Measuring the Effectiveness of Needle Exchange Programs in Los Angeles County

Transcription

1 Measuring the Effectiveness of Needle Exchange Programs in Los Angeles County Rebecca Mulqueen April 24, 2016

2 2 Introduction Needle exchange programs have existed throughout the United States for decades as a public health effort to reduce rates of diseases spread through blood such as HIV/AIDs, Hepatitis C and Hepatitis B. HIV can be contracted through bodily fluids such as blood or semen therefore it can be transmitted through sexual contact with someone who is infected or by using a needle that an infected person has previously used 1. This virus primarily affects minorities in poor communities where access to quality healthcare is limited and people are usually uneducated about the risks of unprotected sex and dirty needles. Intravenous drug users are a particularly high-risk group because they are generally not careful about the source of their needles and tend to live transient, unstable lives therefore they are extremely vulnerable 2. It is extremely easy to contract one of these diseases through sharing needles or using dirty needles therefore needle exchange programs hope to provide a clean alternative. Clients bring a dirty needle to the exchange location and receive a clean needle in return. As a master s student who is very interested in public health and communicable diseases, I was curious if these exchange programs were truly effective and if they led to lower rates of diseases spread with dirty needles. I decided to examine how effective these exchange programs are by comparing rates of HIV diagnoses over several years in locations near to the programs. I chose to analyze the number of new diagnoses in all of the zip codes in Los Angeles County over a 4-year period, , with a focus on the zip codes where needle exchange programs are located. ArcGIS is an ideal program for this type of question because creating chloropleth maps 1 "The Basics of HIV Prevention HIV/AIDS Fact Sheets." AIDSinfo. National Institutes of Health, 22 Sept Web. 15 Apr Ibid.

3 3 where different colors represent different numbers of diagnoses gives a clear visual representation of how effective these programs really are. GIS Analysis Data To answer my question on the effectiveness of needle exchange programs I needed to obtain data from the LA County Department of Public Health (LACDPH) on rates of HIV, locations of needle exchange programs and when they began operating. This data is relevant to my question because it allowed me to create a visual representation of where the programs are located and how prevalent HIV was and is in these zip codes. My original idea was to compare rates of HIV before and after the programs opened so I could easily analyze if they had an immediate and then long-term effect on rates of HIV. I contacted Doug Moralis, GIS Specialist at LACDPH, and he was able to give me the locations of needle exchange programs sponsored by the County but not further information on rates of HIV. After conducting further research, I found an organization called AIDSVu.org that creates interactive maps using data on HIV throughout the United States. They use ArcGIS technology on their website and one is able to look at statistics and rates of HIV using a variety of criteria such as method of transmission, racial groups and age. They also allow comparisons with other data such as rates of poverty, education, access to health insurance etc. I contacted AIDSVu and requested some of the data they use to create their maps, thinking that it would be helpful for my analysis and mapping. They were able to provide me with data in an excel spreadsheet of HIV prevalence for 2011 and 2012 and they suggested that I contact Zhijuan Sheng at LACDPH for additional years. The data from AIDSVu would have been exactly what I needed if Mr. Sheng at

4 4 LACDPH had been able to provide data from more years. I was extremely impressed by Mr. Sheng and his very in-depth assistance to me, he called me on multiple occasions to clarify exactly what data I needed and the limitations to what he was providing. He was very clear about analyzing the data with caution and he informed me that HIV diagnoses by zip code was the most practical information to give me because he was able to provide data for Acquiring this data was a huge challenge because it required me contacting several people in different organizations before I attained what I really needed. Additionally, any data provided by LACDPH takes 10 days to compile therefore I did not have time to request further data from Mr. Sheng and his team. The final data I required were the dates that needle exchange programs began operating throughout LA County. This information was vital if I was going to compare effectiveness of the programs by looking at prevalence directly before and after they opened. I contacted yet another LACDPH employee who focuses on needle exchange programs but I never received a response from him therefore I reached out to each program individually for this information. I was disappointed by this development because unfortunately, most programs were not able to give me the information or the relevant employee was not available. After leaving several messages, it became clear that I was not going to attain the information I needed by the deadline for finishing my maps. At this point I decided to slightly alter my project due to these difficulties and chose to instead compare HIV diagnosis numbers from in zip codes where the programs are located without looking at the immediate effects on prevalence after the clinics opened. The data I received from LACDPH was in Excel spreadsheet format and I only had to do minimal editing of the data before I added it into ArcGIS. Mr. Sheng had listed all the years of

5 5 data in one worksheet and I had to separate each year into separate worksheets since I created separate maps for each year. The limitations to this data are that for each year, it only lists the number of new HIV diagnoses therefore the numbers are extremely low in some zip codes of LA County. Additionally, LACDPH is legally not able to give me specific numbers if the rate of HIV diagnosis is less than 5 and this is to protect the privacy of those people. Therefore, for zip codes where I had <5 as the input, I changed this number to 0. While this slightly skewed my data, I rationalized this idea because if the rate of HIV diagnoses changed from 4 to 3 people, this change is not significant enough to register on the chloropleth maps anyway. Therefore in zip codes where there are fewer than 5 new diagnoses, it will show on my maps as the color that represents 0 cases and then once that number reaches 5 or higher, it will show as a darker color on my map. Other limitations to my data include the fact that rates of new HIV diagnoses are not solely attributable to dirty needles since there are multiple ways to contract HIV. Additionally, while lower rates of diagnoses are certainly positive they may not be the result of needle exchange programs. There may have been other programs operating in the same areas at the same time such as educational programs and free access to condoms that could lead to lower rates too. Therefore, as I analyze my maps to determine how effective these programs are I have to consider that my conclusions are not definitive and there are other factors that are influencing my data. If I had no resource or time limitations, I would have requested significantly more data from LACDPH, especially data on persons currently living with HIV (not just new diagnoses). Mr. Sheng was only able to provide me with this data for 2014 therefore it was not useful for the purposes of my project. I would also have included more variables in my data such as the location of education programs or other efforts that could affect rates of HIV so I could draw more definitive conclusions. While my data is still accurate, my conclusions are weakened since I am not able to

6 6 hold other variables constant to purely look at how the needle exchange programs affected HIV diagnoses. Building the Maps After finally acquiring the data I needed, I began by finding a base map of LA County by zip code. This was easily attainable from ArcGIS Online and then I layered streets in LA County onto my base map so I could begin geocoding. I geocoded the addresses of the needle exchange programs on the list sent to me by Mr. Moralis. This list, I later realized, is not all of the needle exchange programs in LA but only those sponsored and funded by LACDPH. This is also a limitation of my data because there are other needle exchange programs that could be affecting HIV diagnoses rates but I am not analyzing them. My list included 11 programs run by a variety of organizations including Bienestar, Venice Family Clinic and Tarzana Treatment Center among others. After geocoding, I chose a bright red pin to symbolize the programs. To create the multiple chloropleth maps, I joined my data table for each year to the attribute table for LA zip codes. I struggled with this initially because I kept receiving error messages when joining the tables. I eventually realized that I had to match the input layer and joining layer by making them both zip codes, and then the attribute tables successfully joined. While we had practiced this in other exercises in class, I had not done it in over a month and using my own data created some slight complications. As there was a wide variation in numbers between different years, I chose to use the ranges that were selected by ArcGIS for 2010 and keep them for all remaining maps so comparison was easy between years. Additionally, I chose 20 classes because I wanted to show when a small change occurred. Since the vast majority of numbers were low, small changes did make a difference. But as previously mentioned, numbers

7 7 below 5 were classified as 0 since I did not have specific numbers other than <5 to base my ranges on. I chose the yellow through dark blue color spectrum because I wanted the changes between colors to be noticeable and I did not think this would happen if I used 20 shades of the same color, for example. Conclusions The maps I produced for 2010, 2011, 2012 and 2013 demonstrate my data visually so that one can draw conclusions about the trend of HIV diagnoses and the correlation of this trend to the location of needle exchange programs. It is easy to see from the maps that the overall number of HIV diagnoses is decreasing over time in Los Angeles County. This is indicated by the presence of more of the lighter colors on my chrolopleth spectrum in later years such as 2012 and 2013 compared to 2010 and I would expect that if I had more years of data, this trend would continue as more people become educated about HIV and the most effective ways to prevent contracting the disease. The vast majority of the needle exchange programs sponsored by the County are located in South Los Angeles, which is not surprising since this is the poorest area of the County and also where HIV rates appear to be highest. However, there are also some areas further North shown by the dark blue zip codes where HIV diagnosis rates are the highest and these are neighborhoods like Downtown LA and West Hollywood where there are very high numbers of homeless individuals. I believe there are needle exchange programs in these areas too, but the County does not fund them. The zip codes in South LA where the needle exchange programs are located contain levels of diagnosis that are middle to high on the spectrum but not as bad as some other areas of LA. These zip codes generally had HIV diagnoses rates that ranged from 16 to 23 in 2010 and for

8 8 neighborhoods that are some of the poorest in LA county, these numbers are surprisingly manageable. In 2011 and onwards, these numbers continue to decrease in zip codes with needle exchange programs until their numbers are almost entirely in the single digits and very light green on the chrolopleth spectrum. This trend indicates that needle exchange programs are moving HIV diagnosis rates in a positive direction and that they have having a positive impact on the communities they serve. However, I cannot say with certainty that these decreasing numbers are totally due to the presence of needle exchange programs. As previously mentioned, my data has limitations because I did not gather data on other programs in these areas that are working to address HIV. These diminishing numbers could also be due to an increase in awareness and education of HIV and several other factors. I did try to gather data on HIV rates spread solely by needles but LACDPH did not have enough years of data for this to work for my project. As a result of these limitations, I cannot conclude with certainty whether needle exchange programs lead to lower rates of HIV diagnoses in Los Angeles County. I do believe that they have a positive impact on the communities they serve and while HIV diagnosis rates have decreased over time in LA, I do not know whether this is due to needle exchange programs or other factors. If I were able to repeat this project, I would attempt to hold other variables constant if possible or find data that separates the method of transmission of HIV because this would allow more definitive conclusions. The further I progressed with my project, I began to realize the difficulties of forming definitive conclusions given my restrictions but I was still curious to explore overall trends in HIV diagnoses in Los Angeles. While this public health problem is getting better, Los Angeles County and LACDPH still have significant work to accomplish in this arena and I believe that the use of ArcGIS can help them to determine the most effective ways to continue the current level of progress.

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