Oncology Accessibility is a web application based on the methods published
in our paper: "Disparities in accessibility to oncology care centers in France".
The preprint is available on medrxiv.
Cancer is a leading cause of death worldwide,
accounting for nearly
10 million deaths in 2020. Access to health services plays a key role
in cancer survival, and spatial accessibility
methods have been
successfully used for measuring access to healthcare providers.
We propose a method to i) group the care centers based on their oncology
specialization; ii) compute an oncology accessibility score for each
municipality in metropolitan France; iii) im-prove this accessibility
by identifying which care centers to grow and by how much.
Our method will make it possible for health professionals and
administrations to monitor the accessibility to
oncology care. Since the accessibility optimization is very
dependent on the region and local constraints, we
packaged our algorithms into a web application that will let the user
tune every parameter and preview the activity
We use Linear Programming
to optimize the care centers capacities \( S \)
to improve the accessibility \( A \) at a given region. We are interested in
maximizing the total accessibility, i.e the sum of all the municipalities'
accessibility. The form below allows to pick the optimization hyper
Region: The region where the optimization will be run on. The
optimization is ran on metropolitan France, but care centers that
are not from the given region are not allowed to grow/decrease.
Only the care centers and municipalities from the given region and the
surrounding departments will be displayed.
Supply variable: The variable to use as capacity
\( S \). This encodes the care center supply, vs. the population
demand. Choices can be:
Oncology activity: Number of medical or surgery stays related to cancer + number of chemotherapy and radiotherapy patients
MCO activity: Number of medicine, surgery and obstetric stays
Chemotherapy activity: Number of chemotherapy patients
Radiotherapy activity: Number of radiotherapy patients
Oncology medical and surgery activity: Number of medical or surgery stays related to cancer
Additional supply: The activity to be added to
the current overall activity. Setting this parameter to 0 will lead
to an optimization constraint with "constant" activity, meaning
that a care center will have to decrease to let another one grow.
If this number is set between 0 and 1, the corresponding percentage of the current activity
is added. e.g: 0.03 will add 3% of the current activity.
Max growth percentage: The maximum growth percentage
of a care center. If set to 20%, the care center will not be allowed
to grow by more of 20% of its current activity.
Max decrease percentage: The maximum decrease
percentage of a care center. If set to 20%, the care center will
not be allowed to decrease by more of 20% of its current activity.
If set to 0, the care centers activity can't decrease.
Low cluster max capacity: The maximum capacity
that the care centers from the least specialized cluster can reach.
If set to 0, these care centers can't receive any activity and will
be emptied if they originally had some.
High cluster max decrease: This is similar
to the "max decrease percentage" parameter, but only applied
to the care centers from the most specialized cluster. If set to 0,
these care centers won't be allowed to decrease.
Maximum new capacity: The maximum capacity
that the care centers with 0 activity can receive, unless they are
within the least specialized cluster. In this case, this parameter
will be ignored and "low cluster max capacity" will be used.
Plots to better understand the accessibility and oncology activity,
distribution, before running the optimization algorithm.