Purpose

This project supports the Blind and Visually Impaired center of Monterey County, a not-for-profit organization serving individuals on the peninsula. The Monterey Center would like to project the future population of visually impaired in their areas of interest, Monterey, Salinas, Carmel and Carmel Valley, California, to better position thier limited resouces to the population.

This initial report contains the data, methodology, and application to Pacific Grove and Salinas, California. Also, embedded in the document are the computer scripts used to analyze the data.

This project was completed in R Notebook.

Approach

This analysis takes a two-stage approach:

  1. Use existing datasets to determine the risk of Visual Impairment as a function of various factors. In this initial effort, the predictors used are age and geographic region. The differences in sex are not significant for this study.

  2. Apply the risk estimates in step (1) to projected demographics for future years to plan for the number of Visually Impaired services to be provided. We used our knowledge of the unique features of Pacific Grove to assume that the total population would be stable over the near-term.

Datasets

The main source of data for this project is the American Fact Finder, created by the US Census Bureau. Of the variety of data sources examined, it had the advantage of being the cleanest and most reputable.

The most useful single field in the American Fact Finder is table number C18103, “Sex by Age by Vision Difficulty”. Because the Census bureau has different approaches for various municipalities, the data do not always cleanly ‘map’. For example, Pacific Grove, California, is only considered in the decennial Census, while the metropolitan areas of Salinas and San Jose are considered yearly as part of the American Fact Finder. This difference in reporting has minimal - if any - impact on the final outcome.

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 embedded in the this Notebook. This speeds the ability to apply this analysis against other, similar sets of census data, should we so desire.

In addition to the State of California, we also have 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

Analyzing the data.

In this section, we consider the changing demographics of the population as they age, particularly the distribution of visual impairment as populations age.

Figure 3: Trends by year of visual impairment, Monterey County

Figure 4: 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. In the following graphs, we compare Monterey County with the State of California, San Francisco, Salinas, and San Jose. While these boxplots have minor differences, they show the same basic trend of increasing risk:

Under_18 Age19_64 Over64
.1% 1% 6%

For completeness, we present the boxplots of the other communities considered in this analysis:

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Figure 5: Boxplots of Visual Impairment, 4 other areas

We note that the incidence of visual impairment in Monterey county tends to be slightly lower than that of San Francisco. We have no hypothesis as to why.

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

Discussion

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. (compare Pacific Grove demographics with Salinas City, below)

In the City of Salinas, the rates of visual impairment are similar, but the demographics are strikingly different. Applying the method we used for Pacific Grove, we estimate that there will be 1740 persons with visual impairment in Salinas in 2020.

The incidence of Impairment by Age

Census data only reports impairment by age group. We can sketch a very rough attempt at determining the risk per year by assuming a constant risk probability, and fitting the three known points against

\[ Pr(Blind) = 1-(1-p_{risk})^{Age}, \]

where \(Age\) is in years, and \(p_{risk}\) is the unknown quanitity. We can sketch the solution by using Solver in MS Excel to fit the curve against the three datapoints, which are taken to be the center of their age bands; 9, 41, and 77 years, respectively. Using this methodology, we estimate an individual’s per-year unadjusted risk for visual impairment is .067%.

Note that the above figure is actually an exponential curve, but so slight that it appears linear.

Conclusion and Next Steps

This work took a fast look at predicting the incidence of Visual Impairment using census data applied broadly to two geographic areas. 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.

We look forward to serving you again.

Acknoledgments

This work was supported in part by a grant from the CANA Foundation.

Appendix - Data Tables

Data tables for the State of California and Montery County are provided below. Other data tables (Salinas City, San Jose, San Francisco) are available if desired.

California Data

Attribute Year2008 Year2009 Year2010 Year2011 Year2012 Year2013 Year2014 Year2015
3 Total: 36,160,600 36,376,938 36,815,569 37,161,789 37,524,274 37,831,553 38,297,457 38,649,621
4 Male: 17,938,937 18,072,031 18,152,479 18,337,173 18,510,580 18,678,471 18,865,494 19,053,168
5 Under 18 years: 4,781,741 4,833,502 4,749,200 4,731,653 4,711,947 4,684,835 4,657,991 4,643,120
6 With a vision difficulty 28,983 28,689 31,150 32,438 32,968 33,410 31,745 28,530
7 No vision difficulty 4,752,758 4,804,813 4,718,050 4,699,215 4,678,979 4,651,425 4,626,246 4,614,590
8 18 to 64 years: 11,431,915 11,498,591 11,577,968 11,716,855 11,816,597 11,917,914 12,038,061 12,148,870
9 With a vision difficulty 172,881 158,594 152,904 173,459 166,238 189,614 193,727 192,326
10 No vision difficulty 11,259,034 11,339,997 11,425,064 11,543,396 11,650,359 11,728,300 11,844,334 11,956,544
11 65 years and over: 1,725,281 1,739,938 1,825,311 1,888,665 1,982,036 2,075,722 2,169,442 2,261,178
12 With a vision difficulty 119,667 111,340 111,422 116,382 114,461 133,232 133,830 134,708
13 No vision difficulty 1,605,614 1,628,598 1,713,889 1,772,283 1,867,575 1,942,490 2,035,612 2,126,470
14 Female: 18,221,663 18,304,907 18,663,090 18,824,616 19,013,694 19,153,082 19,431,963 19,596,453
15 Under 18 years: 4,565,973 4,589,184 4,539,491 4,520,813 4,511,541 4,473,120 4,477,142 4,460,019
16 With a vision difficulty 27,411 27,370 26,179 30,707 28,274 29,520 29,478 25,576
17 No vision difficulty 4,538,562 4,561,814 4,513,312 4,490,106 4,483,267 4,443,600 4,447,664 4,434,443
18 18 to 64 years: 11,378,951 11,431,245 11,771,939 11,890,613 11,981,784 12,058,604 12,226,448 12,302,981
19 With a vision difficulty 182,745 174,585 164,648 174,986 177,690 200,921 185,501 191,759
20 No vision difficulty 11,196,206 11,256,660 11,607,291 11,715,627 11,804,094 11,857,683 12,040,947 12,111,222
21 65 years and over: 2,276,739 2,284,478 2,351,660 2,413,190 2,520,369 2,621,358 2,728,373 2,833,453
22 With a vision difficulty 191,544 182,557 180,216 183,149 186,310 202,304 194,321 195,368
23 No vision difficulty 2,085,195 2,101,921 2,171,444 2,230,041 2,334,059 2,419,054 2,534,052 2,638,085

Monterey County Data

Attribute Year2008 Year2009 Year2010 Year2011 Year2012 Year2013 Year2014 Year2015
3 Total: 389,468 390,861 398,350 398,398 408,798 411,985 414,823 416,859
4 Male: 194,764 196,854 198,417 195,637 203,820 205,434 206,110 205,943
5 Under 18 years: 56,746 58,525 57,528 57,580 58,781 58,354 58,154 57,909
6 With a vision difficulty 320 313 139 466 747 683 238 501
7 No vision difficulty 56,426 58,212 57,389 57,114 58,034 57,671 57,916 57,408
8 18 to 64 years: 120,132 119,894 122,040 118,747 124,433 125,370 125,864 124,514
9 With a vision difficulty 1,905 1,045 891 1,444 1,158 519 2,358 1,962
10 No vision difficulty 118,227 118,849 121,149 117,303 123,275 124,851 123,506 122,552
11 65 years and over: 17,886 18,435 18,849 19,310 20,606 21,710 22,092 23,520
12 With a vision difficulty 1,001 1,130 857 1,189 1,371 1,386 940 1,895
13 No vision difficulty 16,885 17,305 17,992 18,121 19,235 20,324 21,152 21,625
14 Female: 194,704 194,007 199,933 202,761 204,978 206,551 208,713 210,916
15 Under 18 years: 54,662 53,899 53,761 55,247 55,022 55,378 55,444 56,309
16 With a vision difficulty 209 434 399 413 202 0 145 246
17 No vision difficulty 54,453 53,465 53,362 54,834 54,820 55,378 55,299 56,063
18 18 to 64 years: 117,313 117,162 121,615 122,773 124,220 124,313 125,524 125,705
19 With a vision difficulty 1,990 1,403 1,005 2,024 1,138 1,665 1,340 1,963
20 No vision difficulty 115,323 115,759 120,610 120,749 123,082 122,648 124,184 123,742
21 65 years and over: 22,729 22,946 24,557 24,741 25,736 26,860 27,745 28,902
22 With a vision difficulty 708 1,423 1,297 1,183 1,985 1,682 2,186 2,111
23 No vision difficulty 22,021 21,523 23,260 23,558 23,751 25,178 25,559 26,791