Finisterra, LXI(131), 2026, e41316  
ISSN: 0430-5027  
doi: 10.18055/Finis41316  
Artigo de Investigação  
DETERMINANTS FOR HEALTH DECENTRALISATION IN PORTUGAL:  
POLITICAL, ECONOMIC AND TERRITORIAL FACTORS BEHIND LOCAL ADOPTION  
1
RAFAELA CHOUPEIRO DE OLIVEIRA  
GONÇALO ALVES DE SOUSA SANTINHA  
1
2,3  
JULIAN ALEJANDRO PERELMAN  
4
TERESA SÁ MARQUES  
ABSTRACT – The decentralisation of health competences in Portugal was launched with the aim of bringing decision-  
making closer to local realities and strengthening municipalities’ role in promoting population well-being. A regulatory  
framework introduced between 2018-2019 enabled the transfer of specific responsibilities from central government to local  
governments. However, by 2022, only around a quarter of eligible municipalities had accepted these competences, highlighting  
the existence of structural, political, and financial barriers to reform. While the effects of decentralisation on health system  
have been widely studied, there remains limited empirical evidence on the factors that influence local jurisdictions’ willingness  
to assume new responsibilities. Addressing this gap, the present study analyses 201 eligible Portuguese mainland municipalities  
over the 2020-2022 period, modelling acceptance decisions based on demographic, political, financial, and health-related  
variables through binary logistic regression. Findings reveal that acceptance was more likely in municipalities politically  
aligned with the central government, with greater per capita financial resources, and with younger population profiles. In  
addition, regional dynamics emerged as an important contextual factor. These results highlight the need for decentralisation  
processes to account for territorial diversity, funding adequacy, and local capacity-building in order to ensure equitable and  
effective implementation.  
Keywords: Decentralisation; health; municipalities; decision-making; social determinants of health.  
RESUMO – DETERMINANTES DA DESCENTRALIZAÇÃO DA SAÚDE EM PORTUGAL: FATORES  
POLÍTICOS, ECONÓMICOS E TERRITORIAIS QUE SUSTENTAM A ADOÇÃO LOCAL. A descentralização das  
competências na saúde em Portugal foi implementada com o objetivo de aproximar a tomada de decisões das realidades locais  
e reforçar o papel dos municípios na promoção do bem-estar das populações. Um quadro regulamentar introduzido entre 2018-  
2019 permitiu a transferência de responsabilidades específicas da administração central para as autoridades locais. No entanto,  
até 2022, apenas cerca de um quarto dos municípios elegíveis tinha aceitado estas competências, o que evidencia a existência  
de obstáculos estruturais, políticos e financeiros à reforma. Embora os efeitos da descentralização no sistema de saúde tenham  
sido amplamente estudados, continuam a ser limitados os dados empíricos sobre os fatores que influenciam a vontade das  
autoridades locais de assumir novas responsabilidades. Para colmatar esta lacuna, o presente estudo analisa 201 municípios de  
Portugal Continental elegíveis durante o período de 2020-2022, modelando as decisões de aceitação com base em variáveis  
demográficas, políticas, financeiras e relacionadas com a saúde através de um modelo de regressão logístico binário. Os  
resultados revelam que a aceitação foi mais evidente em municípios politicamente alinhados com o governo central, com  
maiores recursos financeiros per capita e com perfis populacionais mais jovens. Além disso, a dinâmica regional emergiu como  
um importante fator contextual. Estes resultados sublinham a necessidade de os processos de descentralização terem em conta  
a diversidade territorial, a adequação do financiamento e o reforço das capacidades locais, a fim de assegurar uma  
implementação equitativa e eficaz.  
Palavras-Chave: Descentralização; saúde; municípios; tomada de decisão; determinantes sociais da saúde.  
HIGHLIGHTS  
•
•
•
•
Determinants of health decentralisation studied across Portuguese mainland regions.  
Higher funding and younger populations increase odds of accepting health competences.  
Political alignment and regional dynamics shape decentralisation choices.  
Local capacity support is key for effective health decentralisation.  
Recebido: 13/04/2025. Aceite: 04/02/2026. Publicado: 10/04/2026.  
Rafaela Oliveira: rafaelaoliveira@ua.pt  
1 Departamento de Ciências Sociais, Políticas e do Território, Unidade de Investigação sobre Governação, Competitividade e Políticas Públicas (GOVCOPP),  
Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.  
2 Centro de Investigação em Saúde Pública (CISP/PHRC), Escola Nacional de Saúde Pública, Universidade Nova de Lisboa, Lisboa, Portugal.  
3 Comprehensive Health Research Centre (CHRC), Universidade Nova de Lisboa, Lisboa, Portugal.  
4 Departamento de Geografia, Centro de Estudos em Geografia e Ordenamento do Território (CEGOT), Faculdade de Letras, Universidade do Porto, Porto,  
Portugal.  
Published under the terms and conditions of an Attribution-NonCommercial-NoDerivatives 4.0 International license.  
       
Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
1.  
INTRODUCTION  
Decentralisation, understood as the reorganisation of responsibilities across different tiers of  
government, has attracted growing attention in public policy discourse. This multifaceted phenomenon,  
marked by the transfer of competences and resources from central to subnational levels of governance, is  
neither uniform nor linear; rather, it reflects the institutional, political, and cultural particularities of each  
national context (Abimbola et al., 2019; Oliveira et al., 2024). Within the health sector, decentralisation is  
frequently advocated as a means of bringing decision-making closer to communities, thereby enhancing the  
responsiveness of services to local needs and promoting greater efficiency and equity in health systems  
(Nunes & Ferreira, 2022; Organisation for Economic Co-operation and Development [OECD], 2020).  
Decentralisation can take a variety of forms, typically classified as administrative, political, or fiscal  
(see, inter alia, Monte et al., 2022; Tselios, 2022). Administrative decentralisation entails the delegation of  
competences to subnational entities, which exercise operational autonomy while remaining under the legal  
authority of the central government. Political decentralisation involves the transfer of legislative powers and  
autonomous management to locally elected bodies. Fiscal decentralisation, in turn, grants subnational  
authorities the capacity to collect revenues and make independent budgetary decisions. These models often  
coexist and vary widely across European contexts, including Italy, Spain, and Germany, where the  
architecture of decentralisation reflects a combination of historical legacies and institutional arrangements  
(Oliveira et al., 2024).  
In Portugal, healthcare is predominantly delivered through a National Health Service (NHS) funded  
by general taxation and characterised, since its establishment in 1979, by strong centralised control over  
policy and resource allocation. Although the country is territorially organised into regions, districts, and  
municipalities, only the latter are directly elected and possess a legally defined set of responsibilities, albeit  
traditionally limited in scope. However, reforms introduced in 2018 and 2019 marked a significant shift,  
initiating a process of health sector decentralisation. Legal instruments – Law 50/2018 and Decree-Law  
23/2019 – set out the transfer of competences to municipalities, particularly in areas such as the management  
of primary healthcare infrastructure (e.g., maintenance and investment), logistics (e.g., cleaning and utilities),  
and non-clinical personnel (operational assistants). Simultaneously, municipalities were mandated to develop  
Municipal Health Strategies, reinforcing their role in promoting population well-being and tackling health  
inequalities (Nunes & Ferreira, 2022; Oliveira et al., 2022). This reform process remains ongoing, and its  
scope is still evolving.  
Yet the implementation of these reforms has encountered significant obstacles. Portugal's marked  
territorial diversity, coupled with persistent economic and demographic asymmetries, may engender  
considerable variation in municipalities’ capacity to assume new responsibilities, as differences in population  
density, ageing profiles, and available financial resources tend to play a critical role in shaping local responses  
(Capote, 2023).  
In parallel, the financing arrangements underpinning the decentralisation process have come under  
scrutiny, criticised for their opacity and misalignment with local realities, thus hindering the delivery of  
effective and equitable reforms (República Portuguesa, 2023).  
Despite some institutional progress, health decentralisation in Portugal remains highly contested.  
Advocates argue that municipalities, due to their proximity to communities, are better placed to integrate and  
respond to the social determinants of health, such as housing, access to essential services, and community  
engagement (OECD, 2020; Santinha, 2016).  
Critics, however, raise concerns about the uneven capacities of municipalities, particularly in the face  
of population ageing, regional disparities, and increasing pressure on public resources (Rodrigues & Pedreiro,  
2023). In practice, as corroborated by the study of Simões (2023), the decentralisation process has been  
marked by slow and uneven implementation, punctuated by successive legislative revisions. Notably,  
substantial disparities emerged in municipalities’ voluntary acceptance of transferred health competences  
between 2020 and 2024 (see table I and fig. 1), pointing to underlying structural and political asymmetries.  
While the international literature has provided extensive analyses of the consequences of  
decentralisation for health systems, research examining the specific factors that shape municipalities’  
decisions to accept new responsibilities remains limited. Understanding the motivations behind municipal  
decisions – whether to adopt, delay, or reject decentralised competences – is essential for identifying the  
constraints and enablers of policy implementation. Such an understanding also provides critical insights for  
the design of public policies that account for territorial diversity and are capable of fostering more equitable  
governance arrangements (Bruzzi et al., 2022; Hao et al., 2021).  
This article aims to address this gap by examining the factors that influenced Portuguese  
municipalities’ decisions to accept decentralised health competences during the 2020-2022 period, initially  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S ., Finisterra, LX I(131), 2026, e41316  
established as the voluntary phase of the reform. Drawing on a quantitative analysis that integrates  
sociodemographic, political, economic, and health indicators, the study seeks to uncover patterns in local  
decision-making and to contribute to broader debates on health governance and decentralisation. The findings  
aim to inform future policy design, ensuring that decentralisation processes promote both territorial cohesion  
and health system equity.  
Table I – Number of municipalities that implemented decentralised competences in the health area.  
Quadro I – Número de municípios que implementaram a descentralização de competências na Área da Saúde.  
Implementation process of competence decentralisation in the health sector (201 municipalities initially eligible)  
Year  
2020  
Number of municipalities  
Cumulative % of municipalities  
12  
8
6.0  
10.0  
27.4  
89.1  
95.0  
100.0  
2021  
2022  
35  
124  
12  
10  
2023  
2024  
Did not assume  
Source: Ministry of Health  
Fig. 1 – Geographical distribution of the process of implementing the transfer of competences in the Health Area. (cont.)  
Fig.1 – Distribuição geográfica do processo de efetivação da transferência de competências na Área da Saúde. (cont.)  
Source: Authors  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
Fig. 2 – Geographical distribution of the process of implementing the transfer of competences in the Health Area.  
Fig.1 – Distribuição geográfica do processo de efetivação da transferência de competências na Área da Saúde.  
Source: Authors  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S ., Finisterra, LX I(131), 2026, e41316  
2.  
METHODS  
2.1.  
Sample  
The analytical sample comprises the 201 mainland Portuguese municipalities initially eligible to  
voluntarily accept the transfer of competences in the health sector. A total of 77 municipalities were excluded  
from the analysis because they were located in territories where Local Health Units (ULS) were already in  
place. The transfer of competences had become mandatory for these municipalities in 2024 under legislative  
provisions requiring the large-scale implementation of the ULS model across the entire mainland territory,  
which also entailed a restructuring of the decentralisation framework in the health sector for all Portuguese  
municipalities.  
These municipalities lacked effective autonomy in the decision-making process, making them  
analytically distinct from those with discretionary authority. Focusing exclusively on municipalities with  
voluntary decision-making capacity allows for a more accurate assessment of the determinants influencing  
their choices.  
The temporal scope of the study spans the period from 2020 to 2022 corresponding to the initial phase  
of health decentralisation. This interval was selected based on the extension of the voluntary adherence  
deadline to March 2022, as stipulated by Decree-Law 56/2020. Limiting the analysis to this timeframe  
ensures that only municipalities operating under voluntary conditions are considered, thereby avoiding  
confounding effects introduced by subsequent phases in which decentralisation became compulsory.  
2.2.  
Data collection  
To identify the factors influencing municipal acceptance of decentralised competences in the health  
sector, a set of eight indicators was selected and grouped into sociodemographic, political, economic, and  
health-related dimensions. Indicator selection was informed by a review of the relevant literature on  
decentralisation uptake and by the availability of reliable data at the municipal level.  
Data were sourced from official and authoritative repositories, including the National Statistics  
Institute (INE) and legal instruments such as Ordinance 6541-B/2019, which defines the financial allocations  
transferred to municipalities under the decentralisation framework.  
The selected indicators (Appendix A) and corresponding hypotheses were as follows:  
i)  
Sociodemographic Factors  
–
Population density: we hypothesise that more densely populated municipalities may be  
more inclined to accept decentralised competences, as increased population heterogeneity often  
requires more locally responsive and tailored service provision (Wallis & Oates, 1988);  
–
Ageing index: municipalities with older populations may face greater health demands and  
service pressures, which decentralisation could potentially address through more targeted disease  
prevention and health promotion strategies (Andrews & Dollery, 2021).  
ii)  
Political Factors  
Political party of the municipal executive: we expect that municipalities led by political  
–
parties aligned with the central government are more likely to accept decentralisation, due to greater  
perceived institutional alignment and the prospect of increased support (Cuadrado Ballesteros et al.,  
2013). The data for this indicator correspond to the 2021 municipal election results. Comparison with  
2017 showed that only three municipalities accepting decentralisation in 2020-2021 experienced  
changes in the municipal executive, indicating minimal impact on decision-making, as the decision to  
adopt health decentralisation had already been finalised.  
iii)  
Health System Pressure  
Confirmed COVID-19 cases: the COVID-19 pandemic placed unprecedented strain on  
–
municipal resources and highlighted the importance of local public health governance. Higher case  
rates may have increased the perceived urgency or willingness to assume decentralised responsibilities  
(Biase & Dougherty, 2021).  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
iv)  
Human Resources and Accessibility  
Number of doctors and nurses per 1 000 inhabitants: uneven distribution of healthcare  
–
professionals across municipalities may influence perceptions of local capacity to deliver effective  
health services (Melo & Ferreira, 2025);  
–
Percentage of population within 15 minutes of a primary care provider: greater geographical  
accessibility to primary healthcare services may reduce the perceived need for structural changes, such  
as decentralisation, or conversely encourage it as a means to improve reach in underserved areas  
(Costa et al., 2020).  
v)  
Economic factors  
Municipal financial resources per capita the adequacy of financial transfers under the  
–
Decentralisation Financing Fund (FFD) is assumed to be a key enabler of decentralisation, with higher  
per capita funding potentially increasing municipalities’ willingness to accept new responsibilities  
(República Portuguesa, 2023). Due to the unavailability of data on amounts transferred to  
municipalities, the indicator was constructed using the normative amounts specified in Ordinance  
6541-B/2019, assuming that the resulting categorisation (comparing the top 25% of municipalities by  
per capita funding with the remainder) is similar to what would be obtained from the actual transfers.  
The integration of these indicators enabled the construction of a comprehensive and up-to-date  
database, capturing the structural characteristics, contextual dynamics, and governance arrangements of  
municipalities during the study period. This approach supports the analysis of the territorial determinants of  
decentralisation acceptance and enables the identification of key patterns in municipal decision-making.  
3.  
DATA ANALYSIS  
The analytical strategy was structured in two main phases. First, a descriptive analysis was conducted  
to identify spatial patterns and temporal trends in municipal engagement with the decentralisation process.  
This provided preliminary insights into the heterogeneity of acceptance across municipalities. Second, a  
binary logistic regression model was developed to assess the statistical association between selected  
indicators and the likelihood of municipalities voluntarily accepting decentralised health competences. All  
analyses were performed using IBM SPSS Statistics, version 29.  
3.1.  
Descriptive analysis  
The descriptive phase aimed to explore how sociodemographic, political, economic, and health-related  
characteristics might shape municipal decisions. Notably, with the exception of the political affiliation of the  
municipal executive, the remaining indicators did not exhibit clear or consistent patterns of association with  
decentralisation acceptance. This heterogeneity points to the probable influence of unmeasured qualitative  
or contextual factors, such as local political leadership, administrative capacity, or prior experience with  
intersectoral governance.  
In recognition of the growing relevance of Intermunicipal Communities (CIM) and Metropolitan  
Areas (AM) as key territorial governance actors in Portugal, an additional layer of analysis was undertaken  
to explore the relationship between decentralisation acceptance and regional affiliation. By disaggregating  
results by CIM/AM membership, the analysis captured the potential role of inter-municipal cooperation,  
horizontal political dynamics, and regional institutional capacity in shaping decisions at the local level. This  
broader spatial lens complements the individual-level municipal analysis and reinforces the importance of  
supra-municipal coordination mechanisms in decentralised health governance.  
3.2.  
Logistic regression analysis  
To identify the main predictors of decentralisation acceptance between 2020 and 2022, a binary  
logistic regression model was specified. The dependent variable was binary:  
–
–
0 = Municipality did not accept the transfer of health competences.  
1 = Municipality voluntarily accepted the transfer of health competences.  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S ., Finisterra, LX I(131), 2026, e41316  
A total of eight independent variables were inserted into the model. For greater analytical clarity and  
comparability, three continuous variables were recoded into binary categories based on empirical distribution  
thresholds:  
–
Political party of the municipal executive: Categorised as 1 = Socialist Party (PS), which  
held central government during the reform period; 0 = Other parties (including PCP-PEV, PPD/PSD,  
CDS-PP, and independents).  
–
Municipal financial resources per capita: Dichotomised into 1 = Top 25% of municipalities  
with the highest per capita funding under the decentralisation framework; 0 = All others.  
Confirmed COVID-19 cases per capita: Categorised as 1 = Top 25% of municipalities with  
–
the highest reported case rates during the study period; 0 = All others.  
The regression model was tested for compliance with key assumptions, including the independence of  
observations and adequacy of sample size relative to the number of predictors. These conditions were met,  
supporting the internal validity of the estimation.  
In addition to reporting odds ratios, the model's overall explanatory power and classification accuracy  
were assessed using standard diagnostic measures (reported in the Results section). While the model achieved  
statistical significance, the modest classification rate suggests the presence of additional unmeasured  
variables or interaction effects that may influence municipal decision-making. These limitations are further  
considered in the Discussion section.  
4.  
RESULTS  
A full characterization of all variables is displayed in Appendix A.  
4.1.  
Descriptive analysis  
4.1.1. Political party of the municipal executive  
Between 2020 and 2022, municipalities governed by executives affiliated with the Socialist Party (PS)  
were the most frequent adopters of decentralised health competences, accounting for 50.9% of all voluntary  
acceptances during the period.  
The year-by-year distribution reveals distinct adoption patterns. In 2020, 21.8% of municipalities  
engaged with the decentralisation process, with PS-led municipalities comprising the majority (16.4%). This  
early stage coincided with substantial uncertainty regarding the implications of the reform, possibly limiting  
wider engagement.  
In 2021, adoption rates reached their lowest point (14.5%), which may be attributed to the disruptive  
effects of the COVID-19 pandemic, as municipalities redirected attention to crisis management and public  
health emergencies. During this year, acceptance was equally distributed between PS-led and non-PS-led  
municipalities (7.3% each), suggesting a temporary neutralisation of political dynamics.  
A significant increase occurred in 2022, with 63.6% of voluntary adoptions recorded that year, likely  
driven by the approaching deadline established by Decree-Law 56/2020. Interestingly, in this final phase,  
municipalities governed by other political parties exceeded PS-led counterparts in terms of new adoptions  
(36.4% vs. 27.3%), possibly reflecting a shift in strategic or institutional considerations as the deadline  
approached.  
Despite these fluctuations, the Socialist Party maintained a dominant position throughout the analysed  
period. Figure 2 illustrates the relationship between political affiliation and acceptance rates, while figure 3  
maps the geographic distribution of political control across municipalities, revealing regional patterns that  
may help explain local political behaviours.  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
Fig. 2 – Evolution of Competence Transfer Acceptance Rates (%) by Political Affiliation (PS vs. other political parties),  
2020-2022.  
Fig. 2 – Evolução das taxas de aceitação de transferência de competências (%) por filiação política (PS vs. outros partidos  
políticos), 2020-2022.  
Source: Authors  
Fig. 3 – Political parties across the 201 initially eligible municipalities for health-sector competence decentralisation, by  
NUTS III Region.  
Fig. 3 – Partidos políticos nos 201 municípios inicialmente elegíveis para a descentralização de competências nas Área da  
Saúde por NUTS III.  
Source: Authors  
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4.1.2. Inter-Municipal Communities and Metropolitan Areas (NUTS III)  
Analysis at the NUTS III level reveals pronounced regional disparities in the voluntary acceptance of  
health decentralisation. The Alentejo Central Inter-Municipal Community (CIM) registered the highest  
adoption rate, with 78.6% of its municipalities participating in the transfer of competences during the study  
period. High levels of adoption were also observed in Tâmega e Sousa CIM (63.6%) and Algarve CIM  
(56.3%). In contrast, markedly lower acceptance rates were recorded in Lezíria do Tejo CIM (9.1%) and the  
Greater Lisbon Metropolitan Area (11.1%), indicating more cautious or resistant engagement with the  
decentralisation process in these areas.  
A closer examination of the regions with higher adoption rates suggests common characteristics such  
as low population density, ageing demographics, and limited geographical access to primary healthcare  
services. These conditions may have acted as motivating factors, encouraging municipalities to embrace  
decentralisation as a means of improving service responsiveness and addressing long-standing structural  
deficits. The territorial distribution of adoption rates across NUTS III regions is summarised in table II and  
depicted visually in figure 4. The results reflect both the socioeconomic diversity of Portuguese regions and  
the importance of inter-municipal dynamics, including the potential coordinating role of CIMs and AMs, in  
shaping local governance responses.  
Fig. 4 – Municipalities where health competence transfers were implemented (2020-2022) by NUTS III.  
Fig. 4 – Municípios onde se implementaram as transferências de competências em saúde (2020-2022) por NUTS III.  
Source: Authors  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
Table II – Implementation of Health Competence Transfers (2020-2022) by NUTS III. (%)  
Quadro II – Implementação das transferências de competências em saúde (2020-2022) por NUTS III. (%)  
NUTS III  
CIM Alentejo Central (1)  
%
78.57  
CIM Tâmega e Sousa (2)  
Comunidade Intermunicipal de Algarve (3)  
Região de Leiria (4)  
63.64  
56.25  
40.00  
33.33  
33.33  
33.33  
25.00  
21.43  
18.18  
16.67  
15.79  
13.33  
12.50  
11.11  
00.09  
CIM Beiras e Serra da Estrela (5)  
CIM Cávado (6)  
CIM Alto Tâmega e Barroso (7)  
CIM Ave (8)  
CIM Viseu Dão Lafões (9)  
CIM Médio Tejo (10)  
CIM Oeste (11)  
CIM Região de Coimbra (12)  
CIM Douro (13)  
Área Metropolitana do Porto (14)  
CIM Grande Lisboa (15)  
CIM Lezíria do Tejo (16)  
Source: Authors  
4.2.  
Logistic regression model  
The logistic regression model was developed to assess the factors associated with municipal decisions  
to voluntarily accept the transfer of competences in the health sector between 2020 and 2022. The model  
yielded a statistically significant result overall, with a likelihood-ratio chi-square statistic of x² (8) = 22.755,  
p = 0.004, indicating that the set of independent variables contributes meaningfully to explaining variance in  
the dependent outcome. Despite achieving statistical significance, the model's predictive capacity remains  
limited, correctly classifying only 15.5% of cases. The Nagelkerke R² value of 0.155 further suggests that  
the explanatory power is modest (table III). This limitation reflects the inherent complexity of the  
decentralisation process, which is likely shaped by a range of unobserved contextual, institutional, and  
political dynamics not captured by the included indicators.  
Among the covariates analysed, two emerged as statistically significant predictors:  
–
The ageing index showed a negative association with decentralisation acceptance. Specifically,  
for each unit increase in the index, the odds of acceptance decreased by approximately 9.3%  
(Odss Ratio (OR) = 0.907, p = 0.017; 95% Confidence Interval (CI) [0.837, 0.983]). This  
suggests that municipalities with older populations were less likely to take on decentralised  
responsibilities, potentially due to greater service pressure or reduced implementation capacity.  
Conversely, municipal financial resources per capita displayed a positive and significant effect.  
Municipalities in the top quartile of per capita funding were over three times more likely to  
accept the transfer of competences than those with lower funding levels (OR = 3.122, p = 0.009;  
95% CI [1.333, 7.314]). This finding reinforces the importance of fiscal capacity in enabling  
decentralised governance.  
–
Other variables, while not statistically significant at conventional thresholds, provided suggestive  
insights:  
–
Political alignment appeared positively associated with decentralisation acceptance, indicating  
that PS-led municipalities were more inclined to engage with the reform, albeit without  
statistical confirmation;  
–
Population density and proximity to healthcare providers both demonstrated a slight negative  
effect on acceptance, whereas the density of healthcare professionals (doctors and nurses per 1  
000 inhabitants) exhibited weak positive trends, none of which reached statistical significance.  
Additionally, the number of confirmed COVID-19 cases per capita did not significantly influence  
acceptance, although this variable may interact with other local contextual factors not captured by the model.  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S ., Finisterra, LX I(131), 2026, e41316  
The geographic distribution of the significant predictors across Portuguese municipalities is illustrated  
in figure 5, enabling to visualise potential spatial clustering or regional patterns in the influence of financial  
and demographic factors.  
Table III – Logistic Regression Model.  
Quadro III – Modelo de Regressão Logístico.  
2020-2022 Period  
Odds Ratio  
(unadjusted)  
95% CI for Odds Ratio  
(adjusted)  
Odds Ratio (adjusted)  
Model  
25.571  
.999  
Constant  
.999  
Population density  
Aging index  
[.998, 1.000]  
1.002  
.907*  
[.837, .983]  
[.913, 3.504]  
[.556, 3.550]  
[.779, 1.296]  
[.819, 1.134]  
Political party of the municipal executive  
Number of confirmed cases of COVID-19  
Number of doctors per 1000 inhabitants  
Number of nurses per 1000 inhabitants  
Percentage of the population within 15 minutes of  
a primary healthcare provider  
1.619  
1.458  
.849  
1.789  
1.405  
1.005  
.964  
.933  
.979  
.978  
[.948, 1.010]  
[1.333, 7.314]  
Municipal financial resources per capita  
Model summary  
2.810*  
3.122*  
x2 (8) = 22.755 R2 Nagelkerke = .155  
Source: Authors  
A
B
Fig. 3 – (A) Ageing index, and (B) financial value allocated per capita in the 201 municipalities initially eligible for the  
decentralisation of competences in the Health Sector by NUTS III.  
Fig. 5 – (A) Índice de envelhecimento, e (B) valor financeiro atribuído per capita nos 201 concelhos inicialmente elegíveis  
para a descentralização de competências no Sector da Saúde por NUTS III.  
Source: Authors  
5.  
DISCUSSION  
This study offers valuable insights into the drivers of municipal decision-making during the initial  
phase of health decentralisation in Portugal (2020-2022). The process of transferring competences to local  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
governments proved to be shaped by a set of political, demographic, economic, and territorial factors. As  
with decentralisation efforts elsewhere, the Portuguese experience highlights that implementation is rarely  
uniform or linear and often constrained by local capacity and broader systemic conditions (Busygina et al.,  
2018; Cuadrado Ballesteros et al., 2013). While decentralisation holds considerable promise for enhancing  
local responsiveness to health needs, it also faces significant challenges that may compromise its  
effectiveness or equity. In this context, five key dimensions merit close attention.  
5.1.  
Political and ideological factors: alignment with the central government  
Although political affiliation did not emerge as a statistically significant predictor in the regression  
model, the descriptive results indicate a noticeable tendency: municipalities led by the Socialist Party (PS),  
which formed the national government during the period under analysis, were more likely to accept  
decentralised competences. This pattern supports existing literature suggesting that ideological alignment  
between tiers of government eases decentralisation by reducing institutional friction and increasing trust in  
support mechanisms (Toubeau & Wagner, 2015).  
In the Portuguese case, this alignment may have translated into greater confidence among PS-led  
municipalities in their ability to obtain financial or technical assistance from central authorities. It also raises  
questions about the extent to which decentralisation operates as a politically neutral reform, or whether it  
remains embedded in intergovernmental power dynamics.  
5.2.  
Sociodemographic factors: the role of ageing  
The ageing index was one of the few statistically significant predictors, with municipalities exhibiting  
lower levels of population ageing showing a greater propensity to accept decentralised health competences.  
This finding is consistent with studies highlighting the greater service pressure and operational complexity  
faced by ageing territories, which may inhibit their readiness or willingness to take on new responsibilities  
(Franco & Marques da Costa, 2022; Moreira, 2020).  
Conversely, younger municipalities may perceive decentralisation as an opportunity to implement  
preventive and health-promoting policies, fostering life-course approaches to well-being (Ferreira et al.,  
2021). However, the results also raise concerns about the equity of the reform: if decentralisation is more  
readily adopted by municipalities under less pressure, it may inadvertently reinforce what Hart (1971) termed  
the "inverse care law", where resources and responsibilities flow more easily to areas with fewer needs.  
Thus, while decentralisation can be a tool for tailoring services to local contexts, its implementation  
must be carefully calibrated to avoid amplifying territorial health inequalities.  
5.3.  
Economic factors: the challenge of funding  
The logistic regression confirmed the strong predictive power of municipal financial capacity, with  
wealthier municipalities significantly more likely to accept decentralised responsibilities. This highlights a  
fundamental reality: adequate and transparent financial transfers are critical for successful and equitable  
decentralisation.  
In Portugal, however, the funding methodology has been repeatedly criticised for lacking clarity and  
for insufficiently reflecting local needs (República Portuguesa, 2023). While international evidence  
recommends the use of objective criteria such as population, geography, and socioeconomic indicators  
(Abimbola et al., 2019; Sumah et al., 2016), the Portuguese framework remains empirically and politically  
contested.  
From a policy perspective, the findings point to the pressing need for more predictable, needs-based,  
and transparent fiscal mechanisms, capable of ensuring that decentralisation does not exacerbate inequalities  
in municipal capacity or service delivery (Nunes & Ferreira, 2022).  
5.4.  
Territorial dynamics: intermunicipal cooperation  
Descriptive results also highlight the relevance of territorial governance structures, particularly the  
role of CIM and AM. Regions such as Alentejo Central, Tâmega e Sousa, and Algarve demonstrated higher  
rates of acceptance, suggesting that intermunicipal coordination may support shared learning, reduce  
uncertainty, or ease administrative burden-sharing.  
These findings echo arguments by Mourão and Araújo (2023), who contend that decentralisation can  
encourage strategic regional planning and cooperation. Conversely, more complex urban regions such as  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S ., Finisterra, LX I(131), 2026, e41316  
Greater Lisbon displayed lower acceptance rates, possibly due to the administrative fragmentation and higher  
transaction costs involved in adapting large-scale systems.  
Such spatial patterns reinforce the need for decentralisation strategies that are place-sensitive,  
recognising the heterogeneity of local governance ecosystems and avoiding one-size-fits-all approaches.  
5.5.  
Health system and human resources factors: a non-significant role  
Surprisingly, none of the health system-related variables – including the number of doctors and nurses,  
geographical access to primary care, or COVID-19 incidence – emerged as statistically significant predictors.  
While this finding does not imply these factors are irrelevant in practice, it suggests that they were not central  
to the municipal calculus of decentralisation acceptance during the study period.  
This result raises important questions. It may reflect the fact that decisions were shaped more by  
political or structural capacity considerations than by direct assessments of local health needs or service  
readiness. Alternatively, it could suggest a disconnect between decentralisation policy and the operational  
realities of local health systems, thus highlighting the need for more integrated planning and reform across  
sectors.  
Furthermore, this can reflect the scope of the decentralisation process, which, in terms of human  
resources, is confined to transferring responsibility for operational assistants to local governments.  
5.6.  
Strengths and limitations  
This article offers a novel contribution to the literature by analysing, for the first time, the factors  
associated with municipal acceptance of health decentralisation in Portugal. The study covers the full set of  
eligible municipalities during the voluntary adherence phase (2020-2022), and applies a structured  
quantitative approach grounded in theory and policy.  
Nonetheless, several limitations must be acknowledged. First, the model’s predictive power was  
limited, suggesting that other relevant factors, such as disease burden, institutional legacy, or competing  
priorities (e.g., in education or social care), were not captured. Second, regarding political dynamics, the  
analysis was constrained by the lack of data on the composition of municipal assemblies, the political  
longevity of local executives, or their prior links to the health sector, all of which may influence policy  
preferences. Third, the short timeframe of the decentralisation process restricted the sample size and limited  
the capacity to observe temporal or causal relationships. A cross-sectional design, while analytically useful,  
does not fully capture longitudinal trends in policy adaptation or implementation.  
Future studies should consider mixed-method approaches, including qualitative interviews with local  
policymakers, to unpack the political rationales and operational constraints behind decentralisation decisions.  
In addition, there is a pressing need to examine not only acceptance but also the quality and scope of  
implementation, including what municipalities actually did with the competences received, as highlighted by  
other studies on this topic (see, inter alia, Simões, 2023).  
6.  
CONCLUSION  
As the NHS undergoes reform, there is a renewed emphasis on fostering more effective, citizen-  
centred solutions. The decentralisation of health competences to municipalities reflects this ambition by  
enabling local governments to shape strategies tailored to specific territorial realities. More than a shift in  
administrative responsibilities, decentralisation creates opportunities to integrate health with broader social,  
economic, and environmental policies, contributing to a more all-inclusive and place-based vision of public  
well-being (Freitas et al., 2020; Santinha, 2016).  
However, the process of implementation has exposed substantial disparities in municipal engagement.  
This study has shown that the acceptance of health decentralisation was far from uniform and instead shaped  
by a set of interdependent factors. Understanding the rationale behind municipal decisions is therefore  
essential not only for evaluating the current reform, but also for guiding future policy design.  
What drives Portuguese municipalities to accept, or reject, the transfer of health competences? How  
can empirical evidence be used to refine decentralisation mechanisms, ensuring they are responsive to local  
diversity without compromising equity? These questions remain at the heart of an important and ongoing  
policy debate, one that requires robust data, interdisciplinary dialogue, and greater engagement with the  
social and territorial dynamics underpinning governance.  
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Oliveira, R. C., Santinha, G. A. S., Perelman, J. A., Marques, T. S., Finisterra, LXI(131), 2026, e41316  
Our findings suggest that political alignment, municipal financial capacity, population ageing, and  
regional cooperation frameworks all play key roles in shaping local decisions. In particular, the influence of  
financial resources highlights the need for decentralisation to be matched with adequate and equitable  
funding, while the effect of ageing demographics points to the challenges of local implementation in areas  
with heightened service demands.  
If not carefully managed, however, decentralisation may inadvertently reinforce existing inequalities  
between territories, especially where acceptance of competences is concentrated in municipalities with fewer  
needs or greater resources. Policymakers must therefore take into account the heterogeneous conditions of  
municipalities and the strategic role of intermediate governance structures in promoting coordination and  
capacity-building.  
It is also important to acknowledge that, unlike other areas transferred to local governments, such as  
social action, where municipalities already possessed experience, the health sector represented an entirely  
new field of action. The lack of prior experience and the absence of a clear regulatory framework may  
likewise have influenced municipal decision-making.  
Ultimately, decentralisation can be a powerful tool for improving health governance, but only if  
grounded in territorial balance, institutional support, and evidence-informed design. Future stages of the  
reform should build on the lessons of this initial phase to ensure a more effective and balanced public health  
system across Portugal.  
ACKNOWLEDGEMENTS  
This study was funded by the Foundation for Science and Technology (FCT), under reference  
2022.13288.BD, and partially funded by the Centro 2020 Operational Programme through the European Social  
Fund.  
CONTRIBUTOS DOS/AS AUTORES/AS  
Rafaela Oliveira: Conceptualização, Metodologia, Investigação, Escrita – preparação do esboço original,  
Redação – revisão e edição, Visualização. Gonçalo Santinha: Conceptualização, Metodologia, Validação,  
Redação – revisão e edição, Supervisão. Julian Perelman: Conceptualização, Validação, Redação – revisão e  
edição, Supervisão. Teresa Sá Marques: Conceptualização, Redação – revisão e edição, Supervisão.  
ORCID  
Rafaela Choupeiro de Oliveira  
Gonçalo Alves de Sousa Santinha  
https://orcid.org/0000-0003-4568-8884  
https://orcid.org/0000-0002-4732-5959  
Julian Alejandro Perelman  
https://orcid.org/0000-0001-6634-9000  
Teresa Sá Marques  
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República Portuguesa. (2020). Decreto-Lei n.º 56/2020, de 12 de agosto. Prorroga o prazo de transferência das competências  
para as autarquias locais e entidades intermunicipais nos domínios da educação e da saúde [Decree-Law 56/2020, of  
12 August. Extends the deadline for the transfer of competences to local authorities and intermunicipal entities in the  
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República Portuguesa. (2019). Despacho n.º 6541-B/2019, de 19 de julho. Mapa de encargos anuais com as competências  
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decentralised competencies  
123328946  
–
health sector]. https://diariodarepublica.pt/dr/detalhe/despacho/6541-b-2019-  
República Portuguesa. (2019). Decreto-Lei n.º 23/2019, de 30 janeiro. Concretiza o quadro de transferência de competências  
para os órgãos municipais e para as entidades intermunicipais no domínio da saúde [Decree-Law 23/2019, of 30  
January. Implements the framework for the transfer of competences to municipal bodies and intermunicipal entities  
in the field of health]. https://diariodarepublica.pt/dr/legislacao-consolidada/decreto-lei/2019-118813666-118290863  
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APPENDIX  
Table I – Characterization of indicators: definition and descriptive analysis.  
Quadro I – Caracterização dos indicadores: definição e análise descritiva.  
Calculation  
formula  
Standard  
Deviation  
Indicator  
Definition  
N
Mean  
Total number of individuals / Area  
(km²)  
396  
980  
Population density  
N. º/ km²  
952 183  
(Number of people aged 65 or older /  
Number of people aged 0 to 14) * 100  
Ageing index  
%
26 726  
5 732  
(Total number of doctors registered at  
the end of the year / Estimated resident  
population at the end of the year) * 1  
000  
Number of doctors per  
1000 inhabitants  
N. º/1 000 hab.  
N. º/1 000 hab.  
3 279  
5 242  
3 361  
4 111  
201  
(Total number of nurses registered at  
Number of nurses per 1000 the end of the year / Estimated resident  
inhabitants  
population at the end of the year) *1  
000  
Percentage of the  
(Resident population ≤ 15 minutes from  
a primary healthcare provider / Total  
resident population) * 100  
population within 15  
minutes of a primary  
healthcare provider  
%
94 309  
11 974  
N
%
PS  
85  
42.3  
Political party of the  
municipal executive (Local  
election results, 2021)  
PCP-PEV, PPD/PSD, CDS-PP,  
PPD/PSD e Outros, grupos de cidadãos  
e outros  
116  
57.7  
25% of municipalities  
with the most COVID-  
19 cases  
151  
50  
24.9  
75.1  
Number of confirmed  
cases of COVID-19  
(Number of confirmed COVID-19 cases  
/ Resident population) * 10 000  
N. º / 10 000  
hab.  
Remaining  
municipalities  
25% of municipalities  
with the highest per  
capita funding  
50  
24.9  
75.1  
Municipal financial  
resources per capita  
Financial amount allocated under  
decentralisation/ Resident population  
€ / hab.  
Remaining  
municipalities  
151  
Source: Authors  
17