Drug prescription rates in dental health services

Han-A Cho; Sun-Mi Kim; Hosung Shin

doi:10.11149/jkaoh.2014.38.4.212

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Cho, Kim, and Shin: Drug prescription rates in dental health services

Original Article

J Korean Acad Oral Health 2014;38(4):212-219.

Published online: 12 December 2014

DOI: https://doi.org/10.11149/jkaoh.2014.38.4.212

Drug prescription rates in dental health services

Han-A Cho, Sun-Mi Kim, Hosung Shin

Department of Social and Humanity in Dentistry, Wonkwang University School of Dentistry, Iksan, Korea

Corresponding Author: Hosung Shin Department of Social and Humanity in Dentistry, Wonkwang University School of Dentistry, 460 Iksan-dearo, Iksan 570-749, Korea Tel: +82-63-850-6915 Fax: +82-63-850-6934 E-mail: shinhosung@gmail.com

Received 26 August 2014 Revised 16 October 2014 Accepted 4 November 2014

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives

This study examined the misuse and abuse of antibiotics in relation to the demographic and socioeconomic characteristics of patients given prescriptions by dental providers.

Methods

We examined data collected in 2011 by the Korea Health Panel from 3,836 dental visits. The data included multiple visits per individual for 3,738 household members of 2,588 households using outpatient dental services. The data were analyzed by dental service provider type, using four types of -regression. Model analysis and comparison were performed using Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) to select the best model.

Results

Prescription rates according to type of dental service provider are as follows: 18% by dental hospitals and 19%-20% by dental clinics. The patient factors contributing to the prescription rate are gender, age, education, and income level. Higher antibiotics exposure was found in patients who were male, older, with less education, and lower incomes. Patient exposure to antibiotics did not significantly differ between dental hospitals and dental clinics.

Conclusions

When prescribing antibiotics in dental practices, patient safety can be improved by reducing misuse and abuse of antibiotics through consideration of individual patient characteristics.

Keywords: Antibiotics, Korea Health Panel, Prescription rate

References

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Table 1.

Variables of the study subject

	Independent variables		Reference
Demographic	Gender	Male	Male
		Female
	Age	20-40 years	20-40 years
		≤19 years
		40-65 years
		≥65 years
	Martial status	Married	Married
		Divorced/Widowed/Separated
		Unmarried
Socio-economic	Education	≤Primary school	≤Primary school
		Middle school
		High school
		≥College
	Economic	Activity	Active
	Activity	Non-activity
	Household income quartile	Low	Low
		Low-middle
		High-middle
		High
	Private	Uninsured	Uninsured
	Health	Fixed benefit insured
	Insurance	Indemnity insured
Health	Chronic	Yes	Yes
	Disease	No

Table 2.

Handling of beta regression according to occurring 0's and 1's

Model	Analytical methods	Brief description
Model 1	Beta	Ignore 0s and 1s
Model 2	+―0.005 or 0.01	Transform dependent variables to add a small amount using 0.005 or 0.01 to the lower
Model 3	ZOIB (zero one inflated beta)	bound, and to subtract the same amount from the upper bound Consists of three parts : a logistic regression model for whether or not the proportion equals
		0 and 1, a beta model for the proportions between 0 and 1 For situations where the decision for proportions of 0 and/or 1 are governed by a different
Model 4*	y“=(y‘(N-1) + 0.5)/N	process as the other proportions Compress the range to avoid zeros and ones by taking Smithson and Verkuilen propose the formula next to it

*Description of model 4 N=sample size.

Table 3.

Demographic characteristics of dental outpatients

Variables	Classification	Frequency	%
Gender	Male	1,687	43.98
Gender	Female	2,149	56.02
Age	≤19 years	1,127	29.38
	20-40 years	654	17.05
	40-65 years	1,366	35.61
	≥65 years	689	17.96
Marital Status	Married	2,050	53.44
	Unmarried	1,460	38.06
	Divorced/Widowed/Separated	326	8.50
Education	≤Primary school	1,358	35.40
	Middle school	558	14.55
	High school	1,004	26.17
	≥College	916	23.88
Economic Activity	Activity	2,266	59.07
Economic Activity	Non-activity	1,570	40.93
Chronic Disease	Yes	2,398	62.51
Chronic Disease	No	1,438	37.49
Household income	Low	959	25.00
quartile	Low-middle	960	25.03
	High-middle	959	25.00
	High	958	24.97
Private Health	Uninsured	949	24.74
Insurance	Fixed benefit insured	2,310	60.22
	Indemnity insured	577	15.04

Table 4.

Mean of prescribing rates according to demographic characteristics of dental outpatients and the dental institutions

Variables	Classification	Model 1	Model 2	Model 4	P
Variables	Classification	mean±sd	mean±sd	mean±sd	P
Gender	Male	0.21±0.34	0.21±0.33	0.21±0.34	0.002*
Gender	Female	0.18±0.32	0.18±0.31	0.18±0.32	0.002*
Age	≤19 years	0.03±0.16^a	0.04±0.15^a	0.03±0.16^a	<0.001^†
	20-40 years	0.18±0.32^b	0.19±0.31^b	0.18±0.32^b
	40-64 years	0.25±0.36^c	0.25±0.35^c	0.25±0.36^c
	≥65 years	0.34±0.38^d	0.34±0.38^d	0.34±0.38^d
Education	≤Primary school	0.16±0.32^a	0.17±0.31^a	0.16±0.32^a	<0.001^†
	Middle school	0.22±0.35^b	0.23±0.34^b	0.22±0.35^b
	High school	0.21±0.34^b,c	0.22±0.33^b,c	0.21±0.34^b,c
	≥College	0.19±0.32^a,b,c,d	0.19±0.32^a,b,c,d	0.19±0.32^a,b,c,d
Household income quartile	Low	0.28±0.38^a	0.28±0.38^a	0.28±0.38^a	<0.001^†
	Low-middle	0.19±0.32^b,c	0.19±0.32^b,c	0.19±0.32^b,c
	High-middle	0.16±0.30^c,d	0.16±0.29^c	0.16±0.30^c,d
	High	0.14±0.29^d	0.14±0.28^b,c,d	0.14±0.29^d
Dental Institutions	Dental Clinic	0.19±0.33	0.20±0.32	0.19±0.33	0.500*
Dental Institutions	Dental Hospital	0.18±0.31	0.18±0.31	0.18±0.31	0.500*

*P-value is measured by T-test,

^† P-value is measured by ANOVA.

^a -dThe different letter indicates significant difference (P<0.05) in Scheffé's multiple comparison test, within same column.

Table 5.

Beta regression for prescribing rates

Variables	Classification	Model 1	(N=931)	Model 2 (	N=3,836)	Model 3 (	N=3,836)	Model 4 (	N=3,836)
Variables	Classification	exp(b)	P>\|z\|	exp(b)	P>\|z\|	exp(b)	P>\|z\|	exp(b)	P>\|z\|
Gender	Female	0.907	0.093	0.895	0.009	0.702	0.020	0.896	0.014
Age	≤19 years	0.702	0.024	0.580	0.000	0.925	0.403	0.581	0.000
	40-65 years	0.925	0.429	1.180	0.028	0.901	0.365	1.188	0.028
	≥65 years	0.901	0.383	1.433	0.000	0.908	0.083	1.467	0.000
Martial status	Unmarried	0.989	0.928	1.075	0.422	0.990	0.928	1.073	0.451
Martial status	Divorced/Widowed/Separated	0.932	0.428	1.020	0.798	0.933	0.403	1.021	0.805
Education	Middle school	0.886	0.152	1.044	0.489	0.886	0.130	1.052	0.446
	High school	0.933	0.366	0.935	0.265	0.934	0.366	0.937	0.309
	≥College	0.968	0.722	0.856	0.034	0.968	0.712	0.854	0.040
Economic Activity	Non-Activity	0.927	0.204	1.059	0.262	0.927	0.196	1.067	0.231
Chronic Disease	No	0.936	0.365	1.042	0.374	0.937	0.322	1.041	0.401
Household income	Low-middle	0.981	0.796	0.876	0.026	0.981	0.792	0.88	0.042
quartile	High-middle	1.016	0.835	0.831	0.003	1.016	0.831	0.833	0.005
	High	0.860	0.078	0.786	0.000	0.860	0.060	0.791	0.000
Private Health	Fixed benefit insured	1.023	0.729	1.017	0.747	1.023	0.727	1.017	0.758
Insurance	Indemnity insured	1.129	0.242	1.029	0.681	1.128	0.228	1.023	0.751
		5.63		1.669				0.448
AIC		―591.2548		―8971.758		4561.914		―28784.79
BIC		―504.2021		―8859.218		4786.993		―28672.25
Log likelihood		313.62738		4503.8788		―2244.9571		14410.397
Proportion		33.89		28.7		37		29.38

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