Brazilian municipalities and their own expenditures on health: some associations

Araújo, Carmem Emmanuely Leitão; Gonçalves, Guilherme Quaresma; Machado, José Angelo

doi:10.1590/1413-81232017223.15542016

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FREE THEMES • Ciênc. saúde colet. 22 (3) • Mar 2017 • https://doi.org/10.1590/1413-81232017223.15542016 copy

Brazilian municipalities and their own expenditures on health: some associations

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

In a context that combines decentralization and underfunding of public health policy, Brazilian municipalities expressively extend their spending in this sector, allocating a proportion of their own revenues (direct taxation added to the mandatory intergovernmental transfers from the Union and the states) at levels above the ones that are constitutionally determined. However, there have been incipient studies investigating the expenditure composition on health in these federative units, in order to describe its main characteristics and explanations. In order to contribute to eliminating this gap, this article explores some associations between attributes of the municipalities (population size, region of the country, the proportion of older adults, child mortality, hiring private services and SUS transfer) and health expenditure implemented with the municipalities’ own recipes. The study used descriptive statistics and multiple linear regressions in order to investigate such associations with data for 2010. The results point to tax inequities in the sector when comparing municipalities of different population groups and socioeconomic conditions, including the perception of important differences in terms of per capita spending on the analyzed expenditure items.

Public health policy; Decentralization; Health expenditures; Local government

Type of expenditure	Population Size	N	%	Median	Standard deviation
Personnel and payroll	Less than 20,000	3.889	70,4	186,09	101,80
	20,001 – 50,000	1032	18,7	146,36	86,81
	50,001 – 100,000	323	5,8	147,85	89,17
	100,001 – 200,000	148	2,7	149,30	93,68
	200,001 – 400,000	81	1,5	173,48	82,70
	More than 400,000	51	0,9	185,53	91,82
	Total	5.524	100	173,71	99,87
Medical, hospital, dental and laboratory services	Less than 20,000	1.870	69,4	15,89	49,10
	20,001 – 50,000	464	17,2	11,12	47,88
	50,001 – 100,000	178	6,6	25,14	75,30
	100,001 – 200,000	100	3,7	23,31	77,34
	200,001 – 400,000	50	1,9	45,24	104,53
	More than 400,000	33	1,2	87,25	90,27
	Total	2.695	100	16,38	56,02
Medication	Less than 20,000	3.883	70,5	13,82	23,53
	20,001 – 50,000	1.032	18,8	9,05	12,97
	50,001 – 100,000	323	5,9	7,38	12,75
	100,001 – 200,000	144	2,6	7,17	14,17
	200,001 – 400,000	79	1,4	8,13	8,87
	More than 400,00	44	0,8	9,72	8,56
	Total	5.505	100	11,82	21,31
Free of charge medication	Less than 20,000	1.159	69,2	9,44	26,24
	20,001 – 50,000	314	18,8	5,67	10,82
	50,001 – 100,000	106	6,3	5,50	8,01
	100,001 – 200,000	48	2,9	7,27	9,08
	200,001 – 400,000	32	1,9	5,85	6,07
	More than 400,000	15	0,9	1,54	4,63
	Total	1.674	100	7,50	22,93
Permanent equipment and supplies	Less than 20,000	3.776	70,3	8,13	17,75
	20,001 – 50,000	1.009	18,8	4,52	6,95
	50,001 – 100,000	318	5,9	4,22	4,86
	100,001 – 200,000	141	2,6	4,04	4,53
	200,001 – 400,000	81	1,5	3,74	3,45
	More than 400,000	45	0,9	3,17	4,56
	Total	5.370	100	6,22	15,65

Independent variables	Categories of independent variables	Percentage distribution of municipalities	Median Per Capita Expenditure by variable (R$)
Region of the country	North	7,99	181,62
	Northeast	32,11	189,62
	Midwest	8,38	280,65
	Southeast	29,99	250,20
	South	21,53	287,57
Size of Municipalities	Up to 20,000 inh.	70,80	245,52
	Between 20,001 and 50,000 inh.	19,03	187,50
	Between 50,001 and 100,000 inh.	5,84	195,45
	Between 100,001 and 200,000 inh.	2,51	205,62
	Between 200,001 and 400,000 inh.	1,45	264,69
	Equal or higher than 400,001 inh.	0,38	291,88
Proportion of Older Adults	Up to 0.499	7,72	194,35
	From 5.00 to 9.99	67,22	221,64
	From 10.00 to 14.99	24,44	259,71
	Over 15.00	0,62	478,58
Child mortality	Up to 9.9‰	1,61	290,15
	From 10‰ to 14.9‰	33,13	269,35
	From 15‰ to 19.9‰	28,61	255,51
	Over 20‰	36,66	186,75
Hires Private Services exclusively from their own funds	Yes	76,34	235,33
Hires Private Services exclusively from their own funds	No	23,66	210,53
SUS transfers	Up to 100	30,54	214,42
	From 101 to 150	43,17	221,35
	From 151 to 200	17,55	248,93
	From 201 to 300	7,20	284,14
	From 301 to 500	1,36	390,88
	At or above 501	0,19	308,52

Independent variables	Model 1	Model 2	Model 3	Model 4	Model 5
Constant	5,6228***	5,6228***	6,3191***	5,0276***	-
	(0,0111)	(0,0111)	(0,1115)	(0,1168)	-
Region of residence
Southeast	Reference	Reference	Reference	Reference	Reference
North	-0,3566***	-0,3368***	-0,2099***	-0,2823***	-0,1615
	(0,0234)	(0,0228)	(0,0270)	(0,0259)
Northeast	-0,3258***	-0,3160***	-0,1466***	-0,1914***	-0,1885
	(0,0149)	(0,0146)	(0,0227)	(0,0215)
South	0,1094***	0,0885***	0,0308*	0,0260	0,0226
	(0,0166)	(0,0162)	(0,0171)	(0,0162)
Midwest	0,0578**	0,0438*	0,0594**	-0,0678***	-0,0396
	(0,0230)	(0,0223)	(0,0231)	(0,0224)
Size of the Municipality
Up to 20,000 inh.		Reference	Reference	Reference
Between 20,001 and 50,000 inh.		-0,2405***	-0,2524***	-0,2414***	-0,1998
		(0,0148)	(0,0153)	(0,0145)
Between 50,001 and 100,000 inh.		-0,1838***	-0,2178***	-0,2708***	-0,1339
		(0,0246)	(0,0255)	(0,0242)
Between 100,001 and 200,000 inh.		-0,2168***	-0,2597***	-0,3305***	-0,1090
		(0,0367)	(0,0378)	(0,0359)
Between 200,001 and 400,000 inh.		-0,0164	-0,0680	-0,1683***	-0,0424
		(0,0480)	(0,0491)	(0,0465)
At or above 400,001 inh.		-0,5845***	-0,6520***	-0,7812***	-0,1019
		(0,0923)	(0,0923)	(0,0874)
Child Mortality Logarithm			-0,2967***	-0,3632***	-0,2657
			(0,0324)	(0,0307)
Proportion of older adults logarithm			0,0555**	-0,0103	-0,0069
			(0,0226)	(0,0215)
SUS Transfers Logarithm (per capita)				0,3428***	0,3066
				(0,0134)
Hires Private Services				0,0278**	0,0249
				(0,0132)
R²	0,1608	0,2104	0,2249	0,3091	0,3091
Adjusted R²	0,160	0,209	0,223	0,307	0,307
F test	261,6***	161,5***	143,8***	187,6***	187,6***
Sample size (n)	5.466	5.466	5.466	5.466	5.466

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[1] Collaborations

CEL Araújo, GQ Gonçalves and JA Machado worked on research design, data construction and processing and results analysis. Everyone participated in the writing of the article and approval of the final version for the publication.