Geographical distribution and factors associated with physicians’ and nurses’ productivity in Brazilian primary healthcare

Abstract

The aim of this study was to analyze the geographical distribution and factors associated with productivity in Brazilian primary health care (PHC) for the year 2023. Productivity was defined as the number of individual consultations conducted by physicians and nurses per PHC team. The average productivity in Brazil was 247.1 and 138.8 consultations per month for physicians and nurses, respectively. The Southern region (357.7), states such as Santa Catarina (411.2), Rio Grande do Sul (353.6), and Mato Grosso (339.2), and capital cities like Curitiba (413.2), Porto Alegre (377.4), and Palmas (353.8) exhibited the highest physician productivity. Positive associations with physician and nurse productivity were found for APS coverage, registered users per team, number of nursing technicians and assistants per team, and electronic health records. Municipalities with lower social vulnerability and smaller population size demonstrated higher physician productivity. Additionally, per capita APS expenditure was positively associated with physician productivity and negatively associated with nurse productivity. Collective activities such as team meetings, health education, and group consultations were positively correlated with physician and nurse productivity.

Key words:
Productivity; Primary health care; Access to primary care; Social inequalities

Resumo

O objetivo do estudo foi analisar a distribuição geográfica e fatores associados à produtividade da atenção primária à saúde (APS) dos municípios brasileiros em 2023. A produtividade foi definida como o número de atendimentos individuais de médicos e enfermeiros por equipe da APS. A produtividade média no Brasil foi de 247,1 e 138,8 atendimentos de médicos e enfermeiros, respectivamente, por equipe por mês. A região Sul (357,7), os estados de Santa Catarina (411,2), Rio Grande do Sul (353,6) e Mato Grosso (339,2), e as capitais Curitiba (413,2), Porto Alegre (377,4) e Palmas (353,8) foram os locais com a maior produtividade médica. Estiveram associados positivamente à produtividade de médicos e enfermeiros: cobertura da APS, usuários cadastrados por equipe, número de técnicos e auxiliares de enfermagem por equipe e prontuário eletrônico. Municípios com menor vulnerabilidade social e menor porte populacional apresentaram maior produtividade médica. A despesa liquidada em APS por habitante esteve associada positivamente com a produtividade médica e negativamente com a de enfermagem. As atividades coletivas de reuniões de equipe, educação em saúde e atendimento em grupo estiveram associadas positivamente com a produtividade de médicos e enfermeiros.

Palavras-chave:
Produtividade; Atenção primária à saúde; Acesso à atenção primária; Desigualdades sociais

Resumen

El objetivo de este estudio fue analizar la distribución geográfica y los factores asociados a la productividad de la atención primaria de salud (APS) en los municipios brasileños en 2023. La productividad se definió como el número de atenciones individuales de médicos y enfermeras por equipo de APS. La productividad promedio en Brasil fue de 247,1 y 138,8 atenciones de médicos y enfermeras, respectivamente, por equipo al mes. La región Sur (357,7), los estados de Santa Catarina (411,2), Rio Grande do Sul (353,6) y Mato Grosso (339,2), y las capitales Curitiba (413,2), Porto Alegre (377,4) y Palmas (353,8) fueron los lugares con mayor productividad médica. Estuvieron asociados positivamente con la productividad de médicos y enfermeras: cobertura de APS, usuarios registrados por equipo, número de técnicos y auxiliares de enfermería por equipo, y el uso de historia clínica electrónica. Los municipios con menor vulnerabilidad social y menor tamaño poblacional mostraron mayor productividad médica. El gasto liquidado en APS por habitante estuvo asociado positivamente con la productividad médica y negativamente con la de enfermería. Las actividades colectivas como reuniones de equipo, educación en salud y atención grupal estuvieron asociadas positivamente con la productividad de médicos y enfermeras.

Palabras clave:
Productividad; Atención primaria de salud; Acceso a la atención primaria; Desigualdades sociales

Introduction

Brazilian primary health care (PHC) is characterized by individual or collective health actions that involve promotion, prevention, protection, diagnosis, treatment, rehabilitation, harm reduction, palliative care, and health surveillance. Multidisciplinary teams implement these actions, offering services to the population of the defined territory under each team’s responsibility. It is the preferred gateway to the Health Care Network (RAS), free of charge, and which must consider the determinants and conditions of health to meet the territorial needs¹.

Health systems need to be organized to support PHC to achieve equity and social justice, with a structure appropriate to their objectives and guidelines. In this sense, in 1994, the Family Health Strategy (ESF) was established to organize Brazilian PHC to strengthen actions to promote, protect, and restore health comprehensively and continuously, with a location of action in territories assigned to PHC Units (UBS)². Adopting this strategy positively reduced cardiovascular, cerebrovascular, infant, and under-five mortality due to diarrhea and pneumonia, besides hospitalizations due to primary care sensitive conditions (PCSC)^3-7.

The study by Pinto et al.⁸ revealed a decline from 120 to 66 hospitalizations due to PCSC per 10,000 inhabitants in Brazil from 2001 to 2016, a drop of 45%. Considering the capitals and municipalities in the rural area (other municipalities besides the capitals), a 24% and 48.6% reduction was also observed, respectively. Another relevant fact is the representation of PHC in Brazil through the ESF, which had a coverage of 45.3% in 2006 and rose to 64% in 2016, representing an increase of 18.7% in the period, with a tendency for increasing coverage, varying annually by 8.4% in the country⁹. In December 2023, coverage was 76% in Brazil, with 79,255 doctors and 69,236 nurses working in the two main team types: Family Health Team (eSF) and Primary Care Team (eAP).

Despite this expanded coverage, health work can occur differently in each team and municipality, given the units and municipalities’ structure aspects and work process organization models. This can lead to varying results nationwide. Since 2004, municipalities, in general, have been responsible for managing and implementing PHC actions in their territory, resulting in different ways of organizing the PHC work process in each municipality. However, after almost two decades, some municipalities still display weaknesses in their work management capacity¹⁰ regarding administrative and technical power¹¹.

Quality management is important to achieving healthcare excellence services, targeting client satisfaction. Quality management practices develop tools that assess the quality of these actions and services offered through planning, organization, coordination, direction, evaluation, and control based on health indicators that measure a set of characteristics in a given setting. These indicators assess the dimensions of health and its determinants to improve people’s health through evidence-based decision-making¹². A lack of these indicators will result in less technical and more intuitive management, potentially losing efficiency and effectiveness, especially regarding the SUS and PHC principles and guidelines.

Under the logical model Senn et al.¹³ proposed for evaluating PHC performance, productivity is characterized as a performance dimension. PHC productivity will likely be conditioned by several endogenous (structure, care model, work process organization in the team, and professional knowledge, skills, and attitudes) and exogenous aspects of the teams (Municipal PHC management model, PHC financing, municipality size, the population’s socioeconomic profile in the territory). This productivity may be directly related to the Brazilian population’s access to PHC appointments, which may impact people’s health situation.

Productivity is the product/input relationship and reveals the utilization level observed in each production process through the resource employed¹⁴. It is fundamentally influenced by the cost of labor, volume of capital used, management and work methods, product quality level, and the intensive way technology is used. Even so, there may be productivity gains for a Health System without initially providing more resources since increasing the contribution of resources does not guarantee improved health indicators. Therefore, given the gap in scientific knowledge in Brazil on this topic, this study analyzes PHC productivity in Brazilian municipalities in 2023 and investigates its associated factors. These findings may contribute with evidence for the evaluation and decision-making process of managers and professionals in the organization of Brazilian PHC.

Methods

Study design and context

This is a quantitative ecological, exploratory, and observational study. Data from individual appointments with eSF and eAP doctors and nurses of Brazilian municipalities were used to analyze PHC productivity.

Data collection

In Brazilian PHC, the production recorded by professionals is stratified into five types: individual care (provided exclusively by health professionals with higher education, such as doctors, nurses, physiotherapists, psychologists, and others), dental care (provided exclusively by dentists), procedures (e.g., administration of medication, nebulization, blood pressure measurement, dressings, and others), home visits (provided exclusively by community health workers), and collective activities (e.g., health education, group care, team meeting, collective procedure, and others).

The productivity assessed in this study refers specifically to individual care provided by primary care doctors and nurses in Brazilian municipalities, regardless of the care location (in a doctor’s office, at home, and in schools). Data were collected from the Health Information System for Primary Care (SISAB). On the SISAB home page, the health/production report was selected to obtain the data. Then, the data from the production report were collected using the following filters:

• a) Period: January to December 2023.

• b) Production type: individual care.

• c) Team type: eSF and eAP.

• d) Geographical unit: Brazil.

• e) Report line: municipality.

• f) Report column: period of validity.

• g) Professional category: doctor and nurse.

The final data are the sum of individual care provided by eSF and eAP doctors or nurses of each Brazilian municipality in each period. The number of teams was collected from the National Health Establishment Registration System (SCNES), filtering only active teams: 70 - Family Health Team and 76 - Primary Care Team from the list of teams. Data were collected for all Brazilian municipalities from January to December 2023.

Variables

The primary outcome was the mean monthly team productivity, calculated as the ratio between the number of individual doctor and nurse appointments added together from all eSF and eAP teams in the municipality from January to December 2023, divided by the number of eSF and eAP teams in the municipality in the same period. Regarding the possible determinants of the doctor and nurse appointment productivity in PHC teams, Chart 1 shows the variables by dimension and subdimension using the logical model presented by Senn et al.¹³.

Chart 1
Description of the domain and dimensions of the model’s independent variables.

Domain: Organization and structure of PHC practices
Dimensions	Variables	Source
Human resources and management	Mean PHC coverage (2023)	eGestor APS
	Mean number of inhabitants registered per team (2023)	eGestor APS
	Mean number of PHC nursing technicians per team (2023)	SCNES
	Mean number of PHC ACS per team (2023)	SCNES
Information system	Electronic medical record implementation status (December 2023)	eGestor APS
Engaged clients	No information available	-
Clients’ needs	Social Vulnerability Index (SVI) (2010)	IPEA
	Aging index (Census 2022)	IBGE
	Number of deaths due to Primary Care-Sensitive Conditions (PCSC) in 2023 per 100,000 inhabitants	SIM/DATASUS
	Number of hospitalizations due to cardiovascular diseases (Cap. IX ICD 10) in 2023 per 100,000 inhabitants	SIH/DATASUS
	Population density (Census 2022)	IBGE
	Population size (2022 Census)	IBGE
Supplies and equipment	No information available	-
Funding and costs	Mean expenditure paid per inhabitant registered in the PHC subfunction (2023).	SIOPS
Workforce development	No information available	-
Domain: PHC service delivery
Dimensions	Variables per team per year (2023)	Source
Collective actions	Team meeting	SISAB
	Meeting with other health teams	SISAB
	Intersectoral meeting, local health council, or social control	SISAB
	Health education	SISAB
	Group service	SISAB
	Assessment/collective procedure	SISAB
	Social mobilization	SISAB

• SCNES: National Health Establishment Registration System; IPEA: Institute of Applied Economic Research; IBGE: Brazilian Institute of Geography and Statistics; SIOPS: Public Health Budget Information System; SISAB: Primary Care Health Information System.
• Source: Authors.

The IBGE division was adopted to characterize the population size. The Social Vulnerability Index (SVI) created by the Institute of Applied Economic Research (IPEA) was employed to characterize the municipality’s socioeconomic condition, given its recent use by the Ministry of Health to stratify municipalities by vulnerability condition in Ordinance nº 3.493 of April 10, 2024, which defined a new methodology for federal joint financing of the Primary Health Care minimum funding level.

Concerning the “financing and costs” dimension, the mean expenditure settled per inhabitant registered in the PHC subfunction from January to December 2023 was selected based on data from the Public Health Budget Information System (SIOPS). Regarding the information system, the implementation status of the electronic medical record in the PHC in December 2023 was considered, given the difficulty in establishing an average implementation status throughout the year, which ranged from not implemented/partially implemented to implemented, per data from the eGestor APS panel made available by the Ministry of Health.

We could not find municipal data in Brazilian public databases representing user involvement or engagement or PHC supplies and equipment in municipalities from January to December 2023 for some dimensions. The population data for each municipality refer to data from the 2022 IBGE Census since the estimate made by IBGE for 2023 (sent to the Federal Court of Auditors) only considers the update of the territorial grid of municipalities in April 2023 due to some data unavailability.

For the “PHC service delivery” domain, we adapted a dimension called “Collective activities”, introducing all possible activities the teams recorded in 2023 as variables. As activities occur in smaller amounts than individual services, we decided to add the total number for 2023 and divide it by the mean number of teams in the municipality in the same period.

Statistical methods

Municipalities with zero or missing data on individual doctor and nurse appointments were excluded from the database for that period. The interquartile range (IQR) method removed outliers from the database to avoid bias due to recording errors. Productivity rates per doctor and nurse team were then calculated for large regions, states, and capitals.

Four multiple linear regressions were performed using ordinary least squares to investigate the factors associated with doctor and nurse appointment productivity in PHC Brazilian municipalities, two for variables in the “Organization and structure of PHC practices” dimension and two more for variables in the “PHC service delivery” dimension. In each dimension, a regression was performed where the dependent variable was doctor productivity and another where it was nurse productivity. In the dimension “Organization and structure of PHC practices”, given the high number of variables, the final model with the independent variables was selected using the “Stepwise” method with “backward” directionality, considering the best model to be the one with the lowest number of the Akaike information criterion (AIC).

The regression assumptions of model linearity, multivariate normality, low multicollinearity, and autocorrelation were met, while the homoscedasticity of residuals was not. Therefore, standard errors were calculated using the robust standard error technique with White’s correction¹⁶ for the four regressions. Data were stored in EXCEL spreadsheets (version 2019) and processed in R software (version 4.3.3).

Bias

Although the database has been cleaned, problems with recording individual PHC services in Brazil may cause bias. Several teams still use paper systems, which are more susceptible to errors in filling out and typing forms. This bias is expected to be mitigated by removing extreme values ​​using the IQR technique.

Ethical aspects

According to Resolution nº 466/2012 of the National Health Council, this research does not require the Ethics Committee’s appreciation and approval, given the use of secondary, unidentified, and publicly accessible data.

Results

Brazil’s monthly mean medical and nursing appointments in 2023 were 14,219,829 and 7,814,028, respectively, with 57,361 ESF teams. The mean monthly productivity per team was 247.9 medical appointments and 138.8 nursing appointments (Table 1). The mean monthly medical and nursing appointment productivity was higher than the national mean in the South (354.1 and 162.6), Midwest (287.9 ​​and 151.0), and Southeast (273.8 and 143.1) and lower than the national mean in the Northeast (188.7 and 126.6) and North (164.6 and 117.7).

Table 1
Description of the mean number of teams, individual care, and productivity per team of doctors and nurses in PHC, in municipalities, by large region and states. Brazil, 2023.

		Doctors		Nurses
Location	Number of teams	Individual appointments per month	Productivity per team per month	Individual appointments per month	Productivity per team per month
Great Region
South	8,165	2,891,336	354.1	1,327,159	162.6
Midwest	4,310	1,240,839	287.9	631,911	151.0
Southeast	20,519	5,617,778	273.8	2,921,666	143.1
Brazil	57,361	14,219,829	247.9	7,814,028	138.8
Northeast	19,053	3,595,299	188.7	2,311,103	126.6
North	5,313	874,577	164.6	622,189	117.7
State
Santa Catarina	2,179	835,856	383.5	393,688	180.5
Paraná	3,007	1,053,202	350.3	414,210	137.3
Rio Grande do Sul	2,979	1,002,278	336.4	519,262	175.0
Mato Grosso	985	328,691	333.6	145,712	153.0
Mato Grosso do Sul	819	273,140	333.5	119,433	153.3
Minas Gerais	6,440	1,811,846	281.3	984,445	155.2
São Paulo	9,154	2,564,328	280.1	1,183,529	128.6
Goiás	1,880	521,159	277.2	268,543	146.9
Tocantins	612	162,505	265.7	75,789	126.2
Ceará	3,034	805,277	265.4	414,591	177.0
Rio de Janeiro	3,708	979,869	264.3	602,187	164.5
Paraíba	1,570	367,538	234.2	191,420	123.4
Alagoas	974	227,444	233.6	150,166	157.3
Rio Grande do Norte	1,198	260,648	217.7	134,124	111.9
Espírito Santo	1,218	261,735	214.9	151,504	125.3
Rondônia	468	99,927	213.6	41,164	88.0
Federal District	626	117,849	188.3	98,224	157.0
Acre	290	54,376	187.2	29,731	102.4
Sergipe	763	142,290	186.4	84,932	111.3
Pernambuco	2,842	504,671	177.6	374,027	132.8
Amazonas	1,170	205,057	175.2	189,187	163.6
Piauí	1,394	233,576	167.5	161,724	116.5
Bahia	4,666	763,613	163.6	489,341	105.2
Pará	2,345	311,912	133.0	254,196	108.4
Roraima	204	24,480	120.1	15,572	76.4
Maranhão	2,612	290,242	111.1	310,776	120.0
Amapá	225	16,320	72.6	16,550	73.7

• Source: Authors.

The three states with the highest medical appointment productivity were Santa Catarina (383.5), Paraná (350.3), and Rio Grande do Sul (336.4). The three states with the lowest number were Roraima (120.1), Maranhão (111.1), and Amapá (72.6). The three states with the highest nursing appointment productivity were Santa Catarina (180.5), Ceará (177.0), and Rio Grande do Sul (175.0). The three with the lowest number were Rondônia (88.0), Roraima (76.4), and Amapá (73.7).

The three capitals with the highest medical appointment productivity were Fortaleza (470.6), Rio de Janeiro (422.8), and Curitiba (382.0). The three lowest were in Salvador (131.6), Belém (76.0), and Macapá (72.2). Regarding nursing, the three highest means were Fortaleza (420.0), Rio de Janeiro (258.8), and Porto Alegre (241.4). The three lowest were in Salvador (55.0), Macapá (46.4), and Belém (45.6) (Table 2).

Table 2
Description of the mean number of teams, individual care, and productivity per team of doctors and nurses in PHC in the capitals. Brazil, 2023.

Capital	Number of teams	Doctors		Nurses
		Individual appointments per month	Productivity per team per month	Individual appointments per month	Productivity per team per month
Fortaleza	497	233,780	470.6	208,623	420.0
Rio de Janeiro	1,340	566,662	422.8	346,808	258.8
Curitiba	409	156,181	382.0	72,094	176.3
Porto Alegre	398	144,029	362.0	96,024	241.4
Palmas	87	29,431	338.3	14,225	163.5
Belo Horizonte	596	180,493	302.8	133,453	223.9
Campo Grande	258	74,230	287.5	37,885	146.7
Aracaju	156	42,332	271.2	19,261	123.4
São Paulo	2,096	563,645	269.0	324,305	154.8
Florianópolis	197	48,616	246.3	25,688	130.1
Cuiabá	118	28,356	240.1	15,439	130.7
Rio Branco	104	21,476	207.5	12,388	119.7
Goiânia	266	53,809	202.0	27,411	102.9
Natal	165	32,972	199.7	12,684	76.8
Teresina	263	51,086	194.0	28,094	106.7
Manaus	464	87,638	188.9	80,402	173.3
Brasília	626	117,849	188.3	98,224	157.0
Vitória	115	20,534	178.7	6,390	55.6
João Pessoa	206	36,619	177.5	17,613	85.4
Porto Velho	108	16,492	152.9	7,297	67.7
Maceió	98	14,652	149.8	8,094	82.7
São Luís	170	24,447	143.8	23,878	140.5
Boa Vista	108	15,100	139.2	6,498	59.9
Recife	362	49,259	136.2	29,049	80.3
Salvador	501	65,984	131.6	27,589	55.0
Belém	253	19,242	76.0	11,552	45.6
Macapá	100	7,248	72.2	4,658	46.4

• Source: Authors.

After multiple linear regression to investigate the association of the selected variables with the number of individual appointments per team of doctors and nurses in PHC, the variables “PHC coverage” and “users registered per team” were positively associated with the productivity of doctors and nurses. The 1% increase in PHC coverage in the municipality led to an increase of 1.30 and 0.95 in the number of medical and nursing appointments per team per month, respectively. Adding one registered user per team in the municipality led to an increase of 0.06 and 0.01 in the monthly productivity of doctors and nurses, respectively (Table 3).

Table 3
Result of multiple linear regression for the association between selected variables and the number of individual appointments by a team of doctors and nurses in PHC in the municipalities. Brazil, 2023.

Variables	β coefficient (95%CI)
	Doctors	Nurses
PHC coverage	1.30 (1.08, 1.53)***	0.95 (0.82, 1.08)***
Registered users by team	0.06 (0.05, 0.06)***	0.01 (0.01, 0.02)***
Number of nursing technicians/assistants per team	11.13 (8.68, 13.58)***	3.89 (2.28, 5.50)***
Nº of ACS per team	-7.00 (-9.10, -4.90)***	-1.07 (-2.30, 0.16)+
Status of implementation of electronic medical records
Not implemented	Ref.	Ref.
Partially implemented	40.29 (24.01, 56.56)***	17.36 (7.34, 27.38)***
Implemented	62.86 (46.19, 79.53)***	27.47 (17.38, 37.55)***
Socioeconomic Vulnerability Index (SVI)
Very high	Ref.	Ref.
High	18.98 (12.10, 25.87)***	-7.79 (-12.61, -2.96)**
Medium	69.52 (60.60, 78.43)***	-6.22 (-11.65, -0.79)*
Low	103.45 (92.12, 114.79)***	-5.79 (-11.86, 0.28)+
Very low	146.26 (130.24, 162.29)***	-1.24 (-10.16, 7.68)
Aging index	0.15 (0.00, 0.31)+
Number of hospitalizations due to cardiovascular diseases (Chapter IX ICD 10)	0.01 (0.00, 0.02)+
Expenditure paid in PHC per inhabitant	0.07 (0.04, 0.09)***	-0.03 (-0.04, -0.01)***
Population size
Up to 20 thousand	Ref.
20-50 thousand	-12.46 (-19.77, -5.15)***
50-100 thousand	-25.59 (-36.78, -14.40)***
More than 100 thousand	-63.04 (-76.28, -49.80)***

• + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.
• Source: Authors.

The number of nursing technicians or assistants per team was not positively associated with the number of medical and nursing appointments. However, the number of community health workers (ACS) was negatively associated with medical appointments. Municipalities with electronic medical records implemented or partially implemented had a higher mean doctor and nurse productivity than municipalities without implementation.

Municipalities with a lower SVI were positively associated with doctor productivity per team. The “very low” vulnerability group had a mean of 149.26 more medical appointments than the very high SVI group. Regarding nursing, the SVI had the opposite association; the greater the vulnerability, the lower the productivity. However, the size of this effect was smaller, losing statistical significance for the “low” and “very low” groups against the municipalities in the “very high” vulnerability group.

Neither the aging index nor the number of hospitalizations due to cardiovascular diseases (Chapter IX ICD 10) per 100,000 inhabitants showed a statistically significant association with doctor productivity. They did not remain in the final model for nursing. However, the expenditure paid in PHC per inhabitant was positively associated with doctor productivity and negatively with nurse productivity. Municipalities with larger populations had worse doctor productivity than those with smaller populations.

Regarding the “PHC service delivery” domain, in the “Collective activities” dimension, doctor and nurse productivity was positively associated with the annual number of team meetings, health education, and group care per team. Meetings with other health teams were negatively associated only with doctor productivity. Social control or social mobilization activities were not associated with the productivity of either professional (Table 4).

Table 4
Result of multiple linear regression for the association between collective activities and the number of individual appointments by a team of doctors and nurses in PHC in the municipalities. Brazil, 2023.

Variablesª	β coefficient (95%CI)
	Doctors	Nurses
Team meeting	0.66 (0.36, 0.96)***	0.24 (0.10, 0.39)**
Meeting with other health teams	-2.40 (-3.24, -1.57)***	-0.10 (-0.50, 0.30)
Intersectoral meeting, local health council, or social control	0.89 (-0.86, 2.63)	-0.75 (-1.55, 0.05)+
Health education	0.55 (0.35, 0.76)***	0.28 (0.12, 0.44)***
Collective service	0.24 (0.11, 0.36)***	0.13 (0.06, 0.20)***
Assessment/collective procedure	0.06 (-0.04, 0.15)	0.06 (-0.03, 0.15)
Social mobilization	0.11 (-0.25, 0.47)	0.23 (-0.08, 0.55)

• + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001. ^a Collective activities per year per team.
• Source: Authors.

Discussion

Doctor and nurse productivity in PHC in Brazilian municipalities is very heterogeneous, so there was considerable variation between Brazilian municipalities. The standard productivity deviation was 131 appointments per team for doctors and 63.2 for nurses, confirming this variation.

From a technical and legislative viewpoint, the duties of professionals working in eSF and eAP are the same, with changes mainly in the composition of the workload of some professionals. The National Primary Care Policy (PNAB)¹ indicates that all teams in municipalities must offer at least primary actions and procedures related to essential conditions of access and quality in PHC and that expanded standards (strategic actions and procedures for advancing and achieving high standards of access and quality) could respect local specificities of each region or municipality. However, considering that individual care is the basis for access to several essential standard services, the variation found in the number of medical and nursing care per team is troubling, indicating that the functioning of the PHC and possibly access to actions and services among registered people is very different and potentially unequal in our country.

Increasing PHC coverage, i.e., the number of teams (eSF or eAP) per inhabitant in the municipalities, was associated with increased productivity of both professionals under analysis. A study conducted in Porto Alegre identified that walking was the main way to travel to the PHC health unit. In this sense, greater PHC coverage means a greater likelihood of the population having a unit closer to their home. The unit proximity has already been shown to be a barrier to people’s access to PHC services^17,18. Studies in the USA¹⁹ and England^20,21 have shown that the distance from the health unit interferes with people’s service use. Furthermore, the expanded PHC coverage, as shown in this study¹⁷, facilitates access to services in rural population areas. Thus, greater coverage may imply greater proximity of the service to users. Another finding associated with increased productivity was the number of registered users per team. This association was expected since more people registered in the team’s area should generate greater demand for individual care, which can generate greater mean productivity per team in the municipality.

Municipalities with more nursing technicians and assistants per team showed greater productivity for professionals. In several places in the country, nursing technicians and assistants perform a process commonly called triage²², recording vital signs, weight, and height before appointments with doctors and nurses, which can speed up care in the office, favoring productivity throughout the day. Furthermore, suppose health units have few nursing technicians or assistants. In that case, nurses and doctors may perform some procedures (dressings, medication administration, and capillary blood glucose testing) depending on users’ needs, reducing their time on individual care.

The number of ACS per team was negatively associated with doctor productivity. In general, one ACS duty is to identify users’ needs in the territory, indicating to the team that requires a home visit from doctors and nurses²³. The availability of more ACS per team may have provided professionals with a better view of the needs for visits in the territory, investing more time in home visits, which may reduce their productivity since it is unlikely that the same number of appointments will occur compared to office shifts during the visiting shift.

Municipalities with electronic medical records implemented showed higher productivity. Some studies have already revealed that implementing electronic medical records affects the quality of care in diabetes²⁴ and pediatrics²⁵ and increases the quality of records²⁶. The electronic medical records may have reduced time searching for or reopening paper medical records and decreased the number of undue records not computed by the MS in SISAB.

The results of this study differ from those of Garnelo et al.²⁷, where the authors argue that given the high dependence on public health services and the low level of connection to supplementary health plans, there would be a growing demand for public services in municipalities with larger populations, which would result in a greater number of medical and nursing appointments, thus increasing the productivity of these locations. In this study, municipalities with smaller populations were highly productive; however, the greater the municipality’s social vulnerability, the lower the medical productivity.

One point highlighted is doctors’ greater presence and continuity in municipalities with better socioeconomic conditions²⁸. During the period studied, there were some notices from the Mais Médicos Program and the Médicos Pelo Brasil Program, which attempted to allocate professionals mainly in areas with greater socioeconomic vulnerability²⁹. The teams in these areas probably suffer from a higher turnover of doctors, which can harm the monthly team productivity. Cities with better socioeconomic conditions could exert greater power of attraction to retain medical professionals. This situation may explain why this effect is more substantial for medical productivity than nursing, which is also influenced by the same condition but has a different market reality in the country.

Furthermore, dialectically, the net per capita PHC expenditure was positively associated with medical productivity. As mentioned, better socioeconomic conditions in municipalities can offer better possibilities for adequate infrastructure in PHC and better salaries, attracting medical professionals, reducing turnover, and, therefore, enabling increased productivity.

The municipality’s population size was associated with medical productivity, corroborating the study by Girardi et al.²⁹ but not with nursing productivity. A study³⁰ analyzed Brazilian municipalities’ Fiscal, Social, and Management Responsibility Index (IFRS). It concluded that the best management indices were recorded in small Brazilian municipalities, reinforcing the SUS guideline of decentralizing the public administration of health services. This point still requires further studies, but the PHC management process in larger municipalities with a larger number of teams could be more challenging, whether in the planning, implementing, or monitoring of the several actions in the territory. The management process may be closer to the health unit in smaller municipalities, which may be associated with increased productivity of medical professionals.

A study³¹ on the work of doctors in rural-remote municipalities indicated that the professional focused primarily on care at the unit in these locations, with little knowledge of the territory, irregular collective activities, and team meetings occurring sporadically with the argument that such meetings could hinder access during the shift in which they occurred. Another study³² in the municipality of Marília (SP) identified a nursing work process in PHC primarily directed towards individual care, focusing on pathologies. Given these findings, the initial hypothesis was that there was some competition between individual care and group activities, i.e., the more time spent on meetings, health education, and other activities, the less time for individual care. However, this study’s findings contradict this setting, indicating a positive association between the annual number of team meetings, health education, and group care, which is another point that deserves further studies to deepen the understanding of this relationship.

A limitation and, consequently, reflection of this study is that the variance of the residuals explained in the regression (R²) was 44.56% and 9.5% in doctor and nurse productivity, respectively. This fact indicates that, although attempts were made to introduce several variables on several dimensions of analysis at the municipal level, obviously, some other variables were not included in the final model, which also explains part of this variability in productivity. These variables are unavailable in public access databases.

This study aimed not to attempt to construct a structural equation that would explain all the determinants of productivity but to describe the associated factors. Therefore, it is likely that there are individual characteristics of the PHC management process in each municipality, such as the care model, network organization, knowledge, coordination capacity, skills, and attitudes of the management team that may also be associated with productivity. Individual characteristics of professionals (behavioral, life projects, knowledge, skills, attitudes, political and ideological perception, among others) may also influence team productivity, depending on the autonomy of these professionals in defining their work process. This autonomy is also dialectically related to the management characteristics of their working municipality, as mentioned above.

Conclusion

Although exploratory and descriptive, this study provides fundamental elements regarding doctor and nurse productivity in Brazilian PHC. The Ministry of Health has no official recommendation regarding the monthly number of doctors or nurses per team in the Brazilian PHC. Considering that countries such as England³³ and Australia³⁴ have shown an average of 4.5 and 6.3 appointments in PHC per year per citizen, respectively, in recent years, we can consider that the productivity of individual care in Brazilian municipalities is lower than these two countries that also have a priority focus on PHC.

The mean productivity per team in Brazilian municipalities is heterogeneous, indicating that the work process in the municipalities is also very different. Although municipalities consider local or regional specificities, the variation in individual care teams would be lower in the country. The factors associated with productivity in this study can help organize PHC, with the potential to be used to understand better the teamwork process toward expanding people’s access to medical and nursing appointments.

Acknowledgments

We are grateful to the UFBA Scientific Initiation scholarship program (PIBIC), CNPq, and DESID/MS for financing the study project.

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• Chief editors:
  Maria Cecília de Souza Minayo, Romeu Gomes, Antônio Augusto Moura da Silva

Publication Dates

• Publication in this collection
  11 Aug 2025
• Date of issue
  July 2025

History

• Received
  29 July 2024
• Accepted
  18 Feb 2025
• Published
  20 Feb 2025