Factors associated with the cost of child and adolescent mental health crisis care in Brazil

ABSTRACT

OBJECTIVE  To identify the sociodemographic and clinical variables that influenced the daily costs of children and adolescents who stayed overnight in Type III Centro de Atenção Psicossocial Infantojuvenil (CAPSij - Child and Adolescent Psychosocial Care Center) or in hospital reference or emergency and urgent care from the perspective of society in the year 2023.

METHODS  This is a retrospective cost analysis. Direct and indirect costs were collected. A mixed technique was used to measure costs, prioritizing the micro-costing approach and the bottom-up method for valuation. Factors influencing costs were evaluated using univariate statistical models.

RESULTS  The study included 399 users. The average total cost was R$ 6,704.38 (US$ 1,149.99) and the average daily cost was R$ 1,097.97 (US$ 188.33). There was statistical significance in the correlation between daily cost and age, race, diagnostic subgroup, referral to the service, and reason for seeking care. The variable with the greatest positive variation in daily cost was seeking care for drug use (R$ 125.82; US$ 21.58), while the variable with the greatest negative variation in daily cost was being referred by another service (R$ 139.86; US$ 23.99).

CONCLUSIONS  To make more efficient use of available resources, it is up to the child and adolescent mental health care network to invest in the early care of children and adolescents, especially those who are most vulnerable and who may have problems with drug use, and to increasingly encourage coordination between services.

DESCRIPTORS
Mental Health; Costs and Cost Analysis; Child; Adolescent; Crisis Intervention

RESUMO

OBJETIVO  Identificar as variáveis sociodemográficas e clínicas que influenciaram os custos diários de crianças e adolescentes que ficaram em acolhimento noturno em Centros de Atenção Psicossocial Infantojuvenis tipo III ou em internação na referência hospitalar ou de emergência e urgência na perspectiva da sociedade no ano de 2023.

MÉTODOS  Trata-se de uma análise de custos retrospectiva. Foram coletados os custos diretos e indiretos. Para mensuração dos custos, foi empregada técnica mista, priorizando a abordagem de microcusteio e, para valoração, o método bottom-up. Fatores que influenciaram os custos foram avaliados com modelos estatísticos univariados.

RESULTADOS  O estudo incluiu 399 usuários. O custo médio total foi de R$ 6.704,38 (US$ 1.149,99) e o custo médio diário foi de R$ 1.097,97 (US$ 188,33). Houve significância estatística na correlação entre custo diário e idade, raça, subgrupo diagnóstico, encaminhamento para o serviço de atendimento e motivo de busca por cuidado. A variável com maior variação positiva no custo diário foi buscar o serviço por uso de drogas (R$ 125,82; US$ 21,58), enquanto a com maior variação negativa no custo diário foi ter sido encaminhado por outro serviço (R$ 139,86; US$ 23,99).

CONCLUSÕES  A fim de usar de forma mais eficiente os recursos disponíveis, cabe à rede de cuidados em saúde mental infantojuvenil investir no cuidado precoce de crianças e adolescentes, especialmente os mais vulnerabilizados e que possam ter problemas com o uso de drogas, além de incentivar cada vez mais a articulação entre serviços.

DESCRITORES
Saúde Mental; Custos e Análise de Custo; Criança; Adolescente; Intervenção em Crise

INTRODUCTION

Mental health crisis care for children and adolescents is a topic that has been studied more in recent years but is still largely underestimated¹. Although there is data on the importance of early mental health care for children and adolescents, with a view to a healthier future, little is known about the subject in literature². Crisis situations, because they are moments of extreme vulnerability, have the potential to be harmful, leaving the user more fragile and with traumas that will follow them for the rest of their life³. There is therefore an urgent need to think about ways of taking care of these periods in the best possible way⁴, especially as we know that the burden of mental health illness is higher in low- and middle-income countries⁵, such as Brazil.

In Brazil, the Rede de Atenção Psicossocial (RAPS - Psychosocial Care Network) is made up of services at different levels of care in order to provide care at different times of life. For children and adolescents, care is offered in services that must be adequately staffed and structured for their purposes⁶. In a crisis, children, adolescents, and their families, depending on their links with local services and their demands, can turn to different services, such as the Centro de Atenção Psicossocial Infantojuvenil (CAPSij - Child and Adolescent Psychosocial Care Center), general hospitals, or Unidades de Pronto Atendimento (UPA - Emergency Care Units), these last two services are strategic for immediate and more clinical care⁶. The CAPSij are the reference services for mental health and the type III CAPSij have beds for night-time reception⁷ when the user needs more intensive care, thus avoiding hospitalization and strengthening the bond between service, user, and family. There are very few CAPSij III, and only in the city of São Paulo this kind of service regulated⁸.

Faced with this range of services and possibilities, it is important to understand how care is offered in the services and what the cost of each one is, since the health budget is unique and is always in dispute, with a predominance of scarce resources⁹. Health economic evaluation is an important method for presenting financial data in the field of health¹⁰. Cost analysis is a partial economic evaluation, as there is no comparison of outcomes, but it is extremely important as it presents the costs of different services and provides data for future complete economic evaluations¹⁰.

Therefore, the aim of this study was to identify the sociodemographic and clinical variables that influence the daily costs of children and adolescents who stayed at night-time care in CAPSij III or hospitalization in hospital reference or emergency and urgency from the perspective of society in the year 2023.

METHODS

Study Design

This is a partial economic evaluation study¹⁰ with a cross-sectional statistical analysis model using retrospective data on the costs of night-time care in CAPSij III and in an inpatient service (general hospital or UPA). The perspective adopted for this research is that of society. The time horizon of the research is the 12 months of 2023; this was chosen because it is a year in which hospitalizations due to Covid-19 are already stable and because it allows us to monitor possible monthly fluctuations, as well as providing a robust amount of data. The economic analysis plan for this research is available at https://repositorio.usp.br/item/003186572.

Study Population, Data Source, and Costs

The data collected from medical records refers to children and adolescents between the ages of zero and 18 with mental health issues who were admitted for the first time in their lives to the CAPSij III night-time care or to hospitalization in urgent and emergency services. This criterion was adopted to avoid possible bias in the care actions that the teams could carry out for repeat users¹¹ who require other care and coordination.

This research used a mixed technique to identify and measure costs¹⁰, prioritizing the micro-costing approach¹², which was used to obtain the direct medical-hospital and non-medical-hospital costs. For indirect costs, it was necessary to use macro-costing. The bottom-up approach was used to value the costs¹⁰. The Chart shows all the data identified, collected, measured, and valued.

Chart
Description of direct and indirect costs and the identification and measurement approach used.

Approach	Direct costs		Indirect costs
	Medical-hospital	Non-medical-hospital
Micro-costing	Care provided by health professionals	Apportionment of security, cleaning, and hotel services	Days of work lost by the person in charge during treatment
	Exams
	Materials
	Medicines
	Administrative services
Macro costs		Meals	School dropouts
		Transportation	Shelter under state guardianship
		Apportionment of health service bills (rent, electricity, telephone, internet, cooking gas, and laundry)

Socio-demographic data, procedures (health actions), and materials used during each user’s period of care were collected by analyzing individual medical records at the health units. The figures for the direct costs of human resources, administrative work, service bills, and transport were obtained from the service management organizations and service contract analysis. The Brazilian government’s Price Panel was used for medicines and materials and the Sistema de Gerenciamento da Tabela de Procedimentos, Medicamentos e OPM (Sigtap - Procedures, Medications, and OPM Table Management System) of the Sistema Único de Saúde (SUS – Unified Health System) for exams. There are no figures for hospitalization or reception fees in Sigtap.

The figures for indirect costs correspond to: school dropout, which was valued on the basis of the literature¹³; the permanence of users in institutions under the tutelage of the State, such as Institutional Foster Care Services or services for the execution of socio-educational measures, valued using data available for public access (Municipal Secretariats of Social Assistance and Development of São Paulo, Curitiba, and Campo Grande and State Secretariat of Justice and Public Security of Mato Grosso do Sul); and the days lost from work by those responsible, information obtained from users’ medical records and calculated according to the human capital approach¹⁰.

The occupation of the guardians was the most missing information in the medical records, but in the records that did contain this information, it was observed that 75% of the parents were in informal or self-employed low-income jobs, such as domestic workers and waiters/waitresses. Considering this finding and the fact that the literature indicates a low family income among mental health users and their families¹⁴, we decided to use the 2023 minimum wage as the basis for the calculations, thus avoiding overestimates.

All costs, direct and indirect, were calculated by unit price, according to the source of the information, and multiplied by the time and number of times used by each user. To extract data from medical records and costs, instruments developed by the authors in RedCap^® were used.

As the data refers to a single year, no discount rate was used. All costs were collected using values for the month of December 2023 in Brazilian real (R$) and were adjusted by the Broad National Consumer Price Index (IPCA) for January 2025¹⁵. The analyses were carried out in Brazilian currency, but in the Results and Discussion sections, the values are presented in US dollars (US$), whose exchange rate was US$ 1.00 = R$ 5.83, also referring to January 2025¹⁵, to make the data more accessible to different contexts.

Study Context

To select the services, the RAPS were selected if they had at least one CAPSij III in the territory and a reference inpatient service for children and adolescents in mental health crisis. Thus, services in four Brazilian cities were included in the research, namely Campo Grande (MS), Curitiba (PR), Rio de Janeiro (RJ) and São Paulo (SP). All are capitals of Brazilian states and are among the 20 largest cities in the country¹⁶. For RAPS with more than one CAPSij III, the first to become type III was included. For the RAPS that had a reference for admission to a general hospital, the general hospital included was the one with the highest number of attendances in 2023, according to the SUS Hospital Information System. For the RAPS with a UPA as a reference for hospitalization, the UPA in the most populous region of the city was included, since the data on the number of attendances is not included in the SUS Outpatient Information System. In this study, as hospitals and UPAs carried out the same care actions, they were treated in a single category, as “Hospitalization”.

This study was approved by the Research Ethics Committees of the Municipal Health Departments of Campo Grande, Curitiba, Rio de Janeiro, and São Paulo and of Universidade de São Paulo under CAAE code 74548823.0.3004.5162, 74548823.0.3002.0101, 74548823.0.3003.5279, 74548823.0.3001.0086 e 74548823.0.0000.5392, respectively.

Statistical Analysis

The data was stored and exported from RedCap^® and analyzed using Stata software (StataCorp 2007), version 15. Cost data are presented as means and 95% confidence intervals (95%CI) for the mean. An ordinary least squares multiple regression model with standard errors robust to heteroscedasticity was used to test the correlation between daily cost and the independent variables. This type of analysis was chosen because it is ideal for identifying which factors are most strongly associated with daily cost. Heteroscedasticity-robust standard errors were used to correct for possible problems arising from error variance between different groups, thus ensuring robust results¹⁷.

The daily cost of each user was chosen as the dependent variable, since there was great variation in the total cost of treatment between users and the length of stay directly influenced the total cost. On the other hand, the daily cost of users remained similar between users, regardless of the service. Moreover, the daily costs showed a normal and symmetrical distribution. In this way, the average daily cost can also limit the unobserved impacts of heterogeneity by reducing asymmetry and leptokurtosis, presenting more direct data to decision-makers¹⁸.

The independent variables were age, gender, race, school affiliation, diagnostic subgroup¹⁹ — the “Other” group included diagnoses related to self-extermination outside of group F —, referral from another service, reason for seeking treatment, length of stay (in days), having been cared for at CAPSij or in an inpatient service, and medication use (in number of times during the entire treatment). Values of p < 0.05 were considered statistically significant.

RESULTS

In 2023, 399 children and adolescents were admitted to CAPSij III at night or hospitalized in a general hospital or UPA for the first time in their lives, making up the sample of this study. The characterization of the study population is shown in Table 1. It can be seen that the average age is very similar between the services and there is a predominance of cisgender girls in both services, although this percentage is significantly higher in hospitalizations. It is noteworthy that there are no transgender people in the hospitalizations. As for race, the non-black population was the majority in both services, and the significant percentage of lack of data on race/color in the inpatient services is noteworthy. Regarding schooling, it is important to note that 14% of the total sample is not accessing this right.

Table 1
Sociodemographic and clinical characteristics of the sample in absolute, maximum, and average numbers for age, length of stay, and use of medication, and percentage of the total population by service in which they were treated.

	CAPSij (n = 298)	Hospitalization (n = 101)	Total (n = 399)
Age	14.81 (5–18)	14.48 (8–17)	14.73 (5–18)
Gender
Cisgender man	106 (35.6%)	25 (24.8%)	131 (32.8%)
Transgender man	15 (5.0%)	0	15 (3.8%)
Cisgender woman	171 (57.4%)	76 (75.2%)	247 (61.9%)
Transgender woman	5 (1.7%)	0	5 (1.3%)
Non-binary	1 (0.3%)	0	1 (0.2%)
Race
Black population	111 (37.2%)	35 (34.6%)	146 (36.6%)
Non-black population	184 (61.7%)	53 (52.5%)	237 (59.4%)
N/C	3 (0.1%)	13 (12.9%)	16 (4%)
School attendance
Attends school	248 (83.2%)	89 (88.1%)	337 (84.5%)
Does not attend school	49 (16.4%)	7 (6.9%)	56 (14%)
N/C	1 (0.4%)	5 (4.9%)	6 (1.5%)
Diagnostic subgroupa
F00–F09	0	1 (1.0%)	1 (0.2%)
F10–F19	57 (19.1%)	11 (10.9%)	67 (17%)
F20–F29	30 (10.0%)	7 (6.9%)	37 (9.3%)
F30–F39	137 (46.0%)	20 (19.8%)	157 (39.3%)
F40–F49	66 (22.1%)	17 (16.8%)	83 (20.1%)
F50–F59	7 (2.3%)	0	7 (1.7%)
F60–F69	50 (16.8%)	1 (1.0%)	51 (12.8%)
F70–F79	25 (8.4%)	0	25 (6.3%)
F80–F89	18 (6.0%)	0	18 (4.5%)
F90–F99	51 (17.1%)	2 (2.0%)	53 (13.3%)
Other groupsb	6 (2.0%)	53 (52.5%)	59 (14.8%)
N/C	3 (1.0%)	0	3 (0.7%)
Referral
Spontaneous demand	87 (29.2%)	87 (86.1%)	174 (43.6%)
Referred	211 (70.1%)	14 (13.9%)	225 (56.4%)
Living situation
Living with family	263 (88.3%)	86 (88.1%)	349 (87.5%)
Under state guardianship	34 (11.4%)	1 (1.0%)	35 (8.8%)
N/C	1 (0.3%)	14 (13.9%)	15 (3.7%)
Reason for search
Mood swings	11 (3.7%)	11 (10.9%)	22 (5.5%)
Psychotic break	134 (45.0%)	20 (19.8%)	154 (38.6%)
Clinical care	21 (7.0%)	3 (3.0%)	24 (6.0%)
Risk of death	155 (52.0%)	65 (64.4%)	170 (42.6%)
Use of SPA	43 (14.4%)	5 (5.0%)	48 (12.0%)
Vulnerabilities	26 (8.7%)	0	26 (6.5%)
Care service
CAPSij	–	–	298 (74.7%)
Hospitalization	–	–	101 (25.3%)
Length of stay	6.68 (1–63)	3.76 (1–15)	5.94 (1–63)
Use of medication	12.13 (0–110)	6.83 (0–42)	10.79 (0–10)

• N/C: data not in medical records; CAPSij: Child and Adolescent Psychosocial Care Center.
• ^a In these categories, users could correspond to more than one variable.
• ^b The diagnoses applied in this variable correspond to groups R, T, X, Y, and Z related to forms of self-extermination or altered behavior.

When considering the diagnostic subgroup, diagnoses related to mood disorders were predominant in CAPSij and in hospitalization; diagnoses related to attempts at self-extermination stood out in other ICD-10 groups. Regarding referrals, while users arrive at CAPSij referred by another service, most users seek hospitalization on spontaneous demand. Users generally live with their families, but there are a significant number of users under state guardianship who come to CAPSij for overnight care.

As for the reason for seeking services, the risk of death was the main cause, followed by psychotic crisis. The length of stay differed significantly between the services: while CAPSij tended to have inpatient stays lasting an average of almost seven days, inpatient stays lasted an average of almost four days. The frequency of medication use also differed significantly: CAPSij used medication an average of 12 times and inpatient care almost seven times.

The total cost and the daily cost are shown in Table 2. It can be seen that the total costs for CAPSij users had a higher average, since the length of stay directly affects these costs. On the other hand, the daily cost values varied less around the average.

Table 2
Values (in Brazilian reals) referring to the total cost and cost per day of treatment for the overall sample and divided between CAPSij and hospitalization.

	Total cost			Daily cost
	Average	Lower 95%CI	Upper 95%CI	Average	Lower 95%CI	Upper 95%CI
Sample	R$ 6,704.38	R$ 6,076.91	R$ 7,331.85	R$ 1,097.97	R$ 839.84	R$ 1,123.3
CAPSij	R$ 7,641.53	R$ 6,848.46	R$ 8,434.6	R$ 1,121.11	R$ 1,089.79	R$ 1,152.43
Hospitalization	R$ 3,939.32	R$ 3,404.8	R$ 4,473.84	R$ 1,029.7	R$ 994.36	R$ 1,065.04

• CAPSij: Child and Adolescent Psychosocial Care Center.

In hospitalization, direct costs accounted for the largest share of the average daily cost: 86.96% in hospital medical costs and 7.41% in non-medical-hospital costs, for a total of 94.17%. In the CAPSij, direct costs were also the majority, but medical-hospital costs accounted for 88.82% and non-medical-hospital costs for 6.37%. Indirect costs were 4.81% in CAPSij and 5.83% in hospitalization.

Table 3 shows how the categories studied by type of cost contributed to the daily cost per service. There are differences between the services. The use of medication, exams, and materials for procedures has a higher percentage in hospitalization. Non-medical-hospital costs have a greater influence on the daily cost of hospitalization, as do indirect costs. It is worth noting that the biggest difference in percentage between services in non-medical-hospital costs refers to the apportionment of common bills and services, which can be explained by the higher cost of maintaining hospital and urgent and emergency care structures than territorially based services.

Table 3
Description of the categories included in each type of cost and the percentage of participation in the average daily value of users by type of service.

Type of cost		Category	CAPSij	Hospitalization
Direct costs	Medical-hospital costs	Human resources	99.05%	95.97%
		Medication	0.31%	1.09%
		Tests	0.41%	1.89%
		Materials	0.12%	0.84%
		Administrative work	0.11%	0.22%
	Non-medical-hospital costs	Transportation	50.58%	46.95%
		Apportionment of common bills and services	0.74%	5.17%
		Meals	48.68%	47.88%
		Being in institutions	14.23%	2.82%
Indirect costs		School dropouts	42.32%	18.49%
		Working days lost by the guardian	43.47%	78.69%

• CAPSij: Child and Adolescent Psychosocial Care Center.

It can be seen that in indirect costs there are significant differences in the three categories: more CAPSij users were in state-run services, and more were out of school (Table 3). Regarding days of work lost by guardians, this category played a greater role in the daily cost of inpatient treatment, since all hospitals and UPAs have a protocol that requires the user to be accompanied during treatment, while at CAPSij this recommendation can vary depending on the assessment of the case and agreements with the family.

Table 4 shows the correlation between the sample’s sociodemographic and clinical data and the daily cost of the children and adolescents treated. It can be seen that there was statistical significance in the relationships between daily cost and age, race, being from diagnostic subgroup F30-F39, F40-F49, having been referred by another service and having sought care for drug use and vulnerability issues.

Table 4
Ordinary least squares regression analysis by daily treatment value.

Variable	Coefficient	Standard error	p-value	95%CI lower	95%CI higher
Age	13.68	6.45	0.04	1	26.37
Gender
Cisgender man	Reference
Transgender man	-24.11	69.93	0.73	-161.62	113.4
Cisgender woman	-14.08	32.6	0.67	-78.18	50.02
Transgender woman	115.15	93.33	0.22	-68.39	298.69
Non-binary	-107.5	56.03	0.06	-217.68	2.68
Race (black/brown)	-102.6	24.52	p < 0.0001	-150.81	-54.38
School attendance (yes)	13.26	44	0.76	-73.28	99.79
Diagnostic subgroup
F00–F09	-134.76	81.12	0.1	-294.28	24.75
F10–F19	-76.19	44.77	0.09	-164.23	11.85
F20–F29	-38.88	49.57	0.43	-136.36	58.6
F30–F39	-81.3	33.74	0.02	-147.64	-14.95
F40–F49	-100.75	33.6	p < 0.0001	-166.83	-34.67
F50–F59	49.38	87.8	0.57	-123.28	222.05
F60–F69	-28.92	45.61	0.53	-118.61	60.78
F70–F79	-67.78	48.87	0.17	-163.87	28.32
F80–F89	5.16	73.06	0.94	-138.51	148.83
F90–F99	-55.03	43.27	0.2	-140.12	30.07
Other groups	-78.75	42.96	0.07	-163.22	5.72
Referral (Yes)	-139.86	30.47	p < 0.0001	-199.78	-79.94
Reason for search
Mood swings	-0.48	42.53	0.99	-84.11	83.14
Psychotic crisis	20.88	43.03	0.63	-63.73	105.49
Clinical care	21.7	58.38	0.71	-93.11	136.51
Risk of death	-16.66	40.94	0.68	-97.16	63.85
Use of SPA	125.82	55.82	0.03	16.05	235.59
Vulnerabilities	124.12	57.74	0.03	10.58	237.67
Care service (CAPSij)	-16.85	37.12	0.65	-89.85	56.15
Length of stay	-6.25	6.41	0.33	-18.85	6.36
Use of medication	4.1	3.04	0.18	-1.87	10.08
Constant	1,074.09	122.86	p < 0.0001	832.48	1,315.70

• CAPSij: Child and Adolescent Psychosocial Care Center; 95%CI: 95% confidence interval.

With each year of age of the user, the daily cost of treatment increased by R$ 13.68 (US$ 2.35). Belonging to the black population (being of black or brown race/color) decreased the daily cost by R$ 102.60 (US$ 17.60). Being diagnosed in the F30-F39 subgroup had an effect of R$ 81.30 (US$ 13.94) less on the daily cost of treatment and being diagnosed in the F40-F49 subgroup reduced the daily cost by R$ 100.75 (US$ 17.28). Being referred to CAPS or hospitalization by another service reduced the daily cost of treatment by R$ 139.86 (US$ 23.99).

Finally, seeking treatment for drug use increased the daily cost by R$ 125.82 (US$ 21.58) and cases seeking treatment for vulnerability had an extra R$ 124.12 (US$ 21.29) in the average daily cost of care. Therefore, the variables that showed the greatest variation in cost were having been referred by another service for treatment, followed by having sought the service due to drug use and vulnerabilities.

DISCUSSION

The service users were, on average, adolescents. In recent years, especially after the Covid-19 pandemic, there has been an increase in demand for mental health care in this age group²⁰. Diagnoses related to self-extermination and mood swings were predominant and are related to contemporary adolescence²¹. It should also be noted that, in this study, the daily cost increased with the age of the user, which is relevant data for care networks to invest in prevention and promotion of care, both for the population’s own benefit and for the country’s economy.

Cisgender girls made up most of the sample, which seems to be a worldwide trend²². The low demand for transgender people in the services studied, especially in hospitalization, is noteworthy. The lack of identification of gender identity and institutional protocols for the care of transgender children and adolescents, combined with low professional knowledge about transsexuality and an unwelcoming environment, are some of the barriers to access that need to be resolved by the services. Continuing education for professionals is an interesting strategy so that transgender children and adolescents can receive better care and create a bond with the services²³.

The black population in this study did not reflect the national reality of a majority population, and this difference can be explained by the fact that Curitiba was one of the settings where the study was carried out, where the black population is lower in percentage¹⁶. In any case, two points regarding race/color stand out: firstly, the number of medical records without this information was not significant in the total sample, but it is noteworthy that a piece of information that is mandatory in public services²⁴ was missing. And secondly, it is noteworthy that being black influenced the reduction in daily costs. Could it be that black users are less demanding of care, given that human resources account for the largest share of the daily cost? In view of similar findings on the relationship between lower use of medication and access to services and race/color²⁵,²⁶, in addition to human rights violations, it should be pointed out that teams should pay attention to institutional racism. In addition, as they were a minority in the study, teams should be aware of the barriers to access to services for this population.

Dropping out of school had a big impact on the daily cost, but it is mainly a long-term problem for the individual and the country. Having fewer opportunities to access good jobs and social change negatively affects quality of life, as well as accentuating and maintaining social inequalities¹³.

Considering the rights of children and adolescents²⁷, the permanence of the guardian during the period of care is a point to be debated, both clinically and politically. Clinically, it may be that the presence of the guardian is not indicated by the team, which is provided for in the Estatuto da Criança e do Adolescente (ECA - Statute of the Child and Adolescent)²⁷, and the discussion should be carried out cautiously and ethically. Politically, there is the issue of guardians of minors not having the guaranteed right to accompany their children in formal jobs and losing their income from the day’s work in informal jobs, which mainly affects women when they are the main or only caregivers²⁸.

The difference in the participation of the categories in direct medical and hospital costs may be due to the greater use of soft technologies in CAPSij, which demands more from professionals, while in hospitalization services soft-hard and hard technologies are used more often²⁹. Inter- and intra-sectoral networking was shown to be powerful in this study, having the greatest influence on reducing daily costs and being a resource that has already been shown to be cost-effective³⁰. As well as providing users with more integrated care, the link between services makes it easier for users and their families not to get lost in the network or even miss out on services that are necessary³¹. It is also worth remembering that referral does not guarantee careful coordination of the care network³¹, but it is a good path to follow.

Seeking services due to risk of death and psychotic crisis is in line with what the population understands as the reason for the crisis³². The lower cost of affective, mood, and neurotic disorders is in line with the literature review by Christensen et al.³³ Drug use was one of the variables that had the greatest impact on the increase in daily costs, and prevention and harm reduction could help to reduce this³⁴,³⁵. As for vulnerabilities, only CAPSij met this demand as a crisis, which is in line with Dell’Acqua and Mezzina’s definition³², but which is not appropriate in hospital and urgent and emergency care. However, the vulnerabilities showed a significant increase in daily costs and need to be addressed not only by the health network, but by the entire child and adolescent care network, especially to ensure the rights of this population²⁷.

This study has some limitations. Firstly, the costs of medications dispensed by the services’ pharmacies were not considered, as hospitals and UPAs did not have these records in their medical charts, which would overestimate the costs of CAPSij users. High-cost medications not related to mental health care were also disregarded from the analysis in order not to skew the costs. Secondly, the values of indirect costs had to be estimated because there were no records of actual situations, as well as potential future impacts that could not be measured, such as loss of future income from a profession that was not embraced. We suggest investing in training and monitoring the completion of documents, such as medical records, so that future research can be carried out with real-world data. Thirdly, it should be pointed out that, as it is based on data from medical records, some information may not have been recorded. Fourthly, as the study was carried out in cities in the central-southern region of Brazil, the results may not be generalizable to all regions. And lastly, the costs related to some groups, especially the most vulnerable, may be underestimated due to access barriers.

Finally, this study has made an unprecedented contribution by identifying, measuring, and valuing the costs that make up the total and daily costs of two relevant strategies in crisis care for children and adolescents in Brazil, as well as identifying factors that may be related to the variation in daily costs. Based on the findings of this study and considering that mental health care for children and adolescents does not only take place in the health network, but through the guarantee of rights, the importance of the network in providing early care to this population is highlighted, to minimize future suffering and optimize public budget costs. The most vulnerable and those who may have problems with drug use should be monitored more closely. Coordination between services so that actions such as careful referrals can take place must continue and be strengthened. Finally, it is up to the network and professionals to pay attention to how racism and situations that can hinder access to services can affect care. It is suggested that full economic evaluations, such as cost-effectiveness and cost-utility evaluations, be carried out to help decision-makers allocate resources to network services.

REFERENCES

• ¹ Aladro CP, Yu R, Sharma M, Ipince A, Bakrania S, Shokraneh F, et al. D. mind the gap: child and adolescent mental health and psychosocial support interventions: an evidence and gap map of low and middle-income countries. Florence: UNICEF Office of Research - Innocenti; 2022.
• ² Weine S, Horvath Marques A, Singh M, Pringle B. Global child mental health research: time for the children. J Am Acad Child Adolesc Psychiatry. 2020 Nov;59(11):1208-11. https://doi.org/10.1016/j.jaac.2020.06.015
  » https://doi.org/10.1016/j.jaac.2020.06.015
• ³ Mulraney M, Coghill D, Bishop C, Mehmed Y, Sciberras E, Sawyer M, Efron D, Hiscock H. A systematic review of the persistence of childhood mental health problems into adulthood. Neurosci Biobehav Rev. 2021 Oct;129:182-205. https://doi.org/10.1016/j.neubiorev.2021.07.030
  » https://doi.org/10.1016/j.neubiorev.2021.07.030
• ⁴ Lamb CE. Alternatives to admission for children and adolescents: providing intensive mental healthcare services at home and in communities: what works? Curr Opin Psychiatry. 2009;22(4):345-50.
• ⁵ GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017 Sep 16;390(10100):1211-59.
• ⁶ Ministério da Saúde (BR). Atenção psicossocial a crianças e adolescentes no SUS: tecendo redes para garantir direitos. Brasília, DF: Ministério da Saúde; 2014.
• ⁷ Ministério da Saúde (BR). Portaria Nº 854, de 22 de agosto de 2012. Brasília, DF: Ministério da Saúde; 2012 [cited 2025 Jun 22]. Available from: http://bvsms.saude.gov.br/bvs/saudelegis/sas/2012/prt0854_22_08_2012.html
  » http://bvsms.saude.gov.br/bvs/saudelegis/sas/2012/prt0854_22_08_2012.html
• ⁸ Secretaria Municipal da Saúde (São Paulo). Portaria nº 342, de 26 de setembro de 2019. São Paulo (BR): Secretaria Municipal da Saúde; 2019.
• ⁹ Peres UD. The governance of public budgeting: a proposal for comparative analyses: the cases of São Paulo and London. Braz Polit Sci Rev. 2022;16(2):e0005. https://doi.org/10.1590/1981-3821202200020003
  » https://doi.org/10.1590/1981-3821202200020003
• ¹⁰ Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. 4th ed. Oxford: Oxford University Press; 2015.
• ¹¹ Fitzgerald C, Hurst S. Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics. 2017;18(1):19. https://doi.org/10.1186/s12910-017-0179-8
  » https://doi.org/10.1186/s12910-017-0179-8
• ¹² Ministério da Saúde (BR). Secretaria de Ciência, Tecnologia, Inovação e Insumos Estratégicos em Saúde, Departamento de Gestão e Incorporação de Tecnologias e Inovação em Saúde. Diretriz metodológica: estudos de microcusteio aplicados a avaliações econômicas em saúde. Brasília, DF: Ministério da Saúde; 2021.
• ¹³ Barros RP, Franco S, Machado LM, Rocha G, Zanon D, editors. Consequências da violação do direito à educação. Rio de Janeiro: Autografia Editora; 2021.
• ¹⁴ Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc Sci Med. 2013 Aug;90:24-31. https://doi.org/10.1016/j.socscimed.2013.04.026
  » https://doi.org/10.1016/j.socscimed.2013.04.026
• ¹⁵ Banco Central do Brasil. Brasília, DF: Banco Central do Brasil; 2025 [cited 2025 Mar 15]. Available from: https://www3.bcb.gov.br
  » https://www3.bcb.gov.br
• ¹⁶ Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2022 Rio de Janeiro: IBGE; 2022 [cited 2025 Jun 22]. Available from: https://www.ibge.gov.br/estatisticas/sociais/saude/22827-censo-demografico-2022.html
  » https://www.ibge.gov.br/estatisticas/sociais/saude/22827-censo-demografico-2022.html
• ¹⁷ Mansournia MA, Collins GS, Nielsen RO, Nazemipour M, Jewell NP, Altman DG, Campbell MJ. A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration. Br J Sports Med. 2021 Sep;55(18):1009-17. https://doi.org/10.1136/bjsports-2020-103652
  » https://doi.org/10.1136/bjsports-2020-103652
• ¹⁸ May P, Garrido MM, Cassel JB, Morrison RS, Normand C. Using length of stay to control for unobserved heterogeneity when estimating treatment effect on hospital costs with observational data: issues of reliability, robustness, and usefulness. Health Serv Res 2016;51:2020-43. https://doi.org/10.1111/1475-6773.12460
  » https://doi.org/10.1111/1475-6773.12460
• ¹⁹ World Health Organization. ICD-10 version: 2019. Geneva: World Health Organization; 2019 [cited 2025 Jun 22]. Available from: https://icd.who.int/browse10/2019/en
  » https://icd.who.int/browse10/2019/en
• ²⁰ Oliveira JMD, Butini L, Pauletto P, Lehmkuhl KM, Stefani CM, et al. Mental health effects prevalence in children and adolescents during the COVID-19 pandemic: a systematic review. Worldviews Evid Based Nurs. 2022 mar 1;19(2):130-137. https://doi.org/10.1111/wvn.12566
  » https://doi.org/10.1111/wvn.12566
• ²¹ García-Fernández L, Romero-Ferreiro V, Izquierdo-Izquierdo M, Rodríguez V, Alvarez-Mon MA, Lahera G, et al. Dramatic increase of suicidality in children and adolescents after COVID-19 pandemic start: a two-year longitudinal study. J Psychiatr Res. 2023 Jul;163:63-7. https://doi.org/10.1016/j.jpsychires.2023.04.014
  » https://doi.org/10.1016/j.jpsychires.2023.04.014
• ²² Campbell OLK, Bann D, Patalay P. The gender gap in adolescent mental health: A cross-national investigation of 566,829 adolescents across 73 countries. SSM Popul Health. 2021 Jan 26;13:100742. https://doi.org/10.1016/j.ssmph.2021.100742
  » https://doi.org/10.1016/j.ssmph.2021.100742
• ²³ Silveira JCP, Souza DMD, Cardoso CDS, Oliveira MAFD. Barriers and facilitating strategies for healthcare access and reception for transgender children and adolescents. Rev Bras Enferm. 2025;78(suppl 2):e20240266. https://doi.org/10.1590/0034-7167-2024-0266
  » https://doi.org/10.1590/0034-7167-2024-0266
• ²⁴ Ministério da Saúde (BR). Portaria nº 344, de 1º de fevereiro de 2017. Dispõe sobre o preenchimento do quesito raça/cor nos formulários dos sistemas de informação em saúde. Diario Oficail Uniao. 2 fev 2017.
• ²⁵ Ma S, Frick KD, Bleich S, Dubay L. Racial disparities in medical expenditures within body weight categories. J Gen Intern Med. 2012 Jul;27(7):780-6. https://doi.org/10.1007/s11606-011-1983-3
  » https://doi.org/10.1007/s11606-011-1983-3
• ²⁶ Leal MC, Gama SGN da, Pereira APE, Pacheco VE, Carmo CN do, Santos RV. A cor da dor: iniquidades raciais na atenção pré-natal e ao parto no Brasil. Cad Saúde Pública. 2017;33:e00078816. https://doi.org/10.1590/0102-311X00078816
  » https://doi.org/10.1590/0102-311X00078816
• ²⁷ Brasil. Lei nº 8.069, de 13 de julho de 1990. Dispõe sobre o Estatuto da Criança e do Adolescente. Diário Oficial Uniao. 13 jul 1990.
• ²⁸ Sorj B, Fontes A, Machado DC. Políticas e práticas de conciliação entre família e trabalho no Brasil: issues and policies in Brazil. Cad Pesqui. 2007;37(130):573-94. https://doi.org/10.1590/s0100-15742007000300004
  » https://doi.org/10.1590/s0100-15742007000300004
• ²⁹ Merhy EE. Saúde: a cartografia do trabalho vivo. 2a ed. São Paulo: Hucitec; 2005.
• ³⁰ Wright DR, Haaland WL, Ludman E, McCauley E, Lindenbaum J, Richardson LP. The costs and cost-effectiveness of collaborative care for adolescents with depression in primary care settings: a randomized clinical trial. JAMA Pediatr. 2016 nov 1;170(11):1048-54. https://doi.org/10.1001/jamapediatrics.2016.1721
  » https://doi.org/10.1001/jamapediatrics.2016.1721
• ³¹ Kemper MLC, Martins JPDA, Monteiro SFS, Pinto TDS, Walter FR. Integralidade e redes de cuidado: uma experiência do PET-Saúde/Rede de Atenção Psicossocial. Interface (Botucatu). 2015;19(62):995-1003. https://doi.org/10.1590/1807-57622014.1061
  » https://doi.org/10.1590/1807-57622014.1061
• ³² Dell'Acqua G, Mezzina R. Resposta à crise. In: Delgado J, organizador. A loucura na sala de jantar. São Paulo: Resenha; 1991. p. 53-79.
• ³³ Christensen MK, Lim CCW, Saha S, Plana-Ripoll O, Cannon D, Presley F, et al. The cost of mental disorders: a systematic review. Epidemiol Psychiatr Sci. 2020;29:e161. https://doi.org/10.1017/S2045796020000750
  » https://doi.org/10.1017/S2045796020000750
• ³⁴ Wilson DP, Donald B, Shattock AJ, Wilson D, Fraser-Hurt N. The cost-effectiveness of harm reduction. Int J Drug Policy. 2015 Feb;26 Suppl 1:S5-11. https://doi.org/10.1016/j.drugpo.2014.11.007
  » https://doi.org/10.1016/j.drugpo.2014.11.007
• ³⁵ Faller J, Le LK-D, Chatterton ML, Perez JK, Chiotelis O, Tran HNQ, et al. A systematic review of economic evaluations for opioid misuse, cannabis and illicit drug use prevention. BJPsych Open. 2023;9(5):e149. https://doi.org/10.1192/bjo.2023.515
  » https://doi.org/10.1192/bjo.2023.515

• Data Availability:
  As the data is sensitive, it is not available. It may be made available on request.
• Funding:
  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - process no. 408243/2022-8).

Edited by

• Associate Editor:
  Tonantzin Ribeiro Gonçalves https://orcid.org/0000-0003-0249-3358

Data availability

As the data is sensitive, it is not available. It may be made available on request.

Publication Dates

• Publication in this collection
  20 Mar 2026
• Date of issue
  2026

History

• Received
  18 Sept 2025
• Accepted
  16 Dec 2025