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Gouveia, C., Kalakou, S. & Cardoso-Grilo, T. (2023). How to forecast mental healthcare needs? Distinguishing between perceived and unperceived needs and their impact on capacity requirements. Socio-Economic Planning Sciences. 87

C. Gouveia et al.,  "How to forecast mental healthcare needs? Distinguishing between perceived and unperceived needs and their impact on capacity requirements", in Socio-Economic Planning Sciences, vol. 87, 2023

@article{gouveia2023_1716079115481,
	author = "Gouveia, C. and Kalakou, S. and Cardoso-Grilo, T.",
	title = "How to forecast mental healthcare needs? Distinguishing between perceived and unperceived needs and their impact on capacity requirements",
	journal = "Socio-Economic Planning Sciences",
	year = "2023",
	volume = "87",
	number = "",
	doi = "10.1016/j.seps.2023.101552",
	url = "https://www.sciencedirect.com/science/article/pii/S0038012123000526?via%3Dihub"
}

TY  - JOUR
TI  - How to forecast mental healthcare needs? Distinguishing between perceived and unperceived needs and their impact on capacity requirements
T2  - Socio-Economic Planning Sciences
VL  - 87
AU  - Gouveia, C.
AU  - Kalakou, S.
AU  - Cardoso-Grilo, T.
PY  - 2023
SN  - 0038-0121
DO  - 10.1016/j.seps.2023.101552
UR  - https://www.sciencedirect.com/science/article/pii/S0038012123000526?via%3Dihub
AB  - The provision of mental healthcare is vital for the sustainable development of societies. However, current systems in several countries fail to meet citizens' needs and are called to identify new planning approaches to ensure the balance between supply and needs. Nevertheless, the estimation of mental healthcare needs is challenging since there are perceived needs, that are explicitly expressed, and unperceived needs, that are not explicitly expressed and are difficult to measure. To deal with this challenge, this study aims to build estimates for the future needs of mental healthcare services by departing from a simulation model based on a Markov cycle tree that considers both perceived and unperceived needs. The development and application of the proposed model to the Lisbon and Tagus Valley region in Portugal followed a mixed-method approach consisting of: i) a set of interviews with mental healthcare experts, providing information on key aspects entailing mental healthcare needs in general, and in the Portuguese context in particular; ii) a Markov cycle tree model, reflecting the information gathered in the interviews; and iii) a survey conducted to estimate key parameters required to apply the Markov model to the Portuguese context. The results indicate that mental healthcare needs are expected to increase by 2030, reaching 68.62% of the total population, with 21% of these representing perceived and unsatisfied needs, while 20% represents unperceived needs. Also, more than 95% of the required capacity by 2030 is expected to be missing. The suggested tool thus distinguishes among perceived and unperceived needs, helping policy makers and managers who aim to plan the future operations of mental healthcare systems.
ER  -