Are readmissions avoidable?

OBJECTIVE--To examine the possible use of readmission rates as an outcome indicator of hospital inpatient care by investigating avoidability of unplanned readmissions within 28 days of discharge. DESIGN--Retrospective analysis of a stratified random sample of case notes of patients with an unplanned readmission between July 1987 and June 1988 by nine clinical assessors (263 assessments) and categorisation of the readmission as avoidable, unavoidable, or unclassifiable. SETTING--District in North East Thames region. 481 General medical, geriatric, and general surgical inpatients with a readmission at 0-6 days or 21-27 days after the first (index) discharge between July 1987 and June 1988 from whom 100 case notes were selected randomly and of which 74 were available for study. MAIN OUTCOME MEASURES--Assessment of readmissions as avoidable, unavoidable, unclassifiable, variability of assessment within cases and variability among assessors according to specialty and duration to readmission. RESULTS--General medical and geriatric readmissions and surgical readmissions at 0-6 days after discharge were more likely to be assessed as avoidable than those at 21-27 days (medical readmissions 32 v 6%, surgical admissions 49 v 19%). General surgical readmissions were significantly more frequently assessed as avoidable than general medical and geriatric readmissions. The extent of agreement between doctors varied, with general medical and geriatric readmissions at 21-27 days after first discharge causing the greatest variability of judgment. CONCLUSIONS--Differences were apparent in the extent of avoidability of readmissions in different groups of admissions. However, assessors rated only 49.3% of the group with the highest proportion of avoidable admissions (surgical readmissions at 0-6 days) as avoidable. The remainder were thought to be unavoidable except for 2%, which could not be classified. The use of readmission rates as an outcome indicator of hospital inpatient care should be avoided.     

Use of medical record linkage to study readmission rates.

Readmission rates after inpatient care were studied by using routinely collected data from the Oxford record linkage study for 1968-85. Discharges from hospital and subsequent admissions were identified for people who were both resident and treated in the area covered by the linkage study. Rates were calculated for readmissions within 28 days after discharge from the first, index event. Readmission rates for elective readmissions after elective index admissions rose from 3.5% in 1968 to 7.1% in 1985. Those for elective readmissions after immediate (emergency or accident) index admissions rose from 2.4% to 3.5% during the same period. Emergency readmissions after an immediate index admission rose from 4.0% to 7.0%, and emergency readmissions after an elective index admission rose from 1.3% to 2.5%. All these increases were significant. The rise in elective readmissions may in part reflect a trend towards planned discharge with the expectation of readmission. The rise in emergency readmissions, which has been fairly gradual over many years, may, in some cases, be due to pressure on resources and inappropriately short lengths of stay. Further evidence is required to confirm or refute this. Readmission rates are one of the few potential measures available from routine statistics for assessing outcome, but due consideration must be given to issues of method and interpretation.     

Elective total hip replacement: incidence,emergency readmission rate,and postoperative mortality.

OBJECTIVES--To report the incidence of elective total hip replacement and postoperative mortality, emergency readmission rates, and the demographic factors associated with these rates in a large defined population. DESIGN--Analysis of linked, routine abstracts of hospital inpatient records and death certificates. SETTING--10 hospitals in six districts in Oxford Regional Health Authority covered by the Oxford record linkage study. SUBJECTS--Records for 11,607 total hip replacements performed electively in 1976-85. MAIN OUTCOME MEASURES--Incidence of operation, postoperative mortality, relative mortality ratios, and incidence of emergency readmission. RESULTS--NHS operation rates increased over time from 43 to 58 operations/100,000 population. Variation in operation rates between districts reduced over time. Operation rates were on average 25% higher in women than men. There were 93 deaths (11/1000 operations) within 90 days of the operation and 208 emergency readmissions (28/1000 operations) within 28 days of discharge. Postoperative mortality and emergency readmission rates increased with age. No significant trend with time was found. Mortality in the 90 days after the operation was 2.5-fold higher (1.9 to 3.0) than in the rest of the first postoperative year. This represented an estimated excess of 6.5 (4.2 to 8.8) early postoperative deaths/1000 operations. Most deaths were ascribed to cardiovascular events. Thromboembolic disease was the commonest reason for emergency readmission. CONCLUSIONS--The pronounced increase in operations in districts with initially low rates suggests a trend towards greater equity in the local provision of NHS hip arthroplasty. The early postoperative clusters of deaths attributed to cardiovascular disease and of readmissions for thromboembolic disease suggest that there is scope for investigating ways of reducing the incidence of major adverse postoperative events.     

Impact of a COPD Discharge Care Bundle on Readmissions following Admission with Acute Exacerbation: Interrupted Time Series Analysis

ObjectivesWe evaluated the impact of a COPD discharge care bundle on readmission rates following hospitalisation with an acute exacerbation.DesignInterrupted time series analysis, comparing readmission rates for COPD exacerbations at nine trusts that introduced the bundle, to two comparison groups; (1) other NHS trusts in London and (2) all other NHS trusts in England. Care bundles were implemented at different times for different NHS trusts, ranging from October 2009 to April 2011.SettingNine NHS acute trusts in the London, England.ParticipantsPatients aged 45 years and older admitted to an NHS acute hospital in England for acute exacerbation of COPD. Data come from Hospital Episode Statistics, April 2002 to March 2012.ResultsIn hospitals introducing the bundle readmission rates were rising before implementation and falling afterwards (e.g. readmissions within 28 days +2.13% per annum (pa) pre and -5.32% pa post (p for difference in trends = 0.012)). Following implementation, readmission rates within 7 and 28 day were falling faster than among other trusts in London, although this was not statistically significant (e.g. readmissions within 28 days -4.6% pa vs. -3.2% pa, p = 0.44). Comparisons with a national control group were similar.ConclusionsThe COPD discharge care bundle appeared to be associated with a reduction in readmission rate among hospitals using it. The significance of this is unclear because of changes to background trends in London and nationally.     

Hospital Readmission Prevalence and Analysis of Those Potentially Avoidable in Southern Italy

Background

One quality indicator of hospital care, which can be used to judge the process of care, is the prevalence of hospital readmission because it reflects the impact of hospital care on the patient’s condition after discharge. The purposes of the study were to measure the prevalence of hospital readmissions, to identify possible factors that influence such readmission and to measure the prevalence of readmissions potentially avoidable in Italy.

Methods

A sample of 2289 medical records of patients aged 18 and over admitted for medical or surgical illness at one 502-bed community non-teaching hospital were randomly selected.

Results

A total of 2252 patients were included in the final analysis, equaling a response rate of 98.4%. The overall hospital readmission prevalence within 30 days of discharge was 10.2%. Multivariate logistic regression analysis revealed that the proportion of patients readmitted within 30 days of discharge significantly increased regardless of Charlson et al. comorbidity score, among unemployed or retired patients, and in patients in general surgery. A total of 43.7% hospital readmissions were judged to be potentially avoidable. Multivariate logistic regression analysis showed that potentially avoidable readmissions were significantly higher in general surgery, in patients referred to hospital by an emergency department physician, and in those with a shortened time between discharge and readmission.

Conclusion

Additional research on intervention or bundle of interventions applicable to acute inpatient populations that aim to reduce potentially avoidable readmissions is strongly needed, and health care providers are urged to implement evidence-based programs for more cost-effective delivery of health care.     

Thirty-Day Readmission Rates in Orthopedics: A Systematic Review and Meta-Analysis

Background

Hospital readmission rates are being used to evaluate performance. A survey of the present rates is needed before policies can be developed to decrease incidence of readmission. We address three questions: What is the present rate of 30-day readmission in orthopedics? How do factors such as orthopedic specialty, data source, patient insurance, and time of data collection affect the 30-day readmission rate? What are the causes and risk factors for 30-day readmissions?

Methods/Findings

A review was first registered with Prospero (CRD42014010293, 6/17/2014) and a meta-analysis was performed to assess the current 30-day readmission rate in orthopedics. Studies published after 2006 were retrieved, and 24 studies met the inclusion criteria. The 30-day readmission rate was extrapolated from each study along with the orthopedic subspecialty, data source, patient insurance, time of collection, patient demographics, and cause of readmission. A sensitivity analysis was completed on the stratified groups. The overall 30-day readmission rate across all orthopedics was 5.4 percent (95% confidence interval: 4.8,6.0). There was no significant difference between subspecialties. Studies that retrieved data from a multicenter registry had a lower 30-day readmission rate than those reporting data from a single hospital or a large national database. Patient populations that only included Medicare patients had a higher 30-day readmission rate than populations of all insurance. The 30-day readmission rate has decreased in the past ten years. Age, length of stay, discharge to skilled nursing facility, increased BMI, ASA score greater than 3, and Medicare/Medicaid insurance showed statistically positive correlation with increased 30-day readmissions in greater than 75 percent of studies. Surgical site complications accounted for 46 percent of 30-day readmissions.

Conclusions

This meta-analysis shows the present rate of 30-day readmissions in orthopedics. Demonstrable heterogeneity between studies underlines the importance of uniform collection and reporting of readmission rates for hospital evaluation and reimbursement.     

Readmissions after Hospitalization for Heart Failure,Acute Myocardial Infarction,or Pneumonia among Young and Middle-Aged Adults: A Retrospective Observational Cohort Study

Background

Patients aged ≥65 years are vulnerable to readmissions due to a transient period of generalized risk after hospitalization. However, whether young and middle-aged adults share a similar risk pattern is uncertain. We compared the rate, timing, and readmission diagnoses following hospitalization for heart failure (HF), acute myocardial infarction (AMI), and pneumonia among patients aged 18–64 years with patients aged ≥65 years.

Methods and Findings

We used an all-payer administrative dataset from California consisting of all hospitalizations for HF (n = 206,141), AMI (n = 107,256), and pneumonia (n = 199,620) from 2007–2009. The primary outcomes were unplanned 30-day readmission rate, timing of readmission, and readmission diagnoses. Our findings show that the readmission rate among patients aged 18–64 years exceeded the readmission rate in patients aged ≥65 years in the HF cohort (23.4% vs. 22.0%, p<0.001), but was lower in the AMI (11.2% vs. 17.5%, p<0.001) and pneumonia (14.4% vs. 17.3%, p<0.001) cohorts. When adjusted for sex, race, comorbidities, and payer status, the 30-day readmission risk in patients aged 18–64 years was similar to patients ≥65 years in the HF (HR 0.99; 95%CI 0.97–1.02) and pneumonia (HR 0.97; 95%CI 0.94–1.01) cohorts and was marginally lower in the AMI cohort (HR 0.92; 95%CI 0.87–0.96). For all cohorts, the timing of readmission was similar; readmission risks were highest between days 2 and 5 and declined thereafter across all age groups. Diagnoses other than the index admission diagnosis accounted for a substantial proportion of readmissions among age groups <65 years; a non-cardiac diagnosis represented 39–44% of readmissions in the HF cohort and 37–45% of readmissions in the AMI cohort, while a non-pulmonary diagnosis represented 61–64% of patients in the pneumonia cohort.

Conclusion

When adjusted for differences in patient characteristics, young and middle-aged adults have 30-day readmission rates that are similar to elderly patients for HF, AMI, and pneumonia. A generalized risk after hospitalization is present regardless of age. Please see later in the article for the Editors'' Summary     

Relation between general practices' outpatient referral rates and rates of elective admission to hospital.

OBJECTIVE--To examine the variation in rates of admission to hospital among general practices, to determine the relation between referral rates and admission rates, and to assess the extent to which variations in outpatient referral rates might account for the different patterns of admission. DESIGN--A comparison of outpatient referral rates standardised for age and sex and rates of elective admission to hospital for six specialties individually and for all specialties combined. SETTING--19 General practices in three districts in Oxford Regional Health Authority with a combined practice population of 188 610. MAIN OUTCOME MEASURES--Estimated proportion of outpatient referrals resulting in admission to hospital, extent of variation in referral rates and admission rates among practices, and association between admissions and outpatient referrals. RESULTS--Patients referred to surgical specialties were more likely than those referred to medical specialties to be admitted after an outpatient referral. Overall, the estimated proportion of patients admitted after an outpatient referral was 42%. There were significant differences among the practices in referral rates and admission rates for most of the major specialties. The extent of systematic variance in admission rates (0.048) was similar to that in referral rates (0.037). Referral and admission rates were significantly associated for general surgery; ear, nose, and throat surgery; trauma and orthopaedics; and all specialties combined. For most specialties the practices with higher referral rates also had higher admission rates, casting doubt on the view that these practices were referring more patients unnecessarily. CONCLUSION--Rates of elective admission to hospital vary systematically among general practices. Variations in outpatient referral rates are an important determinant of variations in admission rates.     

The Effects of a Comprehensive Multidisciplinary Outpatient Diabetes Program on Hospital Readmission Rates in Patients with Diabetes: A Randomized Controlled Prospective Study

Objective: The diagnosis of diabetes mellitus is associated with an increased risk of hospital readmissions. The goal of this study was to determine whether there was a difference in the rates of 30-day and 365-day hospital readmissions between diabetic patients who, upon their discharge, received diabetes care in a standard primary care setting and those who received their care in a specialized multidisciplinary diabetes program.Methods: This was a randomized controlled prospective study.Results: One hundred and ninety two consecutive patients were recruited into the study, 95 (49%) into standard care (control group) and 97 (51%) into a multidisciplinary diabetes program (intervention group). The 30-day overall hospital readmission rates (including both emergency department and hospital readmissions) were 19% in the control group and 7% in the intervention group (P = .02). The 365-day overall hospital readmission rates were 38% in the control group and 14% in the intervention group (P = .0002).Conclusion: Patients with diabetes who are assigned to a specialized multidisciplinary diabetes program upon their discharge exhibit significantly reduced hospital readmission rates at 30 days and 365 days after discharge.     

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community

Background

Readmissions to hospital are common, costly and often preventable. An easy-to-use index to quantify the risk of readmission or death after discharge from hospital would help clinicians identify patients who might benefit from more intensive post-discharge care. We sought to derive and validate an index to predict the risk of death or unplanned readmission within 30 days after discharge from hospital to the community.

Methods

In a prospective cohort study, 48 patient-level and admission-level variables were collected for 4812 medical and surgical patients who were discharged to the community from 11 hospitals in Ontario. We used a split-sample design to derive and validate an index to predict the risk of death or nonelective readmission within 30 days after discharge. This index was externally validated using administrative data in a random selection of 1 000 000 Ontarians discharged from hospital between 2004 and 2008.

Results

Of the 4812 participating patients, 385 (8.0%) died or were readmitted on an unplanned basis within 30 days after discharge. Variables independently associated with this outcome (from which we derived the nmemonic “LACE”) included length of stay (“L”); acuity of the admission (“A”); comorbidity of the patient (measured with the Charlson comorbidity index score) (“C”); and emergency department use (measured as the number of visits in the six months before admission) (“E”). Scores using the LACE index ranged from 0 (2.0% expected risk of death or urgent readmission within 30 days) to 19 (43.7% expected risk). The LACE index was discriminative (C statistic 0.684) and very accurate (Hosmer–Lemeshow goodness-of-fit statistic 14.1, p = 0.59) at predicting outcome risk.

Interpretation

The LACE index can be used to quantify risk of death or unplanned readmission within 30 days after discharge from hospital. This index can be used with both primary and administrative data. Further research is required to determine whether such quantification changes patient care or outcomes.Readmission to hospital and death are adverse patient outcomes that are serious, common and costly.^1,2 Several studies suggest that focused care after discharge can improve post-discharge outcomes.^3–7 Being able to accurately predict the risk of poor outcomes after hospital discharge would allow health care workers to focus post-discharge interventions on patients who are at highest risk of poor post-discharge outcomes. Further, policy-makers have expressed interest in either penalizing hospitals with relatively high rates of readmission or rewarding hospitals with relatively low expected rates.⁸ To implement this approach, a validated method of standardizing readmission rates is needed.⁹Two validated models for predicting risk of readmission after hospital discharge have been published.^10,11 However, these models are impractical to clinicians. Both require area-level information (e.g., neighbourhood socio-economic status and community-specific rates of admission) that is not readily available. Getting this information requires access to detailed tables, thereby making the model impractical. Second, both models are so complex that risk estimates cannot be attained from them without the aid of special software. Although these models have been used by health-system planners in the United Kingdom, we are unaware of any clinicians who use them when preparing patients for hospital discharge.Our primary objective was to derive and validate a clinically useful index to quantify the risk of early death or unplanned readmission among patients discharged from hospital to the community.     

Factors affecting early unplanned readmission of elderly patients to hospital.

A random sample of 133 elderly patients who had an unplanned readmission to a district general hospital within 28 days of discharge from hospital was studied and compared with a matched control sample of patients who were not readmitted. The total group was drawn from all specialties in the hospital, and by interviewing the patients, their carers, the ward sisters, and the patients'' general practitioners the factors causing early unplanned readmission for each patient were identified. Seven possible principal reasons were found: relapse of original condition, development of a new problem, carer problems, complications of the initial illness, need for terminal care, problems with medication, and problems with services. There were also contributory reasons, and it was usual for several of these to be present in each case. The unplanned readmission rate was 6%; the planned readmission rate was 3%. It was thought that unplanned readmission was avoidable for 78 (59%) patients. Patients in the study group and in the control group showed significant differences in certain characteristics--such as low income, previous hospital admission, already having nursing care, and admission by general practitioners--and this might help to identify patients who are likely to be readmitted in an emergency.     

Development,Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange

ObjectivesIdentifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.MethodsOur objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients.ResultsA risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates.ConclusionsThe risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.     

Hospital Readmissions of Patients with Heart Failure: The Impact of Hospital and Primary Care Organizational Factors in Northern Italy

BackgroundPrimary health care is essential for an appropriate management of heart failure (HF), a disease which is a major clinical and public health issue and a leading cause of hospitalization. The aim of this study was to evaluate the impact of different organizational factors on readmissions of patients with HF.MethodsThe study population included elderly resident in the Local Health Authority of Bologna (Northern Italy) and discharged with a diagnosis of HF from January to December 2010. Unplanned hospital readmissions were measured in four timeframes: 30 (short-term), 90 (medium-term), 180 (mid-long-term), and 365 days (long-term). Using multivariable multilevel Poisson regression analyses, we investigated the association between readmissions and organizational factors (discharge from a cardiology department, general practitioners’ monodisciplinary organizational arrangement, and implementation of a specific HF care pathway).ResultsThe 1873 study patients had a median age of 83 years (interquartile range 77–87) and 55.5% were females; 52.0% were readmitted to the hospital for any reason after a year, while 20.1% were readmitted for HF. The presence of a HF care pathway was the only factor significantly associated with a lower risk of readmission for HF in the short-, medium-, mid-long- and long-term period (short-term: IRR [incidence rate ratio]=0.57, 95%CI [confidence interval]=0.35–0.92; medium-term: IRR=0.70, 95%CI=0.51–0.96; mid-long-term: IRR=0.79, 95%CI=0.64–0.98; long-term: IRR=0.82, 95%CI=0.67–0.99), and with a lower risk of all-cause readmission in the short-term period (IRR=0.73, 95%CI=0.57–0.94).ConclusionOur study shows that the HF care specific pathway implemented at the primary care level was associated with lower readmission rate for HF in each timeframe, and also with lower readmission rate for all causes in the short-term period. Our results suggest that the engagement of primary care professionals starting from the early post-discharge period may be relevant in the management of patients with HF.     

Risk Factors for Hospital Readmission after Radical Gastrectomy for Gastric Cancer: A Prospective Study

Background

Hospital readmission is gathering increasing attention as a measure of health care quality and a potential cost-saving target. The purpose of this prospective study was to determine risk factors for readmission within 30 days of discharge after gastrectomy for patients with gastric cancer.

Methods

We conducted a prospective study of patients undergoing radical gastrectomy for gastric cancer from October 2013 to November 2014 in our institution. The incidence, cause and risk factors for 30-day readmission were determined.

Results

A total of 376 patients were included in our analysis without loss in follow-up. The 30-day readmission rate after radical gastrectomy for gastric cancer was 7.2% (27of 376). The most common cause for readmission included gastrointestinal complications and postoperative infections. On the basis of multivariate logistic regression analysis, preoperative nutritional risk screening 2002 score ≥ 3 was an independent risk factor for 30-day readmission. Factors not associated with a higher readmission rate included a history of a major postoperative complication during the index hospitalization, prolonged primary length of hospital stay after surgery, a history of previous abdominal surgery, advanced age, body mass index, pre-existing cardiopulmonary comorbidities, American Society of Anesthesiology grade, type of resection, extent of node dissection and discharge disposition.

Conclusions

Readmission within 30 days of discharge after radical gastrectomy for gastric cancer is common. Patients with nutritional risk preoperatively are at high risk for 30-day readmission. Preoperative optimization of nutritional status of patients at nutritional risk may effectively decrease readmission rates.     

Risk factors associated with 31-day unplanned readmission in 50,912 discharged patients after stroke in China

Background

Unplanned readmission within 31?days of discharge after stroke is a useful indicator for monitoring quality of hospital care. We evaluated the risk factors associated with 31-day unplanned readmission of stroke patients in China.

Methods

We identified 50,912 patients from 375 hospitals in 29 provinces, municipalities or autonomous districts across China who experienced an unplanned readmission after stroke between 2015 and 2016, and extracted data from the inpatients’ cover sheet data from the Medical Record Monitoring Database. Patients were grouped into readmission within 31?days or beyond for analysis. Chi-squared test was used to analyze demographic information, health system and clinical process-related factors according to the data type. Multilevel logistic modeling was used to examine the effects of patient (level 1) and hospital (level 2) characteristics on an unplanned readmission ≤31?days.

Results

Among 50,912 patients, 14,664 (28.8%) were readmitted within 31?days after discharge. The commonest cause of readmissions were recurrent stroke (34.8%), hypertension (22.94%), cardio/cerebrovascular disease (13.26%) and diabetes/diabetic complications (7.34%). Higher risks of unplanned readmissions were associated with diabetes (OR?=?1.089, P?=?0.001), use of clinical pathways (OR?=?1.174, P?<?0.001), and being discharged without doctor’s advice (OR?=?1.485, P?<?0.001). Lower risks were associated with basic medical insurances (OR ranging from 0.225 to 0.716, P?<?0.001) and commercial medical insurance (OR?=?0.636, P?=?0.021), compared to self-paying for medical services. And patients aged 50?years old and above (OR ranging from 0.650 to 0.985, P?<?0.05), with haemorrhagic stroke (OR?=?0.467, P?<?0.001), with length of stay more than 7?days in hospital (OR ranging from 0.082 to 0.566, P?<?0.001), also had lower risks.

Conclusions

Age, type of stroke, medical insurance status, type of discharge, use of clinical pathways, length of hospital stay and comorbidities were the most influential factors for readmission within 31?days.

     

National Health Service breast screening programme results for 1991-2.

OBJECTIVES--To report the results of the NHS breast screening programme for the year March 1991 to April 1992. DESIGN--A report of statistics was derived from Körner (K62) returns and from the radiology quality assurance programme. MAIN OUTCOME MEASURES--Detection rates for breast cancer and small (< or = 10 mm diameter) invasive cancer, benign biopsy rates, and recall and acceptance rates. RESULTS--The acceptance rate for screening across the United Kingdom was 71.3%. The referral rate for further investigation was 6.2% (regional 4.3-9.0%). The breast cancer detection rate was 6.2 cancers per 1000 women screened (5.1-9.0) and the detection rate of invasive cancers < or = 10 mm was 1.4/1000 (1.0-2.3). 72% of screening programmes reached the target 70% acceptance rate, and 95% of programmes achieved a recall rate of less than 10%. 75% of programmes had a cancer detection rate of more than 5/1000, but only 32% had a detection rate for invasive cancers < or = 10 mm of more than 1.5/1000. CONCLUSIONS--Overall, the results of the screening programme for the year 1991-2 can be regarded as extremely satisfactory, given the size and complexity of the operation.     

Efficacy of Hospital at Home in Patients with Heart Failure: A Systematic Review and Meta-Analysis

Background

Heart failure (HF) is the commonest cause of hospitalization in older adults. Compared to routine hospitalization (RH), hospital at home (HaH)—substitutive hospital-level care in the patient’s home—improves outcomes and reduces costs in patients with general medical conditions. The efficacy of HaH in HF is unknown.

Methods and Results

We searched MEDLINE, Embase, CINAHL, and CENTRAL, for publications from January 1990 to October 2014. We included prospective studies comparing substitutive models of hospitalization to RH in HF. At least 2 reviewers independently selected studies, abstracted data, and assessed quality. We meta-analyzed results from 3 RCTs (n = 203) and narratively synthesized results from 3 observational studies (n = 329). Study quality was modest. In RCTs, HaH increased time to first readmission (mean difference (MD) 14.13 days [95% CI 10.36 to 17.91]), and improved health-related quality of life (HrQOL) at both, 6 months (standardized MD (SMD) -0.31 [-0.45 to -0.18]) and 12 months (SMD -0.17 [-0.31 to -0.02]). In RCTs, HaH demonstrated a trend to decreased readmissions (risk ratio (RR) 0.68 [0.42 to 1.09]), and had no effect on all-cause mortality (RR 0.94 [0.67 to 1.32]). HaH decreased costs of index hospitalization in all RCTs. HaH reduced readmissions and emergency department visits per patient in all 3 observational studies.

Conclusions

In the context of a limited number of modest-quality studies, HaH appears to increase time to readmission, reduce index costs, and improve HrQOL among patients requiring hospital-level care for HF. Larger RCTs are necessary to assess the effect of HaH on readmissions, mortality, and long-term costs.     

Evidence for Natural Horizontal Transfer of the pcpB Gene in the Evolution of Polychlorophenol-Degrading Sphingomonads

The chlorophenol degradation pathway in Sphingobium chlorophenolicum is initiated by the pcpB gene product, pentachlorophenol-4-monooxygenase. The distribution of the gene was studied in a phylogenetically diverse group of polychlorophenol-degrading bacteria isolated from contaminated groundwater in Kärkölä, Finland. All the sphingomonads isolated were shown to share pcpB gene homologs with 98.9 to 100% sequence identity. The gene product was expressed when the strains were induced by 2,3,4,6-tetrachlorophenol. A comparative analysis of the 16S rDNA and pcpB gene trees suggested that a recent horizontal transfer of the pcpB gene was involved in the evolution of the catabolic pathway in the Kärkölä sphingomonads. The full-length Kärkölä pcpB gene allele had approximately 70% identity with the three pcpB genes previously sequenced from sphingomonads. It was very closely related to the environmental clones obtained from chlorophenol-enriched soil samples (M. Beaulieu, V. Becaert, L. Deschenes, and R. Villemur, Microbiol. Ecol. 40:345-355, 2000). The gene was not present in polychlorophenol-degrading nonsphingomonads isolated from the Kärkölä source.