Staffing Levels and COVID‐19 Cases and Outbreaks in U.S. Nursing Homes
BACKGROUND/OBJECTIVES Nursing homes have experienced a disproportionate share of COVID‐19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond...
Saved in:
| Published in | Journal of the American Geriatrics Society (JAGS) Vol. 68; no. 11; pp. 2462 - 2466 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.11.2020
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | BACKGROUND/OBJECTIVES
Nursing homes have experienced a disproportionate share of COVID‐19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond to future outbreaks. We sought to understand whether baseline nurse staffing is associated with the presence of COVID‐19 in nursing homes and whether staffing impacts outbreak severity.
DESIGN
We analyzed Centers for Medicare & Medicaid Services (CMS) facility‐level data on COVID‐19 cases and deaths merged with nursing home and county characteristics. We used logistic regressions to examine the associations of staffing levels from Nursing Home Compare with the outcomes of any COVID‐19 cases and, conditional on at least one case, an outbreak. Among facilities with at least one case, we modeled count of deaths using hurdle negative binomial‐2 regressions.
SETTING
All nursing homes in the CMS COVID‐19 Nursing Home Dataset with reports that passed the CMS Quality Assurance Check as of June 25, 2020.
PARTICIPANTS
Residents of nursing homes that met COVID‐19 reporting requirements.
MEASUREMENTS
A nursing home is defined as having at least one case is if one or more confirmed or suspected COVID‐19 case among residents or staff is reported. Conditional on at least one case, we examine two outcomes: an outbreak, defined as confirmed cases/certified beds >10% or total confirmed and suspected cases/beds >20% or >10 deaths, and the total number of deaths attributed to COVID‐19 among residents and staff.
RESULTS
A total of 71% of the 13,167 nursing homes that reported COVID‐19 data as of June 14 had at least one case among residents and/or staff. Of those, 27% experienced an outbreak. Higher registered nurse‐hours are associated with a higher probability of experiencing any cases. However, among facilities with at least one case, higher nurse aide (NA) hours and total nursing hours are associated with a lower probability of experiencing an outbreak and with fewer deaths. The strongest predictor of cases and outbreaks in nursing homes is per capita cases in the county.
CONCLUSION
The prevalence of COVID‐19 in the community remains the strongest predictor of COVID‐19 cases and deaths in nursing homes, but higher NA hours and total nursing hours may help contain the number of cases and deaths. |
|---|---|
| AbstractList | Nursing homes have experienced a disproportionate share of COVID-19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond to future outbreaks. We sought to understand whether baseline nurse staffing is associated with the presence of COVID-19 in nursing homes and whether staffing impacts outbreak severity.BACKGROUND/OBJECTIVESNursing homes have experienced a disproportionate share of COVID-19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond to future outbreaks. We sought to understand whether baseline nurse staffing is associated with the presence of COVID-19 in nursing homes and whether staffing impacts outbreak severity.We analyzed Centers for Medicare & Medicaid Services (CMS) facility-level data on COVID-19 cases and deaths merged with nursing home and county characteristics. We used logistic regressions to examine the associations of staffing levels from Nursing Home Compare with the outcomes of any COVID-19 cases and, conditional on at least one case, an outbreak. Among facilities with at least one case, we modeled count of deaths using hurdle negative binomial-2 regressions.DESIGNWe analyzed Centers for Medicare & Medicaid Services (CMS) facility-level data on COVID-19 cases and deaths merged with nursing home and county characteristics. We used logistic regressions to examine the associations of staffing levels from Nursing Home Compare with the outcomes of any COVID-19 cases and, conditional on at least one case, an outbreak. Among facilities with at least one case, we modeled count of deaths using hurdle negative binomial-2 regressions.All nursing homes in the CMS COVID-19 Nursing Home Dataset with reports that passed the CMS Quality Assurance Check as of June 25, 2020.SETTINGAll nursing homes in the CMS COVID-19 Nursing Home Dataset with reports that passed the CMS Quality Assurance Check as of June 25, 2020.Residents of nursing homes that met COVID-19 reporting requirements.PARTICIPANTSResidents of nursing homes that met COVID-19 reporting requirements.A nursing home is defined as having at least one case is if one or more confirmed or suspected COVID-19 case among residents or staff is reported. Conditional on at least one case, we examine two outcomes: an outbreak, defined as confirmed cases/certified beds >10% or total confirmed and suspected cases/beds >20% or >10 deaths, and the total number of deaths attributed to COVID-19 among residents and staff.MEASUREMENTSA nursing home is defined as having at least one case is if one or more confirmed or suspected COVID-19 case among residents or staff is reported. Conditional on at least one case, we examine two outcomes: an outbreak, defined as confirmed cases/certified beds >10% or total confirmed and suspected cases/beds >20% or >10 deaths, and the total number of deaths attributed to COVID-19 among residents and staff.A total of 71% of the 13,167 nursing homes that reported COVID-19 data as of June 14 had at least one case among residents and/or staff. Of those, 27% experienced an outbreak. Higher registered nurse-hours are associated with a higher probability of experiencing any cases. However, among facilities with at least one case, higher nurse aide (NA) hours and total nursing hours are associated with a lower probability of experiencing an outbreak and with fewer deaths. The strongest predictor of cases and outbreaks in nursing homes is per capita cases in the county.RESULTSA total of 71% of the 13,167 nursing homes that reported COVID-19 data as of June 14 had at least one case among residents and/or staff. Of those, 27% experienced an outbreak. Higher registered nurse-hours are associated with a higher probability of experiencing any cases. However, among facilities with at least one case, higher nurse aide (NA) hours and total nursing hours are associated with a lower probability of experiencing an outbreak and with fewer deaths. The strongest predictor of cases and outbreaks in nursing homes is per capita cases in the county.The prevalence of COVID-19 in the community remains the strongest predictor of COVID-19 cases and deaths in nursing homes, but higher NA hours and total nursing hours may help contain the number of cases and deaths.CONCLUSIONThe prevalence of COVID-19 in the community remains the strongest predictor of COVID-19 cases and deaths in nursing homes, but higher NA hours and total nursing hours may help contain the number of cases and deaths. BACKGROUND/OBJECTIVES Nursing homes have experienced a disproportionate share of COVID‐19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond to future outbreaks. We sought to understand whether baseline nurse staffing is associated with the presence of COVID‐19 in nursing homes and whether staffing impacts outbreak severity. DESIGN We analyzed Centers for Medicare & Medicaid Services (CMS) facility‐level data on COVID‐19 cases and deaths merged with nursing home and county characteristics. We used logistic regressions to examine the associations of staffing levels from Nursing Home Compare with the outcomes of any COVID‐19 cases and, conditional on at least one case, an outbreak. Among facilities with at least one case, we modeled count of deaths using hurdle negative binomial‐2 regressions. SETTING All nursing homes in the CMS COVID‐19 Nursing Home Dataset with reports that passed the CMS Quality Assurance Check as of June 25, 2020. PARTICIPANTS Residents of nursing homes that met COVID‐19 reporting requirements. MEASUREMENTS A nursing home is defined as having at least one case is if one or more confirmed or suspected COVID‐19 case among residents or staff is reported. Conditional on at least one case, we examine two outcomes: an outbreak, defined as confirmed cases/certified beds >10% or total confirmed and suspected cases/beds >20% or >10 deaths, and the total number of deaths attributed to COVID‐19 among residents and staff. RESULTS A total of 71% of the 13,167 nursing homes that reported COVID‐19 data as of June 14 had at least one case among residents and/or staff. Of those, 27% experienced an outbreak. Higher registered nurse‐hours are associated with a higher probability of experiencing any cases. However, among facilities with at least one case, higher nurse aide (NA) hours and total nursing hours are associated with a lower probability of experiencing an outbreak and with fewer deaths. The strongest predictor of cases and outbreaks in nursing homes is per capita cases in the county. CONCLUSION The prevalence of COVID‐19 in the community remains the strongest predictor of COVID‐19 cases and deaths in nursing homes, but higher NA hours and total nursing hours may help contain the number of cases and deaths. BACKGROUND/OBJECTIVESNursing homes have experienced a disproportionate share of COVID‐19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond to future outbreaks. We sought to understand whether baseline nurse staffing is associated with the presence of COVID‐19 in nursing homes and whether staffing impacts outbreak severity.DESIGNWe analyzed Centers for Medicare & Medicaid Services (CMS) facility‐level data on COVID‐19 cases and deaths merged with nursing home and county characteristics. We used logistic regressions to examine the associations of staffing levels from Nursing Home Compare with the outcomes of any COVID‐19 cases and, conditional on at least one case, an outbreak. Among facilities with at least one case, we modeled count of deaths using hurdle negative binomial‐2 regressions.SETTINGAll nursing homes in the CMS COVID‐19 Nursing Home Dataset with reports that passed the CMS Quality Assurance Check as of June 25, 2020.PARTICIPANTSResidents of nursing homes that met COVID‐19 reporting requirements.MEASUREMENTSA nursing home is defined as having at least one case is if one or more confirmed or suspected COVID‐19 case among residents or staff is reported. Conditional on at least one case, we examine two outcomes: an outbreak, defined as confirmed cases/certified beds >10% or total confirmed and suspected cases/beds >20% or >10 deaths, and the total number of deaths attributed to COVID‐19 among residents and staff.RESULTSA total of 71% of the 13,167 nursing homes that reported COVID‐19 data as of June 14 had at least one case among residents and/or staff. Of those, 27% experienced an outbreak. Higher registered nurse‐hours are associated with a higher probability of experiencing any cases. However, among facilities with at least one case, higher nurse aide (NA) hours and total nursing hours are associated with a lower probability of experiencing an outbreak and with fewer deaths. The strongest predictor of cases and outbreaks in nursing homes is per capita cases in the county.CONCLUSIONThe prevalence of COVID‐19 in the community remains the strongest predictor of COVID‐19 cases and deaths in nursing homes, but higher NA hours and total nursing hours may help contain the number of cases and deaths. Nursing homes have experienced a disproportionate share of COVID-19 cases and deaths. Early analyses indicated that baseline quality was not predictive of nursing home cases, but a more nuanced study of the role of nurse staffing is needed to target resources and better respond to future outbreaks. We sought to understand whether baseline nurse staffing is associated with the presence of COVID-19 in nursing homes and whether staffing impacts outbreak severity. We analyzed Centers for Medicare & Medicaid Services (CMS) facility-level data on COVID-19 cases and deaths merged with nursing home and county characteristics. We used logistic regressions to examine the associations of staffing levels from Nursing Home Compare with the outcomes of any COVID-19 cases and, conditional on at least one case, an outbreak. Among facilities with at least one case, we modeled count of deaths using hurdle negative binomial-2 regressions. All nursing homes in the CMS COVID-19 Nursing Home Dataset with reports that passed the CMS Quality Assurance Check as of June 25, 2020. Residents of nursing homes that met COVID-19 reporting requirements. A nursing home is defined as having at least one case is if one or more confirmed or suspected COVID-19 case among residents or staff is reported. Conditional on at least one case, we examine two outcomes: an outbreak, defined as confirmed cases/certified beds >10% or total confirmed and suspected cases/beds >20% or >10 deaths, and the total number of deaths attributed to COVID-19 among residents and staff. A total of 71% of the 13,167 nursing homes that reported COVID-19 data as of June 14 had at least one case among residents and/or staff. Of those, 27% experienced an outbreak. Higher registered nurse-hours are associated with a higher probability of experiencing any cases. However, among facilities with at least one case, higher nurse aide (NA) hours and total nursing hours are associated with a lower probability of experiencing an outbreak and with fewer deaths. The strongest predictor of cases and outbreaks in nursing homes is per capita cases in the county. The prevalence of COVID-19 in the community remains the strongest predictor of COVID-19 cases and deaths in nursing homes, but higher NA hours and total nursing hours may help contain the number of cases and deaths. |
| Author | Gorges, Rebecca J. Konetzka, R. Tamara |
| AuthorAffiliation | 1 Department of Public Health Sciences The University of Chicago Chicago Illinois USA 2 Department of Medicine The University of Chicago Chicago Illinois USA |
| AuthorAffiliation_xml | – name: 2 Department of Medicine The University of Chicago Chicago Illinois USA – name: 1 Department of Public Health Sciences The University of Chicago Chicago Illinois USA |
| Author_xml | – sequence: 1 givenname: Rebecca J. orcidid: 0000-0003-3300-0579 surname: Gorges fullname: Gorges, Rebecca J. email: rjgorges@uchicago.edu organization: The University of Chicago – sequence: 2 givenname: R. Tamara surname: Konetzka fullname: Konetzka, R. Tamara organization: The University of Chicago |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32770832$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kc1uEzEUhS1URNPCghdAI7GBxaT-Gc_1bJBQgKYoIosUtpZn5k5wmNitPVPUHY_AM_IkOCRFUIE3V7K_c3R8zwk5ct4hIU8ZnbJ0zjbrOGUlKHhAJkwKnsuCySMyoZTyXJWsOCYnMW4oZZwq9YgcCw5AleATMl8NpuusW2cLvME-Zsa12Wz56eLNj2_fWZXNTMT95XIc6oDmS8ysyz5OV9PswxjiTjn3W4yPycPO9BGfHOYpuXz39nI2zxfL84vZ60XeCKkglw0ACtaqykjoREWhFl3LJOd1U8hGVIYLVKIra4CC11BXrKtNqygqJopSnJJXe9ursd5i26Abgun1VbBbE261N1b__eLsZ732NxoKUZZMJIMXB4Pgr0eMg97a2GDfG4d-jJoXgikKJeUJfX4P3fgxuPS7RMkKgJcCEvXsz0S_o9ztOAFne6AJPsaAnW7sYAbrdwFtrxnVuxZ1alH_ajEpXt5T3Jn-iz24f7U93v4f1O_PV3vFT6SUqeQ |
| CitedBy_id | crossref_primary_10_1371_journal_pone_0293336 crossref_primary_10_1016_j_jamda_2024_105322 crossref_primary_10_1377_hlthaff_2021_01687 crossref_primary_10_2196_32442 crossref_primary_10_1111_jgs_17062 crossref_primary_10_1371_journal_pone_0275500 crossref_primary_10_1016_j_gerinurse_2021_10_023 crossref_primary_10_1111_jgs_17224 crossref_primary_10_1287_mnsc_2023_4832 crossref_primary_10_1017_ice_2021_246 crossref_primary_10_1177_23337214241229824 crossref_primary_10_1377_hlthaff_2023_01460 crossref_primary_10_1111_1475_6773_13954 crossref_primary_10_1186_s12913_024_11331_2 crossref_primary_10_1377_hlthaff_2023_00255 crossref_primary_10_3389_fpubh_2021_606364 crossref_primary_10_1590_s1678_9946202163010 crossref_primary_10_1177_00027162211061509 crossref_primary_10_3389_fpubh_2023_1098571 crossref_primary_10_1016_j_jamda_2022_06_028 crossref_primary_10_1017_S095026882200036X crossref_primary_10_3390_v16091461 crossref_primary_10_1016_j_jen_2021_04_006 crossref_primary_10_7759_cureus_32157 crossref_primary_10_3201_eid2703_204508 crossref_primary_10_1007_s41999_023_00793_y crossref_primary_10_3390_ijerph18115805 crossref_primary_10_9778_cmajo_20220022 crossref_primary_10_1007_s40520_021_01959_z crossref_primary_10_3390_healthcare12030301 crossref_primary_10_1001_jamahealthforum_2024_5175 crossref_primary_10_1038_s43587_021_00106_7 crossref_primary_10_1093_ageing_afad165 crossref_primary_10_3389_fpubh_2023_1040851 crossref_primary_10_3390_healthcare11091248 crossref_primary_10_1016_j_jamda_2021_03_029 crossref_primary_10_2105_AJPH_2020_306107 crossref_primary_10_1016_j_ijid_2023_03_007 crossref_primary_10_1177_15271544211056051 crossref_primary_10_3790_sfo_71_10_749 crossref_primary_10_1016_j_jamda_2021_03_024 crossref_primary_10_1371_journal_pcbi_1010042 crossref_primary_10_1016_j_gerinurse_2022_12_018 crossref_primary_10_1177_07334648221132121 crossref_primary_10_1377_hlthaff_2020_01011 crossref_primary_10_1016_j_jhealeco_2022_102592 crossref_primary_10_1016_j_psc_2021_11_010 crossref_primary_10_1093_qjmed_hcaa343 crossref_primary_10_1097_QCO_0000000000000849 crossref_primary_10_2139_ssrn_4820278 crossref_primary_10_3390_epidemiologia3020014 crossref_primary_10_1016_j_ijnurstu_2022_104256 crossref_primary_10_1097_CIN_0000000000000901 crossref_primary_10_1080_01634372_2021_1969714 crossref_primary_10_1007_s10488_023_01311_9 crossref_primary_10_1002_14651858_CD015085 crossref_primary_10_3390_v14010043 crossref_primary_10_1080_10967494_2024_2333389 crossref_primary_10_1016_j_jamda_2021_05_002 crossref_primary_10_1377_hlthaff_2021_00323 crossref_primary_10_2139_ssrn_3659480 crossref_primary_10_1377_hlthaff_2020_02191 crossref_primary_10_1097_JOM_0000000000003181 crossref_primary_10_1377_hlthaff_2020_02351 crossref_primary_10_2196_31502 crossref_primary_10_1016_j_jamda_2024_105116 crossref_primary_10_1016_j_jamda_2021_12_026 crossref_primary_10_1016_j_jamda_2023_04_009 crossref_primary_10_1186_s12912_022_00823_3 crossref_primary_10_1016_j_jamda_2021_08_021 crossref_primary_10_1177_19375867221092149 crossref_primary_10_1590_1518_8345_5581_3557 crossref_primary_10_1590_1518_8345_5581_3556 crossref_primary_10_1016_j_cnur_2022_02_003 crossref_primary_10_1016_j_jamda_2022_07_023 crossref_primary_10_3390_ijerph18147398 crossref_primary_10_1093_rcfs_cfac041 crossref_primary_10_1016_j_jamda_2021_03_001 crossref_primary_10_1016_j_jamda_2021_07_027 crossref_primary_10_1016_j_scitotenv_2020_141946 crossref_primary_10_1097_NJH_0000000000000779 crossref_primary_10_1038_s43587_021_00079_7 crossref_primary_10_1111_jgs_17302 crossref_primary_10_1016_j_gerinurse_2022_08_012 crossref_primary_10_3928_19404921_20230817_04 crossref_primary_10_1177_21650799241268745 crossref_primary_10_1016_j_gerinurse_2021_08_016 crossref_primary_10_1007_s10654_022_00861_w crossref_primary_10_1016_j_jamda_2023_11_003 crossref_primary_10_1016_j_jamda_2022_11_002 crossref_primary_10_18278_jep_1_3_3 crossref_primary_10_3790_sfo_72_7_8_615 crossref_primary_10_1016_j_annepidem_2022_10_013 crossref_primary_10_1186_s12877_022_03218_w crossref_primary_10_3390_healthcare12070807 crossref_primary_10_1177_07334648211040509 crossref_primary_10_1093_infdis_jiab515 crossref_primary_10_1111_jgs_17434 crossref_primary_10_1007_s12351_024_00875_0 crossref_primary_10_1111_jgs_17309 crossref_primary_10_1080_15309576_2023_2295282 crossref_primary_10_1016_j_lanepe_2021_100303 crossref_primary_10_1016_j_jhin_2021_11_008 crossref_primary_10_1186_s12877_021_02530_1 crossref_primary_10_1002_hec_4873 crossref_primary_10_1016_j_jamda_2024_105145 crossref_primary_10_1016_j_gerinurse_2024_01_001 crossref_primary_10_1177_15271544211063828 crossref_primary_10_1016_j_jamda_2022_10_022 crossref_primary_10_1055_a_1745_8780 crossref_primary_10_1111_jgs_17126 crossref_primary_10_1186_s12877_023_04446_4 crossref_primary_10_1111_jgs_16951 crossref_primary_10_1177_00333549221074381 crossref_primary_10_1177_00207314221077649 crossref_primary_10_31389_jltc_237 crossref_primary_10_2139_ssrn_5001353 crossref_primary_10_1016_S2666_7568_21_00025_8 crossref_primary_10_1177_15271544221105996 crossref_primary_10_1186_s13756_023_01292_2 crossref_primary_10_1016_j_jamda_2020_11_022 crossref_primary_10_18332_popmed_149307 crossref_primary_10_31389_jltc_236 crossref_primary_10_1016_j_idm_2022_07_003 crossref_primary_10_2139_ssrn_4113615 crossref_primary_10_1002_14651858_CD015085_pub2 crossref_primary_10_1177_09589287241268490 crossref_primary_10_1001_jamanetworkopen_2020_26702 crossref_primary_10_1590_1518_8345_5581_3516 crossref_primary_10_1016_j_jamda_2021_01_069 crossref_primary_10_1016_j_socscimed_2023_115894 crossref_primary_10_1093_ageing_afab081 crossref_primary_10_1371_journal_pdig_0000047 crossref_primary_10_3390_ijerph18168434 crossref_primary_10_1016_j_yfpn_2024_02_003 crossref_primary_10_1089_heq_2024_0091 crossref_primary_10_1177_2333721420982800 |
| Cites_doi | 10.1177/1527154420938707 10.1111/jgs.16661 10.1111/jgs.16689 10.1111/jgs16642 |
| ContentType | Journal Article |
| Copyright | 2020 The American Geriatrics Society 2020 The American Geriatrics Society. 2020 American Geriatrics Society and Wiley Periodicals, Inc. |
| Copyright_xml | – notice: 2020 The American Geriatrics Society – notice: 2020 The American Geriatrics Society. – notice: 2020 American Geriatrics Society and Wiley Periodicals, Inc. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QP 7TK K9. NAPCQ 7X8 5PM |
| DOI | 10.1111/jgs.16787 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Calcium & Calcified Tissue Abstracts Neurosciences Abstracts ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Premium MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Premium Calcium & Calcified Tissue Abstracts Neurosciences Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic ProQuest Health & Medical Complete (Alumni) MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| DocumentTitleAlternate | NURSING HOME STAFFING AND COVID‐19 |
| EISSN | 1532-5415 |
| EndPage | 2466 |
| ExternalDocumentID | PMC7436613 32770832 10_1111_jgs_16787 JGS16787 |
| Genre | shortCommunication Research Support, U.S. Gov't, P.H.S Journal Article |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: Agency for Healthcare Research and Quality funderid: T32 HS000084 – fundername: NIA NIH HHS grantid: T32 AG000243 – fundername: AHRQ HHS grantid: T32 HS000084 – fundername: ; grantid: T32 HS000084 |
| GroupedDBID | --- --Z -~X ..I .3N .55 .GA .GJ .Y3 05W 0R~ 10A 1CY 1KJ 1OB 1OC 29L 31~ 33P 36B 3O- 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 53G 5GY 5HH 5LA 5RE 5VS 66C 6PF 702 7PT 8-0 8-1 8-3 8-4 8-5 8F7 8UM 930 A01 A03 AAESR AAEVG AAHHS AAHQN AAIPD AAMNL AANHP AANLZ AAONW AAQQT AARRQ AASGY AAWTL AAXRX AAYCA AAYJJ AAZKR ABCQN ABCUV ABDPE ABEML ABIVO ABJNI ABLJU ABOCM ABPPZ ABPVW ABQWH ABXGK ACAHQ ACBWZ ACCFJ ACCZN ACGFO ACGFS ACGOF ACHQT ACMXC ACNCT ACPOU ACPRK ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFNX AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AHMBA AI. AIACR AIAGR AITYG AIURR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BKOMP BMXJE BROTX BRXPI BY8 C45 CAG COF CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 DUUFO EBS EJD EMOBN ESX EX3 F00 F01 F04 F5P FEDTE FUBAC G-S G.N GODZA H.X HF~ HGLYW HVGLF HZI HZ~ H~9 IHE IX1 J0M J5H K48 KBYEO L7B LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N4W N9A NF~ NHB O66 O9- OHT OIG OVD P2P P2W P2X P2Z P4B P4D PALCI PQQKQ Q.N Q11 QB0 QN7 R.K RIWAO RJQFR ROL RX1 SAMSI SUPJJ TAE TEORI TN5 TWZ UB1 UKR UPT V9Y VH1 W8V W99 WBKPD WH7 WHWMO WIH WIJ WIK WOHZO WOW WQ9 WQJ WRC WVDHM WXI WXSBR X7M XG1 XOL YCJ YF5 YFH YOC YQJ YQT YUY YZZ ZGI ZHY ZXP ZY1 ZZTAW ~IA ~S- ~WT AAYXX AEYWJ AGHNM AGQPQ AGYGG CITATION CGR CUY CVF ECM EIF NPM 7QP 7TK AAMMB AEFGJ AGXDD AIDQK AIDYY K9. NAPCQ 7X8 5PM |
| ID | FETCH-LOGICAL-c3587-5c77e31d89a57f3907b3fd1522bc45c39a23e83f6b7742b7b91fbad80e813463 |
| IEDL.DBID | DR2 |
| ISSN | 0002-8614 1532-5415 |
| IngestDate | Thu Aug 21 18:25:25 EDT 2025 Fri Jul 11 10:22:30 EDT 2025 Fri Jul 25 22:36:12 EDT 2025 Thu Apr 03 06:58:38 EDT 2025 Tue Jul 01 03:55:32 EDT 2025 Thu Apr 24 22:59:51 EDT 2025 Wed Jan 22 16:31:26 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Keywords | COVID-19 nursing homes long-term care |
| Language | English |
| License | 2020 The American Geriatrics Society. This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3587-5c77e31d89a57f3907b3fd1522bc45c39a23e83f6b7742b7b91fbad80e813463 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This article was published online on 28 August 2020. An error was subsequently identified in the author name. This notice is included in both the online and print versions to indicate that they have been corrected on 3 September 2020. |
| ORCID | 0000-0003-3300-0579 |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC7436613 |
| PMID | 32770832 |
| PQID | 2459772637 |
| PQPubID | 41968 |
| PageCount | 5 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7436613 proquest_miscellaneous_2431807602 proquest_journals_2459772637 pubmed_primary_32770832 crossref_citationtrail_10_1111_jgs_16787 crossref_primary_10_1111_jgs_16787 wiley_primary_10_1111_jgs_16787_JGS16787 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | November 2020 |
| PublicationDateYYYYMMDD | 2020-11-01 |
| PublicationDate_xml | – month: 11 year: 2020 text: November 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken, USA |
| PublicationPlace_xml | – name: Hoboken, USA – name: United States – name: New York |
| PublicationTitle | Journal of the American Geriatrics Society (JAGS) |
| PublicationTitleAlternate | J Am Geriatr Soc |
| PublicationYear | 2020 |
| Publisher | John Wiley & Sons, Inc Wiley Subscription Services, Inc |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc |
| References | 2017 2020 2020; 68 2020; 21 e_1_2_6_9_1 e_1_2_6_8_1 e_1_2_6_5_1 e_1_2_6_4_1 e_1_2_6_10_1 e_1_2_6_7_1 e_1_2_6_6_1 Deb P (e_1_2_6_12_1) 2017 e_1_2_6_3_1 e_1_2_6_11_1 e_1_2_6_2_1 |
| References_xml | – year: 2017 – volume: 21 start-page: 174 year: 2020 end-page: 186 article-title: Nurse staffing and coronavirus infections in California nursing homes publication-title: Policy Polit Nurs Pract – year: 2020 article-title: Variation in SARS‐CoV‐2 prevalence in US skilled nursing facilities publication-title: J Am Geriatr Soc. – volume: 68 start-page: 1653 year: 2020 end-page: 1656 article-title: Characteristics of U.S. nursing homes with COVID‐19 cases publication-title: J Am Geriatr Soc. – volume: 68 issue: 9 year: 2020 article-title: COVID‐19 infections and deaths among Connecticut nursing home residents: facility correlates publication-title: J Am Geriatr Soc. – year: 2020 – ident: e_1_2_6_6_1 doi: 10.1177/1527154420938707 – ident: e_1_2_6_11_1 – ident: e_1_2_6_3_1 – ident: e_1_2_6_4_1 doi: 10.1111/jgs.16661 – ident: e_1_2_6_9_1 – ident: e_1_2_6_2_1 – ident: e_1_2_6_10_1 – ident: e_1_2_6_7_1 doi: 10.1111/jgs.16689 – ident: e_1_2_6_5_1 doi: 10.1111/jgs16642 – ident: e_1_2_6_8_1 – volume-title: Health Econometrics Using Stata year: 2017 ident: e_1_2_6_12_1 |
| SSID | ssj0012088 |
| Score | 2.6402538 |
| Snippet | BACKGROUND/OBJECTIVES
Nursing homes have experienced a disproportionate share of COVID‐19 cases and deaths. Early analyses indicated that baseline quality was... Nursing homes have experienced a disproportionate share of COVID-19 cases and deaths. Early analyses indicated that baseline quality was not predictive of... BACKGROUND/OBJECTIVESNursing homes have experienced a disproportionate share of COVID‐19 cases and deaths. Early analyses indicated that baseline quality was... |
| SourceID | pubmedcentral proquest pubmed crossref wiley |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2462 |
| SubjectTerms | Brief Report COVID-19 COVID-19 - epidemiology COVID‐19‐Related Content Disease Outbreaks - statistics & numerical data Humans long‐term care Nursing homes Nursing Homes - organization & administration Nursing Staff - supply & distribution Outbreaks Pandemics Personnel Staffing and Scheduling Prevalence Quality assurance United States Workforce planning |
| Title | Staffing Levels and COVID‐19 Cases and Outbreaks in U.S. Nursing Homes |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fjgs.16787 https://www.ncbi.nlm.nih.gov/pubmed/32770832 https://www.proquest.com/docview/2459772637 https://www.proquest.com/docview/2431807602 https://pubmed.ncbi.nlm.nih.gov/PMC7436613 |
| Volume | 68 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JS8QwFA7iQby4L-NGFA9epkyTdJLiScZlFBdww4NQkjRRUarYmYsnf4K_0V_iS7rguIB4K80rSfry8r6XvHxBaJ1KblKmdNOCM4AARclmLFPZ1NRYiB6kLLMtjtvdC3ZwFV0Noc3qLEzBD1EvuDnL8PO1M3Cp8s9GfpMHIUy17iS5y9VygOi0po4KSUuIGvqCDypZhXwWT_XloC_6BjC_50l-xq_eAe2Oo-uq6UXeyX3Q76lAv3xhdfxn3ybQWAlM8VYxkibRkMmm0MhRufU-jbruol8Ljg4fujyjHMssxZ2Ty_3t99e3MMYdcIfFyxNoGkDR-xzfZfgiOAtwuSSB3Z3s-Qw6390573Sb5TUMoLAIpqBIc25omIoYFGcpRNOK2hT8PlGaRZrGklAjqG0rgJJEcRWHVslUtIwIKWvTWTScPWZmHmEtuE0toUxElgltJTM2ammIGBmgMsobaKPSR6JLinJ3U8ZDUocqN3nif0wDrdWiTwUvx09CS5VSk9I084QwR7lH2q661boYjMrtlMjMPPadDEx1bs-SNNBcMQbqWijhHHArlPCB0VELOMLuwZLs7tYTdwNaAzhEoZte-b83PDnYO_MPC38XXUSjxK0F-HOSS2i499w3ywCYemrFW8YHN7oRAg |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1fTxQxEJ8AJuCLCgqeoBTjgy-3uW27127CCznEAw5I5DC8kE3bbU-CWYx79-KTH8HP6Cdx2v0TTjQxvm22s9l2p9P5TTv7G4A3TAmbc226Dp0BBihadVOVq65h1mH0oFSdbXHaH17wo8vkcgF2m39hKn6IdsPNW0ZYr72B-w3pu1Y-KaMY11qxCA98Re8QUH1oyaNi2pOyBb_ohWpeoZDH0zw6743uQcz7mZJ3EWxwQQeP4arpfJV5chPNpjoy337jdfzf0T2BRzU2JXvVZFqFBVuswfJJffr-FIa-1q9DX0dGPtWoJKrIyeDs4-H-z-8_4pQM0CNWN8-wb4hGb0pyXZCL6Dwi9a4E8WXZy2cwPng3Hgy7dSUG1FmCq1BihLAszmWKunMMA2rNXI6un2rDE8NSRZmVzPU1okmqhU5jp1Uue1bGjPfZOiwVt4V9DsRI4XJHGZeJ49I4xa1LegaDRo7AjIkOvG0UkpmapdwXy_ictdHKpMzCh-nA61b0S0XN8SehrUarWW2dZUa5Z92jff-6nbYZ7coflqjC3s68DK52_tiSdmCjmgTtWxgVAqErtoi56dEKeM7u-Zbi-lPg7kbAhoiI4TCD9v_e8ezo_Xm4ePHvotuwMhyfjLLR4enxJjykfmsg_Da5BUvTrzP7EvHTVL8KZvILNh0VHQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VIlVceBRotxQwiAOXjTa2EzvqCe2ybB-0iLaoh0qR7dilapVWZPfCqT-B38gvYew81KUgVb1F8US2Mx7PN_b4M8A7poQtuDZ9h84AAxSt-pkqVN8w6zB6UKrJtthNJ4d86yg5WoCN9ixMzQ_RLbh5ywjztTfwy8JdN_KTKopxqhX34D5PB9IP6dHXjjsqpgMpO-yLTqihFQppPO2n887oBsK8mSh5HcAGDzR-BMdt2-vEk7NoNtWR-fkXreMdO_cYHjbIlHyoh9ITWLDlMix9bvben8LE3_Tr0NORHZ9oVBFVFmS4921z9PvqV5yRIfrD-uUeNg2x6FlFTktyGO1HpFmTIP5S9uoZHIw_Hgwn_eYeBtRYgnNQYoSwLC5khppzDMNpzVyBjp9qwxPDMkWZlcylGrEk1UJnsdOqkAMrY8ZT9hwWy4vSrgIxUrjCUcZl4rg0TnHrkoHBkJEjLGOiB-9bfeSm4Sj3V2Wc512sclLl4cf04G0nelkTc_xLaL1Vat7YZpVT7jn3aOqre9MVo1X5rRJV2ouZl8G5zm9a0h6s1GOgq4VRIRC4YomYGx2dgGfsni8pT78H5m6Ea4iHGHYzKP__Dc-3Pu2Hh7Xbi76GpS-jcb6zubv9Ah5Qvy4Qzkyuw-L0x8y-RPA01a-CkfwBBugT1Q |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Staffing+Levels+and+COVID+%E2%80%9019+Cases+and+Outbreaks+in+U.S.+Nursing+Homes&rft.jtitle=Journal+of+the+American+Geriatrics+Society+%28JAGS%29&rft.au=Gorges%2C+Rebecca+J.&rft.au=Konetzka%2C+R.+Tamara&rft.date=2020-11-01&rft.issn=0002-8614&rft.eissn=1532-5415&rft.volume=68&rft.issue=11&rft.spage=2462&rft.epage=2466&rft_id=info:doi/10.1111%2Fjgs.16787&rft.externalDBID=n%2Fa&rft.externalDocID=10_1111_jgs_16787 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0002-8614&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0002-8614&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0002-8614&client=summon |