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Back to Journals » Clinical Epidemiology » Volume 11
Benchmarking Danish hospitals on mortality and readmission rates after cardiovascular admission
Authors Ridgeway G, Nørgaard M, Rasmussen TB, Finkle WD, Pedersen L, Bøtker HE, Sørensen HT
Received 30 September 2018
Accepted for publication 26 November 2018
Published 4 January 2019 Volume 2019:11 Pages 67—80
DOI https://doi.org/10.2147/CLEP.S189263
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Amy Norman
Peer reviewer comments 2
Editor who approved publication: Professor Irene Petersen
Greg Ridgeway,1–3 Mette Nørgaard,4 Thomas Bøjer Rasmussen,4 William D Finkle,3 Lars Pedersen,4 Hans Erik Bøtker,5 Henrik Toft Sørensen4
1Department of Criminology, University of Pennsylvania, Philadelphia, PA, USA; 2Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA; 3Consolidated Research, Inc., Los Angeles, CA, USA; 4Department of Clinical Epidemiology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark; 5Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
Objective: The aim of this study was to examine hospital performance measures that account more comprehensively for unique mixes of patients' characteristics.
Design: Nationwide cohort registry-based study within a population-based health care system.
Participants: In this study, 331,513 patients discharged with a primary cardiovascular diagnosis from 1 of 26 Danish hospitals during 2011–2015 were included. Data covering all Danish hospitals were drawn from the Danish National Patient Registry and the Danish National Health Service Prescription Database.
Main outcome measures: Thirty-day post-admission mortality rates, 30-day post-discharge readmission rates, and the associated numbers needed to harm were measured.
Methods: For each index hospital, we used a non-parametric logistic regression model to compute propensity scores. Propensity score weighted patients treated at other hospitals collectively resembled patients treated at the index hospital in terms of age, sex, primary discharge diagnosis, diagnosis history, medications, previous cardiac procedures, and comorbidities. Outcomes for the weighted patients treated at other hospitals formed benchmarks for the index hospital. Doubly robust regression formally tested whether the outcomes of patients at the index hospital differed from the outcomes of the patients used to form the benchmarks. For each index hospital, we computed the false discovery rate, ie, the probability of being incorrect if we claimed the hospital differed from its benchmark.
Results: Five hospitals exceeded their benchmark for 30-day mortality rates, with the number needed to harm ranging between 55 and 137. Seven hospitals exceeded their benchmark for readmission, with the number needed to harm ranging from 22 to 71. Our benchmarking approach flagged fewer hospitals as outliers compared with conventional regression methods.
Conclusion: Conventional methods flag more hospitals as outliers than our benchmarking approach. Our benchmarking approach accounts more thoroughly for differences in hospitals' patient case mix, reducing the risk of false-positive selection of suspected outliers. A more comprehensive system of hospital performance measurement could be based on this approach.
Keywords: performance measurement, propensity score, doubly robust estimation, case mix adjustment, cohort study
Measures of hospitals' quality may identify poorly performing hospitals in need of improvement, point to high-performing hospitals that can share best practices, and inform patients, providers, and administrators about hospital performance.1,2 Methods of assessing quality of care have been debated for several years,3–5 with hospital-related mortality used widely as a marker.6,7 Mortality measures have the advantage that reporting of death is mandatory and comprehensive in many countries.8 The hospital standardized mortality ratio has been publicly available in the UK since 2001.9 Defined as the ratio of observed to expected deaths multiplied by 100, expected deaths are derived from statistical models that adjust for available case mix factors such as age, sex, primary diagnosis, and comorbidity.10
Hospital readmission is another potential surrogate measure of quality of care.11,12 In the USA, ~20% of the Medicare patients are readmitted within 30 days after hospital discharge.13 Although most hospital readmissions seem to have non-modifiable causes, high readmission rates of patients with defined conditions may reflect quality-of-care barriers.14
Variation in hospital performance measures such as 30-day mortality or 30-day readmission rates can stem in part from patient differences,3,6 ie, one hospital may have higher mortality and readmission rates than other hospitals, but it might also be treating patients with more complex and life-threatening conditions. The adequacy of adjustment for case mix thus remains a major challenge. In this study, we examined an alternative approach; instead of comparing hospitals head-to-head with adjustment for measured differences, we benchmarked an index hospital by comparing its performance measures with those calculated for a group of patients selected from other hospitals, who have characteristics similar to those of patients treated at the index hospital.
In this study, we define the benchmarking methodology, apply it to 30-day mortality and 30-day readmission rates for Danish hospitals, compute the number needed to harm for each hospital, and compare the outlier classification of hospitals using this approach with the classification obtained using conventional, regression-based adjustment methods. Hence, we examined performance measures for hospitals that account comprehensively for unique mixes of patients' characteristics, including age, sex, diagnoses, medications, procedures, and comorbidities. Although the benchmarking method is broadly applicable to any class of patients, we apply it here to patients discharged with a circulatory disorder diagnosis.
Data sources and study population
We conducted our nationwide cohort study in Denmark during 2011–2015. Denmark has a mixed rural and urban population of ~5.6 million persons. The Danish National Health Service provides tax-funded medical care to all residents, guaranteeing free access to general practitioners and inpatient/outpatient care in 1 of 26 hospital facilities, as well as partial reimbursement for prescription drugs (unpublished data).15
All Danish residents have a civil registration number (CRN), which allows unambiguous linkage between medical databases and public administrative registries, provided by the Danish Civil Registration System, which has recorded all changes in vital status and migration for the entire Danish population since 1968, with daily electronic updates.16
This study was based on the data from the Danish National Patient Registry (DNPR), which has recorded all admissions to Danish hospitals since 1977.17 In the DNPR, diagnoses have been classified according to the ICD-8 up to 1995 and the ICD-10 since then.17
We identified a cohort consisting of all patients with acute inpatient admissions to Danish hospitals between 2011 and 2015, with diseases of the circulatory system (ICD codes: I00–I99) as the primary discharge diagnosis. We linked transfers between hospitals, considering them as a single admission, and used the primary diagnosis from the first admission as the diagnosis code. To focus the study on more common diagnosis categories and to facilitate the process of finding matching patient cases across hospitals, we included only ICD-10 codes assigned to at least 400 patients during the 5-year study period and present in at least six of the hospitals.
We linked the cohort to the National Health Service Prescription Database, which includes data on all reimbursable prescriptions redeemed at community pharmacies and hospital-based outpatient pharmacies in Denmark since 2004. This database contains dispensing information including the Anatomical Therapeutic Chemical Classification code, date of dispensing, number of prescription refills, and pack size.18
To ascertain study outcomes, we obtained information from the Danish Civil Registration system on overall mortality within 30 days after admission.16 Information on 30-day readmissions, defined as any hospital admission occurring from 1 to 30 days after the discharge date from the index admission, was obtained from the DNPR.
In addition to computing mortality and readmission rates for each hospital, we computed 30-day mortality and 30-day readmission rates for a benchmark population of patients treated at other hospitals as specified in the section "Statistical analyses". To characterize the magnitude of the difference between a given hospital and the benchmarked population at other hospitals, we computed the number needed to harm, ie, the number of patients needed to be treated at a given hospital to cause an additional fatality/readmission that would not have occurred at another hospital.19 This was computed as the inverse of the difference in rates between the hospital and its benchmark population.
For each hospital, we also computed a false discovery rate for 30-day mortality and a false discovery rate for 30-day admissions (described in more detail in the section "Statistical analyses"). The false discovery rate is defined as the probability that a hospital flagged as an outlier is in truth not an outlier.20,21 We used the false discovery rate to weigh the evidence that a hospital is an outlier, based on the precision of the rate estimates and the magnitude of the difference between a hospital's mortality/readmission rate and the associated benchmark rate. We flagged as an outlier any hospital with a false discovery rate <0.05, signaling a probability in excess of 0.95 that the hospital was an outlier.
Confounding factors
For all patients, we obtained age and sex details from their CRN in the Civil Registration System. We used the DNPR to ascertain diagnosis history in the 5 years prior to admission, including ICD-8 and ICD-10 diagnostic codes for ischemic heart disease, heart failure, cardiomyopathy, arterial hypertension, cardiac arrhythmias, valvular heart disease, stroke, diabetes, COPD, renal disease, liver disease, venous thromboembolism, connective tissue disease, cancer (except non-melanoma skin cancer), and dementia (see Table 1 for specific codes). We also recorded whether the patient in the 5 years prior to admission had cardiac procedures including any percutaneous coronary intervention, coronary artery bypass grafting, cardiac resynchronization therapy, radiofrequency ablation, cardiac pacemaker, implantable cardiac defibrillator, mitral valve surgery, or aortic valve surgery. The Danish NHPD provided each patient's history of prescriptions in the 6 months prior to admission for cardiovascular medications: nitrates, antiplatelets, anticoagulants, beta blockers, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, calcium channel blockers, diuretics, amiodarone, and lipid-lowering drugs. The NHPD also provided prescription data for the 6 months prior to admission for anti-depressants, selective serotonin reuptake inhibitors, antipsychotics, opioids, NSAIDs, aspirin, paracetamol, glucocorticoids, antidiabetics, disease-modifying anti-rheumatic drugs, antibiotics, anti-ulcer drugs, and drugs used to treat alcohol dependence (see Table 1 for codes). We also recorded the month and year of admission.
Table 1 Distribution of selected characteristics in an index hospital population (Hospital Z), its benchmark population, and all patients at other hospitals in Denmark
Notes: For primary cardiovascular discharge diagnosis at index admission, the model also matched on 105 specific ICD-10 codes, but we aggregate them here to show the results more compactly. Except for age, which is presented as the average age, all other figures are percentages. aDefined as at least one prescription within 6 months before admission.
Abbreviations: ACE, angiotensin-converting enzyme; NMSC, non-melanoma skin cancer.
Our approach to benchmarking hospitals involved the following three steps: 1) for each index hospital we applied weights to patients treated at other hospitals so that they resembled patients treated at the index hospital; 2) we compared the outcomes for each index hospital with the outcomes for their benchmark patients using a weighted regression model; and 3) we calculated each hospital's false discovery rate.
To select a benchmark population of patients as a comparison group for each hospital, we constructed a propensity score model. This model estimated the probability that a patient would be admitted to the index hospital (vs any other Danish hospital) conditional on covariates observed at discharge. We used generalized boosted modeling (GBM) to estimate these probabilities.22 GBM is a machine learning method that can capture a flexible non-parametric relationship between a large collection of patients' features and an outcome – in this case the probability of treatment at the index hospital. Advantages of using GBM for propensity scores are its ability to account for non-linear effects, interaction effects, and handling of numeric, ordinal, and categorical features (see Supplementary materials for details). We converted the propensity score estimated with GBM to a patient weight. If the index hospital treated patient i, then patient i received a weight of 1. For patient i treated at any other hospital the weight was , where p(xi) is the propensity score and xi represents the covariates of patient i. Previous research has used propensity score stratification to adjust for provider case mix (the mix of patient types treated at hospitals23) based on a small set of patients' features.24
To compare 30-day mortality and 30-day readmission rates between a hospital and its benchmark population, we used a propensity score-weighted logistic regression in a doubly robust estimation step (see Supplementary materials for a detailed description) to obtain doubly robust adjusted mortality and readmission rates.25 The weighted regression includes all patient covariates and produces consistent estimates of the adjusted rates if either the propensity score model or the regression model is correct. We extracted the P-value for the index hospital effect from the weighted regression model as a measure of the difference between a specific hospital's average outcomes and average outcomes for patients treated at other hospitals. For each of the 26 hospitals in turn, we refitted a new propensity score model, estimated a new doubly robust estimation step, and extracted the index hospital effect P-value. A similar approach has been used previously to analyze public safety performance.26
Finally, we converted the P-values to false discovery rates.20,21,27 The false discovery rate is the expected fraction of type I errors. In our example, this is the fraction of hospitals flagged as outliers that are not actually outliers. False discovery rates, rather than P-values, are essential for benchmarking. If we flagged every hospital with a P-value less than the traditional 0.05, even if no hospital differed from its benchmark in reality, we would expect to flag one hospital as an outlier (26 hospitals ×0.05=1.3). The Benjamini–Hochberg false discovery rate procedure presents a collection of P-values in increasing order, , and rejects hypotheses for which . The rejected hypotheses will have a false discovery rate less than or equal to q (closest to q if outliers are rare). We applied the Benjamini–Yekutieli modification21 that allows for dependence among the P-values. Rather than select a specific q, we report each hospital's estimated q-value, the smallest q for which that hospital would be flagged as an outlier.28 For example, any hospital flagged as an outlier with a q-value <0.05 would have at most a 5% chance of being a false positive. Although adjusting P-values for multiple testing is not always necessary,29 we approached the detection of outlier expecting that most hospitals will match their benchmark and few will deviate greatly from their benchmark.
For each of the 26 Danish hospitals, we indicated whether its 30-day mortality and readmission rates substantially differed from a set of similar patients treated at other Danish hospitals and reported the probability of its truly being an outlier.
Finally, we compared the results of our proposed benchmarking approach with common practices. Perhaps, the most common practice is to compare hospital performance to a national or regional average, flagging as outliers those hospitals whose performance differs from this average (ie, with a P-value of <0.05).30 Alternative approaches account only for a small set of patients' features, typically age, sex, and comorbidities, using a regression model. They flag as an outlier any hospital with an outcome that differs from that predicted by the model for the hospital (flagging as outliers those hospitals with fixed effects that have an associated P-value of <0.05).24 We compared hospitals that our benchmarking method flagged as outliers with hospitals flagged as outliers using unadjusted and regression-adjusted methods and counted the number of agreements and disagreements in outlier labeling.
We identified 365,636 patients admitted to Danish hospitals during 2011–2015 with a circulatory system disease recorded as the primary discharge diagnosis. After excluding ICD-10 codes assigned to fewer than 400 patients over the 5-year study period or present at fewer than six hospitals, as well as ICD-10 code I99.9 (unspecific circulatory disorder), 331,513 patients remained for analysis.
The patients' median age was 70 years and 57% were men. The three most common diagnoses were atrial fibrillation and flutter (ICD-10 code I48, 24%), acute myocardial infarction (ICD-10 code I21, 11%), and stroke (ICD-10 code I63, 9%). However, the hospitals exhibit substantial variation in their patient case mix. The average age of patients at each of the 26 hospitals ranged from a minimum of 66 years to a maximum of 73 years. At one hospital, 11% of the patients had a primary discharge diagnosis of atrial fibrillation and flutter, whereas at another hospital 41% had this diagnosis. Hospitals ranged from having 5% acute myocardial infarction patients to 22%. For one hospital, 38% of the patients had recent prescriptions for a beta blocker and at another hospital 58% of the patients had recent beta blocker prescriptions. The patients treated at the 26 Danish hospitals differ in numerous factors that are highly relevant to mortality and readmission.
Table 1 focuses on one particular index hospital compared to all others. Table 1 shows the selected characteristics of: 1) patients at an example index hospital (Hospital "Z"), 2) patients used to form Hospital Z's benchmark, and 3) all patients at all other hospitals (Table 1 does not describe all patients in the study, just a comparison of Hospital Z's patients with its benchmark and patients at other hospitals). For example, compared to patients treated at all other hospitals, Hospital Z's circulatory system patients were more likely to be admitted for angina pectoris (15.7% vs 11.4%), but less likely to be admitted for stroke (5.8% vs 13.1%). Although Table 1 only shows the data for a few selected primary diagnosis categories, the hospital and its benchmark matched on 105 specific ICD-10 codes. For example, Table 1 shows that Hospital Z not only matches its benchmark on the percentage discharged with a stroke diagnosis but the hospital and its benchmark also match on each of the specific stroke ICD-10 codes (eg, 3.1% of the patients were discharged with an I63.9 ICD-10 code for both Hospital Z and its benchmark population of patients). Our propensity score model successfully created a weighted group of patients treated at other hospitals that uniformly resembled Hospital Z's patients. The largest difference between Hospital Z's patients and the benchmark population across all patients' features was less than a percentage point.
Not only did the mean values and percentages of Hospital Z's patients' features match those of the benchmark population but also the entire age distribution of patients at Hospital Z and patients in its benchmark population was nearly identical (Figure 1A).
Figure 1 Comparison of the age distribution of Hospital Z (black) to the age distribution of Hospital Z's benchmark population of patients (red).
Notes: (A) Comparison of the distribution for all patients. (B) The age distribution also matches within (arbitrarily selected) I20.0 patients on statins, demonstrating the balance of patients' features in three-way interactions.
Furthermore, combinations of patients' features matched. Figure 1B shows the age distribution of patients with an unstable angina diagnosis and a prior statin prescription, an arbitrarily selected combination. More variability is evident because of the smaller number of patients with these features, but the age distributions still matched closely.
We created 25 other tables similar to Table 1 for each of the other 25 hospitals. In each case, we successfully established a set of benchmark patients closely resembling the index hospital's patients. In all cases, the largest difference for any patient feature between the index hospital and its benchmark population was 0.8%.
Figure 2 shows each hospital's 30-day post-admission mortality rate compared to the mortality rate for the hospital's benchmark population. For this measure, hospitals on the right side of Figure 3 substantially exceeded the mortality rate in their benchmark population. For example, the 30-day mortality rate for circulatory system patients in Hospital Z was 6.9% (marked with a black point), whereas similar patients treated at other Danish hospitals had a mortality rate of 5.1% (marked with a red point). The false discovery rate for this hospital was <1%, indicating a high probability that this hospital was an outlier. Table 1 shows that this hospital closely resembled its benchmark patients, so the 1.8 percentage point observed difference in mortality rates cannot be due to differences in patient case mix for any of the features shown in the table. Five hospitals (V, T, W, Y, and Z) exceeded the mortality rate in their benchmark population and had false discovery rates <0.05. To put the differences in mortality rates into perspective, Figure 2 shows the number needed to harm for each of these outlier hospitals.
Figure 2 Mortality within 30 days of admission by hospital (black) compared with its benchmark population (red).
Notes: Lines connect hospitals to their benchmark population when the false discovery rate exceeds 5%. The numbers above the dots indicate the number needed to harm for hospitals exceeding their benchmark, which are flagged as outliers. The horizontal line is the average mortality rate (5.4%).
Figure 3 Readmission within 30 days of discharge by hospital (black) compared to its benchmark population (red).
Notes: Lines connect hospitals to their benchmark population when the false discovery rate exceeds 5%. Numbers above the dots indicate the number needed to harm for hospitals exceeding their benchmark, which are flagged as outliers. The horizontal line marks the average 30-day readmission rate (18.5%).
At the other extreme, Hospital C had a mortality rate of 5.5%, a full percentage point lower than its benchmark population's mortality rate of 6.5%. Hospital N, near the middle of Figure 2, had a relatively high mortality rate of 6.3%, about 1 percentage point higher than the national average of 5.4% (shown by the horizontal line). However, because of its patient case mix, its benchmark population had a similarly elevated mortality rate. Comparisons with the national average would have identified this hospital as an outlier. In Figure 2, hospitals with lines connecting the hospital and benchmark points indicate those with false discovery rates >5%, which signal that these rates may be statistically indistinguishable. However, no public health standard has emerged for false discovery rate thresholds.
We also compared 30-day readmission rates among hospitals, as shown in Figure 3. Hospital G had a readmission rate exceeding 23%, far greater than the rate for its benchmark population (18.8%) and the national average (18.5%). A total of seven hospitals exceeded their benchmark populations' readmission rates, with false discovery rates <5%. Hospital U, in contrast, had a benchmark readmission rate of 19.5%, indicating that this hospital's case mix would be consistent with high readmission rates. However, Hospital U had among the lowest readmission rates of any hospital, 4 percentage points lower than the rate for its benchmark population.
Benchmarking vs conventional approaches
Table 2 shows the number of hospitals that our benchmarking methodology flagged as outliers (using a false discovery rate <5%). We compared these with hospitals flagged as outliers using the unadjusted and regression-adjusted methods (using P-values <0.05). Table 2 differentiates between positive outliers (those with high mortality rates) and negative outliers (those with low mortality rates). The counts on the diagonals indicate the number of hospitals for which the diverse methods produced similar results. The benchmarking and unadjusted comparison methods agreed on the identification of four hospitals as positive outliers, two hospitals as negative outliers, and ten hospitals as non-outliers. The benchmarking and regression adjustment methods agreed on the identification of four positive outliers, three negative outliers, and five non-outliers.
Table 2 Comparison of 30-day mortality outliers flagged with benchmarking, unadjusted comparisons, and regression-adjusted comparisons
However, for 10 hospitals benchmarking disagreed with the unadjusted comparison and for 14 hospitals benchmarking disagreed with the regression-adjusted comparison. In some cases, benchmarking flagged a hospital as a negative outlier, whereas conventional methods flagged it as a positive outlier. Both conventional methods flagged Hospital N as a positive outlier, but benchmarking, with more complete control of confounding than conventional methods, ascertained lower-than-expected mortality rates. Both conventional methods were more likely to flag hospitals as outliers than the benchmarking method. The benchmarking method that relies on thorough control of confounding and accounting of multiple comparisons reduced the risk of false positives.
Table 3 shows the selected hospitals for which the level of disagreement is striking. Four hospitals in Table 3 (N, Q, K, and S) appear in the middle of Figure 2, indicating that their 30-day mortality rates are nearly identical to that expected based on their patients' features. For these hospitals, either one or both unadjusted and regression-adjusted methods would indicate that they were significant outliers. Hospital N was noted previously as having a high mortality rate compared to the national average, but benchmarking indicated that this hospital's case mix is consistent with a higher mortality rate. In the case of Hospital V, benchmarking indicated that it was an outlier, but regression adjustment suggested it was not.
Table 3 Selected hospitals for which benchmarking, unadjusted comparisons, and regression adjustment disagree, including their status as an outlier for 30-day mortality rate
We defined a benchmarking approach and applied it to cardiovascular health care quality metrics, in line with Denmark's focus on monitoring the quality of cardiovascular treatment. Our analysis flags specific hospitals that exceed their mortality rate and readmission rate benchmarks and likely require closer review. Our benchmarking approach uses a flexible machine learning approach to estimate propensity scores, adjusting for patient case mix based on a thorough appraisal of clinically relevant patients' features. Importantly, the propensity score model addressed differences in patients treated at different hospitals. Our use of doubly robust estimation further reduced confounding stemming from any remaining differences between a hospital's patients and the group of patients used to construct benchmarks for the hospital. Our rigorous accounting of patients' features precludes hospitals flagged as outliers from claiming that they treat a more complex patient population than their comparison hospital, which adversely affects their performance. We included false discovery rates to estimate the outlier probabilities for each hospital. Unlike a P-value, the false discovery rate is a measure that can be used to define thresholds that would trigger further steps.
If the proposed methods are not adopted as a standard, based on statistical arguments and simulation experiments, classification of hospitals using conventional methods will result in frequent false-positive selection of outliers due to chance. Our proposed benchmarking approach aids in decision-making about hospital quality. Action should be taken (such as an investigation or review) whenever the cost of addressing an outlier times the false discovery rate is less than the cost of failing to address an outlier (excess injuries, fatalities, and liability) times one minus the false discovery rate.
We demonstrated that conventional methods (unadjusted comparisons or regression adjustments) flagged more hospitals as outliers than our benchmarking approach. Since conventional methods have limited capacity to rigorously address confounding and simultaneous testing of hospital differences, the excess number of outliers they generate is likely due to insufficient adjustment for differences in patient case mix and higher false-positive rates. Benchmarking also corrects for simultaneous comparison with a false discovery rate calculation, which conventional methods often ignore.
Several potential limitations of our approach also must be considered. Benchmarking is only as good as the data on which it is based. For example, the propensity score model produced close alignment between Hospital Z's patients and its benchmark population. Therefore, differences in readmission rates between the hospitals were not due to observed patient demographics, diagnoses, medications, previous cardiac procedures, or comorbidities. Other unmeasured factors, such as lifestyle (eg, smoking and obesity), organizational, economic (eg, income and employment), social (eg, family structure), or geographic characteristics (eg, distance to hospitals and transfers between hospitals)31 must have produced this difference.
Benchmarking is useful if it identifies potential problematic practices, but it also could reflect differences between hospitals in coding practices, such as use of non-specific codes. Data may contain misclassified diagnoses and the rate of misclassifications could vary by hospital, although positive predictive values are generally good for cardiovascular diseases and procedures in Denmark.32,33 Even if data contain properly coded diagnoses, they may not capture differences in severity of the disease or of comorbidities. It must also be noted that our analysis excluded 9% of the patients, who had either a diagnosis of unspecific circulatory disorder or uncommon ICD-10 codes. These exclusions offset the analysis toward common diagnoses. However, if rare cases offer the greatest opportunity to detect outlier hospitals, then a method that excludes such cases will not be able to flag them as outliers.
We chose to examine circulatory disorders because of their seriousness, the large number of patients, and the constitutional focus on monitoring quality of cardiovascular health care through specialized registries in Denmark. The Danish Heart Registry, established in 1998, serves as a data source for nationwide cardiovascular research within the field of ischemic heart and heart valve disease.34 An annual report summarizes activity, waiting times, and prognostic factors. The Western Denmark Heart Registry, founded in 1999, submits data to the Danish Heart Registry in collaboration with Western Denmark's three major cardiac centers. As well, it collects data on cardiac patients, invasive procedures, and cardiac surgery and monitors 30-day mortality following treatment.35 The Danish Heart Failure Registry is a nationwide registry established in 2003 as a part of a large nationwide quality improvement initiative aimed at monitoring and improving the quality of care for patients with specific severe diseases.36 This Registry monitors 1-year mortality following initial admission for heart failure. The Danish health system has shown a substantial reduction in 1-year heart failure mortality since the Registry's inception, decreasing from 20.5% to 12.8% between 2003 and 2010.37 Finally, the Danish Stroke Registry has monitored the quality of stroke care since 2003 and transient ischemic attack since 2013. This Registry maintains data on key processes of care and patient outcomes and includes hospital-specific reports of quality measures such as 30-day readmission and 30-day mortality.38
Correctly measuring the performance of health care providers, including doctors, nurses, medical groups, and hospitals, critically depends on adequately addressing differences in patient case mix. Commonly used methods fail to adjust for case mix and make comparisons with a regional average, such as Medicare's Hospital Compare.30 They also may make modest regression adjustments with a limited number of features24 using models with stringent parametric assumptions.39,40 Regression adjustment is particularly problematic in the presence of many highly correlated features, which frequently occur in patient data. In these circumstances, the estimated provider effect is extremely sensitive to the exact form of the regression model specification. For example, inclusion or exclusion of an interaction term can change the magnitude or even the sign of the estimated provider effect.
Nothing about the benchmarking methodology described here is unique to cardiovascular patients, hospital-level performance measurement, or Denmark. The methodology, in principle, is applicable to any collection of patients. Rigorous case mix adjustment when benchmarking for other categories of patients almost certainly requires different sets of patients' features than those used here. Although our analysis compared performance at the hospital level, the methodology could provide benchmarks for individual providers. Finally, other nations providing single-payer health care systems as well as a wide range of networks of providers, insurers, and patients worldwide need benchmarks to measure the performance of their health care providers. This need is not unique to Denmark, indicating that our proposed methodology has wide-ranging applicability.
We defined a benchmarking methodology that provides more thorough adjustment for differences in patient case mix when comparing hospitals. Demonstrating the methodology on 26 Danish hospitals, we found that five hospitals greatly exceed their benchmarks for 30-day mortality rates and seven hospitals greatly exceed their benchmarks for 30-day readmission rates for cardiovascular patients. Our proposed benchmarking methodology flags fewer outliers than conventional methods that have less capacity for controlling for confounding. Given the magnitude of hospital expenditures, the stakes of misclassifying outliers are high. Further study of outlier classification methods in different settings with more hospitals is imperative to identify misleading classification methods and to introduce methods with fairer rewards and incentives for practitioners.
Studies based solely on data from the Danish national registers, by Danish legislation, do not require approval from the Danish Health Research Ethics committees, as study participants are never contacted, and consent is not required for the use of register information.
The authors report no conflicts of interest in this work.
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Rothman KJ. Six persistent research misconceptions. J Gen Intern Med. 2014;29(7):1060–1064.
Paddock SM, Adams JL, Hoces de la Guardia F. Better-than-average and worse-than-average hospitals may not significantly differ from average hospitals: an analysis of Medicare Hospital Compare ratings. BMJ Qual Saf. 2015;24(2):128–134.
Terkelsen CJ, Sørensen JT, Maeng M, et al. System delay and mortality among patients with STEMI treated with primary percutaneous coronary intervention. JAMA. 2010;304(7):763–771.
Sundbøll J, Adelborg K, Munch T, et al. Positive predictive value of cardiovascular diagnoses in the Danish National Patient Registry: a validation study. BMJ Open. 2016;6(11):e012832.
Adelborg K, Sundbøll J, Munch T, et al. Positive predictive value of cardiac examination, procedure and surgery codes in the Danish National Patient Registry: a population-based validation study. BMJ Open. 2016;6(12):e012817.
Özcan C, Juel K, Flensted Lassen J, von Kappelgaard LM, Mortensen PE, Gislason G. The Danish Heart Registry. Clin Epidemiol. 2016;8:503–508.
Schmidt M, Maeng M, Jakobsen CJ, et al. Existing data sources for clinical epidemiology: the Western Denmark Heart Registry. Clin Epidemiol. 2010;2:137–144.
Schjødt I, Nakano A, Egstrup K, Cerqueira C. The Danish Heart Failure Registry. Clin Epidemiol. 2016;8:497–502.
Nakano A, Johnsen SP, Frederiksen BL, et al. Trends in quality of care among patients with incident heart failure in Denmark 2003-2010: a nationwide cohort study. BMC Health Serv Res. 2013;13:391.
Johnsen SP, Ingeman A, Hundborg HH, Schaarup SZ, Gyllenborg J. The Danish stroke registry. Clin Epidemiol. 2016;8:697–702.
Finison K, Mohlman M, Jones C, et al. Risk-adjustment methods for all-payer comparative performance reporting in Vermont. BMC Health Serv Res. 2017;17(1):58.
Shahian DM, Normand SL. What is a performance outlier? BMJ Qual Saf. 2015;24(2):95–99.
Details of propensity score estimation with generalized boosted models, doubly robust estimation, and false discovery rates
Propensity score estimation to match patients
We assign weights to patients treated at other hospitals so that, after weighting, those patients have features that collectively resemble the hospital for which we are constructing a benchmark. Mathematically, for a hypothetical index hospital "Hospital A", this means solving for a weight function w(x) such that:
where x represents a patient's features, f(x|hospital = A)is the distribution of patients' features at Hospital A, and f(x|hospital ≠ A) is the distribution of features of all other patients not treated at Hospital A. Solving for w(x) yields:
where p(x) is the propensity score, the probability that a patient with features x received treatment at Hospital A. Patient i not treated at Hospital A will receive weight p(xi)/(1-p(xi)). K is a constant that will cancel out in calculations of any weighted statistics.
We estimate p(x) from the patient data using generalized boosted modeling. This modeling strategy is similar to logistic regression except that, rather than using the individual x's as covariates, we use a linear combination of transformations of the x's:
Specifically, the functions hj(x) are all piecewise constant functions of x and their interactions involving up to three patients' features, sufficiently flexible to capture non-linear relationships, threshold and saturation effects, and higher order interactions.
Estimating Equation 3 without constraints results in an unidentifiable and numerically unstable model. Boosting approximates the use of the lasso penalty when estimating models with maximum likelihood.1 That is, we estimate the coefficients in Equation 3 by finding the βj that maximizes:
where Ai is a 0/1 indicator of whether patient i was at Hospital A. If l = 0 then maximizing Equation 4 is equivalent to standard logistic regression, but with the h's as covariates. When λ is large, then the penalty forces all βj's to be close to 0 and will actually set most βj's to be equal to 0. Boosting iteratively relaxes the size of λ, determining at each step which of the hj(x)'s will have a non-zero coefficient, and includes them in the model. Although the set of basis functions may be extremely large, most have coefficients equal to 0 and never need to be computed or stored. The boosting algorithm iterates until the features of patients at Hospital A most closely resemble the features of patients at other hospitals. This approach has been shown to outperform alternative methods for estimating propensity scores.2
We use the resulting set of βj's and hj(x)'s to compute propensity score weights for patients at other hospitals:
Doubly robust estimation to estimate the hospital effect
To compute the hospital effect, we fit a weighted logistic regression of the form shown in Equation 6, where Hospital A's patients have weight 1 and the other patients have weight wi.
where yi is the 0/1 indicator for death or readmission within 30 days for patient i. Since the propensity score weights uncorrelate the confounders from A, their inclusion can reduce bias in the estimate of g by adjusting for any remaining imbalance between Hospital A's patients and the other patients.
We extract the p-value for testing g=0 from Equation 6 as a measure of the difference between Hospital A's outcomes and the benchmark outcomes. For each of the 26 hospitals in turn, we refit a new propensity score model and the doubly robust estimation step. At the end, we have 26 p-values.
False discovery rates for detecting outliers
Converting p-values to false discovery rates was developed by Seeger3 and refined and popularized by Benjamini and Hochberg.4 For cases where p-values are not independent, Benjamini and Yekutieli proposed5 a modification, which we adopted. We converted a set of m p-values arranged in descending order, P(m), P(m-1),..., P(1), into q-values as:
where m is the number of comparisons (m=26 hospitals in our example). Any q(i) that exceeds 1 is set to equal 1. The false discovery rate for hospital (i) will be less than or equal to q(i).
Conventional covariate adjustment
To compare the benchmarking approach with conventional covariate adjustment, we fit a logistic regression model of the form:
where yi is the 0/1 outcome for 30-day mortality or readmission, x is a vector of patients' features (age, sex, and comorbidity indicators), and aj is a fixed effect for Hospital j. We then compute the average predicted mortality and readmission rates for Danish patients as if they had been treated at each of the 26 hospitals. We then compare the covariate-adjusted rates to the national average.
Zhao P, Yu B. Stagewise Lasso. J Mach Learn Res. 2004;8:2701–2726.
Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337–346.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B. 1995;57(1):289–300.
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Liberal Arts for Leaders
Explore how a liberal arts education prepares individuals to lead ambitiously
Wed., Dec 1, 2021 | 6:00 - 7:00 pm CST
Join us to examine how a liberal arts education can prepare you to be a better leader. Alongside a brief opening presentation on the value of the liberal arts, you will hear from three executives who are harnessing their liberal arts education to lead ambitiously.
Who's Speaking
Fabiola Delgado
Assistant Vice President for Finance and Chief of Staff for National Laboratories, University of Chicago
Fabiola (Fabi) provides leadership for finance, budget, accounting and internal financial controls for the operations under the office of the Vice President for National Laboratories, Science Strategy, Innovation and Global Initiatives. Building strong collaborative relationships with the finance and leadership teams deployed in these offices, she provides guidance, advice, coordination, and resources to aid the units in the development and management of their respective strategic plans, audit engagements and financial planning and reporting.
Fabi previously served as Chief of Staff for National Laboratories, serving as a key advisor on the design, coordination and execution of strategic initiatives and operational plans related to the Argonne and Fermi National Laboratories; building strong partnerships with the Department of Energy (DOE) and the National Laboratories leadership team.
Fabi has an exceptional strong background in financial planning, internal and external audit, change management, global business process excellence, international treasury and business development. Prior to joining the University of Chicago, Fabi worked across the globe in a series of increasingly responsible leadership roles in public accounting firms, Fortune 500 corporations and private equity-owned enterprises, including such companies as Deloitte, Abbott Laboratories, Aon Hewitt, and most recently Ensono, where she was the Global Vice President for Financial Planning and Analysis.
She received her B.S. and C.P.A. from the Universidad Nacional Autónoma de México (UNAM) with the highest honors, and is a candidate for a Master of Liberal Arts with concentration in Ethics and Leadership at the University of Chicago. Fabi has a passion for learning about the physical and social sciences and has been an active participant in and sponsor of charities focused on empowering the diverse youth of Chicago. In her spare time, Fabi enjoys traveling, going for walks with her family and pets, and attending performing arts and world culture events.
Anne Gehring
Founding Partner, Stanton Blackwell
Anne has established a reputation as a dynamic change agent skilled in solving large, complex and persistent problems. She is noted for her candor and practical leadership style, focused on fast and measurable business impact.
Anne has over 30 years' experience across multiple industries and functions with extensive experience in strategic planning, finance, capital adequacy, project and change management and process excellence. Anne has deep financial services experience, most recently as a Managing Director for JP Morgan Chase in New York where she led Financial Planning and Analysis and regulatory stress testing for the mortgage business. Anne was also part of the turnaround team at Fannie Mae in Washington, D.C. where she led the Business Planning and Public Policy organization, as well as the Enterprise Program Management Office. Anne spent a portion of her corporate career in regional banking with tenure at both Fifth Third and ABN-AMRO LaSalle, where she was a senior executive leading finance, operations and business transformation functions. In addition to her work in the for-profit sector, Anne was the founder of Avenell, a boutique consulting firm that assisted non-profit organizations respond to the 2008 financial crisis. Anne began her career at General Electric where she spent fifteen years working in the US, Europe and Southeast Asia in both industrial and financial services industries. Anne is a board member of the Coleman Entrepreneurship Center at DePaul University, Chicago and is also one of the "Founding 40" members of Coleman's Women in Entrepreneurship Institute.
Anne received her undergraduate degree from Miami University (Ohio) and completed her graduate studies at the University of Chicago.
Don Phillips
Managing Director, Morningstar
Don Phillips is a managing director with Morningstar. He originally joined Morningstar in 1986 as the company's first mutual fund analyst and soon became editor of its flagship publication, Morningstar Mutual Funds™, establishing the editorial voice for which the company is best known. Investment Advisor magazine named him to its list of the most influential people in the financial planning industry.
Register for the Event
Register for Liberal Arts for Leaders by clicking the offsite Registration button below.
Lectures & Panels
Why Philanthropy Is Failing Democracy, and How to Redeem It
Thu, February 3, 2 - 3:30pm CST
MLA Information Session
Wed, February 9, 6 - 7pm CST
Exploring the History of Normality: A Conversation with Tal Arbel
Fri, February 25, 12 - 12:45pm CST

redpajama_set_name: RedPajamaCommonCrawl


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Q: Standard form Linear Program How do i convert this LP
$$\text{maximize  }x_4$$
$$\text{subject to  }\epsilon\leq x_1\leq 1\\\epsilon{x_{j-1}}\leq x_j\leq 1-\epsilon{x_{j-1}}, \quad j=2,...,4 $$
 to the standard form $$\text{minimize  } c^Tx\\\text{subject to  Ax=b}\\x\geq 0$$

redpajama_set_name: RedPajamaStackExchange


Éric Dupont (born June 16, 1970) is a Canadian writer from Quebec. His 2006 novel La Logeuse was the winner of the 2008 edition of Le Combat des livres, and his 2012 novel La fiancée américaine was a competing title in the 2013 edition of the program; the latter novel's English translation, Songs for the Cold of Heart, was shortlisted for the 2018 Scotiabank Giller Prize.
Dupont was born and raised in Amqui, Quebec, and studied at Carleton University, the Free University of Berlin, the Université de Montréal and the University of Toronto. In addition to his writing career, he teaches English to French translation and continuing education courses at McGill University.
His debut novel, Voleurs de sucre, was published in 2005, and won the Prix Senghor and the Prix Jovette-Bernier. Its English translation, Sugar Thieves, was issued in 2012.
La Logeuse, his second novel, was published in 2006. It was defended by journalist Nicolas Langelier in the 2008 edition of Le Combat des livres, winning the competition.
His third novel, Bestiaire, followed in 2008. Its English translation, Life in the Court of Matane, appeared in 2016 as the first title ever published by QC Fiction, a publishing house which specializes in English translations of Québécois literature. La fiancée américaine followed in 2012, with its English translation Songs for the Cold of Heart following in 2018. In addition to its Giller Prize nomination the novel's translator, Peter McCambridge, was shortlisted for the Governor General's Award for French to English translation at the 2018 Governor General's Awards.
His newest novel, La route du lilas, was published in 2018.
^ a b "Un nouveau roman pour Éric Dupont". La Presse, August 22, 2018.
^ a b "L'explosion Éric Dupont". Le Devoir, October 13, 2012.
^ "Le Combat des livres kicks off with talk of independence and throwing books". Montreal Gazette, March 18, 2013.
^ "Esi Edugyan, Patrick deWitt among finalists for $100,000 Giller Prize". Toronto Star, October 1, 2018.
^ "Éric Dupont : Et dehors ce n'est pas la mer". Voir, April 27, 2006.
^ "Éric Dupont : les enfants d'Expo 67". La Presse, September 4, 2008.
^ "Life in the Court of Matane". Quill & Quire, July 2016.
^ "The Dupont Phenomenon". Montreal Review of Books, Summer 2018.
^ "Here are the finalists for the 2018 Governor General's Literary Awards". CBC Books, October 3, 2018.


This post was contributed by Tracey Crosby.
When you think of studying abroad in Europe, you probably imagine traveling to different cities and countries every weekend. I mean, why not? Everything is so close that spending Oktoberfest in Germany is as easy as booking a flight for 60€ and hopping on a plane for an hour. Why not spend a few nights in Portugal and the next week in Switzerland? I'll tell ya' why. Now that I am at the halfway point of my semester abroad in Paris, I don't ever want to leave. Like, ever. Before embarking on this wild adventure, I viewed myself solo traveling every weekend to anywhere and everywhere. But the truth is, I've been lacking in the 'abroad' part of 'studying abroad'; this is because I've been investing all my time in making this city my home as much as possible. Now, before you start thinking I'm a loser who is wasting her time abroad doing nothing all day, let me explain.
I think it's great that people travel every weekend to see new cities. My sister is abroad in Rome at the moment, and she is the definition of 'abroad'. She has gone away almost every weekend to the south of Italy, Spain, Germany, France – you get the picture… but she's not the only one with this idea. A girl in my program told me that this upcoming weekend was going to be her first one actually spent in Paris.
My jaw literally dropped to the floor. I couldn't believe that she had gone almost nine weeks living in Paris, yet hasn't even spent a single weekend in Paris! This goes the same for plenty of other people in my program. Every week, I hear something about someone spending a weekend somewhere in Barcelona, Prague, Stockholm, Venice, Berlin – you name it, someone's going. Sure, they've all been to a ton of beautiful places during the semester, but how much are they actually seeing of the place they're currently living in?
Are they merging with the culture or living as a visitor?
I feel like this is the case with a lot of people studying abroad. They book trips as soon and as often as possible that they're gone almost half of the week from the place they're supposed to be living in. I'll admit, I used to get a little jealous of people traveling to see a new city every weekend. It felt like I was going about the whole 'study abroad' thing all wrong. So many of my friends would be traveling while I stayed in Paris – yet again. But the reason I stay every weekend is simply that I love it so much. Now after all these weeks, I know Paris like the back of my hand- sorta. When I say it's important to put the 'study' in 'study abroad', I don't necessarily mean with school. In fact, I mean the opposite of school. What I mean is you should get to know your neighborhood, the language, cultural habits – heck, get to know the locals. My friends and I go to this ramen place called Hokkaido every Thursday after our language class, and we wait outside in a line to get in because it's just that good. We always see this woman there (bless her hardworking self), and I order the same damn thing every time.
My order is a little complicated because I'm a vegetarian, but this woman always knows, without fail, what I'm about to say. One time, this guy was taking our order and I was having trouble explaining what I wanted (in French of course), and I was certain that my food was about to come out wrong. But the heavens opened up, and the woman literally came out of left field to explain my order to him.
That's when I knew I was a regular there.
Every morning before class, I stop at the Franprix by my house before hopping on the metro and I buy a single green apple and it's always the same man who cashes me out and I make sure to wish him a "bonne journée!" After French class, my friends and I get paninis for lunch at this place by my school because their paninis are no joke. You'd be mistaken if you think I'd pass up a panini for 3,50€. But, not everything I do in Paris involves food believe it or not. I've even pushed myself to interact with people at the post office to send letters to family back home in the States. My first attempt I accidentally spent 20€ on the wrong stamps, but hey, now I know.
I've made it a point to be home every night for my dinners with my host mom because it's an opportunity to practice my french for an hour and get to know her and her customs a little better (plus it's free food, so that's a bonus). It's these interactions that make studying abroad so special. Anyone can go to a city and see the big tourist attractions. But to completely adjust and immerse yourself into another culture is something else. Since being here, I've completely changed. I dress differently, I speak differently, I metro everywhere, I eat different foods on a different schedule, I tell time differently, I count money differently, yadda yadda yadda.
During the few excursions where I have left Paris, I find myself seriously missing the city. It genuinely feels like home now, instead of some way of life separate from my own.
The life I had back in the States seems so foreign to me now.
The habits I had no longer apply to my life in Paris. I had to say goodbye to English, large coffees, early dinners, driving, makeup, long showers, fresh air (if you know, you know). Sacrificing these things wasn't the end of the world, because I know I'm gonna be back in the states in no time. But you only have so many opportunities to truly adopt another way of life.
I've gotten to know amazing places here hidden in the streets of Paris and connect with people on a personal level whom I might never have met. There is so much to see if you let yourself veer off the typical route to school and just explore for the hell of it. You could potentially find your new favorite bakery or garden or monument or book store.
Yeah, I'll admit a small part of me wishes I took the opportunity to travel more around Europe. But, there is no right or wrong way to do study abroad. Some people simply just want to travel more than others. It doesn't mean their experience is necessarily better or worse, it's just different for different reasons. Of course, I dream of traveling to many more countries and cities in the future.
But for now, I still have a lot more to learn in Paris.
Meet Tracey: I'm Tracey and I'm a Fashion Photographer from Massachusetts. I love anything and everything to do with the French language and culture- especially the pain au chocolats. I'm always either reading, writing, editing, or all of the above. But no matter what, I'm always living my best life. Keep up with me on IG.


The Svadhisthana rooms are two private rooms within our 'Japanese Garden Villa' that adjoins the front wall of the retreat centre, located as you enter the main door to the retreat centre. The villa is the newest addition our growing retreat centre space, and provide a great option for complete privacy from the retreat centre itself, but literally adjoining the retreat centre. Room 1 has two single beds (can be joined to make a king), and Room 2 a queen bed – both have private bathrooms, and each room has it's own private level and huge living space of the two storey villa. The villa theme is "minimalist Japanese" and can be a great option for groups 2 or 3 guests who would love their very own private villa within the retreat centre compound.
Named after the sacral chakra, Svadhisthana is defined as the support or foundation to your vital life force / soul.Whilst the base chakra gets our material needs in order, the sacral chakra then allows our emotional nature to be attended to. This is where the unconscious and emotions reside, and emotions, like water, are fluid, ever changing provides us with the ability to accept change and go with the flow of life. Svadhisthana encourages us not to rush decisions but to allow the answers to bubble up from your core in their own time ~ so the bubbling fountain of the Garden Villa sets the perfect backdrop to let go and let things flow.


THEATREMATTERS! is a partnership between the Boys & Girls Clubs of Sarasota County and Venice Theatre. The program offers children who live in disadvantaged circumstances in Venice to participate in performance art education – for the fourth consecutive year! 24 members from the Robert and Joan Lee Boys & Girls Club between ages 8-14 participate in the Venice Theatre arts program known as THEATREMATTERS!
The THEATREMATTERS! program is designed to develop each student's individual talents and skills, as well as teach them to work together as a team, increase their problem-solving skills, and improve their presentation and communication skills. Participants learn about performing, as well as the technical aspects of theatre such as lighting, sets, sound, and costumes. The program runs Monday through Wednesday at Venice Theatre and Thursday at the Robert and Joan Lee Boys & Girls Club.
Recent studies suggest that children who participate in the arts are more likely to be recognized for academic achievement, participate in math and science fairs, and win awards for writing. Arts education also plays an integral role in enhancing children's developmental skills including motor skills, language development, decision-making skills, visual learning, creativity, cultural awareness, and overall academic performance.
About our Education & Outreach Dept.
Calling all Scouts! Call 941-486-8679 to earn your theatre badges with us.


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Q: SSIS Excel source not mapping all columns I have a data that I need to import on a xlsx file.
The flow is simple, a person complete the information on this file, then put the file on a shared folder. Finally the ETL move the file to other location and load the data and put it on a SQL Table.
But for some strange reason now, the file load correct, but then fails. The error says

SSIS.Pipeline] Error: Error in the validation of "component" Excel Origin "(150)". The validation status "VS_NEEDSNEWMETADATA" was returned.

I realized that the dataource is not loading some columns that exists in the file.
I've tried creating a new datasource and new connection, but the problem persits. I think the problem is on the file, but how can I change the metadata to map the columns tha are missing.

A: Try removing then adding the Excel Source component in the DataFlow Task, Also try to set the DataFlow Task Delay Validation property to True
If the Error persists, i think that the issue is not with the columns name, it can be generated by the column data type, try adding ;IMEX=1 to the excel connection string so the data type are taken from the first row


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Reading: Mariah Carey's "Christmas Time is In the Air Again" Through the Years
THE 97 > Blog > Artists > Mariah Carey > Mariah Carey's "Christmas Time is In the Air Again" Through the Years
FeaturedMariah CareyMusicRetrospectives
Mariah Carey's "Christmas Time is In the Air Again" Through the Years
Vincent Anthony
Mariah Carey's honorary title as "Queen of Christmas" is attributed to the colossal success of her #1 hit single, 1994's "All I Want For Christmas is You." However, to be honest, that song isn't even her best Christmas song. Nope. You didn't read wrong. In my humble opinion, Mariah Carey's best Christmas song is 2010's "Christmas Time is in the Air Again."
"Christmas Time is in the Air Again" Live Through the YearsOn her ABC Christmas special in 2010:At the Rockefeller Center Tree Lighting in 2012:At Michael Buble's Christmas Special in 2013:At the Beacon Theatre "All I Want For Christmas is You" residency, 2014-18:With Babyface, at Mariah Carey's Merriest Christmas special, 2015:On The Late Late Show, in 2019:In Mariah Carey's Magical Christmas special, 2020:
First released on her second Christmas album, 2010's Merry Christmas II You, "Christmas Time is in the Air Again" is simply perfection. Composed by a pair who are independently legendary, together, Mariah Carey and Marc Shaiman literally crafted up magic.
The exquisitely orchestrated ballad is has that classic, timeless feel that one would expect to hear from a vintage Christmas song. Lyrically, Mariah expertly weaves ornaments of Christmas into a relatable love story. The song is structured into four verses, with no real hook — just the repetition of the song's title. Really, each verse is like a hook itself, the way Mariah has skillfully structured it. Throughout the four verses, she mentions Christmas chimes and cheer, evergreens that "sparkle with lights," falling "like the snow, so deep in love," occupied mistletoe, and the memories that come along with it all. Even Christmas icons Mr. Grinch (who "can't resist warming up") and Ebenezer Scrooge (who "makes a Christmas wish for a honey to hold") are mentioned.
Melodically and vocally, Mariah delivers on both fronts with a melody that sticks and vocals that warms the soul. There is no reason this song should not be perched among the most beloved Christmas songs of all time. To her credit, Ms. Carey has certainly done her due diligence in promoting the song throughout the years. Let's go back in time, shall we (with the Ghost of Christmas Past)?
"Christmas Time is in the Air Again"
Live Through the Years
On her ABC Christmas special in 2010:
At the Rockefeller Center Tree Lighting in 2012:
At Michael Buble's Christmas Special in 2013:
At the Beacon Theatre "All I Want For Christmas is You" residency, 2014-18:
With Babyface, at Mariah Carey's Merriest Christmas special, 2015:
On The Late Late Show, in 2019:
In Mariah Carey's Magical Christmas special, 2020:
TAGGED: Christmas, Featured, Mariah Carey, Retrospective, review
Vincent Anthony December 21, 2021
By Vincent Anthony
Vincent is the founder of the magazine and has had a strong passion for popular music since, well, 1997! If it's not obvious, his favorite artists include Destiny's Child, Mariah Carey, Janet Jackson, Michael Jackson, P!nk, and many more. Vincent lives in New York and is a graduate of St. John's University. When he's not writing for the magazine, he's writing lesson plans, as he is also a high school English teacher!


I've managed to make quite a bit of progress in a couple of areas over the past few days.
The hilly streets of San Francisco are an ideal place to do a bit of hill walking training, as I now have less than a month until my Colorado "7 peaks" challenge. The other morning I found myself at the dual summits of Twin Peaks, which I imagine on a clearer day would give spectacular views over the whole city. However, since I have been here the peaks have always been hidden in cloud, and the other morning was no different. Despite being warm when I set off, it was cold and windy on the summit, and I didn't pause there for long.
This morning I took another walk around some of the steeper streets nearby. I am quite fascinated by both the geography and architecture of the city. It is so different to Perth for example, where everything is flat and open, and houses are often single story dwellings with large gardens. Here everything is packed in close together, and is thin and tall, built on amazingly inclined streets, so that in some areas no two houses are on the same level.
Yesterday I went back to Bridge Storage, just north of the city, where my RV has been stored for the past six months. It was parked in a different location to where I had left it, and I was encouraged that it had obviously been started and driven at least once in the time I had been away.
I checked all around, and the tyres looked okay, all still being fully inflated. I put the key in the ignition, and the battery meter showed plenty of power. A couple of cranks and the engine fired and ran smoothly without any trouble. The brakes were a little bit seized, but soon freed, and after running the engine for a brief while, I checked the oil and water. All looked good.
I went and questioned three different RV repair places about fixing the damage I had done to the waste water tanks and pipes last year. The most encouraging was a guy called Mike at the last place I visited, who was more optimistic about being able to repair the damage, rather than replacing everything with new stuff.
So I hurried back to the storage place, collected the RV and dropped it off with Mike just before he closed for the day. I'm now waiting for a final quote, and if all works out well, I will be able to use the toilet and wash dishes when I am at Bonneville Salt Flats, without water just pouring out onto the ground from the broken tanks. What luxury!
Previous Post Birthday weekend in San Francisco. Next Post More goals invites…. come and join in.


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Celebrating the successes of science in post-independence India - IndiaBioscience
indiabioscience April 26, 2018, 5:35am #1
On 25th April 2018, The Indian National Science Academy (INSA) released a book on the impact of science on the growth of independent India. Titled, "Indian Science-Transforming India", the book is a showcase of areas where Indian science has had a positive transformative impact; for example, the setting up of Amul co-operative, developing of plastic bags suited for storing blood in blood banks, the manufacturing of generic drugs and several others. The stories included in the book have been written by a group of four authors- Adita Joshi, Dinesh C. Sharma, Kavita Tiwari and Nissy Nevil. L S Shashidhara of IISER, Pune is the editor of the book.
The book attempts to address the "lacuna in communicating how science has affected our society, and how it is upto the scientists to ensure that the public at large, and policymakers in specific, know the scientific contributions that helped in bringing leaps in all aspects of our nation's health", says, Ajay Sood, President, INSA in his opening note in the book.
At the time of India's independence, the literacy rate was just 12%, life expectancy a mere 32 years, poverty incidence rate of 80% and a population of over 340 million people. Not many imagined that in 70 years, the country would be a trillion dollar economy, among the seventh largest in the world. But for our insistence on using science and technology to build the nation, India would not have been able to overcome its colonial past at this rapid pace. Indeed, India is the only country that includes "scientific temper, humanism and spirit of enquiry and reform as a duty of its every citizen" in its constitution.
The book holds a mirror to the same spirit of scientific temper visible in various successes our country has seen in the years since independence. It also tries to reinforce the idea that rather than looking out for individual heroes, people – general public and policymakers, should realise that our country progresses when the average scientific capability of the country is high.
The stories engage the reader in interesting backgrounds against which scientists, engineers, businessmen and technopreneurs got together to solve needs of the society. The first story in the book, written by Joshi, brings to spotlight the need for developing indelible ink, which later became an irreplaceable part of the electoral process. In fact, India is now an exporter of this ink to several countries across the globe. Other stories written by Joshi talk about shaping of India's biotechnology sector by Shantha Biotechnics (first ones to make Hepatitis B vaccine widely available to people of India); ensuring the quality of Basmati rice using DNA fingerprinting and finally, making of the Improved Samba Mahsuri variety of rice which benefits both farmers and traders.
Tiwari takes the readers into the village of Anand, to meet the reluctant dairy engineer Verghese Kurien, who changed the fortunes of not only that village, but of the entire country. People know the story of the beloved 'Amul Man', but not that "basic nutrition security was achieved when milk and milk products were delivered to the doors of Indian middle- and lower-middle class families. This helped the next generation become physically and cognitively more competent" says Shashidhara. Tiwari also covers the story of generic drugs and the birth of blood bags –both of which are saving millions of life today. Other stories by the same author explore the use of plant tissue culture in revolutionising the growth of agriculture and the role Central Institute of Brackishwater Aquaculture (CIBA) played in reviving India's aquaculture.
Another author, Nevil portrays the story of two engineers who built an empire of laser technology in Surat, transforming India into a world leader in diamond cutting and polishing.
And finally, the story written by Sharma reminds me, if it were not for the contributions of basic science behind the revolution of information technology, India's status as an IT giant (and my unfettered access to the computer on which I write this article) would not have been possible.
The book leaves you feeling positive about India's 70-year journey, but more than that it gives you hope that with science and technology we can conquer more challenges facing our country.
Going ahead, INSA plans to "bring out several such books on various aspects of Science and Society from 2019 onwards. These will cover India centric science issues such as water, drug discovery, crop genomics and provide an up to date account of where we are and the future projections", says A.K Singhvi, Vice-President INSA.
The printed copies of the books would be distributed at very low cost, with an online version available freely.
This is a companion discussion topic for the original entry at https://indiabioscience.org/news/2018/celebrating-the-successes-of-science-in-post-independence-india
sansritifoundation April 26, 2018, 6:48am #2
Thanks Urvashi for putting this up.
The prime reason behind writing this book was to connect and communicate the strength of Indian science with young students, citizens and policy makers. All the authors have chosen stories which highlight scientific innovations, products and processes that have touched and improved the life of common man, either directly or indirectly.
One unique aspect of the book is that the narration is based on personal interviews, e-mail correspondences and telephonic calls with the scientists, researchers and administrators. Thus, each story has information which readers would not get easily from the web or other sources.While reading the stories, the readers may appreciate that how the confluence of basic science and applicative technologies have helped us solve problems that matter to the community in a way which suits our resources and local conditions.
I hope the readers enjoy the book ,and young students get inspired to pursue science with a dream that a few years later their stories might also be written as celebration of Indian science.
Adita Joshi
smita April 26, 2018, 12:26pm #3
The stories are amazing - I am myself in awe of the innovations that were made by some real brilliant and dedicated people at the time when the resources were scarce, resulting in defining of the Indian Science canvas. Everyone should read this book to understand and appreciate the importance of science and technology.
mona.sharma1169 April 26, 2018, 7:46pm #4
Looking forward to buying the book for my son and his friends !!
A good start and look forward to more…
Urvashi April 27, 2018, 5:16am #5
Thanks Adita, I thoroughly enjoyed reading the book. The stories have been narrated beautifully and the hope is they will be equally engaging to the target audience as they were to me. I was curious about how the interviews were conducted, so it's good to hear about that from you.
I will be doing my bit to highlight the book in my circle. Looking forward to buying it for my parents.
Urvashi
Thanks Urvashi,
Here is some information on how the interviews were conducted.
Authors select the topics
Authors read about the topic and put down a list of questions that they need to ask.
Authors and editor would contact the scientists , researchers and administrators.
Interviews were conducted ( Authors would need to travel out of their hometowns or workplaces to other geographical locations to conduct the interviews)
smita April 28, 2018, 3:36am #7
The book should be soon available to download freely on INSA website.
smita May 4, 2018, 4:35am #8
eBook: Indian Science - Transforming India. Impact of Science in Independent India can be downloaded freely here: http://www.insaindia.res.in/scroll_news_pdf/ISTI.pdf
rajeshkrsingh194 May 7, 2018, 6:17am #9
Yes downloaded this book and it's amazing
aryaricha August 10, 2019, 1:42am #10
Great book, thank you all for the effort. From where I could buy it ? I wish to buy multiple copies to distribute amongst family and friends.


text:
Q: VS2019 custom template - parent variable prefix (ext_) not working I am trying to create a custom template for VS2019 comprised of multiple projects (based on this guide), and I'm having problems with the references.
My solution is based on the classic 3-tier architecture (data/business/ui), so when I create a new solution - call it "MySolution", I get "MySolution.Data", "MySolution.Business" and "MySolution.UI" - all good. I need the business layer to have a reference to the data layer - however, using the $ext_safeprojectname$ variable just gives me an unresolved reference in the projects node of my dependencies to "$ext_safeprojectname$.Data"
Other variables seem to work fine - for example, if I use $safeprojectname$, I get "MySolution.Business.Data" in my reference - correct parsing of the variable, for the business layer project, but not the desired result.
Has the ext_ prefix been replaced? I cannot seem to find any documentation for this anywhere.
Thanks!

A: I was able to solve it after commenting on your question. I was missing the CopyParameters=true attribute for ProjectTemplateLink element in the .vstemplate file. It is mentioned as a tip on Create a multi-project template from an existing solution. My ext_ variables were replaced once I did this. Your ProjectTemplateLink element should look something like below:
<ProjectTemplateLink ProjectName="MyProject" CopyParameters="true">...</ProjectTemplateLink>


Axis Direct vs Monarch Networth Capital– Compare Brokerage, Demat etc.
Here is a detailed comparison of Axis Direct vs Monarch Networth Capital. Axis Direct is the only banking broker known to offer great deals to its clients like happy hour trading, free demat account & more. Monarch Networth Capital is also a full service broker and comparatively less branches across India. This comparison between Axis Direct & Monarch Networth Capital is a neck to neck affair.
This Axis Direct & Monarch Networth Capital comparison will give a very detailed overview of which stock broker is suitable for you.


SUBMISSION DEADLINE: October 15th, 2012.
We have almost 100 applications in to speak at TEDxMaui 2013. Remarkable proposals! Our team has got their work cut out, but we are so grateful!
Thank you everyone! Stay tuned!


EnWave Corporation (TSX-V:ENW | FSE:E4U) ("EnWave", or the "Company") announced today that it will discontinue its exploratory equipment development project with Sutro Biopharma Inc. ("Sutro"). EnWave has been working under an equipment construction contract to deploy the Company's Radiant Energy Vacuum ("REV™") technology to dehydrate a specific cell-free extract since September 2012.
EnWave was unable to satisfy certain quantitative bioactivity criteria for the dried cell-free extract; however, the throughput and process time metrics were achieved using REV™ technology. Due to project time metrics and additional investment required to pursue a solution, both companies decided to put any further development on hold for the foreseeable future. Sutro will independently continue development work to identify a suitable drying process for its cell-free extract.
As a result of this project ending, EnWave will record an impairment to its 'Due from Customers on Contract' balance in the amount of $865,000. The impairment charge will be reported in the Company's third quarter financial statements for the three and nine months ended June 30, 2018.
EnWave Corporation, a Vancouver-based advanced technology company, has developed Radiant Energy Vacuum ("REV™") – an innovative, proprietary method for the precise dehydration of organic materials.


text:
Q: How to create per-process variables in a kernel module? Scenario
I'm implementing a simple IPC system in linux kernel. To simply the question, assume we only have 2 system calls. and the interface cannot be changed.

*

*SYSCALL_DEFINE1(add, int, id)

*SYSCALL_DEFINE1(del, int, id)
if add has an existing element with id, then it needs to report 'already exist', EEXIST.
So I thought it needs to store the added data somewhere. My choice is to store it inside my syscall implementation .c file, as a global value. Like:
struct mylist {
    int id;
    struct list_head list;
};

LIST_HEAD(id_list);

SYSCALL_DEFINE1(add, int, id) {
    // kmalloc struct mylist
    // operate on `id_list` (add, check existence)
}

Problem
When I write a user space program to have a test.

*

*The first time I ran the test it is ok.

*The second time it fails, because it reports id already exists!

The memory does not appear as I thought.
Questions

*

*What is the lifetime of kernel space data id_list? (as long as the system is up?)

*How can I make id_list's lifetime equal to userspace program?


CUMBERLAND, W.Va. — Officials from private, nonprofit and governmental agencies are continuing an effort to re-establish Allegany and Garrett County as well as the Potomac Highlands area of West Virginia as a popular destination for motor coach tourism.
Evan Slonaker, executive director of the Allegany County Historical Society, who helps to operate several historic sites including the Gordon-Roberts House at 218 Washington St., spoke about the changes that have occurred in motor coach tourism.
"We used to have (at the Gordon-Roberts House) three to four buses visit on a single day. Now we might get 10 to 15 all season," said Slonaker.
Slonaker said a season is April to October.
Efforts are under way to bring back the once thriving bus tourism industry.
"We need to get the word out and let people know we are still here," said Vicki Koch, the owner of Westmar Tours.
Koch said the benefits to the local economy and the service it provides in promoting the attractions and the history are invaluable.
For the full story, go to the CumberlandTimes-News.com.


Kani History Video... Do you know your history?
Shout out to the ARCO team in Barcelona!!!
Kani Life Denim & Linen !!!
The Come Back CUBS featuring Karl Kani Jr.!!!!
Karl Kani on Good Day LA!!!
So Fresh and So Klean!!!
Zappos.com in the house baby!
Fashion Designer, Karl Kani has spread his vision internationally for the last 10 years. Karl Kani has built the brand name from Brooklyn, New York to become a household name in over 25 countries. From Brooklyn to Rome!


Bio Ermanno Pitacco is full professor of Actuarial Mathematics and Life Insurance Technique in the University of Trieste, and academic director of the Master in Insurance and Risk Management in the MIB School of Management in Trieste. He is an actuary and a member of several actuarial associations and committees, among which the Actuarial Association of Europe, the AFIR/ERM Section of the International Actuarial Association (ICA) and the Mortality Working Group of the ICA. He is editor of the Springer Actuarial Series, and coeditor or associate editor of several journals in the field of actuarial mathematics and insurance. Main fields of scientific interest are: life and health insurance mathematics, longevity risk, portfolio valuations. He authored or co-authored papers and textbooks in the field of actuarial mathematics and actuarial techniques. He was awarded with the 1996 INA prize for Actuarial Mathematics from Accademia Nazionale dei Lincei, and the 2011 Bob Alting von Geusau Memorial Prize, together with Annamaria Olivieri, for the best paper published in the ASTIN Bulletin on an AFIR related topic: "Stochastic Mortality: the Impact on Target Capital".
This couse aims at introducing technical and financial aspects of the insurance business, with a special emphasis on the actuarial valuation of insurance products.
professionals and technicians operating in insurance and pension areas, whose job may regard investments, risk analysis, financial reporting, and so on, hence implying communication with actuarial professionals and managers.
E. Pitacco and A. Olivieri (1997), Introduzione alla teoria attuariale delle assicurazioni di persone, Pitagora Editrice, Bologna.


HomeBlogsTradingWhat are the Advantages and Disadvantages of Discount Stock Brokers?
In India 2010 Discount Broker concept lunched by Zerodha – Leading Stock Broker. Before we start let's see what is discount broker?
Discount Broker who offer lowest Brokerage or cheapest brokerage for buy and sell stocks. They offer you online execution platform. Discount Stock Broker offer very low brokerage compare to full service broker. But mostly discount broker doesn't offer advisory like full service broker. Discount Broker mostly operates centralised.
Full-service broker: Full service brokerage basically traditional Offline Broker who offer client dedicated relationship manager, advisory, Sit and trade facilities and charges Brokerage higher compare to discount broker. Full service has wide Branch Network in Pan India. Full Service Broker provides Fundamental reports and technical calls for Investor & trader.
Unbiased Offer: Either you big player or small investor Discount Broker offer you same.
Low Transitional Cost: Mostly Discount Broker offer lowest Transactional cost due to their high volume of trades.
Clients Education: Discount Broker offer online educational video tutorial for their client so client can educate and trade with them for longer time. Mostly full service broker not provided this type of facilities.
No Misleading: Discount broker doesn't give brokerage target or third product sales target to their employees.
Paperless Account Opening: With help of Aadhar Number Discount Broker offer Paperless Account Opening – Open Online Trading account in 10 mins . Whereas full service broker still process same way.
No Minimum Brokerage: Unlike full service broker discount broker don't charge minimum brokerage for their client. Means if you want to buy or trade in panny stock then discount broker is best option for you.
Flat Brokerage Charges: Discount Broker offer their client Flat brokerage charges irrespective of Trade volume means if you buy 1 lot or 100 lot of nifty they charges you flat Rs 20* (Zerodha Leading discount Brokerage Charges for F&O) where Full service broker charges you as per % on volume you trade.
No Branch Support: Mostly Discount Broker worked centrally so if you required Branch Support for Paper work then Discount Stock Broker not for you. You should search Broker who offer Branch Support and offer discount brokerage like Zerodha.
No Relationship Manager: Discount Broker don't offer relationship manager like Full service broker so if you service class people and don't have time to check online portfolio regularly and required a person who can manage your stocks and mutual fund portfolio then Discount stock broker is not you cup of tea.
No advisory: Discount Broker don't offer Advisory service where as full service broker offer their client advisory service free of cost.
PMS Service: Discount Broker doesn't offer Portfolio management service to their client. If you are looking fund manager who can manage your fund then Full service broker is best option for you – Motilal Oswal is your choice.
3 – in – one: Unlike Full service banking broker Discount broker don't offer 3-in-one (Banking + Trading + Demat) account opening. If you are looking for 3-in-one account then ICICIDirect, Kotak Sec or SBICAP Sec your best option to select.
Above comparison shows that Discount Broker is best option for who want to trade in stock market and don't required advisory.
What to watch out for Selection of Discount Broker.
Online Trading Terminal: As you aware that Discount Broker works online you should check their Trading Terminal software what they offer to their client. And review of that Terminal. Currently Zerodha Kite and Upstox Pro is best Online Trading terminal offered by Discount Stock Broker.
Hidden Charges: Discount Broker mostly show their charges sheet on their website. Compare their Transaction cost with other discount Broker. Someone you found that one discount broker brokerage charges is less than other discount brokers but they charge heavy on Transaction cost. Will end up more cost than others.
Broker Review:Before Selecting Discount Broker Read Discount Broker review online. Clients reviews are the best way to understand where they are lacking.
Branch Network : Discount broker also have branches in mostly major cities check before opening Trading account with Discount broker are they provide Branch service in your area.
Discount Broker Financial Strength: Check broker financial strength before put all your investment amount broker. Best way to understand that check Broker Risk management system if broker don't allow more risk leverage to their client means they are under control.
Which is the Best Discount Broker for Beginners 2019?
← Which are the Best Investment Apps for Beginners?


Pride season in South Florida is finally drawing to a close, and a little bird told me (I think her name was Vanessa Williams) that they saved (one of) the best for last.
Wilton Manors Stonewall Parade and Pride Festival will rock the Island City on Saturday, June 16, as more than 45,000 of us arrive to get our rainbow on. It all kicks off when the Marketplace opens at 4pm, with more than 100 booths spread along the length of Wilton Drive, including vendors of every stripe, artists, activists, service agencies, and, of course, plenty of food and drank.
Meanwhile, parade contingents will have been queuing up just south of the river (and the Dairy Queen!) at Fort Lauderdale High School, preparing to step off at 5pm. The parade lasts about 90 minutes, then The Drive slips into full-on street-party mode.
Who am I kidding? The Drive is in full-on street-party mode all day and evening, with folks enjoying the Marketplace — and each other's company. The focus of much of the partying will be the four major stages set up along the parade route, where the best, the brightest, the queerest, and the most outrageous among us will be strutting their stuff from mid-afternoon.
Read on for the skinny on all the hot stage action.
The iHeart Stage is ground zero for your Stonewall Pride celebrations, and it all kicks off with the official opening ceremonies at 4:15pm, featuring appearances by local leaders including Wilton Manors Mayor Gary Resnick, Vice Mayor (and mayoral candidate!) Justin Flippen, and Congresswoman Debbie Wasserman Schultz.
After the parade wraps up around 6:30, the stage kicks into high gear with DJs including iHeartRadio's Mobile Mike as well as special guest DJs from 93.9 MIA, not to mention dozens of local performers. Keeping them all in line will be your lovely and talented hosts: Misty Eyez, Nicole Halliwell, and Hotspots LIVE's very own Kitty Meow.
The Manor parking lot will be its own little street party. They're bringing in food trucks! Meanwhile, out on The Drive, the megaclub showcases two of its most popular resident DJs on its outdoor stage: DJ Sushiman gets things going at 3pm, and DJ JPS takes over from 6pm to 11. Between beats, expect plenty of performers.
To help you transition from the street into the club (no cover tonight!), DJ Kidd Madonny will be stepping up to the decks on The Manor's indoor stage at 10pm, where the Miami fave will keep the crowd jumping until 4am.
Also appearing: The Big Dipper, who Vice described a few years ago as "a hairy, filthy-mouthed queer rapper out of Chicago who has a theater background, and way better style than you," will be jetting in from his new base in L.A.
He'll be joined by NYC-bred rapper (and dancer) Reijo Rych, who will not need to fly in because he's now local talent. This won't be Rych's first time on The Drive. He's played Stonewall before. He even won Rumors Talent Search back in the day.
Speaking of Rumors, the Upper Drive staple is promising its largest Stonewall stage ever. And they're gonna need it to hold all the talent because this year the popular bar and grill is springing for a 7-piece live band. Fronted by both male and female vocalists, the band will be serving up your pop favorites for the ages: Britney to Bruno Mars, Bon Jovi to Lady Gaga.
Sounds like if you need a break from drag performances during Stonewall — say it isn't so! — Rumors will be your official lip-sync-free zone.