Variation of GP antibiotic prescribing tendency for contacts with out-of-hours primary care in Denmark – a cross-sectional register-based study

Figures & data

Table 1. Flow of data, per contact type (n).

	Clinic consultation				Home visit				Telephone consultation
	Contacts	Patients	GPs	Prescriptions	Contacts	Patients	GPs	Prescriptions	Contacts	Patients	GPs	Prescriptions
	853,036	483,178	955	^a	292,319	150,889	975	^a	2,816,720	852,086	898	^a
Merging of contacts and prescriptions	↓				↓				↓
	853,036	483,178	955	137,160	292,319	150,889	975	33,657	2,816,720	852,086	898	94,258
Excluding contacts with invalid GP ID	↓				↓				↓
	840,381	478,697	954	134,189	286,085	148,560	974	32,901	2,781,046	847,509	897	92,915
Exclusion due to too few contacts	↓				↓				↓
	794,220	461,170	584	127,539	281,141	146,844	829	32,496	2,724,556	840,519	585	91,304
Removal of contacts ending in referral	↓				↓				↓
	794,220	461,170	584	127,539	281,141	146,844	829	32,496	1,583,919	624,953	585	91,244

^aInitial number of antibiotic prescriptions was 275,466 spread across 265,075 contacts with OOH primary care.

Table 2. Contact-, patient- and GP-related characteristics, proportion by contact type (in %).

	Clinic consultation		Home visit		Telephone consultation
	N	%	N	%	N	%
Number of contacts	794,220	29.9	281,141	10.6	1,583,919	59.6
Antibiotic prescriptions	127,539	16.1	32,496	11.6	91,244	5.8
Contact characteristics
Time to next in-hours period (hours)
0–16	350,484	44.1	143,651	51.1	745,349	47.1
>16	443,736	55.9	137,490	48.9	838,570	52.9
Regional patient load, past hour
First hour of shift	65,316	8.2	3263	1.2	136,866	8.6
1st quintile	150,601	19.0	60,708	21.6	292,980	18.5
2nd	146,371	18.4	55,138	19.6	296,210	18.7
3rd	148,032	18.6	59,323	21.1	280,398	17.7
4th	140,562	17.7	51,981	18.5	288,534	18.2
5th quintile	143,338	18.0	50,728	18.0	288,931	18.2
Patient characteristics
Age in groups (years)
0–3	123,626	15.6	11,142	4.0	237,778	15.0
4–17	163,130	20.5	13,032	4.6	231,450	14.6
18–39	253,280	31.9	39,198	13.9	499,454	31.5
40–64	183,704	23.1	66,315	23.6	351,052	22.2
≥65	70,480	8.9	151,454	53.9	264,185	16.7
Sex
Female	409,657	51.6	147,611	52.5	893,919	56.4
Male	384,563	48.4	133,530	47.5	690,000	43.6
Highest educational level (years)
<10	207,886	26.2	123,117	43.8	467,248	29.5
10–15	230,679	29.0	85,933	30.6	453,365	28.6
>15	86,627	10.9	26,187	9.3	188,087	11.9
Children	250,065	31.5	19,769	7.0	418,478	26.4
Missing values	18,963	2.4	26,135	9.3	56,741	3.6
Income^a (deciles)
1st–3rd	249,452	31.4	109,575	39.0	542,473	34.2
4th–7th	329,701	41.5	129,774	46.2	675,526	42.6
8th–10th	215,067	27.1	41,792	14.9	365,920	23.1
Living status
Married/cohabitating	543,295	68.4	127,988	45.5	954,064	60.2
Unmarried/widow(er)/divorced	250,925	31.6	153,153	54.5	629,855	39.8
Ethnicity
Native born	699,824	88.1	262,125	93.2	1,425,728	90.0%
1st generation immigrants	57,638	7.3	15,458	5.5	97,344	6.1
2nd generation immigrants	36,758	4.6	3,558	1.3	60,847	3.8
Urbanisation (no. of inhabitants)
>100,000	161,894	20.4	43,176	15.4	362,605	22.9
20,000–100,000	219,290	27.6	76,817	27.3	414,781	26.2
1000–20,000	214,882	27.1	98,585	35.1	456,862	28.8
<1000	174,718	22.0	58,792	20.9	306,327	19.3
Missing values	23,436	3.0	3771	1.3	43,344	2.7
Charlson Co-morbidity Index
No comorbidities	681,020	85.7	140,675	50.0	1,253,337	79.1%
1	87,498	11.0	73,295	26.1	211,581	13.4
2	18,577	2.3	39,465	14.0	73,889	4.7
3	5072	0.6	17,584	6.3	28,376	1.8
≥4	2053	0.3	10,122	3.6	16,736	1.1
Patient GP/OOH contacts in the past 12 months (quintiles)
1st quintile	196,013	24.7	62,579	22.3	385,635	24.3
2nd	170,531	21.5	52,170	18.6	266,394	16.8
3rd	123,772	15.6	54,829	19.5	316,520	20.0
4th	147,121	18.5	58,111	20.7	317,791	20.1
5th quintile	156,783	19.7	53,452	19.0	297,579	18.8
GP characteristics
Number of GPs	584	29.2	829	41.5%	585	29.3
Sex
Female	305,467	38.5	118,443	42.1	587,378	37.1
Male	488,753	61.5	162,698	57.9	996,541	62.9
Age in groups (years)
31–40	154,484	19.5	59,987	21.3	277,527	17.5
41–50	303,572	38.2	103,873	36.9	548,966	34.7
51–60	221,466	27.9	75,254	26.8	497,531	31.4
>60	114,698	14.4	42,027	14.9	259,895	16.4
GP experience (years)
6–10	118,123	14.9	49,537	17.6	204,233	12.9
11–20	346,430	43.6	115,259	41.0	633,477	40.0
>20	329,667	41.5	116,345	41.4	746,209	47.1
Primary care specialist
No	132,270	16.7	46,339	16.5	256,698	16.2
Yes	404,080	50.9	141,992	50.5	698,033	44.1
Missings	257,870	32.5	92,810	33.0	629,188	39.7
OOH shifts in the past 180 days, quintiles
First 180 days of follow-up	100,772	12.7%	37,160	13.2%	203,848	12.9%
1st quintile	159,487	20.1%	53,781	19.1%	286,159	18.1%
2nd	136,566	17.2%	48,390	17.2%	282,160	17.8%
3rd	124,639	15.7%	50,214	17.9%	272,232	17.2%
4th	141,608	17.8%	44,278	15.7%	271,064	17.1%
5th quintile	131,148	16.5%	47,318	16.8%	268,456	16.9%
Patients seen in the past hour, quintiles
First hour of shift	129,878	16.4%	89,847	32.0%	246,562	15.6%
1st quintile	202,711	25.5%	106,350	37.8%	309,319	19.5%
2^nd	106,885	13.5%	0	0.0%	275,376	17.4%
3^rd	112,784	14.2%	68,429	24.3%	271,272	17.1%
4^th	161,247	20.3%	0	0.0%	246,581	15.6%
5th quintile	80,715	10.2%	16,515	5.9%	234,809	14.8%

^aWe used parental characteristics to categorise children.

Figure 1. Histogram presenting raw antibiotic prescription rates, per contact type.

Figure 2. Adjusted^a APTs, per contact type. The simulated curves (grey) represent the situation in which all GPs act similarly. ^aAdjusted for contact characteristics (year, month, time to next in-hours period, and patient load regionally past hour) and patient characteristics (age, sex, education level, income, living status, ethnicity, urbanisation, comorbidity, and patient GP/OOH contacts in the past 12 months). Left Y-axis: The adjusted APT presents the individual GP’s likelihood of prescribing antibiotics compared to the average GP. Right Y-axis: The APT is converted to an adjusted prescribing rate by multiplying the APT by the observed average prescribing rate for each contact type.

Figure 3. Adjusted relative risk of antibiotic prescribing tendency (APT) according to GP characteristics, stratified by contact type. Antibiotic prescribing tendency (APT) is the tendency of each individual GP to prescribe antibiotics compared to the average GP, calculated by dividing the number of observed antibiotic prescriptions by the number of expected prescriptions predicted by a model correcting for the case-mix of the patient population of each individual GP. Presented estimates were mutually adjusted, meaning, for example, GP sex effect was adjusted for GP age and vice versa. OOH shifts past 180 days refer to the number of similar shifts at out-of-hours (OOH) primary care done by the individual GP. Patients seen in the past hour refers to the total number of patients seen in the hour up to the index contact.