Need for dental care among medical staff working in the China Antarctic stations

Figures & data

Table 1. Antarctic doctor characteristics.

Doctor’s characteristics	n	%
Age
<45 years old (including 45)	17	51.5
>45 years old	16	48.5
Sex
Male	30	90.9
Female	3	9.1
Antarctic work duration
<6 months (including 6)	11	33.4
>6 months	22	66.6

Table 2. Clinic visits frequency.

Reason for clinic visits	%
Orthopaedics	71.88
Dermatologic	65.63
Empyrosis	28.13
Frostbite	46.88
Influenza	15.63
Diarrhea	53.13
Acute abdominal pain	25
Cardiovascular	43.75
Psychological	15.63
Dental	65.63
other	15.63

Table 3. Dental problem experienced by doctors in the Antarctic.

Dental problem experienced and Treatment aspects	n	%
Dental problem
dental pain	18	54.55
tooth cracked	4	12.12
bleeding/swollen gum	17	51.52
loose tooth	9	27.27
Oral mucosa diseases	17	51.52
wound in the crania-facial area	3	9.09
Uneasiness caused by dental problem
eating food	24	72.73
talking	8	24.24
drinking	6	18.18
emotion	19	57.58
sleeping	16	48.48
working	11	33.33
other	3	9.09
Treatment
got treatment	22	66.7
treatment on time	20	90.9
local doctor	9	40.9
Licenced dentist	5	22.7
essential dental equipment and instrument	6	27.3
satisfied to the outcome	15	68.2

Table 4. Dental screen and education.

Dental screening features	n	%
Dental education (Yes)	11	33.33
Dental screen pre-departure (Yes)	12	36.36
Dental check-up at one’s will (Yes)	7	36.84
Dental screen after-departure (Yes)	0	0
Dental knowledge
Q1	10	30.3
Q2	30	90.91
Q3	28	84.85
Q4	23	69.7
Q5	23	69.7
Q6	11	33.33
Q7	18	54.55
Q8	26	78.79

Q = Question. Q1:Fresh squeezed juice is easier to cause tooth decay than fresh fruits. Q2:Together toothbrush with dental floss, the teeth can be cleaned thoroughly. Q3:Brush the outer and inner surface of your teeth up and down, occlusal surface back and forth. Q4:Cracked teeth can happen when chewing on hard foods. Q5:Alcohol-based mouthwash can’t be used as an everyday-routine. Q6:The teeth should be cleaned by dental hygienist every 6–12 months. Q7:If you see black spot in your teeth, you should go to the dentist for help. Q8:None of the above.

Table 5. Diet and dental related behaviour in the Antarctic.

Dietary and dental related behavioural factors	n	%
The ratio of your nutrition structure
Carbohydrates	-	33
Poultry	-	34
Milk	-	14
Vegetable and Fruits	-	18
Daily fluid intake
<1500 ml	26	78.78
>1500 ml	7	21.21
eat snack routinely (Yes)	16	48.48
The times brush teeth
<2 times (including 2)	27	81.82
>2 times	6	18.18
Smoke (Yes)	10	40.9
Drink (Yes)	13	22.7

Table 6. The correlation coefficients between important variables.

	v9a	v9b	v9c	v9d	v9e	v9f
gender	−0.173	−0.430	0.254	−0.098	0.254	−0.230
age	0.147	0.145	0.107	0.151	0.129	0.274
cigarette	0.120	−0.094	0.396	0.314	−0.248	0.260
alcohol	0.373	0.162	0.613	0.107	0.442	0.538
v4a	0.093	0.210	0.165	0.395	−0.428	0.176
v4b	0.373	0.162	0.060	0.107	−0.329	0.136
v5	0.814	0.194	0.557	0.245	−0.019	0.406
v6t	0.064	0.028	0.452	−0.004	0.033	0.202
v14a	0.061	−0.323	0.156	−0.032	0.099	0.319
v14b	−0.139	0.094	−0.046	−0.035	0.167	−0.101
v14c	0.156	0.460	−0.071	0.242	0.047	−0.099
v14d	−0.046	−0.001	−0.097	−0.087	−0.286	−0.217
v15	−0.214	−0.028	0.080	0.358	−0.292	0.611
v16	0.733	0.647	0.144	0.367	0.144	0.019
v18	−0.073	−0.514	−0.518	−0.406	−0.011	−0.126

v = variable, a/b/c/d/e/f = the first/second/third/fourth/fifth/sixth choice. All the correlation coefficients range from −1 to 1, with positive values indicating positive correlations and vice versa. We coded “YES” as 1, “No” as 0.

Table 7. P values of the univariate analysis.

	v9a	v9b	v9c	v9d	v9e	v9f
gender	0.662	0.271	0.518	0.805	0.518	0.997
age	0.400	0.414	0.541	0.389	0.462	0.175
cigarette	0.679	0.806	0.170	0.286	0.386	0.543
alcohol	0.178	0.646	0.024	0.717	0.107	0.996
v4a	0.741	0.550	0.555	0.169	0.121	0.683
v4b	0.178	0.646	0.829	0.717	0.231	0.753
v5	0.023	0.687	0.121	0.543	0.960	0.997
v6t	0.715	0.874	0.018	0.984	0.851	0.276
v14a	0.727	0.097	0.373	0.853	0.571	0.118
v14b	0.431	0.595	0.791	0.840	0.350	0.564
v14c	0.376	0.035	0.685	0.177	0.787	0.572
v14d	0.791	0.993	0.578	0.617	0.114	0.279
v15	0.396	0.821	0.828	0.169	0.208	0.111
v16	0.005	0.996	0.598	0.209	0.598	0.965
v18	0.700	0.180	0.090	0.181	0.822	0.736

v = variable, a/b/c/d/e/f = the first/second/third/fourth/fifth/sixth choice.

Table 8. Hierarchical regression model of snacks taking.

	Estimate	Std. Error	z value	Pr(>|z|)	Resid.Dev	Df	R^2	Pr(>Chi)
Model 1
(Intercept)	−0.96	2.149	−0.447	0.655	44.377	2	0.024	0.578
age	0.041	0.044	0.934	0.35
gender	−0.778	1.306	−0.596	0.551
Model 2
(Intercept)	−1.967	2.508	−0.784	0.433	35.619	1	0.217	0.003
age	0.032	0.05	0.632	0.528
gender	−0.365	1.526	−0.239	0.811
snacks taking	2.293	0.841	2.727	0.006

Estimate=coefficients of variables, Std.Error=standard error of estimate, Z values=statistics for Wald-test, Pr(>|Z|)=Significance of Wald-test, Resid.Dev=Residual deviance, Df=degree of freedom, R2=coefficient of determination, Pr(> Chi)=Significance of the model compared with the previous model. The first model was compared with the NULL model.

Table 9. Hierarchical regression model of alcohol taking.

	Estimate	Std. Error	z value	Pr(>|z|)	Resid.Dev	Df	R^2	Pr(>Chi)
Model 1
(Intercept)	−1.57	2.134	−0.735	0.462	45.02	2	0.015	0.706
age	0.022	0.043	0.503	0.615
gender	0.715	1.298	0.551	0.581
Model 2
(Intercept)	0.378	2.447	0.155	0.877	39.678	1	0.132	0.021
age	−0.026	0.052	−0.505	0.614
gender	0.142	1.332	0.106	0.915
snacks taking	2.016	0.944	2.134	0.033

Estimate = coefficients of variables, Std.Error = standard error of estimate, Z values = statistics for Wald-test, Pr(>|Z|) = Significance of Wald-test, Resid.Dev = Residual deviance, Df = degree of freedom, R2 = coefficient of determination, Pr(> Chi) = Significance of the model compared with the previous model. The first model was compared with the NULL model.