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Articles
Effects of nurse-to-patient ratio legislation on nurse staffing
and patient mortality, readmissions, and length of stay:
a prospective study in a panel of hospitals
Matthew D McHugh, Linda H Aiken, Douglas M Sloane, Carol Windsor, Clint Douglas, Patsy Yates
Summary
Background Substantial evidence indicates that patient outcomes are more favourable in hospitals with better nurse
staffing. One policy designed to achieve better staffing is minimum nurse-to-patient ratio mandates, but such policies
have rarely been implemented or evaluated. In 2016, Queensland (Australia) implemented minimum nurse-to-patient
ratios in selected hospitals. We aimed to assess the effects of this policy on staffing levels and patient outcomes and
whether both were associated.
Lancet 2021; 397: 1905–13
Methods For this prospective panel study, we compared Queensland hospitals subject to the ratio policy (27 intervention
hospitals) and those that discharged similar patients but were not subject to ratios (28 comparison hospitals) at two
timepoints: before implementation of ratios (baseline) and 2 years after implementation (post-implementation). We
used standardised Queensland Hospital Admitted Patient Data, linked with death records, to obtain data on patient
characteristics and outcomes (30-day mortality, 7-day readmissions, and length of stay [LOS]) for medical-surgical
patients and survey data from 17 010 medical-surgical nurses in the study hospitals before and after policy
implementation. Survey data from nurses were used to measure nurse staffing and, after linking with standardised
patient data, to estimate the differential change in outcomes between patients in intervention and comparison
hospitals, and determine whether nurse staffing changes were related to it.
School of Nursing, Center for
Health Outcomes and Policy
Research, University of
Pennsylvania, Philadelphia, PA,
USA (Prof M D McHugh PhD,
Prof L H Aiken PhD,
D M Sloane PhD); School of
Nursing, Queensland
University of Technology,
Kelvin Grove, QLD, Australia
(C Windsor PhD, C Douglas PhD,
Prof P Yates PhD); Centre for
Healthcare Transformation,
Faculty of Health, Queensland
University of Technology,
Brisbane, QLD, Australia
(C Windsor, C Douglas,
Prof P Yates); Metro North
Hospital and Health Service,
Royal Brisbane and Women’s
Hospital, Herston, QLD,
Australia (C Douglas)
Findings We included 231 902 patients (142 986 in intervention hospitals and 88 916 in comparison hospitals) assessed
at baseline (2016) and 257 253 patients (160 167 in intervention hospitals and 97 086 in comparison hospitals) assessed
in the post-implementation period (2018). After implementation, mortality rates were not significantly higher than at
baseline in comparison hospitals (adjusted odds ratio [OR] 1·07, 95% CI 0·97–1·17, p=0·18), but were significantly
lower than at baseline in intervention hospitals (0·89, 0·84–0·95, p=0·0003). From baseline to post-implementation,
readmissions increased in comparison hospitals (1·06, 1·01–1·12, p=0·015), but not in intervention hospitals (1·00,
0·95–1·04, p=0·92). Although LOS decreased in both groups post-implementation, the reduction was more
pronounced in intervention hospitals than in comparison hospitals (adjusted incident rate ratio [IRR] 0·95, 95% CI
0·92–0·99, p=0·010). Staffing changed in hospitals from baseline to post-implementation: of the 36 hospitals with
reliable staffing measures, 30 (83%) had more than 4·5 patients per nurse at baseline, with the number decreasing to
21 (58%) post-implementation. The majority of change was at intervention hospitals, and staffing improvements by
one patient per nurse produced reductions in mortality (OR 0·93, 95% CI 0·86–0·99, p=0·045), readmissions (0·93,
0·89–0·97, p30 days) patients were
excluded.
To adjust for differences in patient mix across hos
pitals, our readmission and mortality models included
risk scores for each outcome derived from models that
regressed the different outcomes on 17 indicators
(eg, diabetes, cancer, and so on) from the Charlson
Comorbidity Index to account for confounding co
morbidities,27–30 as well as sex, age, and dummy variables
for the Diagnosis-Related Group (DRG). These scores
were derived from separate logistic regression models in
which we estimated a risk score for death or readmission
based on the patient characteristics described. These
models showed excellent discrimination (c statistics
were approximately 0·90). Readmission models were
restricted to short-term patients (LOS ≤30 days) with
discharge to home. Models for LOS were also restricted
to short-term patients and controlled for whether patients
died during hospitalisation and for age, mortality risk,
comorbidities, and DRG.
Statistical analysis
We first described the patients in intervention and
comparison hospitals before and after implementation
of ratios, including their sex, age, comorbidities, and
outcomes (ie, mortality, readmissions, and LOS).
We then provided the results of estimating multilevel
random-intercept logistic regression models for
mortality and readmissions and zero-truncated negative
binomial regression models (LOS was a count variable)
to produce odds ratios (ORs) for mortality and
readmissions and incident rate ratios (IRRs) for LOS,
1908
indicating the differential change in outcomes between
patients in intervention and comparison hospitals, after
accounting for hospital characteristics (ie, size and
time-invariant factors) and patient characteristics. The
specification of multilevel models for panels of macro
units with observations on nested micro units is detailed
in Fairbrother,31 and its elaboration in the context of a
prospective panel study of nurses nested within hospitals
is presented in Sloane and colleagues.24 Finally, after
showing how nurse staffing had changed over time,
we used similar models to estimate whether staffing
improvements were associated with patient outcome
improvements. We did not have missing data; all models
were adjusted for clustering of patients in hospitals and
controlled for hospital size. Using expected frequencies
derived from our models, we estimated the counter
factual for each outcome, that is, what outcomes would
we expect in intervention hospitals if ratios had not been
implemented. We then used published cost data to make
a rough estimate of return on investment derived by
preventing additional LOS and readmissions.
Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report.
Results
For this study, we included 231 902 patients (142 986 in
intervention hospitals and 88 916 in comparison hospitals)
assessed at baseline (2016) and 257 253 patients (160 167 in
intervention hospitals and 97 086 in comparison hos
pitals) assessed in the post-implementation period (2018).
Patients in intervention hospitals were slightly younger
and less likely to be women than those in comparison
hospitals (table 1). The differences in comorbidities
between timepoints were minimal in most cases for
patients in both intervention and comparison hospitals.
Although slightly higher rates of diabetes without
complications and cancer were observed in patients
in comparison hospitals, all other comorbidities were
somewhat more common in patients in intervention
hospitals.
Regarding the average number of patients per nurse,
comparison hospitals averaged 6·13 patients per nurse
(SD 0·75) at baseline and improved slightly after
implementation to 5·96 patients per nurse (0·98).
Intervention hospitals were better staffed on average at
baseline (4·84 patients per nurse, SD 1·05) but improved
by a greater margin to 4·37 patients per nurse (0·54)
after implementation (table 2). The differences in these
SDs, while unadjusted, indicate that the variation across
intervention hospitals was reduced by half, whereas the
variation across comparison hospitals increased some
what over time. Regarding patient outcomes, 30-day
mortality was somewhat higher overall at each timepoint
for patients in intervention hospitals than for those in
www.thelancet.com Vol 397 May 22, 2021
Articles
Number of patients
Comparison hospitals (n=28)
Intervention hospitals (n=27)
Baseline
Post-implementation Total
Baseline
Post-implementation
Total
88 916
97 086
142 986
160 167
303 153
Age, years
63·1 (17·7)
63·6 (17·6)
Female
45 344 (51·0%)
Male
43 572 (49·0%)
Acute myocardial
infarction
186 002
63·4 (17·6)
57·0 (20·0)
58·3 (19·8)
57·7 (19·9)
49 762 (51·3%)
95 106 (51·1%)
67 066 (46·9%)
76 862 (48·0%)
143 928 (47·5%)
47 323 (48·7%)
90 895 (48·9%)
75 920 (53·1%)
83 305 (52·0%)
159 225 (52·5%)
662 (0·7%)
461 (0·5%)
1123 (0·6%)
1552 (1·1%)
1744 (1·1%)
3296 (1·1%)
Congestive heart failure
1871 (2·1%)
1910 (2·0%)
3781 (2·0%)
4224 (3·0%)
4878 (3·0%)
9102 (3·0%)
Cerebrovascular disease
667 (0·8%)
568 (0·6%)
1235 (0·7%)
1844 (1·3%)
2200 (1·4%)
4044 (1·3%)
Dementia
1652 (1·9%)
1403 (1·4%)
3055 (1·6%)
3726 (2·6%)
3599 (2·2%)
7325 (2·4%)
Chronic obstructive
pulmonary disease
1974 (2·2%)
1905 (2·0%)
3879 (2·1%)
3579 (2·5%)
5243 (3·3%)
8822 (2·9%)
Sex*
Comorbidities
Mild liver disease
686 (0·8%)
758 (0·8%)
1444 (0·8%)
3894 (2·7%)
4092 (2·6%)
7986 (2·6%)
Diabetes
8386 (9·4%)
9129 (9·4%)
17 515 (9·4%)
12 136 (8·5%)
12 890 (8·0%)
25 026 (8·3%)
Diabetes with
complications
4302 (4·8%)
5995 (6·2%)
10 297 (5·5%)
12 566 (8·8%)
17 024 (10·6%)
29 590 (9·8%)
Hemiplegia or paraplegia
655 (0·7%)
566 (0·6%)
1221 (0·7%)
3088 (2·2%)
2938 (1·8%)
6026 (2·0%)
Renal disease
3201 (3·6%)
2359 (2·4%)
5560 (3·0%)
8154 (5·7%)
6435 (4·0%)
14 589 (4·8%)
Cancer
1366 (1·5%)
1349 (1·4%)
2715 (1·5%)
1654 (1·2%)
1730 (1·1%)
3384 (1·1%)
Metastatic cancer
1804 (2·0%)
1830 (1·9%)
3634 (2·0%)
3105 (2·2%)
3625 (2·3%)
6730 (2·2%)
Data are n (%) or mean (SD). Comorbidities present for fewer than 1% of patients are included in the analyses but excluded from this table. These include peripheral vascular
disease, rheumatoid disease, peptic ulcer disease, moderate or severe liver disease, and AIDS. *Some values do not add up to the total; categorisations with fewer than
ten individuals were suppressed to maintain confidentiality.
Table 1: Patient characteristics by baseline or post-implementation time period and by intervention or comparison hospitals
Comparison hospitals (n=28)
Intervention hospitals (n=27)
Baseline
Postimplementation
Total
Baseline
Postimplementation
Total
30-day mortality, deaths per
cases (%)
952/88 916
(1·07%)
1092/97 086
(1·12%)
2044/186 002
(1·10%)
2290/142 986
(1·60%)
2419/160 167
(1·51%)
4709/303 153
(1·55%)
7-day readmissions,
readmissions per cases (%)
3226/115 463
(2·79%)
3660/123 778
(2·96%)
6886/239 241
(2·88%)
5208/162 910
(3·20%)
5769/178 699
(3·23%)
10 977/341 609
(3·21%)
Length of stay, mean (SD; cases)
3·66
(3·83; 117 809)
3·51
(3·63; 126 236)
3·58
(3·73; 244 045)
3·45
(3·85; 176 396)
3·13
(3·42; 193 318)
3·29
(3·63; 369 714)
6·13
(0·75)
5·96
(0·98)
6·04
(0·86)
4·84
(1·05)
4·37
(0·54)
4·60
(0·80)
Patient outcomes
Hospital staffing
Medical-surgical nurse staffing,
mean patients per nurse (SD)
Data are n/N (%), unless otherwise specified.
Table 2: Patient mortality, readmissions, and length of stay, by timepoint and by intervention or comparison hospitals
comparison hospitals, but although the percentage of
patient deaths increased over time for patients in com
parison hospitals, it decreased for those in intervention
hospitals (table 2). Readmissions were slightly higher
overall and in each timepoint for patients in inter
vention hospitals than for those in comparison hospitals,
though the only change that occurred across time
points—to the extent there was any change at all—was
restricted to patients in comparison hospitals. Fewer
than 2·8% of these patients were readmitted at baseline,
www.thelancet.com Vol 397 May 22, 2021
whereas nearly 3% were readmitted post-implementation
(table 2). By contrast, mean LOS was shorter and declined
by a greater amount for patients in intervention hospitals
than for those in comparison hospitals.
These results are tentative because they were not
adjusted for differences in patient characteristics (eg, sex,
age, and comorbidities) or differences in the size of
intervention and comparison hospitals. To make these
adjustments and assess differences across the two hospital
groups over time, we used multilevel and multivariable
1909
Articles
30-day mortality*
7-day readmissions†
Length of stay‡
OR (95% CI)
p value
OR (95% CI)
p value
IRR (95% CI)
p value
1·34
Intervention vs
comparison at baseline (1·09–1·64)
0·0052
1·15
(0·98–1·34)
0·090
0·78
(0·72–0·84)
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