Juvenile Hospitalization Fraction From HHS State Level Release
Jacob Fenton
Introduction
This is a look at HHS’ COVID-19 data on pediatric hospital admissions using prior day hospital admissions as reported daily. This page reflects the data as released April 24 2021, and covers daily reported numbers through April 23.
Data Source
The raw HHS file “provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities” (and a third collection method used prior to July 2020). The file can be downloaded here.
# Libraries
library(ggplot2)
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✔ tibble 3.1.0 ✔ dplyr 1.0.2
## ✔ tidyr 1.1.2 ✔ stringr 1.4.0
## ✔ readr 1.4.0 ✔ forcats 0.5.0
## ✔ purrr 0.3.4## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()# setwd("/Users/jacob/github-whitelabel/covid-kids/juvenile_covid_analysis/hhs/")
file = "0430_Timeseries.csv"
hospdf <- read.csv(file, header=TRUE, sep=",")
hospdf$dateob = as.Date(hospdf$date)
# Sum total adult COVID: confirmed + suspected
hospdf$total_adult = hospdf$previous_day_admission_adult_covid_confirmed + hospdf$previous_day_admission_adult_covid_suspected
# Sum total pediatric COVID: confirmed + suspected
hospdf$total_ped = hospdf$previous_day_admission_pediatric_covid_confirmed + hospdf$previous_day_admission_pediatric_covid_suspectedResult
Here’s what this data shows when we remove non-U.S. states (VI and PR) as well as Oregon and Washington (which have data issues, see data validation below). Prior to November a sizable number of hospitals were not reporting, so we’ve focused on the greatest time period with a relatively stable number of reporting hospitals.
hosp <- hospdf %>% filter (!state %in% c("OR","WA", "PR", "VI")) %>% group_by (dateob) %>% summarise(all_ped = sum(total_ped), all_adult=sum(total_adult), all_ped_conf = sum(previous_day_admission_pediatric_covid_confirmed), all_adult_conf =sum(previous_day_admission_adult_covid_confirmed))
hosp$prcnt_conf = (100*hosp$all_ped_conf / (hosp$all_adult_conf + hosp$all_ped_conf))
hosp$prcnt = (100*hosp$all_ped / (hosp$all_adult + hosp$all_ped))
hosp %>% ggplot(aes(x=dateob)) + geom_line(aes(y=prcnt_conf, color="Confirmed only")) + geom_line(aes(y=prcnt, color="Confirmed plus suspected")) + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + ylim(0,6) + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab("Percent of total hospitalizations") + xlab("Date") + labs(title = "U.S. Juvenile COVID-19 Hospitalizations As Percent Of Total") + theme(legend.title = element_blank(),
legend.position = c(0.4, 0.8), legend.direction = "horizontal")
In absolute terms, the number of adults hospitalizations is much greater than children. Note the weekly variation, which like reflects a fraction of hospitals not providing data on weekends.
hosp %>% ggplot(aes(x=dateob)) + geom_line(aes(y=all_adult_conf, color="Adult confirmed")) + geom_line(aes(y=all_ped_conf, color="Pediatric confirmed")) + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab("Confirmed Daily Hospitalizations") + xlab("Date") + labs(title = "U.S. COVID-19 Confirmed Hospitalizations") + theme(legend.title = element_blank(),
legend.position = c(0.4, 0.4), legend.direction = "horizontal")
For all children and adults
hosp %>% ggplot(aes(x=dateob)) + geom_line(aes(y=all_adult, color="Adult")) + geom_line(aes(y=all_ped, color="Pediatric")) + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab("Confirmed Daily Hospitalizations") + xlab("Date") + labs(title = "U.S. COVID-19 Hospitalizations, Confirmed And Suspected") + theme(legend.title = element_blank(),
legend.position = c(0.4, 0.4), legend.direction = "horizontal")
The absolute hospitalization count in children is mostly unchanged.
hosp %>% ggplot(aes(x=dateob)) + geom_line(aes(y=all_ped_conf, color="Confirmed Only")) + geom_line(aes(y=all_ped, color="All Pediatric")) + ylim(0,900) + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab(" Daily Hospitalizations") + xlab("Date") + labs(title = "U.S. Pediatric COVID-19 Hospitalizations, Confirmed And Suspected") + theme(legend.title = element_blank(),
legend.position = c(0.4, 0.4), legend.direction = "horizontal")
hosp %>% ggplot(aes(x=dateob)) + geom_point(aes(y=prcnt_conf, color="Confirmed only", size=all_ped_conf, fill="firebrick", alpha=0.4)) + geom_point(aes(y=prcnt, color="Confirmed plus suspected",size=all_ped, alpha=0.4)) + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + ylim(0,6) + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab("Percent of total hospitalizations") + xlab("Date") + labs(title = "Daily U.S. Pediatric COVID-19 Hospitalizations Percent") + theme(legend.title = element_blank()) + guides(alpha = FALSE) + guides(fill=FALSE)
Data review
Summaries
hosp_summary <- hospdf %>% filter (!state %in% c("OR","WA", "PR", "VI")) %>% group_by (dateob) %>% summarise(all_ped = sum(total_ped), all_adult=sum(total_adult), all_ped_conf = sum(previous_day_admission_pediatric_covid_confirmed), all_adult_conf =sum(previous_day_admission_adult_covid_confirmed))
hosp_summary %>% summarise(grand_total_all_ped = sum(all_ped, na.rm=TRUE),grand_total_all_adult = sum(all_adult, na.rm=TRUE),grand_total_adult_conf = sum(all_adult_conf, na.rm=TRUE),grand_total_ped_conf = sum(all_ped_conf, na.rm=TRUE)) ## # A tibble: 1 x 4
## grand_total_all_ped grand_total_all_adult grand_total_adult_conf grand_total_ped_conf
## <int> <int> <int> <int>
## 1 154067 3864518 2121553 37992hosp_summary <- hospdf %>% filter (!state %in% c("OR","WA", "PR", "VI"))%>% filter (dateob >= as.Date('2020-11-01')) %>% group_by (dateob) %>% summarise(all_ped = sum(total_ped), all_adult=sum(total_adult), all_ped_conf = sum(previous_day_admission_pediatric_covid_confirmed), all_adult_conf =sum(previous_day_admission_adult_covid_confirmed))
hosp_summary %>% summarise(grand_total_all_ped = sum(all_ped, na.rm=TRUE),grand_total_all_adult = sum(all_adult, na.rm=TRUE),grand_total_adult_conf = sum(all_adult_conf, na.rm=TRUE),grand_total_ped_conf = sum(all_ped_conf, na.rm=TRUE)) ## # A tibble: 1 x 4
## grand_total_all_ped grand_total_all_adult grand_total_adult_conf grand_total_ped_conf
## <int> <int> <int> <int>
## 1 107330 2777799 1696110 28605View As Table
Review the raw numbers in tabular format. The output variables starting with n_ are the number of reporting hospitals that day. The sample size we’re looking at, beginning Nov. 1, is about 5800 reporting on weekdays and 5400 reporting on weekends. It appears to be fairly stable since then.
byday <- hospdf %>% filter (!state %in% c("OR","WA", "PR", "VI")) %>% filter (dateob > (as.Date('2020-07-01'))) %>% group_by (dateob) %>% summarise(adult_admits = sum(total_adult), ped_admits=sum(total_ped), daily_prcnt = 100*(sum(total_ped) / (sum(total_adult) + sum(total_ped))),n_adult_conf=sum(previous_day_admission_adult_covid_confirmed_coverage), n_adult_susp=sum(previous_day_admission_adult_covid_suspected_coverage), n_ped_conf=sum(previous_day_admission_pediatric_covid_confirmed_coverage), n_ped_susp=sum(previous_day_admission_pediatric_covid_suspected_coverage) )
print(byday,n=350)## # A tibble: 303 x 8
## dateob adult_admits ped_admits daily_prcnt n_adult_conf n_adult_susp n_ped_conf n_ped_susp
## <date> <int> <int> <dbl> <int> <int> <int> <int>
## 1 2020-07-02 NA NA NA 193 151 5 5
## 2 2020-07-03 NA NA NA 197 156 4 4
## 3 2020-07-04 NA NA NA 217 162 3 3
## 4 2020-07-05 NA NA NA 224 164 4 4
## 5 2020-07-06 NA NA NA 197 145 4 4
## 6 2020-07-07 NA NA NA 212 154 6 6
## 7 2020-07-08 NA NA NA 215 156 6 6
## 8 2020-07-09 NA NA NA 216 155 5 5
## 9 2020-07-10 NA NA NA 222 161 6 6
## 10 2020-07-11 NA NA NA 279 221 5 52
## 11 2020-07-12 NA NA NA 283 232 5 52
## 12 2020-07-13 NA NA NA 286 228 4 54
## 13 2020-07-14 NA NA NA 385 317 5 78
## 14 2020-07-15 NA NA NA 2485 2315 1838 1883
## 15 2020-07-16 NA NA NA 2948 2665 2134 2165
## 16 2020-07-17 NA NA NA 3098 2964 2427 2441
## 17 2020-07-18 NA NA NA 3163 2967 2399 2431
## 18 2020-07-19 NA NA NA 2988 2860 2309 2338
## 19 2020-07-20 NA NA NA 3216 3016 2458 2483
## 20 2020-07-21 NA NA NA 3269 3069 2581 2608
## 21 2020-07-22 NA NA NA 3074 2887 2594 2526
## 22 2020-07-23 NA NA NA 3096 3059 3180 3153
## 23 2020-07-24 NA NA NA 3671 3580 3393 3336
## 24 2020-07-25 NA NA NA 3822 3766 3455 3412
## 25 2020-07-26 NA NA NA 3823 3764 3497 3463
## 26 2020-07-27 NA NA NA 3742 3660 3525 3475
## 27 2020-07-28 13333 478 3.46 3974 3885 3724 3681
## 28 2020-07-29 12590 501 3.83 4166 4096 3903 3861
## 29 2020-07-30 12863 578 4.30 4190 4119 3962 3888
## 30 2020-07-31 12949 480 3.57 4200 4123 3994 3923
## 31 2020-08-01 12545 540 4.13 4152 4078 3812 3742
## 32 2020-08-02 10231 469 4.38 4087 4003 3770 3696
## 33 2020-08-03 10530 497 4.51 4194 4146 3987 3913
## 34 2020-08-04 12648 448 3.42 4202 4132 3986 3899
## 35 2020-08-05 12107 496 3.94 4083 4006 3850 3755
## 36 2020-08-06 11948 530 4.25 4136 4047 3899 3786
## 37 2020-08-07 11924 547 4.39 4317 4283 4110 4038
## 38 2020-08-08 11489 505 4.21 4352 4306 4137 4066
## 39 2020-08-09 10314 441 4.10 4277 4258 4074 4015
## 40 2020-08-10 10091 435 4.13 4325 4331 4102 4090
## 41 2020-08-11 11991 577 4.59 4347 4334 4121 4125
## 42 2020-08-12 11505 548 4.55 4369 4345 3975 4142
## 43 2020-08-13 12083 526 4.17 4740 4743 4023 4192
## 44 2020-08-14 12688 511 3.87 5160 5151 4125 4296
## 45 2020-08-15 12505 485 3.73 5160 5151 4090 4263
## 46 2020-08-16 10988 440 3.85 5113 5099 4053 4230
## 47 2020-08-17 11263 393 3.37 5179 5169 4128 4306
## 48 2020-08-18 12618 442 3.38 5285 5149 4119 4291
## 49 2020-08-19 11648 460 3.80 5294 5161 4126 4304
## 50 2020-08-20 11699 512 4.19 5295 5160 4137 4314
## 51 2020-08-21 11245 445 3.81 5340 5208 4170 4359
## 52 2020-08-22 11325 467 3.96 5313 5181 4201 4388
## 53 2020-08-23 9716 414 4.09 5310 5178 4194 4380
## 54 2020-08-24 10403 456 4.20 5337 5204 4261 4443
## 55 2020-08-25 12060 475 3.79 5316 5188 4259 4439
## 56 2020-08-26 11941 451 3.64 5342 5212 4337 4523
## 57 2020-08-27 11770 467 3.82 5331 5204 4307 4487
## 58 2020-08-28 11435 474 3.98 5366 5242 4356 4541
## 59 2020-08-29 11712 428 3.53 5380 5257 4379 4550
## 60 2020-08-30 9977 394 3.80 5358 5224 4340 4503
## 61 2020-08-31 9918 386 3.75 5393 5261 4413 4584
## 62 2020-09-01 11638 434 3.60 5415 5285 4438 4616
## 63 2020-09-02 11499 431 3.61 5446 5316 4509 4681
## 64 2020-09-03 11060 443 3.85 5463 5333 4533 4707
## 65 2020-09-04 11074 441 3.83 5487 5356 4752 4743
## 66 2020-09-05 10846 776 6.68 5477 5349 4729 4718
## 67 2020-09-06 9390 705 6.98 5474 5346 4720 4717
## 68 2020-09-07 9666 412 4.09 5464 5337 4711 4706
## 69 2020-09-08 10243 428 4.01 5492 5361 4774 4767
## 70 2020-09-09 12017 533 4.25 5507 5379 4791 4780
## 71 2020-09-10 11537 435 3.63 5513 5389 4805 4793
## 72 2020-09-11 11217 507 4.32 5538 5414 4836 4818
## 73 2020-09-12 10707 464 4.15 5539 5415 4808 4791
## 74 2020-09-13 9647 448 4.44 5518 5385 4795 4778
## 75 2020-09-14 9010 413 4.38 5525 5396 4841 4824
## 76 2020-09-15 10837 507 4.47 5535 5408 4859 4839
## 77 2020-09-16 10220 474 4.43 5534 5410 4868 4846
## 78 2020-09-17 10337 437 4.06 5560 5434 4884 4862
## 79 2020-09-18 10210 454 4.26 5562 5435 4902 4880
## 80 2020-09-19 9841 450 4.37 5558 5432 4886 4860
## 81 2020-09-20 8784 389 4.24 5552 5419 4876 4855
## 82 2020-09-21 8871 509 5.43 5557 5428 4919 4904
## 83 2020-09-22 10770 428 3.82 5549 5420 4930 4916
## 84 2020-09-23 10676 408 3.68 5557 5429 4947 4932
## 85 2020-09-24 10521 487 4.42 5573 5444 4981 4958
## 86 2020-09-25 10427 475 4.36 5604 5478 5016 5000
## 87 2020-09-26 10418 496 4.54 5596 5470 5000 4984
## 88 2020-09-27 9208 443 4.59 5583 5457 4997 4977
## 89 2020-09-28 9621 435 4.33 5604 5478 5039 5026
## 90 2020-09-29 11202 479 4.10 5601 5480 5058 5043
## 91 2020-09-30 10814 513 4.53 5608 5487 5065 5049
## 92 2020-10-01 10763 507 4.50 5614 5493 5072 5057
## 93 2020-10-02 10576 437 3.97 5625 5504 5094 5077
## 94 2020-10-03 10953 504 4.40 5618 5497 5060 5043
## 95 2020-10-04 9222 453 4.68 5610 5489 5065 5046
## 96 2020-10-05 10834 493 4.35 5625 5504 5175 5153
## 97 2020-10-06 13589 570 4.03 5626 5505 5186 5169
## 98 2020-10-07 12402 558 4.31 5633 5511 5195 5177
## 99 2020-10-08 11786 549 4.45 5666 5545 5276 5256
## 100 2020-10-09 11432 476 4.00 5679 5558 5314 5293
## 101 2020-10-10 11608 553 4.55 5679 5558 5351 5335
## 102 2020-10-11 10333 516 4.76 5669 5548 5345 5331
## 103 2020-10-12 10315 424 3.95 5648 5527 5325 5304
## 104 2020-10-13 12501 552 4.23 5635 5514 5332 5311
## 105 2020-10-14 12008 507 4.05 5697 5576 5401 5388
## 106 2020-10-15 11909 587 4.70 5718 5597 5435 5422
## 107 2020-10-16 12235 577 4.50 5750 5629 5473 5455
## 108 2020-10-17 12229 502 3.94 5739 5618 5472 5460
## 109 2020-10-18 10459 467 4.27 5728 5607 5463 5449
## 110 2020-10-19 10880 446 3.94 5673 5552 5428 5411
## 111 2020-10-20 12903 512 3.82 5656 5535 5428 5417
## 112 2020-10-21 12511 530 4.06 5765 5644 5558 5547
## 113 2020-10-22 12599 493 3.77 5789 5668 5584 5570
## 114 2020-10-23 12565 479 3.67 5803 5678 5598 5584
## 115 2020-10-24 12586 490 3.75 5779 5652 5582 5571
## 116 2020-10-25 11019 405 3.55 5766 5639 5570 5559
## 117 2020-10-26 11018 434 3.79 5498 5375 5308 5293
## 118 2020-10-27 13354 551 3.96 5496 5372 5332 5323
## 119 2020-10-28 13430 585 4.17 5797 5675 5638 5638
## 120 2020-10-29 13346 531 3.83 5818 5697 5662 5658
## 121 2020-10-30 13955 567 3.90 5836 5715 5687 5681
## 122 2020-10-31 13039 532 3.92 5815 5694 5668 5660
## 123 2020-11-01 11736 394 3.25 5806 5685 5668 5660
## 124 2020-11-02 12480 491 3.79 5482 5361 5343 5338
## 125 2020-11-03 14964 540 3.48 5467 5346 5332 5328
## 126 2020-11-04 14849 572 3.71 5814 5693 5683 5678
## 127 2020-11-05 15192 615 3.89 5834 5713 5705 5699
## 128 2020-11-06 15601 637 3.92 5840 5719 5712 5706
## 129 2020-11-07 15953 644 3.88 5835 5714 5708 5703
## 130 2020-11-08 14281 583 3.92 5830 5709 5703 5698
## 131 2020-11-09 14655 529 3.48 5451 5330 5324 5322
## 132 2020-11-10 17838 650 3.52 5457 5336 5333 5332
## 133 2020-11-11 18012 718 3.83 5833 5712 5709 5707
## 134 2020-11-12 17803 721 3.89 5865 5744 5740 5736
## 135 2020-11-13 18013 716 3.82 5870 5749 5745 5741
## 136 2020-11-14 17859 682 3.68 5868 5747 5742 5740
## 137 2020-11-15 16548 722 4.18 5860 5739 5736 5732
## 138 2020-11-16 16819 572 3.29 5473 5352 5349 5346
## 139 2020-11-17 20028 694 3.35 5455 5334 5331 5330
## 140 2020-11-18 19439 806 3.98 5871 5749 5746 5743
## 141 2020-11-19 19683 737 3.61 5875 5754 5750 5747
## 142 2020-11-20 19732 672 3.29 5881 5760 5755 5753
## 143 2020-11-21 19500 708 3.50 5876 5755 5750 5748
## 144 2020-11-22 17866 641 3.46 5871 5750 5746 5743
## 145 2020-11-23 17429 598 3.32 5449 5328 5325 5324
## 146 2020-11-24 21195 648 2.97 5468 5347 5343 5342
## 147 2020-11-25 20216 706 3.37 5888 5767 5764 5761
## 148 2020-11-26 19635 591 2.92 5888 5767 5763 5763
## 149 2020-11-27 16794 477 2.76 5888 5767 5764 5762
## 150 2020-11-28 20258 552 2.65 5887 5766 5763 5762
## 151 2020-11-29 18934 526 2.70 5879 5758 5754 5754
## 152 2020-11-30 18352 571 3.02 5446 5325 5323 5323
## 153 2020-12-01 22155 766 3.34 5453 5332 5330 5330
## 154 2020-12-02 21611 715 3.20 5893 5772 5770 5769
## 155 2020-12-03 21075 635 2.92 5906 5785 5783 5782
## 156 2020-12-04 20978 692 3.19 5906 5785 5783 5782
## 157 2020-12-05 20708 740 3.45 5908 5787 5785 5784
## 158 2020-12-06 18657 565 2.94 5907 5786 5784 5783
## 159 2020-12-07 18953 570 2.92 5474 5353 5351 5350
## 160 2020-12-08 22563 679 2.92 5469 5348 5346 5345
## 161 2020-12-09 22343 692 3.00 5920 5799 5796 5796
## 162 2020-12-10 22677 701 3.00 5912 5791 5788 5788
## 163 2020-12-11 22290 657 2.86 5916 5795 5792 5792
## 164 2020-12-12 22084 668 2.94 5919 5798 5794 5794
## 165 2020-12-13 19620 499 2.48 5912 5791 5788 5788
## 166 2020-12-14 19709 540 2.67 5462 5338 5338 5338
## 167 2020-12-15 23566 703 2.90 5461 5337 5339 5338
## 168 2020-12-16 22729 669 2.86 5932 5811 5810 5809
## 169 2020-12-17 22024 653 2.88 5928 5807 5803 5804
## 170 2020-12-18 21864 639 2.84 5928 5807 5805 5805
## 171 2020-12-19 21920 581 2.58 5927 5806 5803 5804
## 172 2020-12-20 19749 528 2.60 5926 5805 5803 5803
## 173 2020-12-21 20116 528 2.56 5445 5324 5322 5322
## 174 2020-12-22 24063 635 2.57 5530 5409 5408 5408
## 175 2020-12-23 23409 616 2.56 5924 5803 5802 5802
## 176 2020-12-24 22287 568 2.49 5920 5799 5798 5798
## 177 2020-12-25 20063 492 2.39 5922 5801 5800 5800
## 178 2020-12-26 18753 492 2.56 5921 5800 5799 5799
## 179 2020-12-27 21700 544 2.45 5916 5795 5794 5794
## 180 2020-12-28 21071 539 2.49 5430 5309 5308 5308
## 181 2020-12-29 23981 679 2.75 5433 5312 5311 5311
## 182 2020-12-30 23840 741 3.01 5936 5815 5814 5814
## 183 2020-12-31 23537 646 2.67 5933 5812 5810 5810
## 184 2021-01-01 22347 635 2.76 5935 5814 5812 5812
## 185 2021-01-02 21351 589 2.68 5934 5813 5810 5810
## 186 2021-01-03 22614 606 2.61 5931 5810 5807 5807
## 187 2021-01-04 21168 603 2.77 5438 5317 5315 5315
## 188 2021-01-05 25663 795 3.00 5432 5311 5308 5307
## 189 2021-01-06 25031 713 2.77 5930 5809 5808 5807
## 190 2021-01-07 23862 721 2.93 5933 5812 5810 5810
## 191 2021-01-08 23678 739 3.03 5937 5816 5814 5814
## 192 2021-01-09 23426 712 2.95 5936 5815 5812 5813
## 193 2021-01-10 20350 534 2.56 5929 5808 5805 5806
## 194 2021-01-11 19924 549 2.68 5444 5323 5319 5321
## 195 2021-01-12 23521 688 2.84 5440 5319 5316 5317
## 196 2021-01-13 23162 732 3.06 5935 5814 5812 5813
## 197 2021-01-14 22980 664 2.81 5927 5806 5804 5804
## 198 2021-01-15 23107 671 2.82 5928 5807 5804 5805
## 199 2021-01-16 22316 620 2.70 5930 5809 5806 5806
## 200 2021-01-17 19131 537 2.73 5928 5807 5804 5804
## 201 2021-01-18 18579 579 3.02 5437 5316 5314 5313
## 202 2021-01-19 21183 691 3.16 5444 5323 5320 5321
## 203 2021-01-20 20779 721 3.35 5936 5815 5814 5814
## 204 2021-01-21 20257 709 3.38 5932 5811 5809 5809
## 205 2021-01-22 20832 703 3.26 5932 5811 5808 5808
## 206 2021-01-23 19655 649 3.20 5930 5809 5804 5805
## 207 2021-01-24 16716 568 3.29 5929 5808 5805 5805
## 208 2021-01-25 16076 612 3.67 5435 5314 5311 5311
## 209 2021-01-26 20011 705 3.40 5435 5314 5311 5311
## 210 2021-01-27 18688 720 3.71 5941 5820 5819 5819
## 211 2021-01-28 18435 677 3.54 5940 5819 5818 5818
## 212 2021-01-29 17200 658 3.68 5936 5815 5813 5814
## 213 2021-01-30 17142 664 3.73 5938 5817 5814 5814
## 214 2021-01-31 14977 496 3.21 5936 5815 5812 5812
## 215 2021-02-01 14304 567 3.81 5442 5321 5319 5319
## 216 2021-02-02 16811 677 3.87 5442 5321 5319 5319
## 217 2021-02-03 16440 686 4.01 5942 5821 5820 5820
## 218 2021-02-04 16713 731 4.19 5936 5815 5813 5813
## 219 2021-02-05 16427 679 3.97 5937 5816 5814 5814
## 220 2021-02-06 15489 673 4.16 5935 5814 5812 5812
## 221 2021-02-07 12846 594 4.42 5934 5813 5811 5811
## 222 2021-02-08 12021 518 4.13 5442 5321 5319 5319
## 223 2021-02-09 15009 660 4.21 5438 5317 5315 5315
## 224 2021-02-10 14412 639 4.25 5918 5815 5814 5814
## 225 2021-02-11 13872 626 4.32 5936 5815 5813 5813
## 226 2021-02-12 13080 621 4.53 5933 5812 5811 5811
## 227 2021-02-13 12675 598 4.51 5931 5810 5809 5809
## 228 2021-02-14 10926 462 4.06 5932 5811 5808 5808
## 229 2021-02-15 10082 469 4.45 5435 5314 5313 5313
## 230 2021-02-16 12236 558 4.36 5437 5316 5314 5315
## 231 2021-02-17 12538 557 4.25 5941 5820 5820 5820
## 232 2021-02-18 12162 560 4.40 5937 5816 5815 5815
## 233 2021-02-19 11681 530 4.34 5937 5816 5814 5815
## 234 2021-02-20 11832 565 4.56 5936 5815 5813 5813
## 235 2021-02-21 10381 464 4.28 5933 5812 5810 5811
## 236 2021-02-22 10208 487 4.55 5437 5316 5312 5313
## 237 2021-02-23 12423 594 4.56 5435 5314 5311 5312
## 238 2021-02-24 12095 595 4.69 5937 5816 5814 5815
## 239 2021-02-25 11712 559 4.56 5931 5810 5807 5808
## 240 2021-02-26 11174 577 4.91 5933 5812 5809 5810
## 241 2021-02-27 10571 535 4.82 5934 5813 5810 5811
## 242 2021-02-28 9171 440 4.58 5931 5810 5807 5808
## 243 2021-03-01 8770 436 4.74 5372 5251 5248 5249
## 244 2021-03-02 10653 565 5.04 5373 5252 5249 5250
## 245 2021-03-03 10247 549 5.09 5876 5755 5753 5754
## 246 2021-03-04 10262 614 5.65 5933 5812 5809 5810
## 247 2021-03-05 10124 507 4.77 5928 5822 5820 5819
## 248 2021-03-06 9672 533 5.22 5931 5810 5809 5809
## 249 2021-03-07 8340 425 4.85 5929 5808 5807 5807
## 250 2021-03-08 8267 442 5.08 5439 5318 5317 5317
## 251 2021-03-09 10142 560 5.23 5432 5311 5310 5310
## 252 2021-03-10 10069 554 5.22 5935 5829 5829 5828
## 253 2021-03-11 9725 536 5.22 5937 5816 5815 5815
## 254 2021-03-12 10193 524 4.89 5936 5815 5814 5814
## 255 2021-03-13 9452 499 5.01 5936 5815 5813 5813
## 256 2021-03-14 8171 442 5.13 5934 5813 5811 5811
## 257 2021-03-15 8017 440 5.20 5430 5309 5306 5307
## 258 2021-03-16 9809 569 5.48 5425 5304 5302 5302
## 259 2021-03-17 9649 561 5.49 5936 5815 5814 5814
## 260 2021-03-18 9544 555 5.50 5932 5811 5809 5809
## 261 2021-03-19 9666 559 5.47 5933 5812 5809 5810
## 262 2021-03-20 9176 530 5.46 5933 5812 5808 5810
## 263 2021-03-21 7931 446 5.32 5932 5811 5807 5809
## 264 2021-03-22 8285 473 5.40 5433 5312 5308 5310
## 265 2021-03-23 10266 490 4.56 5431 5310 5307 5308
## 266 2021-03-24 9841 577 5.54 5937 5816 5815 5815
## 267 2021-03-25 9932 507 4.86 5932 5811 5810 5810
## 268 2021-03-26 9737 552 5.36 5931 5810 5809 5809
## 269 2021-03-27 9451 528 5.29 5929 5808 5807 5807
## 270 2021-03-28 8436 556 6.18 5929 5808 5807 5807
## 271 2021-03-29 8258 435 5.00 5423 5302 5301 5301
## 272 2021-03-30 10181 556 5.18 5416 5295 5294 5294
## 273 2021-03-31 9893 548 5.25 5934 5813 5813 5813
## 274 2021-04-01 9823 565 5.44 5930 5809 5808 5808
## 275 2021-04-02 9754 578 5.59 5930 5809 5806 5807
## 276 2021-04-03 9474 510 5.11 5930 5809 5807 5808
## 277 2021-04-04 8497 487 5.42 5927 5806 5804 5805
## 278 2021-04-05 8554 524 5.77 5410 5289 5288 5288
## 279 2021-04-06 10905 519 4.54 5408 5287 5286 5286
## 280 2021-04-07 10688 576 5.11 5931 5810 5809 5809
## 281 2021-04-08 10339 601 5.49 5929 5808 5806 5806
## 282 2021-04-09 10355 528 4.85 5927 5806 5804 5804
## 283 2021-04-10 10051 530 5.01 5929 5808 5806 5806
## 284 2021-04-11 9088 504 5.25 5927 5806 5805 5805
## 285 2021-04-12 8892 477 5.09 5441 5320 5319 5319
## 286 2021-04-13 10731 572 5.06 5426 5305 5304 5304
## 287 2021-04-14 10460 589 5.33 5937 5816 5816 5816
## 288 2021-04-15 10443 550 5.00 5932 5811 5810 5809
## 289 2021-04-16 10674 607 5.38 5932 5811 5810 5810
## 290 2021-04-17 10384 569 5.19 5932 5811 5811 5811
## 291 2021-04-18 8911 449 4.80 5930 5809 5809 5809
## 292 2021-04-19 8831 504 5.40 5417 5296 5295 5295
## 293 2021-04-20 10422 623 5.64 5413 5292 5291 5291
## 294 2021-04-21 10029 630 5.91 5932 5811 5811 5811
## 295 2021-04-22 9686 577 5.62 5928 5807 5805 5806
## 296 2021-04-23 9539 582 5.75 5928 5807 5805 5806
## 297 2021-04-24 9379 613 6.13 5926 5805 5803 5804
## 298 2021-04-25 8287 486 5.54 5925 5804 5802 5803
## 299 2021-04-26 8139 514 5.94 5411 5290 5286 5287
## 300 2021-04-27 9829 582 5.59 5408 5287 5284 5284
## 301 2021-04-28 9460 618 6.13 5895 5774 5772 5773
## 302 2021-04-29 9453 624 6.19 5885 5764 5762 5763
## 303 2021-04-30 9352 608 6.10 5881 5761 5759 5759State Summaries
Calculate the totals, by state. This allows us to compare (a very differently assembled count) of child hospitalizations (released by the American Academy of Pediatricians)[https://downloads.aap.org/AAP/PDF/AAP%20and%20CHA%20-%20Children%20and%20COVID-19%20State%20Data%20Report%202.25.21%20FINAL.pdf].
bystate <- hospdf %>% filter (!state %in% c("PR", "VI", "OR", "WA")) %>% filter (dateob < as.Date("2021-05-01")) %>% group_by (state) %>% summarise(adult_admits = sum(total_adult, na.rm=TRUE), ped_admits=sum(total_ped, na.rm=TRUE), prcnt_ped = 100* ( sum(total_ped, na.rm=TRUE) / ( sum(total_adult, na.rm=TRUE) + sum(total_ped, na.rm=TRUE) ) ), ped_susp = sum(previous_day_admission_pediatric_covid_suspected, na.rm=TRUE), ped_conf = sum(previous_day_admission_pediatric_covid_confirmed, na.rm=TRUE), adult_susp = sum(previous_day_admission_adult_covid_suspected, na.rm=TRUE), adult_conf = sum(previous_day_admission_adult_covid_confirmed, na.rm=TRUE) )
print(bystate,n=100)## # A tibble: 49 x 8
## state adult_admits ped_admits prcnt_ped ped_susp ped_conf adult_susp adult_conf
## <fct> <int> <int> <dbl> <int> <int> <int> <int>
## 1 AK 3472 115 3.21 60 55 1341 2131
## 2 AL 80244 2663 3.21 2058 605 31137 49107
## 3 AR 70699 3294 4.45 2962 332 46519 24180
## 4 AZ 115972 1879 1.59 1221 658 57108 60404
## 5 CA 386556 8218 2.08 4475 3743 156025 230531
## 6 CO 49820 2918 5.53 2160 758 22065 27755
## 7 CT 39333 809 2.02 487 322 18922 20411
## 8 DC 26317 3189 10.8 2776 413 20873 5444
## 9 DE 12790 752 5.55 267 485 6965 5825
## 10 FL 259693 7569 2.83 4086 3483 91826 167867
## 11 GA 149486 15654 9.48 11938 3716 48766 100720
## 12 HI 10751 189 1.73 163 26 6975 3776
## 13 IA 26997 1197 4.25 891 306 7170 20354
## 14 ID 9676 189 1.92 59 130 1521 8155
## 15 IL 192067 9813 4.86 8828 985 116118 75949
## 16 IN 94599 3545 3.61 2187 1358 46709 47890
## 17 KS 38156 870 2.23 688 182 16861 21295
## 18 KY 129406 1973 1.50 1686 287 57401 72005
## 19 LA 52542 1097 2.05 457 640 11848 40694
## 20 MA 67844 1609 2.32 1093 516 36623 31221
## 21 MD 113487 2328 2.01 2005 323 77755 35732
## 22 ME 9497 226 2.32 206 20 7126 2371
## 23 MI 96671 2297 2.32 1272 1025 31149 65522
## 24 MN 41692 1359 3.16 768 591 18293 23399
## 25 MO 99879 6065 5.72 5275 790 56840 43039
## 26 MS 36517 1474 3.88 1138 336 12790 23727
## 27 MT 15239 1166 7.11 878 288 5969 9270
## 28 NC 126163 4744 3.62 3667 1077 70962 55201
## 29 ND 8384 584 6.51 415 169 3079 5305
## 30 NE 17217 757 4.21 392 365 6589 10628
## 31 NH 10539 230 2.14 199 31 5985 4554
## 32 NJ 109154 2612 2.34 1905 707 48489 60665
## 33 NM 21331 681 3.09 539 142 6531 14800
## 34 NV 47818 1498 3.04 1110 388 24596 23222
## 35 NY 200402 7604 3.66 5532 2072 71701 128701
## 36 OH 164987 17706 9.69 16237 1469 83332 81655
## 37 OK 71182 2010 2.75 1342 668 23031 48151
## 38 PA 204979 7414 3.49 6101 1313 120744 84235
## 39 RI 4898 147 2.91 54 93 474 4424
## 40 SC 59163 1084 1.80 728 356 24762 34401
## 41 SD 10793 486 4.31 335 151 3383 7410
## 42 TN 78833 2199 2.71 1456 743 28457 50376
## 43 TX 412913 19560 4.52 14334 5226 174943 237970
## 44 UT 16641 816 4.67 132 684 3796 12845
## 45 VA 104369 4255 3.92 3257 998 66143 38226
## 46 VT 3052 99 3.14 87 12 1989 1063
## 47 WI 76055 2109 2.70 1385 724 26481 49574
## 48 WV 24620 412 1.65 280 132 13792 10828
## 49 WY 8535 170 1.95 86 84 4808 3727State View
This is the percent of hospitalizations that are among children when both suspected and confirmed are includded in the tally.
hospdf %>% filter (!state %in% c("PR", "VI")) %>% ggplot((aes(x=dateob,y= 100*total_ped/(total_ped+total_adult) ) )) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-08-01'), as.Date('2021-05-01'))## Warning: Removed 153 row(s) containing missing values (geom_path).
Prior day suspected pediatrics admission counts
Total Hospitalizations: State View
This is the total count of hospitalizations per state
hospdf %>% ggplot((aes(x=dateob,y= percent_of_inpatients_with_covid_numerator ) )) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-08-01'), as.Date('2021-05-01'))## Warning: Removed 153 row(s) containing missing values (geom_path).
Total daily COVID inpatients
Total ICU: State View
This is the total count of hospitalizations per state
hospdf %>% ggplot((aes(x=dateob,y= staffed_icu_adult_patients_confirmed_and_suspected_covid ) )) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-08-01'), as.Date('2021-05-01'))## Warning: Removed 153 row(s) containing missing values (geom_path).
Total daily COVID inpatients
Total %ICU: State View
This is the total count of hospitalizations per state
hospdf %>% filter (100*staffed_icu_adult_patients_confirmed_and_suspected_covid/percent_of_inpatients_with_covid_numerator < 100) %>% ggplot((aes(x=dateob,y= 100*staffed_icu_adult_patients_confirmed_and_suspected_covid/percent_of_inpatients_with_covid_numerator ) )) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-08-01'), as.Date('2021-05-01'))## Warning: Removed 17 row(s) containing missing values (geom_path).
Total daily COVID inpatients
Pediatric Percent: State View Smoothed
This is the percent of hospitalizations that are among children when both suspected and confirmed are includded in the tally.
hospdf %>% filter (!state %in% c("WA", "OR", "PR", "VI")) %>% ggplot((aes(x=dateob,y= 100*total_ped/(total_ped+total_adult) ) )) + geom_smooth() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-08-01'), as.Date('2021-05-01'))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 7759 rows containing non-finite values (stat_smooth).
Prior day suspected pediatrics admission counts
Pediatric: suspected vs confirmed
hospdf %>% filter (!state %in% c("OR","WA", "PR", "VI")) %>% group_by (dateob) %>% summarise(ped_susp = sum(previous_day_admission_pediatric_covid_suspected), ped_conf=sum(previous_day_admission_pediatric_covid_confirmed)) %>% ggplot(aes(x=dateob,y=(100*ped_conf/(ped_susp+ped_conf)))) + geom_line() + xlab("") + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + ylim(0,50) + labs(title = "Confirmed pediatric cases vs all (suspected + confirmed)") + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab("Percent of total hospitalizations") + xlab("Date")
Adult: suspected vs confirmed
hospdf %>% filter (!state %in% c("OR","WA", "PR", "VI")) %>% group_by (dateob) %>% summarise(adult_susp = sum(previous_day_admission_adult_covid_suspected), adult_conf=sum(previous_day_admission_adult_covid_confirmed)) %>% ggplot(aes(x=dateob,y=(100*adult_conf/(adult_susp+adult_conf)))) + geom_line() + xlab("") + xlim(as.Date('2020-11-01'), as.Date('2021-05-01')) + ylim(0,100) + labs(title = "Confirmed adult cases vs all (suspected + confirmed)") + labs(caption="Source: HHS. Does not include erroneous data for Oregon and Washington. Graphic: Jacob Fenton.") + theme( plot.caption = element_text(size = 8,hjust = 0), axis.title=element_text(size=10)) + ylab("Percent of total hospitalizations") + xlab("Date")
Data screening
Known anomalies
Oregon and Washington state both appear to have a constant value added to their data beginning Oct. 19, as if a hospital org with branches in Oregon and Washington erroneously reported a wrong value beginning then.
hospdf %>% filter(state=="OR" | state=="WA" ) %>% ggplot(aes(x=dateob,y=previous_day_admission_pediatric_covid_suspected, color=state)) + geom_line() + xlab("Date") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Oregon overall
This uses as total the inpatients_with_covid_numerator used in hhs’ percent calculations
hospdf %>% filter(state=="OR" ) %>% ggplot(aes(x=dateob,y=percent_of_inpatients_with_covid_numerator, color=state)) + geom_line() + xlab("Date") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01')) + ylab("Total COVID inpatients")
Tabular view
bystate <- hospdf %>% filter(state=="OR" ) %>% filter(dateob > as.Date('2020-11-01')) %>% select (dateob, percent_of_inpatients_with_covid_numerator, total_adult_patients_hospitalized_confirmed_covid, total_adult_patients_hospitalized_confirmed_and_suspected_covid, staffed_icu_adult_patients_confirmed_and_suspected_covid) %>% rename(covid_inpatients_numerator=percent_of_inpatients_with_covid_numerator, adult_hosp_confirmed=total_adult_patients_hospitalized_confirmed_covid, adult_icu_total=staffed_icu_adult_patients_confirmed_and_suspected_covid,total_adult=total_adult_patients_hospitalized_confirmed_and_suspected_covid)%>% arrange(dateob)
tibble.print_max = 500
print(bystate)## dateob covid_inpatients_numerator adult_hosp_confirmed total_adult adult_icu_total
## 1 2020-11-02 279 173 273 71
## 2 2020-11-03 285 177 270 65
## 3 2020-11-04 264 183 258 63
## 4 2020-11-05 275 182 262 62
## 5 2020-11-06 306 204 295 65
## 6 2020-11-07 340 230 325 68
## 7 2020-11-08 350 254 337 68
## 8 2020-11-09 363 262 351 70
## 9 2020-11-10 382 283 368 83
## 10 2020-11-11 393 284 376 75
## 11 2020-11-12 391 293 378 76
## 12 2020-11-13 444 323 429 85
## 13 2020-11-14 456 329 437 90
## 14 2020-11-15 455 343 442 99
## 15 2020-11-16 466 351 455 95
## 16 2020-11-17 493 371 481 107
## 17 2020-11-18 511 403 500 102
## 18 2020-11-19 545 412 536 117
## 19 2020-11-20 525 416 516 108
## 20 2020-11-21 604 434 592 110
## 21 2020-11-22 534 429 525 103
## 22 2020-11-23 536 437 518 112
## 23 2020-11-24 570 454 558 121
## 24 2020-11-25 590 468 576 125
## 25 2020-11-26 594 488 583 123
## 26 2020-11-27 611 519 601 123
## 27 2020-11-28 614 516 605 115
## 28 2020-11-29 667 542 654 125
## 29 2020-11-30 685 565 668 136
## 30 2020-12-01 670 561 656 128
## 31 2020-12-02 665 560 656 120
## 32 2020-12-03 643 532 637 124
## 33 2020-12-04 644 541 631 119
## 34 2020-12-05 662 544 650 117
## 35 2020-12-06 674 556 664 121
## 36 2020-12-07 666 543 657 126
## 37 2020-12-08 660 547 648 132
## 38 2020-12-09 686 563 673 129
## 39 2020-12-10 686 567 674 133
## 40 2020-12-11 690 580 679 141
## 41 2020-12-12 669 549 658 139
## 42 2020-12-13 632 527 619 140
## 43 2020-12-14 643 533 634 125
## 44 2020-12-15 667 538 657 125
## 45 2020-12-16 672 547 663 123
## 46 2020-12-17 669 534 665 119
## 47 2020-12-18 677 542 672 121
## 48 2020-12-19 664 531 653 121
## 49 2020-12-20 636 515 629 121
## 50 2020-12-21 622 517 614 118
## 51 2020-12-22 606 497 599 123
## 52 2020-12-23 589 491 585 115
## 53 2020-12-24 577 472 571 114
## 54 2020-12-25 542 447 535 110
## 55 2020-12-26 553 460 546 109
## 56 2020-12-27 564 485 556 114
## 57 2020-12-28 598 493 590 127
## 58 2020-12-29 626 507 615 128
## 59 2020-12-30 633 507 618 126
## 60 2020-12-31 596 474 587 118
## 61 2021-01-01 584 474 578 118
## 62 2021-01-02 569 465 560 115
## 63 2021-01-03 573 475 564 124
## 64 2021-01-04 564 468 514 97
## 65 2021-01-05 592 484 586 114
## 66 2021-01-06 583 460 568 118
## 67 2021-01-07 560 465 551 109
## 68 2021-01-08 521 435 511 96
## 69 2021-01-09 539 431 533 105
## 70 2021-01-10 494 408 489 102
## 71 2021-01-11 498 412 489 105
## 72 2021-01-12 521 417 518 114
## 73 2021-01-13 512 414 507 115
## 74 2021-01-14 468 399 464 106
## 75 2021-01-15 470 398 462 107
## 76 2021-01-16 475 394 466 108
## 77 2021-01-17 462 381 453 107
## 78 2021-01-18 418 348 409 109
## 79 2021-01-19 429 345 422 108
## 80 2021-01-20 445 367 437 95
## 81 2021-01-21 432 352 420 100
## 82 2021-01-22 429 353 417 99
## 83 2021-01-23 432 344 422 99
## 84 2021-01-24 403 320 393 81
## 85 2021-01-25 445 360 432 88
## 86 2021-01-26 412 333 405 83
## 87 2021-01-27 405 335 400 80
## 88 2021-01-28 410 314 400 77
## 89 2021-01-29 399 305 395 80
## 90 2021-01-30 397 308 391 77
## 91 2021-01-31 383 307 377 80
## 92 2021-02-01 383 300 370 70
## 93 2021-02-02 384 289 368 81
## 94 2021-02-03 381 298 368 77
## 95 2021-02-04 357 283 351 76
## 96 2021-02-05 350 282 341 75
## 97 2021-02-06 338 262 330 68
## 98 2021-02-07 337 261 329 65
## 99 2021-02-08 304 241 293 66
## 100 2021-02-09 319 247 308 72
## 101 2021-02-10 315 244 303 69
## 102 2021-02-11 312 236 300 70
## 103 2021-02-12 314 245 305 66
## 104 2021-02-13 313 248 303 65
## 105 2021-02-14 315 248 306 75
## 106 2021-02-15 304 236 292 74
## 107 2021-02-16 303 225 290 71
## 108 2021-02-17 312 217 300 74
## 109 2021-02-18 285 207 275 67
## 110 2021-02-19 287 207 269 62
## 111 2021-02-20 272 200 261 57
## 112 2021-02-21 285 204 277 64
## 113 2021-02-22 260 201 253 55
## 114 2021-02-23 257 192 246 57
## 115 2021-02-24 245 177 232 48
## 116 2021-02-25 248 182 240 50
## 117 2021-02-26 238 172 231 50
## 118 2021-02-27 227 160 220 47
## 119 2021-02-28 215 155 209 45
## 120 2021-03-01 246 163 239 44
## 121 2021-03-02 241 163 230 42
## 122 2021-03-03 223 151 213 42
## 123 2021-03-04 205 156 199 42
## 124 2021-03-05 210 140 199 39
## 125 2021-03-06 214 133 202 45
## 126 2021-03-07 193 121 187 48
## 127 2021-03-08 183 127 175 71
## 128 2021-03-09 203 145 195 36
## 129 2021-03-10 202 141 193 43
## 130 2021-03-11 196 133 192 39
## 131 2021-03-12 203 133 198 46
## 132 2021-03-13 179 117 175 40
## 133 2021-03-14 178 116 173 42
## 134 2021-03-15 187 130 181 43
## 135 2021-03-16 186 132 182 45
## 136 2021-03-17 182 125 177 42
## 137 2021-03-18 182 128 177 44
## 138 2021-03-19 200 131 192 44
## 139 2021-03-20 189 117 184 44
## 140 2021-03-21 176 122 169 31
## 141 2021-03-22 171 122 165 32
## 142 2021-03-23 182 128 173 29
## 143 2021-03-24 172 123 166 32
## 144 2021-03-25 173 134 167 33
## 145 2021-03-26 152 107 144 36
## 146 2021-03-27 183 130 175 33
## 147 2021-03-28 196 132 191 42
## 148 2021-03-29 213 148 205 52
## 149 2021-03-30 211 141 202 50
## 150 2021-03-31 210 143 201 46
## 151 2021-04-01 225 150 217 48
## 152 2021-04-02 211 158 202 55
## 153 2021-04-03 223 154 212 57
## 154 2021-04-04 210 147 198 51
## 155 2021-04-05 243 172 233 53
## 156 2021-04-06 238 157 224 49
## 157 2021-04-07 239 169 226 48
## 158 2021-04-08 251 169 236 51
## 159 2021-04-09 241 156 230 57
## 160 2021-04-10 246 179 235 56
## 161 2021-04-11 258 179 245 59
## 162 2021-04-12 261 194 248 61
## 163 2021-04-13 280 203 270 62
## 164 2021-04-14 277 203 269 63
## 165 2021-04-15 269 199 263 64
## 166 2021-04-16 286 203 280 62
## 167 2021-04-17 283 205 275 57
## 168 2021-04-18 305 229 294 62
## 169 2021-04-19 316 255 307 63
## 170 2021-04-20 326 252 319 63
## 171 2021-04-21 332 263 323 78
## 172 2021-04-22 337 266 327 72
## 173 2021-04-23 346 277 338 72
## 174 2021-04-24 371 298 361 73
## 175 2021-04-25 369 307 356 77
## 176 2021-04-26 397 327 388 88
## 177 2021-04-27 393 328 381 81
## 178 2021-04-28 392 318 380 76
## 179 2021-04-29 384 326 376 76
## 180 2021-04-30 384 326 376 76Additional data screening
Because Oregon and Washington contain erroneous data, we performed a cursory manual review to show that all the variables used in the chart at the top are 1. reported by a fairly constant number of hospitals and 2. do not arbitrarily change. This light data screening turns up fairly obvious data errors, like those encountered in Oregon and Washington, but will not be sufficient to highlight more insidious data quality problems.
Review: previous_day_admission_pediatric_covid_suspected
Here’s the value of previous_day_admission_pediatric_covid_suspected per state. The anomalies in WA and OR are pretty easy to spot. No other state seems as obviously wrong, although Rhode Island does seem quite low. We ignore values less than zero so the scale isn’t skewed.
hospdf %>% filter(previous_day_admission_pediatric_covid_suspected>0) %>% ggplot(aes(x=dateob,y=previous_day_admission_pediatric_covid_suspected)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day suspected pediatrics admission counts
Here’s the number of hospitals that reported a value for this variable. It seems like the number of reporting hospitals rises till mid October or early November. Puerto Rico has other issues, but we are only including U.S. states.
hospdf %>% filter(previous_day_admission_pediatric_covid_suspected>0) %>% ggplot(aes(x=dateob,y=previous_day_admission_pediatric_covid_suspected_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day suspected pediatrics admission count coverage
Review: previous_day_admission_pediatric_covid_confirmed
Here’s the value of previous_day_admission_pediatric_covid_confirmed per state. It looks like Utah has a similar data issue, adding one or two to it’s data until roughly November.
hospdf %>% filter(previous_day_admission_pediatric_covid_confirmed>0) %>% ggplot(aes(x=dateob,y=previous_day_admission_pediatric_covid_confirmed)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day confirmed pediatrics admission counts
Here’s the number of hospitals that reported a value for this variable.
hospdf %>% ggplot(aes(x=dateob,y=previous_day_admission_pediatric_covid_confirmed_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day confirmed pediatrics admission count coverage
Review: previous_day_admission_adult_covid_confirmed
Here’s the value of previous_day_admission_adult_covid_confirmed per state.
hospdf %>% filter(previous_day_admission_adult_covid_confirmed>0) %>% ggplot(aes(x=dateob,y=previous_day_admission_adult_covid_confirmed)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day confirmed adult admission count
Here’s the number of hospitals that reported a value for this variable.
hospdf %>% ggplot(aes(x=dateob,y=previous_day_admission_adult_covid_confirmed_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day confirmed adult admission count coverage
Review: previous_day_admission_adult_covid_suspected
Here’s the value of previous_day_admission_adult_covid_suspected per state.
hospdf %>% filter(previous_day_admission_adult_covid_suspected>0) %>% ggplot(aes(x=dateob,y=previous_day_admission_adult_covid_suspected)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day suspected adult admission count
Here’s the number of hospitals that reported a value for this variable.
hospdf %>% ggplot(aes(x=dateob,y=previous_day_admission_adult_covid_suspected_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Prior day suspected adult admission count coverage
Review: Differences in coverage between adult confirmed vs suspected
Are the same number of hospitals reporting values for adult numbers–confirmed vs suspected ? Because we are subtracting one from the other, we are looking for this to be about zero by November.
hospdf %>% ggplot(aes(x=dateob,y=previous_day_admission_adult_covid_confirmed_coverage - previous_day_admission_adult_covid_suspected_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Coverage differences, adult confirmed vs. adult suspected
Review: Differences in coverage between pediatric confirmed vs suspected
Are the same number of hospitals reporting values for pediatric numbers–confirmed vs suspected ?
hospdf %>% ggplot(aes(x=dateob,y=previous_day_admission_pediatric_covid_confirmed_coverage - previous_day_admission_pediatric_covid_suspected_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Coverage differences, pediatric confirmed vs. adult suspected
Review: Differences in coverage between pediatric suspected vs adult suspected
hospdf %>% ggplot(aes(x=dateob,y=previous_day_admission_adult_covid_suspected_coverage - previous_day_admission_pediatric_covid_suspected_coverage)) + geom_line() + facet_wrap( ~ state, scales = "free" ) + xlab("") + xlim(as.Date('2020-07-01'), as.Date('2021-05-01'))
Coverage differences, adult suspected vs. pediatric suspected