How full are ICUs summed by metropolitan area?

How full is the ICU at all reporting hospitals overall in metropolitan areas with more than 200 ICU beds? Enter the state's two digit postal code below and hit 'Run SQL'. To see hospital level detail for this metro area, copy the cbsacode and paste it into this query

Note that this query leaves out hospitals averaging less than 4 ICU COVID beds weekly, or that don't report their weekly staffed bed count, so consider the results an estimate only. This query also ignores lines where this data is blank.

The fractions are calculated as:
icu_occupancy_fraction = staffed_adult_icu_bed_occupancy_7_day_avg /total_staffed_adult_icu_beds_7_day_avg
How full is the ICU in terms of confirmed or suspected COVID patients? This is calculated as:
covid_icu_fraction = staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg /total_staffed_adult_icu_beds_7_day_avg

This database can be queried directly using Structured Query Language (SQL) supported by SQLite. For more on that syntax, see here. Read more about datasette, the technology powering these interactive queries.

Custom SQL query returning 72 rows (hide)

select  cbsatitle as metro_area, cbsacode, count(*) as num_hospitals, (0.0 + sum(staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg))/sum(total_staffed_adult_icu_beds_7_day_avg) as covid_icu_fraction, (0.0 + sum(staffed_adult_icu_bed_occupancy_7_day_avg))/sum(total_staffed_adult_icu_beds_7_day_avg) as icu_occupancy_fraction, sum(total_staffed_adult_icu_beds_7_day_avg) as total_staffed_icu_beds, sum(staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg) as total_covid_icu_beds from   hosp_capacity left join cbsaxfips on hosp_capacity.fips_code = cbsaxfips.fips_code where  not total_staffed_adult_icu_beds_7_day_avg = '' and staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg > 0 and staffed_adult_icu_bed_occupancy_7_day_avg > 0 and staffed_adult_icu_bed_occupancy_7_day_avg > 0 and collection_week = '2021/09/17' group by cbsacode, cbsatitle having sum(total_staffed_adult_icu_beds_7_day_avg) > 200 order by covid_icu_fraction desc;

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metro_areacbsacodenum_hospitalscovid_icu_fractionicu_occupancy_fractiontotal_staffed_icu_bedstotal_covid_icu_beds
    55 0.6388992190405354 0.9148382298252137 537.8000000000001 343.59999999999997
Charlotte-Concord-Gastonia, NC-SC 16740 12 0.5326934613077385 0.9547090581883624 333.4 177.60000000000002
North Port-Sarasota-Bradenton, FL 35840 7 0.5032369443245576 0.8847647820457488 231.7 116.6
Memphis, TN-MS-AR 32820 7 0.46640862593309373 0.9704174730439591 361.7 168.7
Knoxville, TN 28940 8 0.4659993096306524 0.9147393855712806 289.7 135.0
Atlanta-Sandy Springs-Roswell, GA 12060 27 0.4574459058124736 0.9085277895630038 1178.4999999999998 539.1
Dallas-Fort Worth-Arlington, TX 19100 13 0.4458858413639733 0.863232023721275 539.6 240.6
Greenville-Anderson-Mauldin, SC 24860 8 0.4423135464231355 0.8417047184170472 328.5 145.3
Augusta-Richmond County, GA-SC 12260 4 0.4267487901451826 0.9665640123185216 227.3 97.0
Houston-The Woodlands-Sugar Land, TX 26420 8 0.403252480705623 0.9233737596471885 362.8 146.3
Sacramento--Roseville--Arden-Arcade, CA 40900 13 0.3899922620582924 0.9249419654371936 387.70000000000005 151.2
Louisville/Jefferson County, KY-IN 31140 9 0.3885296598256958 0.9370255833567613 355.7 138.2
Portland-Vancouver-Hillsboro, OR-WA 38900 14 0.37560602194437354 0.824189844348048 391.9 147.2
Lexington-Fayette, KY 30460 6 0.3662597114317425 0.9593044765075842 270.3 99.0
Baton Rouge, LA 12940 4 0.35960801022582023 0.8449083936940777 234.7 84.4
Indianapolis-Carmel-Anderson, IN 26900 19 0.359142894702248 0.8314710135401603 760.7 273.20000000000005
Jackson, MS 27140 5 0.35807860262008734 0.9213973799126638 229.0 82.0
Tulsa, OK 46140 7 0.3566901408450704 0.906338028169014 284.0 101.3
San Antonio-New Braunfels, TX 41700 7 0.3556207535515751 0.907350216182829 647.6 230.3
New Orleans-Metairie, LA 35380 8 0.3513784461152882 0.8310776942355891 399.0 140.2
Nashville-Davidson--Murfreesboro--Franklin, TN 34980 17 0.34964622641509435 0.7141509433962263 848.0 296.5
Orlando-Kissimmee-Sanford, FL 36740 12 0.3436817472698908 0.9889235569422776 641.0 220.3
Birmingham-Hoover, AL 13820 11 0.34292691435548583 0.9398220826792257 573.3 196.60000000000002
Oklahoma City, OK 36420 9 0.3418549346016647 0.9289536266349584 336.4 115.0
Columbia, SC 17900 5 0.3392945851962245 0.7868852459016393 201.3 68.3
Little Rock-North Little Rock-Conway, AR 30780 7 0.3321428571428572 0.8846153846153846 364.0 120.9
Charleston-North Charleston, SC 16700 6 0.33118971061093244 0.8755167661920075 217.7 72.1
Raleigh, NC 39580 4 0.3239683933274803 0.8241147205150718 341.7 110.7
Miami-Fort Lauderdale-West Palm Beach, FL 33100 38 0.3239404613112147 0.7903647312805364 1760.1999999999998 570.2
Minneapolis-St. Paul-Bloomington, MN-WI 33460 14 0.3225806451612903 0.9255583126550868 403.0 130.0
Riverside-San Bernardino-Ontario, CA 40140 24 0.318815331010453 0.7860876057740169 803.5999999999999 256.2
Cincinnati, OH-KY-IN 17140 14 0.31384383251603165 0.8651452282157676 530.2 166.4
Tampa-St. Petersburg-Clearwater, FL 45300 24 0.31289962447985387 0.8262458134578302 985.3 308.3
Chattanooga, TN-GA 16860 3 0.3043637697103044 0.6380638063806381 272.7 83.0
Jacksonville, FL 27260 6 0.298992443324937 0.9100755667506297 397.0 118.7
Seattle-Tacoma-Bellevue, WA 42660 14 0.2962466487935657 0.757372654155496 596.8 176.8
Springfield, MO 44180 2 0.2930118512464242 0.9141806293420515 244.7 71.7
Las Vegas-Henderson-Paradise, NV 29820 6 0.29136566907464745 0.9243206054351565 290.7 84.7
Dayton, OH 19380 5 0.27883959044368595 0.8552901023890785 293.0 81.69999999999999
Denver-Aurora-Lakewood, CO 19740 15 0.2750503329719684 0.804243456713644 645.7 177.60000000000002
San Diego-Carlsbad, CA 41740 11 0.27256567252617026 0.8188820857199289 506.3 138.0
St. Louis, MO-IL 41180 14 0.26867451915908747 0.9284329804681676 670.7 180.2
Winston-Salem, NC 49180 4 0.26666666666666666 0.8866666666666667 240.0 64.0
Durham-Chapel Hill, NC 20500 3 0.2633587786259542 0.9427480916030534 262.0 69.0
San Francisco-Oakland-Hayward, CA 41860 17 0.25759441707717573 0.7926929392446634 487.2 125.5
Phoenix-Mesa-Scottsdale, AZ 38060 26 0.24782986111111116 0.6469907407407408 1382.4 342.6000000000001
Fort Wayne, IN 23060 3 0.24312267657992567 0.8884758364312267 269.0 65.4
Kansas City, MO-KS 28140 13 0.24125560538116592 0.8085201793721973 446.0 107.6
Toledo, OH 45780 5 0.2397586535408066 0.6192442045093681 314.9 75.5
Salt Lake City, UT 41620 5 0.23968565815324167 0.6595565534661801 356.3 85.4
Albuquerque, NM 10740 5 0.22862368541380887 0.9524462734339278 218.7 50.0
Virginia Beach-Norfolk-Newport News, VA-NC 47260 12 0.21876430205949657 0.6336384439359268 437.0 95.6
Richmond, VA 40060 6 0.2121037463976945 0.9086455331412104 347.0 73.6
Columbus, OH 18140 11 0.21078514498897744 0.8862133288112599 589.7 124.3
Los Angeles-Long Beach-Anaheim, CA 31080 43 0.21065375302663442 0.8121065375302665 1652.0 348.00000000000006
Harrisburg-Carlisle, PA 25420 4 0.20994229915667995 0.8952507767421215 225.3 47.3
Omaha-Council Bluffs, NE-IA 36540 6 0.20807324178110695 0.8431127756970453 240.3 50.0
Pittsburgh, PA 38300 17 0.2069520437721275 0.8847763115545542 621.4 128.60000000000002
Milwaukee-Waukesha-West Allis, WI 33340 9 0.20582362728785358 0.610648918469218 601.0 123.7
Tucson, AZ 46060 6 0.20094339622641508 0.5754716981132075 318.0 63.89999999999999
Cleveland-Elyria, OH 17460 14 0.19846892138939673 0.8088436928702007 875.2 173.70000000000002
Baltimore-Columbia-Towson, MD 12580 10 0.19002557544757032 0.8217391304347826 391.0 74.3
Washington-Arlington-Alexandria, DC-VA-MD-WV 47900 20 0.17902621722846443 0.7149812734082398 801.0 143.4
Syracuse, NY 45060 4 0.1778301886792453 0.8037735849056604 212.0 37.7
Rochester, NY 40380 6 0.16215139442231077 0.7864541832669322 251.0 40.7
Detroit-Warren-Dearborn, MI 19820 13 0.15535444947209653 0.8194570135746606 663.0 103.0
Chicago-Naperville-Elgin, IL-IN-WI 16980 38 0.15092224028704376 0.7454168301844482 1783.7 269.2
San Jose-Sunnyvale-Santa Clara, CA 41940 6 0.13890857547838414 0.8710134656272148 282.2 39.2
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 37980 19 0.12435586541376173 0.7446953622309791 1319.6 164.09999999999997
Boston-Cambridge-Newton, MA-NH 14460 9 0.11819923371647507 0.8465517241379311 522.0 61.69999999999999
New York-Newark-Jersey City, NY-NJ-PA 35620 41 0.11320224719101123 0.6207553058676654 3204.0 362.7
Buffalo-Cheektowaga-Niagara Falls, NY 15380 3 0.0822861775435752 0.7105796513984598 246.7 20.3
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