covid-geography: mmsa-icu-beds.csv

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This table contains the data behind the story How One High-Risk Community In Rural South Carolina Is Bracing For COVID-19.

mmsa-icu-beds.csv combines data from the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS), a collection of health-related surveys conducted each year of more than 400,000 Americans, and the Kaiser Family Foundation to show the number of people who are at high risk of becoming seriously ill from COVID-19 per ICU bed in each metropolitan area, micropolitan area or metropolitan division for which we have data.

Being high risk is defined by a number of health conditions and behaviors. Based on the CDC’s list of the relevant underlying conditions that put people at higher risk of serious illness from COVID-19, plus the advice of experts from the Cleveland Clinic, the American Lung Association and the American Heart Association, we counted people as at risk if they’re 65 or older; if they have ever been told they have hypertension, coronary heart disease, a myocardial infarction, angina, a stroke, chronic kidney disease, chronic obstructive pulmonary disease, emphysema, chronic bronchitis or diabetes; if they currently have asthma or a BMI over 40; if they smoke cigarettes every day or some days or use e-cigarettes or vaping products every day or some days; or if they’re currently pregnant. We included every individual who meets at least one of these conditions but counted them only once each, so anyone with multiple conditions doesn’t get counted multiple times. We were not able to include a number of conditions for which we did not have location-based data from the BRFSS, such as liver disease, having smoked, vaped or dabbed marijuana in the last 30 days, and getting cancer treatment or being on immunosuppression medications.

Column Description
MMSA The name of the metropolitan area, micropolitan area or metropolitan division available in the CDC’s BRFSS
total_percent_at_risk The percent of individuals in that area that are at high risk of becoming seriously ill from COVID-19, per CDC’s BRFSS
high_risk_per_icu_bed The number of high risk individuals per ICU bed in that area
high_risk_per_hospital The number of high risk individuals per hospital in that area
icu_beds The number of ICU beds in the area, based on the Kaiser Family Foundation’s data
hospitals The number of hospitals in the area, based on the Kaiser Family Foundation’s data
total_at_risk The total number of high risk individuals in the area, per CDC’s BRFSS

Data license: CC Attribution 4.0 License · Data source: fivethirtyeight/data on GitHub · About: simonw/fivethirtyeight-datasette

136 rows

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Link rowid ▼ MMSA total_percent_at_risk high_risk_per_ICU_bed high_risk_per_hospital icu_beds hospitals total_at_risk
1 1 San Juan-Carolina-Caguas, PR 52.88%         923725.203
2 2 Manhattan, KS 47.29% 4489.84875 8979.6975 8 4 35918.79
3 3 Hilton Head Island-Bluffton-Beaufort, SC 62.72% 3904.163571 36438.86 28 3 109316.58
4 4 Kahului-Wailuku-Lahaina, HI 59.13% 3860.557 19302.785 20 4 77211.14
5 5 Spartanburg, SC 66.12% 3786.115556 85187.6 45 2 170375.2
6 6 Baton Rouge, LA 66.60% 3459.7325 39000.62091 124 11 429006.83
7 7 Rockingham County-Strafford County, NH, Metropolitan Division 57.72% 3365.052 40380.624 60 5 201903.12
8 8 Salisbury, MD-DE 68.32% 3292.271176 37312.40667 68 6 223874.44
9 9 Wichita Falls, TX 67.11% 3279.425 19676.55 24 4 78706.2
10 10 Colorado Springs, CO 55.96% 3251.603053 77225.5725 95 4 308902.29
11 11 Cambridge-Newton-Framingham, MA, Metropolitan Division 52.17% 3161.025223 62035.12 314 16 992561.92
12 12 Albuquerque, NM 60.33% 3091.331014 71100.61333 138 6 426603.68
13 13 Portland-South Portland, ME 60.60% 3051.228953 43734.28167 86 6 262405.69
14 14 Silver Spring-Frederick-Rockville, MD, Metropolitan Division 48.90% 2929.015833 70296.38 168 7 492074.66
15 15 Charlotte-Concord-Gastonia, NC-SC 60.63% 2919.072935 65192.62889 402 18 1173467.32
16 16 Ogden-Clearfield, UT 53.80% 2906.274767 35705.66143 86 7 249939.63
17 17 Cedar Rapids, IA 57.99% 2739.539318 30134.9325 44 4 120539.73
18 18 Tallahassee, FL 52.69% 2737.105667 54742.11333 60 3 164226.34
19 19 Portland-Vancouver-Hillsboro, OR-WA 55.37% 2648.925704 63106.75941 405 17 1072814.91
20 20 Grand Rapids-Wyoming, MI 58.12% 2538.918207 42469.17727 184 11 467160.95
21 21 Riverside-San Bernardino-Ontario, CA 56.62% 2530.862418 56646.65588 761 34 1925986.3
22 22 Des Moines-West Des Moines, IA 57.99% 2518.830893 40301.29429 112 7 282109.06
23 23 Fayetteville-Springdale-Rogers, AR-MO 56.85% 2517.092088 38175.89667 91 6 229055.38
24 24 Minneapolis-St. Paul-Bloomington, MN-WI 50.99% 2474.26659 43763.59031 566 32 1400434.89
25 25 Worcester, MA-CT 58.26% 2472.280229 43264.904 175 10 432649.04
26 26 Wilmington, DE-MD-NJ, Metropolitan Division 61.40% 2441.704685 69832.754 143 5 349163.77
27 27 Houston-The Woodlands-Sugar Land, TX 55.82% 2421.507063 52172.47036 1185 55 2869485.87
28 28 Spokane-Spokane Valley, WA 61.67% 2413.394779 38959.08714 113 7 272713.61
29 29 Grand Island, NE 60.48% 2402.975 9611.9 16 4 38447.6
30 30 Oakland-Hayward-Berkeley, CA, Metropolitan Division 48.01% 2385.409101 55868.79211 445 19 1061507.05
31 31 Myrtle Beach-Conway-North Myrtle Beach, SC-NC 73.79% 2385.046891 47303.43 119 6 283820.58
32 32 Washington-Arlington-Alexandria, DC-VA-MD-WV, Metropolitan Division 50.03% 2356.378968 59204.02156 804 32 1894528.69
33 33 Seattle-Bellevue-Everett, WA, Metropolitan Division 51.35% 2329.607529 57907.38714 522 21 1216055.13
34 34 Montgomery County-Bucks County-Chester County, PA, Metropolitan Division 59.69% 2328.824246 40298.78478 398 23 926872.05
35 35 Topeka, KS 68.10% 2322.307885 30190.0025 52 4 120760.01
36 36 Sioux City, IA-NE-SD 60.83% 2319.862121 19138.8625 33 4 76555.45
37 37 Springfield, MA 59.45% 2307.629846 49998.64667 130 6 299991.88
38 38 Deltona-Daytona Beach-Ormond Beach, FL 71.11% 2304.373554 63754.335 166 6 382526.01
39 39 New York-Jersey City-White Plains, NY-NJ, Metropolitan Division 54.38% 2302.129085 75184.16695 2678 82 6165101.69
40 40 Rochester, NY 59.63% 2254.532389 42460.36 226 12 509524.32
41 41 Newark, NJ-PA, Metropolitan Division 55.88% 2237.802582 68252.97875 488 16 1092047.66
42 42 College Station-Bryan, TX 51.71% 2230.095 21408.912 48 5 107044.56
43 43 Virginia Beach-Norfolk-Newport News, VA-NC 60.85% 2228.447378 58894.68071 370 14 824525.53
44 44 Albany-Schenectady-Troy, NY 58.67% 2225.128717 41609.907 187 10 416099.07
45 45 Greenville-Anderson-Mauldin, SC 62.84% 2198.10445 43962.089 200 10 439620.89
46 46 North Port-Sarasota-Bradenton, FL 67.32% 2183.080962 64868.69143 208 7 454080.84
47 47 Fort Worth-Arlington, TX, Metropolitan Division 58.76% 2180.375795 37367.13 497 29 1083646.77
48 48 Warren-Troy-Farmington Hills, MI, Metropolitan Division 61.20% 2165.925455 56314.06182 572 22 1238909.36
49 49 Hartford-West Hartford-East Hartford, CT 57.26% 2150.121289 55043.105 256 10 550431.05
50 50 Providence-Warwick, RI-MA 60.98% 2110.081467 56520.03929 375 14 791280.55
51 51 Camden, NJ, Metropolitan Division 63.56% 2109.945288 77804.2325 295 8 622433.86
52 52 Burlington-South Burlington, VT 54.84% 2104.114565 48394.635 46 2 96789.27
53 53 Sioux Falls, SD 58.02% 2084.996111 16084.25571 54 7 112589.79
54 54 Atlanta-Sandy Springs-Roswell, GA 55.19% 2059.592863 64931.375 1198 38 2467392.25
55 55 South Bend-Mishawaka, IN-MI 61.57% 2058.605 38084.1925 74 4 152336.77
56 56 Anchorage, AK 58.80% 2055.441034 44705.8425 87 4 178823.37
57 57 Phoenix-Mesa-Scottsdale, AZ 57.75% 2043.376384 55171.16237 1026 38 2096504.17
58 58 Aberdeen, SD 61.13% 2031.057 6770.19 10 3 20310.57
59 59 Dallas-Plano-Irving, TX, Metropolitan Division 52.49% 2027.770063 38324.8542 945 50 1916242.71
60 60 Hagerstown-Martinsburg, MD-WV 66.46% 2022.890441 68778.275 68 2 137556.55
61 61 St. Cloud, MN 54.67% 2022.670976 20732.3775 41 4 82929.51
62 62 Claremont-Lebanon, NH-VT 63.34% 2016.79875 12548.97 56 9 112940.73
63 63 Boise City, ID 55.40% 2013.776027 42001.61429 146 7 294011.3
64 64 Buffalo-Cheektowaga-Niagara Falls, NY 65.12% 2000.767381 65358.40111 294 9 588225.61
65 65 Billings, MT 60.09% 1942.979024 26554.04667 41 3 79662.14
66 66 Minot, ND 61.17% 1940.797368 18437.575 19 2 36875.15
67 67 Austin-Round Rock, TX 51.27% 1937.338662 39300.29857 426 21 825306.27
68 68 Los Angeles-Long Beach-Anaheim, CA 51.13% 1924.917328 53454.9542 2777 100 5345495.42
69 69 Sacramento--Roseville--Arden-Arcade, CA 54.51% 1923.676063 65148.496 508 15 977227.44
70 70 Memphis, TN-MS-AR 62.33% 1920.567866 52495.52167 328 12 629946.26
71 71 Cincinnati, OH-KY-IN 62.75% 1919.68433 55064.62947 545 19 1046227.96
72 72 Corpus Christi, TX 61.89% 1868.164174 53709.72 115 4 214838.88
73 73 Baltimore-Columbia-Towson, MD 61.84% 1865.840219 59139.02261 729 23 1360197.52
74 74 Omaha-Council Bluffs, NE-IA 58.80% 1832.191563 27360.72733 224 15 410410.91
75 75 Rapid City, SD 65.61% 1817.71878 14905.294 41 5 74526.47
76 76 Pensacola-Ferry Pass-Brent, FL 62.98% 1810.896176 41046.98 136 6 246281.88
77 77 Denver-Aurora-Lakewood, CO 53.58% 1750.519638 60392.9275 690 20 1207858.55
78 78 Miami-Fort Lauderdale-West Palm Beach, FL 55.77% 1738.823877 63932.10581 1581 43 2749080.55
79 79 Dayton, OH 62.63% 1735.071991 56018.03857 226 7 392126.27
80 80 Columbus, OH 59.99% 1734.587336 52808.54778 548 18 950553.86
81 81 Tampa-St. Petersburg-Clearwater, FL 63.54% 1717.404843 60835.76385 921 26 1581729.86
82 82 Charleston-North Charleston, SC 61.97% 1713.245747 63104.55167 221 6 378627.31
83 83 Port St. Lucie, FL 63.70% 1712.978951 81651.99667 143 3 244955.99
84 84 Orlando-Kissimmee-Sanford, FL 55.92% 1694.240231 91771.34583 650 12 1101256.15
85 85 Allentown-Bethlehem-Easton, PA-NJ 60.18% 1666.819708 36366.97545 240 11 400036.73
86 86 Little Rock-North Little Rock-Conway, AR 73.48% 1664.52612 37830.13909 250 11 416131.53
87 87 Nassau County-Suffolk County, NY, Metropolitan Division 54.88% 1664.47748 61752.1145 742 20 1235042.29
88 88 Oklahoma City, OK 62.22% 1661.77368 25182.26269 394 26 654738.83
89 89 Crestview-Fort Walton Beach-Destin, FL 65.45% 1638.910824 27861.484 85 5 139307.42
90 90 Pittsburgh, PA 65.74% 1629.838558 46058.03037 763 27 1243566.82
91 91 Kingsport-Bristol-Bristol, TN-VA 80.73% 1624.14 39953.844 123 5 199769.22
92 92 Philadelphia, PA, Metropolitan Division 61.32% 1622.193615 53617.76789 628 19 1018737.59
93 93 Salina, KS 62.98% 1618.733889 9712.403333 18 3 29137.21
94 94 Boston, MA, Metropolitan Division 55.19% 1614.752369 52526.94471 553 17 892958.06
95 95 El Paso, TX 52.32% 1586.418128 35782.54222 203 9 322042.88
96 96 Jacksonville, FL 65.10% 1578.970678 64079.89333 487 12 768958.72
97 97 Richmond, VA 60.29% 1572.474522 60854.764 387 10 608547.64
98 98 San Antonio-New Braunfels, TX 56.62% 1558.322415 58783.38444 679 18 1058100.92
99 99 Salt Lake City, UT 51.14% 1556.712483 40474.52455 286 11 445219.77
100 100 Chicago-Naperville-Elgin, IL-IN-WI 56.02% 1548.761447 45614.71214 2474 84 3831635.82

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CREATE TABLE "covid-geography/mmsa-icu-beds" (
"MMSA" TEXT,
  "total_percent_at_risk" TEXT,
  "high_risk_per_ICU_bed" REAL,
  "high_risk_per_hospital" REAL,
  "icu_beds" INTEGER,
  "hospitals" INTEGER,
  "total_at_risk" REAL
);