort on PPY.
Table 5 presents the parameter estimates and standard error of mean number of PPY from general linear regression for the age-period and age-cohort models. A parameter estimate of 0 indicated which group was the reference group. A parameter estimate less than 0 indicated a lower average number of PPY and greater than 0 indicated higher average number of PPY, compared with the reference group. The reference categories used were the 30–34 year age group, the data collection from 2009–10 and the dentist birth cohort that was aged 25–29 years in 1983.
Table 5
Age-period and age-cohort models of PPY
 
Age-period model
Age-cohort model
 
Parameter estimate
SE
P
Parameter estimate
SE
P
Age group
      
20-24
-575.72
167.87
0.0006
-227.75
183.01
0.21
25-29
-215.46
91.67
0.0188
21.21
97.33
0.83
30-34
0.00
  
0.00
  
35-39
81.60
91.10
0.3705
-148.17
94.88
0.12
40-44
259.74
92.16
0.0049
-163.53
99.14
0.10
45-49
168.54
94.43
0.0744
-447.04
105.24
<.0001
50-54
200.62
96.90
0.0385
-623.58
112.21
<.0001
55-59
197.47
104.71
0.0594
-811.74
131.32
<.0001
60-64
-85.57
114.86
0.4563
-1244.09
144.70
<.0001
65-69
-685.31
152.71
<.0001
-2006.21
185.03
<.0001
70-74
-805.75
199.77
<.0001
-2291.59
245.17
<.0001
75-79
-465.99
341.60
0.1726
-2067.34
392.57
<.0001
80-84
-2153.59
546.17
<.0001
-3731.77
620.80
<.0001
85-89
-2104.31
1170.04
0.0722
-2054.34
1167.05
0.08
Period
      
1983
993.34
85.61
<.0001
   
1988
706.21
79.16
<.0001
   
1993
409.65
80.80
<.0001
   
1998
188.70
79.83
0.0182
   
2003
107.99
76.62
0.1588
   
2009
0.00
     
Cohort
      
80-84 (a)
-
 
-
648.55
958.64
0.50
75-79 (a)
-
 
-
1267.25
686.92
0.07
70-74 (a)
-
 
-
1393.63
356.46
<.0001
65-69 (a)
-
 
-
1223.43
274.77
<.0001
60-64 (a)
-
 
-
1175.36
161.98
<.0001
50-59 (a)
-
 
-
772.51
153.49
<.0001
45-49 (a)
-
 
-
833.53
137.42
<.0001
40-44 (a)
-
 
-
690.22
117.07
<.0001
35-39 (a)
-
 
-
551.33
102.35
<.0001
30-34 (a)
-
 
-
545.69
92.84
<.0001
25-29 (a)
-
 
-
0.00
  
20-24 (a)
-
 
-
-330.88
89.43
0.00
20-24 (b)
-
 
-
-352.63
102.26
0.00
20-24 (c)
-
 
-
-539.13
114.43
<.0001
20-24 (d)
-
 
-
-778.79
131.20
<.0001
20-24 (e)
-
 
-
-847.71
162.35
<.0001
20-24 (f)
   
-793.63
455.75
0.08
Note: a = period 1 (1983), b = period 2 (1988), c = period 3 (1993), d = period 4 (1998), e = period 5 (2003), f = period 6 (2009).
In the age-period model, the age effect showed that compared to the reference group of 30–34 years there were negative parameter estimates indicating lower PPY in younger (20–24 and 25–29 years) and older age groups (65–69, 70–74 and 80–84 years). Age groups 40–44 and 50–54 years had positive parameter estimates indicating higher PPY. The period effect showed that compared to the reference group of 2009, the periods 1983 to 1998 had positive parameter estimates indicating higher PPY.
In the age-cohort model, the age effect showed that the age groups 45–49 to 80–84 years all had negative parameter estimates indicating lower PPY than the reference group of 30–34 years. The cohort effect showed that compared to the reference group of 25–29 years in 1983, older cohorts aged 30–34 to 70–74 years in 1983 had positive parameter estimates indicating higher PPY, while younger cohorts aged 20–24 in 1983 to 20–24 years in 2003 had negative parameter estimates indicating lower PPY.
In summary, the cross-sectional age curve from the age-period model shows that the younger age dentists (20–29 years) and older dentists (65–74 and 80–84 years) have lower PPY than middle-aged dentists. The longitudinal age curve from this age-cohort model shows intra-cohort ageing effects of declining PPY over time within cohorts aged 45–84 years. Cohort parameters from the age-cohort model generally show higher PPY among earlier cohorts, and lower PPY among more recent cohorts.

Discussion

The present study investigated time trends and estimated age, period and cohort effects in patient visits supplied per dentist per year. The findings of the study have shown that the mean number of PPY decreased across most age groups of dentists over time, and the younger age dentists (20–29 years) and older dentists (65–74 and 80–84 years) have lower PPY than middle-aged dentists. There were intra-cohort ageing effects of declining PPY over time within cohorts aged 45–84 years, and higher PPY among earlier cohorts, and PPY was lower among more recent cohorts.
This longitudinal study was from a national survey and samples were selected randomly from a comprehensive sampling frame and achieved about 70 per cent response rates at each wave. Because the majority of dentists in Australia were from general practice and were from the private sector [3, 6, 13], the data was weighted to reflect the age and sex distribution of private general practitioners in Australia. Therefore, the results can be generalized to represent the main Australian dentist context of private general practice.
These findings point to a fundamentally different pattern of work for younger cohorts of dentists than older dentist cohorts. The findings show that younger cohorts are providing fewer patients visits each working year, and this work pattern appears to be relatively stable over time as they move into middle age. Previous reports have shown that the trend towards fewer patient visits was related to increased provision of services per visit and a shift in the types of services provided [1].
There are many factors that may influence and impact on PPY. The increasing proportion of female dentists (from 10% in 1980 to 33% in 2009) can have a substantial influence on total aggregate capacity to provide dental services. Similarly, the proportion of female dentists increased in some other western developed countries, such as in Canada (from 17% in 1991 to 37% in 2008) [14] and America (from 3% in 1980 to 19% in 2000) [15]. This is because female dentists were undertaking more part time work [3], and taking more career breaks than male dentists [1618]. The average worked hours per week decreased (from 39 hours in 2000 to 37 hours in 2009). The percentage working in solo private practice decreased (from 44% in 2000 to 29% in 2009) to reflect a more flexible working pattern, such as solo with assistant, partnership and associateship arrangements [3].
Retaining more natural teeth in middle and older aged adults may consequently result in an increased burden of dental disease in older mouths [19] that may lead to demand for dental services. More complex dental treatment needs may lead to increased length of dental appointments, resulting in lower PPY. For instance, more endodontic and crown and bridge services have been associated with trends towards greater retention of teeth among adults [1, 19], as well as age-related oral diagnoses and insurance status [20, 21].
Age-period-cohort models provide a formal framework to guide the analysis through an explicit consideration of all effects with assessing goodness-of-fit of models. Using the modelling approach to analyse age, period and cohort effects provides information to understand the time trends and inter-related time-dependent variables of age, period and cohort effects.
These findings are important to labour force planning in relation to the capacity to supply dental services. Australia’s National Oral Health Plan included consideration of a sufficient, sustainable and appropriately skilled labour force to meet identified oral health needs across the Australian population [22] while the National Advisory Council on Dental Health conclude that advancement of foundational activities (such as those relating to the dental labour force) was integral to dental services delivery [23]. A review of Australian government health labour force programs noted the importance of data to inform dental policy debate [24]. Previous projections of the dental labour force in Australia have noted the importance of supply of dental visits to capacity to supply services [25]. Health Workforce Australia is investigating the number and mix in the oral health workforce to meet the changing demographics and policy requirements to 2025 [26].
The synthetic cohort approach used in this study was representative in terms of cross-sectional estimates, rather than being based on longitudinal changes. PPY was a key marker of practice activity. However, the component variables of HPY and PPH were not explicitly modelled. For instance, increased numbers of services per visit over time could decrease PPH [1], and an increase in the number of dentists per practice, dental assistants per practice, or the size of private practice (single handed or group) could reduce HPY. The age-period-cohort approach suffers from a confounding of age, period and cohort effects. This confounding makes the separation of age, period and cohort effects difficult unless all comparisons are pronounced and consistent [5]. Despite these limitations, this study of age, period and cohort effects in relation to patient dental visits in Australia is significant to future planning of the dental labour force in Australia.

Conclusion

The capacity of dentists to supply services might be influenced by age, period and cohort effects. Understanding dentists’ capacity to supply dental services over time is a key element in the process of planning for the future. The study found a period effect of declining PPY over the observation period. More recent cohorts of dentists provided lower numbers of PPY than earlier cohorts at similar ages, but the provision of PPY among these younger cohorts appeared to be stable as they moved into middle age.

Acknowledgments

The Longitudinal Study of Dentists’ Practice Activity has been supported by the Australian Government Department of Health and Ageing, the National Health and Medical Research Council (NHMRC), and the Australian Institute of Health and Welfare (AIHW). This paper was written with support from a Career Development Award (627037) and CRE (1031310) from the NHMRC. The contents are solely the responsibility of the administering institution and authors, and do not reflect the views of NHMRC.

Competing interests

The authors have no competing interests to declare.

Authors’ contributions

XJ performed analyses and drafted the manuscript. DSB was involved in interpretation of data and contributing of drafting of the manuscript. AJS was involved in developing the project and revising the manuscript. The authors have read and approved the manuscript.
Zusatzmaterial
Authors’ original file for figure 1
12913_2013_2963_MOESM1_ESM.pdf
Authors’ original file for figure 2
12913_2013_2963_MOESM2_ESM.pdf
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