Improving treatment and survival: a population‐based study of current outcomes after a hepatic resection in patients with metastatic colorectal cancer

AbstractBackgroundPopulation‐based studies historically report underutilization of a resection in patients with colorectal metastases to the liver. Recent data suggest limitations of the methods in the historical analysis. The present study examines trends in a hepatic resection and survival among Medicare recipients with hepatic metastases.MethodsMedicare recipients with incident colorectal cancer diagnosed between 1991 and 2009 were identified in the SEER(Surveillance, Epidemiology and End Results)‐Medicare dataset. Patients were stratified into historical (1991–2001) and current (2002–2009) cohorts. Analyses compared treatment, peri‐operative outcomes and survival.ResultsOf 31 574 patients with metastatic colorectal cancer to the liver, 14 859 were in the current cohort treated after 2002 and 16 715 comprised the historical control group. The overall proportion treated with a hepatic resection increased significantly during the study period (P< 0.001) with pre/post change from 6.5% pre‐2002 to 7.5% currently (P < 0.001). Over time, haemorrhagic and infectious complications declined (both P ≤ 0.047), but 30‐day mortality was similar (3.5% versus 3.9%, P = 0.660). After adjusting for predictors of survival, the use of a hepatic resection [hazard ratio (HR) = 0.40, 95% confidence interval (CI): 0.38–0.42, P < 0.001] and treatment after 2002 (HR = 0.88, 95% CI: 0.86–0.90, P < 0.001) were associated with a reduced risk of death.ConclusionsCase identification using International Classification of Diseases, 9th Revision (ICD‐9) codes is imperfect; however, comparison of trends over time suggests an improvement in multimodality therapy and survival in patients with colorectal metastases to the liver

A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database

ImportanceThe United States (US) Medicare claims files are valuable sources of national healthcare utilization data with over 45 million beneficiaries each year. Due to their massive sizes and costs involved in obtaining the data, a method of randomly drawing a representative sample for retrospective cohort studies with multi-year follow-up is not well-documented.ObjectiveTo present a method to construct longitudinal patient samples from Medicare claims files that are representative of Medicare populations each year.DesignRetrospective cohort and cross-sectional designs.ParticipantsUS Medicare beneficiaries with diabetes over a 10-year period.MethodsMedicare Master Beneficiary Summary Files were used to identify eligible patients for each year in over a 10-year period. We targeted a sample of ~900,000 patients per year. The first year's sample is stratified by county and race/ethnicity (white vs. minority), and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county population size with oversampling of minorities. Patients who were alive, did not move between counties, and stayed enrolled in Medicare fee-for-service (FFS) were retained in the sample for subsequent years. Non-retained patients (those who died or were dropped from Medicare) were replaced with a sample of patients in their first year of Medicare FFS eligibility or patients who moved into a sampled county during the previous year.ResultsThe resulting sample contains an average of 899,266 ± 408 patients each year over the 10-year study period and closely matches population demographics and chronic conditions. For all years in the sample, the weighted average sample age and the population average age differ by &lt;0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. Rates of 21 comorbidities estimated from the samples for all 10 years were within 0.12% of the population rates. Longitudinal cohorts based on samples also closely resembled the cohorts based on populations remaining after 5- and 10-year follow-up.Conclusions and relevanceThis sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries or other national claims files for longitudinal follow-up with possible oversampling of sub-populations

Aetiology and incidence of diarrhoea requiring hospitalisation in children under 5 years of age in 28 low-income and middle-income countries: findings from the Global Pediatric Diarrhea Surveillance network.

Introduction: Diarrhoea remains a leading cause of child morbidity and mortality. Systematically collected and analysed data on the aetiology of hospitalised diarrhoea in low-income and middle-income countries are needed to prioritise interventions. Methods: We established the Global Pediatric Diarrhea Surveillance network, in which children under 5 years hospitalised with diarrhoea were enrolled at 33 sentinel surveillance hospitals in 28 low-income and middle-income countries. Randomly selected stool specimens were tested by quantitative PCR for 16 causes of diarrhoea. We estimated pathogen-specific attributable burdens of diarrhoeal hospitalisations and deaths. We incorporated country-level incidence to estimate the number of pathogen-specific deaths on a global scale. Results: During 2017–2018, 29 502 diarrhoea hospitalisations were enrolled, of which 5465 were randomly selected and tested. Rotavirus was the leading cause of diarrhoea requiring hospitalisation (attributable fraction (AF) 33.3%; 95% CI 27.7 to 40.3), followed by Shigella (9.7%; 95% CI 7.7 to 11.6), norovirus (6.5%; 95% CI 5.4 to 7.6) and adenovirus 40/41 (5.5%; 95% CI 4.4 to 6.7). Rotavirus was the leading cause of hospitalised diarrhoea in all regions except the Americas, where the leading aetiologies were Shigella (19.2%; 95% CI 11.4 to 28.1) and norovirus (22.2%; 95% CI 17.5 to 27.9) in Central and South America, respectively. The proportion of hospitalisations attributable to rotavirus was approximately 50% lower in sites that had introduced rotavirus vaccine (AF 20.8%; 95% CI 18.0 to 24.1) compared with sites that had not (42.1%; 95% CI 33.2 to 53.4). Globally, we estimated 208 009 annual rotavirus-attributable deaths (95% CI 169 561 to 259 216), 62 853 Shigella-attributable deaths (95% CI 48 656 to 78 805), 36 922 adenovirus 40/41-attributable deaths (95% CI 28 469 to 46 672) and 35 914 norovirus-attributable deaths (95% CI 27 258 to 46 516). Conclusions: Despite the substantial impact of rotavirus vaccine introduction, rotavirus remained the leading cause of paediatric diarrhoea hospitalisations. Improving the efficacy and coverage of rotavirus vaccination and prioritising interventions against Shigella, norovirus and adenovirus could further reduce diarrhoea morbidity and mortality

Use of quantitative molecular diagnostic methods to identify causes of diarrhoea in children: a reanalysis of the GEMS case-control study.

BACKGROUND: Diarrhoea is the second leading cause of mortality in children worldwide, but establishing the cause can be complicated by diverse diagnostic approaches and varying test characteristics. We used quantitative molecular diagnostic methods to reassess causes of diarrhoea in the Global Enteric Multicenter Study (GEMS). METHODS: GEMS was a study of moderate to severe diarrhoea in children younger than 5 years in Africa and Asia. We used quantitative real-time PCR (qPCR) to test for 32 enteropathogens in stool samples from cases and matched asymptomatic controls from GEMS, and compared pathogen-specific attributable incidences with those found with the original GEMS microbiological methods, including culture, EIA, and reverse-transcriptase PCR. We calculated revised pathogen-specific burdens of disease and assessed causes in individual children. FINDINGS: We analysed 5304 sample pairs. For most pathogens, incidence was greater with qPCR than with the original methods, particularly for adenovirus 40/41 (around five times), Shigella spp or enteroinvasive Escherichia coli (EIEC) and Campylobactor jejuni o C coli (around two times), and heat-stable enterotoxin-producing E coli ([ST-ETEC] around 1·5 times). The six most attributable pathogens became, in descending order, Shigella spp, rotavirus, adenovirus 40/41, ST-ETEC, Cryptosporidium spp, and Campylobacter spp. Pathogen-attributable diarrhoeal burden was 89·3% (95% CI 83·2-96·0) at the population level, compared with 51·5% (48·0-55·0) in the original GEMS analysis. The top six pathogens accounted for 77·8% (74·6-80·9) of all attributable diarrhoea. With use of model-derived quantitative cutoffs to assess individual diarrhoeal cases, 2254 (42·5%) of 5304 cases had one diarrhoea-associated pathogen detected and 2063 (38·9%) had two or more, with Shigella spp and rotavirus being the pathogens most strongly associated with diarrhoea in children with mixed infections. INTERPRETATION: A quantitative molecular diagnostic approach improved population-level and case-level characterisation of the causes of diarrhoea and indicated a high burden of disease associated with six pathogens, for which targeted treatment should be prioritised. FUNDING: Bill & Melinda Gates Foundation

Minimally biased nonparametric regression and autoregression

A nonparametric regression estimator is introduced which adapts to the smoothness of the unknown function being estimated. This property allows the new estimator to automatically achieve minimal bias over a large class of locally smooth functions without changing the rate at which the variance converges. Optimal convergence rates are shown to hold for both i.i.d. data and autoregressive processes satisfying strong mixing conditions

Banded and Tapered Estimates for Autocovariance Matrices and the Linear Process Bootstrap

We address the problem of estimating the autocovariance matrix of a stationary process. Under short range dependence assumptions, convergence rates are established for a gradually tapered version of the sample autocovariance matrix and for its inverse. The proposed estimator is formed by leaving the main diagonals of the sample autocovariance matrix intact while gradually down-weighting o�-diagonal entries towards zero. In addition we show the same convergence rates hold for a positive de�nite version of the estimator, and we introduce a new approach for selecting the banding parameter. The new matrix estimator is shown to perform well theoretically and in simulation studies. As an application we introduce a new resampling scheme for stationary processes termed the linear process bootstrap (LPB). The LPB is shown to be asymptotically valid for the sample mean and related statistics. The e�ectiveness of the proposed methods are demonstrated in a simulation study

Banded and Tapered Estimates for Autocovariance Matrices and the Linear Process Bootstrap

We address the problem of estimating the autocovariance matrix of a stationary process. Under short range dependence assumptions, convergence rates are established for a gradually tapered version of the sample autocovariance matrix and for its inverse. The proposed estimator is formed by leaving the main diagonals of the sample autocovariance matrix intact while gradually down-weighting o�-diagonal entries towards zero. In addition we show the same convergence rates hold for a positive de�nite version of the estimator, and we introduce a new approach for selecting the banding parameter. The new matrix estimator is shown to perform well theoretically and in simulation studies. As an application we introduce a new resampling scheme for stationary processes termed the linear process bootstrap (LPB). The LPB is shown to be asymptotically valid for the sample mean and related statistics. The e�ectiveness of the proposed methods are demonstrated in a simulation study.autocovariance matrix, stationary process, boostrap, block bootstrap, sieve bootstrap, Econometrics