Research Paper on Uncover Solutions with Asset Mapping in Community Health Assessments

Introduction

There are many benefits associated with asset mapping in community health assessment. The main benefit associated with asset mapping is that it yields crucial information regarding community resources and strengths, thus helping uncover solutions (North Carolina Institute for Public Health, n.d.). For instance, in a community health assessment aimed at implementing physical exercise aimed at decreasing obesity, the following community assets that are useful in the success of the program may include health-related non-governmental organizations, association of residents, physical infrastructure (e.g., recreational centers), and capacity (skills and knowledge) of community members.

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Despite its immense benefits, asset mapping has also been reported to have some limitations. One of these limitations is that finding the appropriate maps can be challenging, and mapping software can be expensive to the users and difficult to use (UCLA Center for Health Policy Research, n.d.). If it takes a long time to identify the maps, then the benefits that could have been accrued from the community health assessment are delayed - thus compounding the community health problems. Similarly, if the program is not adequately funded, it might not be capable of acquiring the software needed to create maps as well as to recruit experts in the use of asset mapping software.

An Example of When I Might Use Asset Mapping and Reasons for Using

Asset mapping refers to the process of documenting features of the built environment that influence health, e.g., access to physical exercise facilities such as gyms and fields. In my proposed community health assessment, I will use asset mapping to locate health-related non-governmental organizations, physical infrastructure, and community health experts who might help in the success of the program. The identification of these community assets is vital in various ways. For instance, health-related non-governmental organizations can provide grants or funds for the program. On the other hand, identifying the physical infrastructure, such as open spaces, is necessary for ensuring that there is enough space for the implementation of exercise intervention.

Explanation of One Benefit and One Limitation of Using GIS Community Health Assessment

The use of GIS in asset mapping has been reported to have many benefits. One of the advantages that are attributed to the use of GIS in community health assessment process is that it can be effectively utilized in the entire assessment process, from planning phase to the evaluation phase (Kazda et al., 2009; Pacquiao & Douglas, 2018; Panagiotis, 2016; Stanhope & Lancaster, 2016). The power of GIS lies in its ability to provide answers to spatial-related questions- which have been reported to be vital in health science research (Kazda et al., 2009). The data collected from GIS can then be used to create the community's demographic profile- which further supplements the existing community's health risk profile such as ethnicity, income, and housing conditions. In conclusion, GIS provides large volumes of data that is useful in designing robust programs as well as in seeking grants for the community health assessment.

Even though asset mapping using GIS has been crucial in addressing various health problems in communities, limitations associated with GIS have been reported. For instance, Beyer, Comstock, and Seagren (2010) reported that using GIS technique in the investigation of health questions is made difficult by limitations associated with community access to private and confidential data at fine-scaled geographical population. Even though some researchers have directly conducted mapping of diseases using point locations, the data used primarily come from informal surveys rather than more robust disease registries, thus limiting the reliability and validity of the collected data.

The issue of privacy and confidentiality in the use of GIS is associated with the risk of reverse geocoding (Zandbergen, 2014). Through reverse geocoding, an individual who is knowledgeable in map interpretation can pinpoint the household related to a specific condition (Richardson, Kwan, Alter, & McKendry, 2015). The risk of reverse geocoding has raised concerns related to patients' confidentiality (Bader, Mooney, & Rundle, 2016; Blatt, 2014). Consequently, the disease registries and other organizations tasked with storing patients' data are careful in giving geocoded health data to third parties, such as researchers (Haley et al., 2016; Soares, Dewalle, & Marsh, 2017). Haley et al. (2016) have also noted that despite the extensive literature that have outlined the approaches that can be taken to protect patient confidentiality when using social-spatial data, there still exist peer-reviewed manuscripts showing quasi-identifiers and identifiable personal point data in maps.

References

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North Carolina Institute for Public Health. (n.d.). Asset Mapping. Retrieved from https://nciph.sph.unc.edu/cha-learning-congress/Asset-Mapping.pdf

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Richardson, D. B., Kwan, M.-P., Alter, G., & McKendry, J. E. (2015). Replication of scientific research: Addressing geoprivacy, confidentiality, and data sharing challenges in geospatial research. Annals of GIS, 21(2), 101-110. https://doi.org/10.1080/19475683.2015.1027792

Soares, N., Dewalle, J., & Marsh, B. (2017). Utilizing patient geographic information system data to plan telemedicine service locations. Journal of the American Medical Informatics Association, 24(5), 891-896. https://doi.org/10.1093/jamia/ocx011

Stanhope, M., & Lancaster, J. (Eds.). (2016). Public health nursing: Population-centered health care in the community (9th edition). St. Louis, Missouri: Elsevier.

UCLA Center for Health Policy Research (n.d.). Technical Assistance Series Article #1: Asset Mapping. Retrieved from http://healthpolicy.ucla.edu/Documents/Newsroom%20PDF/democ_data_assetmap.pdf

Zandbergen, P. A. (2014). Ensuring confidentiality of geocoded health data: Assessing geographic masking strategies for individual-level data. Retrieved https://www.hindawi.com/journals/amed/2014/567049/abs/