CANA Advisors took on a pro-bono project supporting the Blind and Visually Impaired Center of Monterey County, a not-for-profit organization serving individuals on the peninsula. The Monterey Center needed to project the future population of visually impaired in their areas of interest - Monterey, Salinas, Carmel, and Carmel Valley, California - to better position their limited resources to the population.
To approach this problem, we took a two-step approach using existing data to determine the risk of vision loss as a function of location, age and sex and applying those factors to the projected demographics of communities on the Central Coast.
The main source of data for this project is the American Fact Finder, created by the U.S. Census Bureau, specifically table C18103 "Sex by Age by Vision Difficulty."
Figure 1: Screenshot of the American Fact Finder interface, captured 17 October 2016
Summary data from the years 2008-2015 is captured as separate Microsoft Excel files from the web page. These files are then processed and brought together as summary data for predictions. This somewhat mundane task is aided by a set of automated routines built in the R. Specifically, we captured each spreadsheet in a list, and then iterated over the list to extract the by-year columns.
In addition to the State of California, we also collected data for Monterey County, San Francisco County, the City of Salinas, and the San Jose metro area. This dataset lacks some elements we would like to include, such as veteran status, income, and ethnicity. However, we feel that these variables are controlled for by the metropolitan area sufficient for purposes of this project.
Figure 2: Map of the Central California Region
We then turn to the first task, which is analyzing the loss of vision as persons age by location and sex. While we compiled this information for each area, we present the results for Monterey County only.
Figure 3: Boxplot of Proportion of Visually Impaired population by Sex and Age
The boxplot of risk, showing the average incidence of Visual Impairment (solid line) variability per year (box), is the most interesting artifact.
Projecting the future impaired population of Pacific Grove, CA
Pacific Grove, California (zip code 93950), is a small city. It is unique because there is, for all practical purposes, no undeveloped land, and the local government actively works to keep the city size stable. For our purposes, we consider the city's population to be fixed.
From our previous analysis, we determined that the percentage of the Visually Impaired population by age is: under 18, .6%, 18-64, 1.1%, and over 64, 6.2%. Sex is not a significant determinant in visual impairment for this population. The demographics of Pacific Grove are below:
Based on the information determined above, we estimate that in 2020, the Visually Impaired population of Pacific Grove, California will be approximately 335 persons. This is slightly higher than our current estimate of 315 in 2015.
There are two factors that contribute to the very low (less than 1%) growth in visual impairment in Pacific Grove. These are
1. No population growth in the city. There are no undeveloped areas in Pacific Grove; the current population number will almost certainly remain constant. By way of comparison, Pacific Grove had negative population growth between 2000 and 2010.
2. No growth in the over 64 population. The population greatest at risk, those over 64 years old, are unlikely to see strong growth in the near- to mid- term. This is because the city already has a substantial older population. See comparison of Pacific Grove demographics with Salinas City, below.
Conclusion and Next Steps
This work took a fast look at predicting the incidence of Visual Impairment using census data. This work did not account for differences in education, work experience, or veteran's status with respect to loss of sight. We were surprised to discover how uniform the rates of visual impairment are across the populations of Central California; in the future, we may consider how California compares with other States and/or Countries.
It would be worthwhile to consider the data that various agencies may have as part of their records. This work only considered the incidence of Visual Impairment, but did not consider the causes or different treatments/services required by that population.
This work was supported in part by a grant from the CANA Foundation.