A brief abstract describing Azmat's work: delineating catchment areas of medical facilities is essential for estimating the quality of a health-care system and to maximise the efficiency of health service provision. One critical shortcoming of previous approaches is manifested in comprehensive assumptions about a hospital’s patients by using census data or gravity models. In contrast, this approach uses anonymised mobile and landline phone data to derive hospital catchment areas. The overall goal is not to assess the quality of the health care system, but to identify the geographic areas from which people actually communicate with, and thus likely use a hospital. Thus, results from this research reveal new insights into the de facto catchment areas of hospitals by minimising assumptions about demographic factors.
Catchment Areas: (a) Port of Spain General Hospital,
(b) San Fernando General Hospital,
(c) Caura Chest Hospital, and
(d) Sangre Grande General Hospital.
In performing this research, a number of limitations are identified. First, not all calls made or received within a hospital are necessarily related to the hospital’s “business”, which induces an un-known bias. Moreover, it is not accounted for the mobile cells’ spatial density in this analysis. Thus, the current method does not consider the probability of a cell phone connected to a particular antenna decreaseing if many cells’ coverage areas overlap. Furthermore, the use of grid cells in the calculation of the catchment areas may lead to a modifiable areal unit problem (MAUP). This could potentially be mitigated by intersecting all ellipse segments, which was too computationally intensive given our extensive dataset. Finally, evaluation and validation of our results are difficult as no data for ground-truthing or comparable studies exist.
Congratulations to this distinction, and to continuing a tradition of excellence established by previous recipients of this award like Laura Knoth in 2016!