Risk Factors Associated with Reduced Access to Healthcare
A GIS project by Anthony Wang
INTRODUCTION
In September 1978, world leaders gathered at a conference in Alma-Ata, Kazakhstan, to discuss the state of the world regarding urgent health issues. It is well accepted that there are enormous gaps in health care internationally and those who gathered at this conference sought to develop a plan of action to implement primary health care throughout the world, especially in developing countries. The Declaration of Alma-Ata stated that health is a basic human right and the existing inequalities of the health status of individuals are unacceptable and must be addressed(Alma-Ata 1978). Unfortunately thirty years later, people all over the world are still suffering from a lack of even the most basic health care. Furthermore, the disparity between those with and those without adequate health care is exacerbated in the United States due to a lack of universal health insurance, gaps in available health insurance, physician maldistribution, and inequities in the provision of health care(Fiscella and Shin 2005). Thus, it is important to better understand the characteristics of those who are at the highest risk of not being able to access primary health care.
Several studies that have explored the demographic characteristics of those who do not have access to health care point to those with a high school diploma or less education, living below the federal poverty level, living with fewer adults and more children in the household to be positively associated with a reduced level of health care access(Fiscella and Shin 2005; Mier, Ory et al. 2008; Lykens, Fulda et al. 2009). In this study we aim to map out the areas of Los Angeles County where these risk factors or barriers to healthcare access are the most concentrated, develop a model that combines these barriers, and geocode the locations of free healthcare clinics to determine if free health care services are adequately distributed in the county.
METHODS
Data collection
Data on average family size, the number of people with a high school diploma or less education, the number of single parents, and the number of people living below the federal poverty limit was collected from the 2000 US Census Summary File 1 and 3 for all census tracts in Los Angeles County (www.census.gov). The addresses of free clinics in Los Angeles County, excluding those in the San Fernando Valley, were located on the internet. The latitude and longitude of each free clinic was obtained by inputting these addresses into Yahoo’s search engine.
Spatial Interpolation
Although data on all the census tracts in Los Angeles County were obtained from the US 2000 Census website the shapefiles for the census tracts that were obtained contained extra census tracts not included in the census data, so the census data could not be joined to the shapefile. Thus, instead of mapping out the risk factors for each census tract, spatial interpolation was utilized in order to create a map of those at who are at the highest risk of reduced access to health care.
The first step was to determine which census tract each free clinic belonged to, so the census data collected could be linked to the individual clinics. Then the free clinics were geocoded using ArcGIS 9.1 and the kriging spatial interpolation procedure was performed for each demographic variable collected (family size, education, single parent status, and poverty level) to create a raster grid of the distribution of the particular variable. The values of each raster were then grouped into eight categories according to the number of people with the particular characteristic. A 1.5 mile circular buffer was then drawn around each geocoded free clinic in order to visualize the catchment area of each clinic. In order to fit the four variables into one model, each the categories of each variable were reclassified from 1-8. Finally, the four variables were combined in one model and weighted according to their hypothesized contribution to risk of reduced access to health care.
RESULTS
From the map it is obvious that the four variables each have different distributions across Los Angeles County. Average family size is the greatest around South Central Los Angeles, those living in East Los Angeles have lower levels of education, South Central and West Los Angeles have the highest concentration of single parents, and the majority of those living under the federal poverty limit tend to cluster in Hollywood, South Central, and Downtown Los Angeles. According to the weighted model, the areas with the most barriers to healthcare access are the Downtown and South Central areas. There are some free clinics scattered about in these high risk areas but they are the mostly concentrated in the North Hollywood area.
DISCUSSION
This study has shown that free clinics are not distributed to appropriately to respond to the most critically at risk populations in Los Angeles County. The majority of free clinics are located in northern region of the county whereas those that live in the central and south central regions have the greatest need for accessible health care.
The issue of access to health care is very complex, with a multitude of contributing factors. Thus, a strength of this study is that it explores a handful of specifics risk factors and combines them into one model to determine overall risk of reduced access to health care. Another strength is that the data used in this study is also of good quality as it was obtained from the US 2000 Census datasets. Kriging is also an appropriate method of spatial interpolation since the distribution of people with certain demographic characteristics is not random. A final strength of this study is that the juxtaposition of the locations of free clinics and the areas with the most people at risk of reduced health care access allows one to easily see which areas are in the most need for additional free clinics.
However, this study also suffers many limitations. Due to the mismatch of the US 2000 census data with the ArcGIS census tract shapefile, there was a significant amount of risk factor data that was not able to be used. Spatial interpolation is useful but is only accurate in areas surrounding the geocoded free clinics. Furthermore, it is very difficult to quantify the affect that specific risk factors have on access to healthcare, thus there is uncertainty about whether or not the model described in this study is accurate. Given more resources and time to complete this study, the centroids of the census tracts could be geocoded, allowing the spatial interpolation procedure to utilize more data and thus more accurately produce a map of the demographic characteristics associated with health care access.
Alma-Ata (1978). Declaration of Alma-Ata. International Conference on Primary Health Care. Alma-Ata, USSR.
Fiscella, K. and P. Shin (2005). "The inverse care law: implications for healthcare of vulnerable populations." J Ambul Care Manage 28(4): 304-12.
Lykens, K. A., K. G. Fulda, et al. (2009). "Differences in risk factors for children with special health care needs (CSHCN) receiving needed specialty care by Socioeconomic Status." BMC Pediatr 9: 48.
Mier, N., M. G. Ory, et al. (2008). "Health-related quality of life among Mexican Americans living in colonias at the Texas-Mexico border." Soc Sci Med 66(8): 1760-71.
