Dataset Information

The association between physician staff numbers and mortality in English hospitals.

ABSTRACT:

Background

Physician medical specialties place specific demands on medical staff. Often patients have multiple co-morbidities, frailty is common, and mortality rates are higher than other specialties such as surgery. The key intervention for patients admitted under physician subspecialties is the care provided on the ward. The current evidence base to inform staffing in physician medical specialty wards is limited. The aim of this analysis is to investigate the association between medical staffing levels within physician medical specialties and mortality.

Methods

This study is a cross-sectional analysis of national data, which is aggregated at provider level. Medical beds per senior, middle grade and junior physicians employed in physician medical specialties were calculated from national employment records for acute hospitals in England, in 2017. Outcome measures included unadjusted mortality rate and Summary Hospital-level Mortality Indicator (SHMI) in physician medical specialties. Both Raw mortality and SHMI include deaths during admission or within 30 days following discharge. Linear regression models were constructed for each medical staffing grade for unadjusted mortality, SHMI and SHMI adjusted for local provider factors.

Findings

The mean number of medical beds per senior, middle grade and junior physicians were 7.3(SD 2.5), 19.7(11.5), 10.1(3.1) respectively. Lower bed numbers per medical staff grade were associated with lower than expected mortality by SHMI; senior(Coefficient 0.012(95%CI:0.005-0.018),p = 0.001), middle grade(0.002(0.0002-0.005),p = 0.032) and junior(0.008(0.002-0.015),p = 0.014). Hospital providers were more likely to achieve a better than expected mortality (SHMI<1) if beds per physician were lower than; 5.3, 14.6 and 9.0 for senior, middle grade and junior doctors respectively.

Interpretation

Acute hospital providers with fewer beds per medical staff of all grades are associated with lower than expected mortality.

Funding

No external funding is associated with this analysis.

SUBMITTER: Harvey PR

PROVIDER: S-EPMC7910697 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The association between physician staff numbers and mortality in English hospitals.

Harvey Philip R PR Trudgill Nigel J NJ

EClinicalMedicine 20210107

<h4>Background</h4>Physician medical specialties place specific demands on medical staff. Often patients have multiple co-morbidities, frailty is common, and mortality rates are higher than other specialties such as surgery. The key intervention for patients admitted under physician subspecialties is the care provided on the ward. The current evidence base to inform staffing in physician medical specialty wards is limited. The aim of this analysis is to investigate the association between medica ...[more]

PMID: 33681734

Similar Datasets

Project description:Few studies have analyzed contemporary data on outcomes at US teaching hospitals vs nonteaching hospitals.To examine risk-adjusted outcomes for patients admitted to teaching vs nonteaching hospitals across a broad range of medical and surgical conditions.Use of national Medicare data to compare mortality rates in US teaching and nonteaching hospitals for all hospitalizations and for common medical and surgical conditions among Medicare beneficiaries 65 years and older.Hospital teaching status: major teaching hospitals (members of the Council of Teaching Hospitals), minor teaching hospitals (other hospitals with medical school affiliation), and nonteaching hospitals (remaining hospitals).Primary outcome was 30-day mortality rate for all hospitalizations and for 15 common medical and 6 surgical conditions. Secondary outcomes included 30-day mortality stratified by hospital size and 7-day mortality and 90-day mortality for all hospitalizations as well as for individual medical and surgical conditions.The sample consisted of 21?451?824 total hospitalizations at 4483 hospitals, of which 250 (5.6%) were major teaching, 894 (19.9%) were minor teaching, and 3339 (74.3%) were nonteaching hospitals. Unadjusted 30-day mortality was 8.1% at major teaching hospitals, 9.2% at minor teaching hospitals, and 9.6% at nonteaching hospitals, with a 1.5% (95% CI, 1.3%-1.7%; P?<?.001) mortality difference between major teaching hospitals and nonteaching hospitals. After adjusting for patient and hospital characteristics, the same pattern persisted (8.3% mortality at major teaching vs 9.2% at minor teaching and 9.5% at nonteaching), but the difference in mortality between major and nonteaching hospitals was smaller (1.2% [95% CI, 1.0%-1.4%]; P?<?.001). After stratifying by hospital size, 187 large (?400 beds) major teaching hospitals had lower adjusted overall 30-day mortality relative to 76 large nonteaching hospitals (8.1% vs 9.4%; 1.2% difference [95% CI, 0.9%-1.5%]; P?<?.001). This same pattern of lower overall 30-day mortality at teaching hospitals was observed for medium-sized (100-399 beds) hospitals (8.6% vs 9.3% and 9.4%; 0.8% difference between 61 major and 1207 nonteaching hospitals [95% CI, 0.4%-1.3%]; P?=?.003). Among small (?99 beds) hospitals, 187 minor teaching hospitals had lower overall 30-day mortality relative to 2056 nonteaching hospitals (9.5% vs 9.9%; 0.4% difference [95% CI, 0.1%-0.7%]; P?=?.01).Among hospitalizations for US Medicare beneficiaries, major teaching hospital status was associated with lower mortality rates for common conditions compared with nonteaching hospitals. Further study is needed to understand the reasons for these differences.

Project description:OBJECTIVE:To evaluate the association between systems characteristics and esophagectomy mortality at low-volume hospitals BACKGROUND:High-volume hospitals have lower esophagectomy mortality rates, but receiving care at such centers is not always feasible. We examined low-volume hospitals and sought to identify characteristics of those with better outcomes. METHODS:Using national data from Medicare and the American Hospital Association, we studied 4498 elderly patients who underwent an esophagectomy from 2004 to 2007. We divided hospitals into terciles based on esophagectomy volume and examined characteristics of patients and hospitals (size, nurse ratios, and presence of advanced medical, surgical, and radiological services). Our primary outcome was mortality. We identified 5 potentially beneficial systems characteristics in our data set and used multivariable logistic regression to determine whether these characteristics were associated with lower mortality rates at low-volume hospitals. RESULTS:Of the 874 hospitals that performed esophagectomies, 83% (723) were low-volume hospitals whereas only 3% (25) were high-volume. Low-volume hospitals performed a median of 1 esophagectomy during the 4-year study period and cared for patients that were older, more likely to be minority, and more likely to have multiple comorbidities compared with high-volume centers. Low-volume hospitals that had at least 3 of 5 characteristics (high nurse ratios, lung transplantation services, complex medical oncology services, bariatric surgery services, and positron emission tomography scanners) had markedly lower mortality rates compared with low-volume hospitals with none of these characteristics (12.5% vs. 5.0%; P value = 0.042). CONCLUSIONS:Low-volume hospitals with certain systems characteristics seem to achieve better esophagectomy outcomes. A more comprehensive study of the beneficial characteristics of low-volume hospitals is warranted because high-volume hospitals are difficult to access for many patients.

Project description:ObjectivesTo develop a transparent and reproducible measure for hospitals that can indicate when deaths in hospital or within 30 days of discharge are high relative to other hospitals, given the characteristics of the patients in that hospital, and to investigate those factors that have the greatest effect in changing the rank of a hospital, whether interactions exist between those factors, and the stability of the measure over time.DesignRetrospective cross sectional study of admissions to English hospitals.SettingHospital episode statistics for England from 1 April 2005 to 30 September 2010, with linked mortality data from the Office for National Statistics.Participants36.5 million completed hospital admissions in 146 general and 72 specialist trusts.Main outcome measuresDeaths within hospital or within 30 days of discharge from hospital.ResultsThe predictors that were used in the final model comprised admission diagnosis, age, sex, type of admission, and comorbidity. The percentage of people admitted who died in hospital or within 30 days of discharge was 4.2% for males and 4.5% for females. Emergency admissions comprised 75% of all admissions and 5.5% died, in contrast to 0.8% who died after an elective admission. The percentage who died with a Charlson comorbidity score of 0 was 2% in contrast with 15% who died with a score greater than 5. Given these variables, the relative standardised mortality rates of the hospitals were not noticeably changed by adjusting for the area level deprivation and number of previous emergency visits to hospital. There was little evidence that including interaction terms changed the relative values by any great amount. Using these predictors the summary hospital mortality index (SHMI) was derived. For 2007/8 the model had a C statistic of 0.911 and accounted for 81% of the variability of between hospital mortality. A random effects funnel plot was used to identify outlying hospitals. The outliers from the SHMI over the period 2005-10 have previously been identified using other mortality indicators.ConclusionThe SHMI is a relatively simple tool that can be used in conjunction with other information to identify hospitals that may need further investigation.

Project description:ObjectivesThe primary aims of this study were to investigate if exposure to antenatal corticosteroids (ACS) was associated with lower rates of perinatal mortality (primary outcome) and other adverse perinatal outcomes (i.e., stillbirth, early neonatal mortality, APGAR score of < 7 at 5 mins, neonatal sepsis and respiratory distress syndrome) in preterm infants in hospitals in Tanzania. We also examine factors associated with administration of ACS among women at risk of preterm delivery.MethodsA hospital-based prospective chart review study was undertaken in four hospitals located in Nyamagana and Sengerema districts, Tanzania. The study population included all stillborn and live born preterm infants delivered between 24 to 34 weeks of gestation between July 2019 to February 2020. A total 1125 preterm infants were delivered by 1008 women (895 singletons, 230 multiple). Sociodemographic and medical data were recorded from participants' medical records.ResultsThree hundred and fifty-six (35.3%) women were administered at least one dose of ACS between 24 to 34 weeks' gestation and 385 (34.2%) infants were exposed to ACS. Infants exposed to ACS had a lower rate of perinatal mortality (13.77%) compared to those who were not exposed (28.38%). Multivariate analysis indicated that infants exposed to ACS were less likely to die during perinatal period, aRR 0.34 (95%CI 0.26-0.44). Only one-third of the sample was provided with ACS. Administration of ACS was associated with maternal education, attending antenatal care more than 3 times, method used to assess gestational age, maternal infection, exposure to maternal antibiotics, delivery mode and level of health facility.ConclusionACS significantly reduced the risk in perinatal mortality among infants born preterm in a limited resource setting. However, only about one-third of eligible women were provided with ACS, indicating low usage of ACS. Numerous factors were associated with low usage of ACS in this setting.

Dataset Information

The association between physician staff numbers and mortality in English hospitals.

Background

Methods

Findings

Interpretation

Funding

Publications

The association between physician staff numbers and mortality in English hospitals.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets