Wmo forecasting model

Municipality of Roosendaal

The Wmo prediction model provides a prediction of the number of unique users with a prediction horizon of five years. Predictions are made at district level for both the Wmo total and sub-products of the Wmo (Help with Household, Support at Home and Assistive Technology and Services).

Last change on 7th of August 2024, at 9:45 (CET) | Publication Standard 1.0

• Publication category: Other algorithms
• Impact assessment: Field not filled in.
• Status: In use

General information

Theme

Health and Healthcare

Begin date

012022

Contact information

[e-mail]

Link to publication website

https://wmovoorspelmodel.vng.nl/

Responsible use

Goal and impact

The algorithm was developed because the municipality wants to have more insight into the use and costs of Wmo in the coming years. The Roosendaal population is growing and ageing, as a result of which the use of Wmo is also expected to increase. The question is by how many people and in which districts and for which Wmo facilities? The forecasting model answers these questions. The insights from the Wmo forecasting model support policy and implementation in answering tactical and strategic questions. These are questions and topics that are relevant in the longer term (e.g. 5 to 10 years). The model is not intended to answer operational questions (short-term issues) and does not involve automated decisions.

Considerations

Only open data were used. From CBS, the following files were used: Key figures Districts and Neighbourhoods, Wmo numbers, Age categories, Number of people with dispensed medicines and Forecast population development 2020-2050. Further use is made of Vektis (numbers of mental health care users) and VNG (socio-economic status).

Human intervention

There is no automated system. The insights from the prediction model can only be used through human intervention and with the context knowledge and experience of employees involved.

Risk management

Because the prediction model does not make statements about individuals but about the use of facilities in neighbourhoods, there is no risk that the results from the model could violate the privacy of specific individuals. Furthermore, we only worked with neighbourhoods that were sufficiently large (more than 100 inhabitants) and where enough people used the Wmo (more than 100 users). One reason for this is that it does not allow for disclosure. That is, by combining characteristics, we cannot identify who the potential users of Wmo facilities are.

Legal basis

There is no legal basis for the forecasting model, but the municipality is responsible for implementing the Wmo and the model allows the municipality to better anticipate expected developments and better support citizens.

Elaboration on impact assessments

None. There is no processing of personal data.

Operations

Data

Only open data were used. From CBS, the following files were used: Key figures Districts and Neighbourhoods, Wmo numbers, Age categories, Number of people with dispensed medicines and Forecast population development 2020-2050. Further use is made of Vektis (numbers of mental health care users) and VNG (socio-economic status).

Technical design

A first differences regression model is used to estimate the relationship between Wmo use and predictors. This involves searching for the characteristics with the smallest prediction error (determined using the measure: MAPE). The characteristics best able to predict Wmo use are used for forecasting.

External provider

VNG