Final Project

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.

week 10 quiz

1) 10 most populous countries
1 - China
2 - India
3 - USA
4 - Indonesia
5 - Russia
6 - Brazil
7 - Pakistan
8 - Japan
9 - Bangladesh
10 - Nigeria

2) 3 most populous countries in Africa
1 - Nigeria
2 - Guinea
3 - Egypt

3) 5 least populous countries in South America
1 - French Guiana
2 - Suriname
3 - Guyana
4 - Uruguay
5 - Paraguay

4) 15 rivers in the Amazon river system

5) 60 cities are within 500 km of the Amu Darya and Syr Darya rivers

6) Population of countries within 300 km of Iran: 452,300,000 rounded to the nearest 100,000

7) Of the landlocked countries the least populous is the Vatican City and the most populous is Ethiopia.

8)Poland, Czech Republic, Slovakia, Austria, Slovenia, Hungary, Romania, Croatia, Bosnia, and Yugoslavia are within 30 km of Veszprem, Hungary.

9) Monaco the sovereign nation with the 4th smallest land area.

10) The countries that border Chad include: Libya, Niger, Sudan, Nigeria, Central African Republic, and Cameroon.

Spatial Interpolation

In this lab we were tasked to create a map of precipitation of Los Angeles county. We were given data on total and normal precipitation collected from stations that measuerd rainfall. The first task was to convert the latitude and longitude to decimal degrees and then manually type in the rainfall data. This data was then imported into a GIS and various interpolation techniques were applied.

Inverse distance weighting (IDW)would not be an appropriate interpolation technique because the distribution of rain stations that actually collected both normal and total rain fall was not dense enough to capture an accurate extent of precipitation across Los Angeles county. IDW becomes less accurate as the distance to data points increases so the precipitation map would be accurate at rain station locations and inaccurate for areas in between stations.

Kriging would be a more suitable interpolation technique because it takes into account the fact that precipitation may be spatially correlated. So this method of interpolation should provide a better estimate of the level of precipation across Los Angeles county.

Below is a map of precipitation in Los Angeles County based on IDW and kriging.

Lab 7 - Fire Hazard

In this lab we had to create a fire hazard map of the mountainous area in northern Los Angeles county where the station fire occurred. Luckily I already had the DEM raster, county boundary, and fire perimeter shapefiles from the Intro to GIS class last quarter, so I did not have to search for the majority of the information needed to perfrom this analysis. On the California Department of Forestry and Fire Protection website I was able to find a map of fuel hazard rating based on vegetation. I then created a slope map using ArcMaps surface analysis tool. I then created a model that added the fuel hazard rating to the slope to create a fire hazard map. I changed classification of the fire hazard map to a continuous scale and changed the color ramp to 'yellow-red' in order to represent fire hazards.

Here is the map that I created.


As you can see, the area with the most flammable vegatation and slope conducive to the spreading of fires are the Malibu area and the mountainous areas where the station fire occured. ArcGIS was very useful in performing analysis of fire hazards due to its ability to combine data to create models based on factors that contribute to fire hazard.

The main challenge of this lab were understanding the rationale behind the model of fire hazard. We were assigned a tutorial to complete before creating our own fire hazard maps that taught us how to create models from raster data. The tutorial used data that had to be reclassified according to NFPA 1144 standards, but did not explain how those were created and what the rationale behind the reclassification. So when I set out to make my own fire hazard map it was difficult to come up with a way of reclassifying fuels and slope to match the NFPA 1144 standard. Instead I just created a rank order classification scheme for both the fuels and slope data and added the two together to derive the fire hazard map. Thus, while my fire hazard map does not conform to the NFPA 1144 standard it is still represents a rank ordered level of fire hazard within LA county.

Here is the map I created from the tutorial.

Landfill Suitability Analysis

Landfill Suitability Analysis Maps





Spatial analysis is a very powerful tool for policy makers to utilize in helping to make decisions when major decisions need to be made regarding controversial subjects such as building or expanding landfills and its effect on the health of those living near the areas in question. GIS helps to manage and analyze data of the various factors affecting the decision. Spatial analysis of the suitability of an area of land to build a landfill on is especially complicated as it may involve slope analysis, distance to bodies of water, land cover, soil drainage, and distance to other landfills. Thus, using GIS to help inform policy makers about the suitability of building landfills on certain plots of land will be important.

A toxic waste landfill in Kettleman City, California, has been planning to expand its capacity to take in more . However, residents of Kettleman City have reported increased rates of birth defects and blamed the landfill for this increase. Senators and the EPA have gotten involved with the investigation due to the outcry of the residents regarding the death of infants who suffered from birth defects and have suspended the expansion of the landfill pending further investigation of the health risks associated with the landfill. The owners of the landfill were surprised to hear about the suspension but were cooperative with the investigation.

A preliminary report concluded that birth defects occurring in the area surrounding the landfill were comparable to nearby communities and there were no common factors between birth defect cases . The report did not sit well with advocacy groups and residents who complained that the study was conducted haphazardly and did not include other factors that may contribute to birth defects.

It is difficult to comment on the report without knowing the specific details on the analysis that was conducted. However, in general situations when rare cases are involved it will be difficult to find associations with any factor due to the lack of statistical power. With only four cases to work with it is not surprising that the report did not find any meaningful associations between environmental factors with birth defects. Historical analysis of past rates of birth defects will need to be conducted so more cases can be involved and statistical power can be increased.

GIS also can play a major role in the investigation of the landfill and other environmental contaminants’ effect of the development of birth defects. Spatial analysis can be run on the slope, soil drain, nearby water sources, and land cover to determine which areas would be the most affected by toxic waste leakage. Wells can be plotted and see if they draw water from contaminated water sources. Pesticide use maps reported by farmers can be used to determine ambient exposure to agriculturally applied pesticides. These are just a few spatial analyses that can be conducted with GIS which can be combined to provide a more complete assessment of the factors that contribute to birth defects.

So in conclusion, I would agree with the advocacy groups and residents that the preliminary report is inadequate to determine whether or not living near the landfill are associated with birth defects.

midterm

Policy Regarding LA City Council Decision of Marijuana Dispensaries

I support the city council's decision to restrict marijuana dispensaries from being within 1000 feet of schools, libraries, and parks.




The purple areas indicate the restricted areas (within 1000 feet of schools, libraries, and parks) while the yellow areas represent areas where dispensaries can operate. According to the data obtained from our GIS, it appears that there is adequate area for dispensaries to do business outside of the 1000 feet restriction.

The costs incurred to the county by this decision include loss in revenue from marijuana dispensaries since less dispensaries would be able to operate under this new policy and there may also be potential illegal selling of marijuana in restricted areas, increasing the level of crime in these areas.

The benefits of this policy include reducing the access that children have to marijuana dispensaries and heightened regulation of the marijuana industry.

I believe that the benefits outweigh the costs in this case. The motivation behind the legalization of marijuana is to be able to regulate it. A tough stance such as the one California took against smoking is also needed against marijuana since it does have psychologically altering effects and should definitely be regulated.

Lab 4: The Digitization of Iraq

This is a digitized map of Iraq. Provinces, cities, and the Euphrates and Tigris rivers were digitized using ArcGis.