There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Why most published research findings are false

The Summary of Notifiable Diseases - United States, 2011 contains the official statistics, in tabular and graphic form, for the reported occurrence of nationally notifiable infectious diseases in the United States for 2011. Unless otherwise noted, the data are final totals for 2011 reported as of June 30, 2012. These statistics are collected and compiled from reports sent by state health departments and territories to the National Notifiable Diseases Surveillance System (NNDSS), which is operated by CDC in collaboration with the Council of State and Territorial Epidemiologists (CSTE).

/pdf/summary-of-notifiable-diseases-united-states-2011-2ycpfu6g2m.pdf

Summary of Notifiable Diseases - United States, 2011.

The Summary of Notifiable Infectious Diseases and Condition-United States, 2013 (hereafter referred to as the summary) contains the official statistics, in tabular and graphic form, for the reported occurrence of nationally notifiable infectious diseases and conditions in the United States for 2013. Unless otherwise noted, data are final totals for 2013 reported as of June 30, 2014. These statistics are collected and compiled from reports sent by U.S. state and territory, New York City, and District of Columbia health departments to the National Notifiable Diseases Surveillance System (NNDSS), which is operated by CDC in collaboration with the Council of State and Territorial Epidemiologists (CSTE). This summary is available at http://www.cdc.gov/mmwr/mmwr_nd/index.html. This site also includes summary publications from previous years.

/pdf/summary-of-notifiable-infectious-diseases-and-conditions-1h4kmx6yio.pdf

Summary of Notifiable Infectious Diseases and Conditions - United States, 2013.

Haemolytic uraemic syndrome.

The Summary of Notifiable Infectious Diseases and Conditions - United States, 2015 (hereafter referred to as the summary) contains the official statistics, in tabular and graphical form, for the reported occurrence of nationally notifiable infectious diseases and conditions in the United States for 2015. Unless otherwise noted, data are final totals for 2015 reported as of June 30, 2016. These statistics are collected and compiled from reports sent by U.S. state and territories, New York City, and District of Columbia health departments to the National Notifiable Diseases Surveillance System (NNDSS), which is operated by CDC in collaboration with the Council of State and Territorial Epidemiologists (CSTE). This summary is available at https://www.cdc.gov/MMWR/MMWR_nd/index.html. This site also includes summary publications from previous years.

/pdf/summary-of-notifiable-infectious-diseases-and-conditions-3wlh1e9c50.pdf

Summary of Notifiable Infectious Diseases and Conditions - United States, 2015.

Cronobacter spp. are gram-negative bacteria mainly perceived to cause serious infections in infants (1). Cases are most common among newborns or young infants; estimated mortality rates are as high as 80% (2). Infections also occur in older children and adults (3). In adults, Cronobacter spp. cause septicemia, pneumonia, osteomyelitis, wound infections, and splenic abscesses (4).

Little is known about reservoirs or routes of transmission other than ingestion of contaminated powdered infant formula (5–7). However, organisms have been isolated from other foods and environmental sources, and infections have occurred among persons who did not consume or handle formula (8,9).

In the United States, the incidence of Cronobacter spp. infection is unknown, but evidence suggests it is low. In 1998, only 1 case was found among 10,660 hospitalized infants of low birth weight (10). In 2002, the Foodborne Diseases Active Surveillance Network (FoodNet) estimated an incidence of 1 case per 100,000 infants (11). However, these estimates might be unreliable because populations studied were small and the disease is rare. There is no national surveillance for Cronobacter spp. infections; such infections are reportable only for infants in Minnesota. To increase understanding of the public health effects and demographic distribution of Cronobacter spp. infections, we investigated incidence of laboratory-confirmed infection and characteristics of infected persons.

/pdf/incidence-of-cronobacter-spp-infections-united-states-2003-4e3vftp41k.pdf

Incidence of Cronobacter spp. infections, United States, 2003-2009.

Postdiarrheal hemolytic uremic syndrome in United States children: clinical spectrum and predictors of in-hospital death.

To the Editor: As part of the public health response to the current pandemic (H1N1) 2009, surveillance for adverse events following vaccination for influenza A (H1N1) is a high priority (1). Surveillance for Guillain-Barre syndrome (GBS) has been of particular interest, because the syndrome was associated with the 1976–1977 swine influenza vaccine (1,2). To study this association, reliable ascertainment of recent incident cases of GBS is necessary.

GBS is an acute, immune-mediated paralytic disorder of the peripheral nervous system (3–5) with an estimated annual incidence of 0.8–1.9/100,000 (6). Most cases are associated with an antecedent infection (6). Several surveillance systems are in place to monitor rates of postvaccination GBS (1–3), most of which include a component of electronic administrative record review for case detection. Analysis of computerized medical databases is a well-established method of monitoring for vaccine adverse events (7). Although the validity of such data varies, depending on the diagnosis and region, few studies have evaluated the use of hospital discharge data for GBS specifically (8,9).

We reviewed the Tennessee Department of Health Uniform Hospital Discharge Dataset for all hospital discharge diagnoses in 4 major metropolitan regions of Tennessee in 2002–2003 with codes from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), that might indicate acute GBS. Records with ICD-9-CM code 357.0 (acute infective polyneuritis) or other combinations suggestive of GBS within the top 10 diagnoses were requested. These data were compared with information on cases identified by directly requesting lists of patients with discharge diagnoses of GBS from hospital medical record departments. Charts of all reported cases were validated by chart review. Patients were classified as having acute GBS if they met Brighton Criteria Levels 1, 2, or 3 (10).

A total of 344 records of possible cases of acute GBS were identified. Of these cases, 215 (63%) were identified through the state hospital discharge database, 315 (92%) were reported directly by hospitals, and 186 (54%) were identified by both systems. Among all suspected cases identified, only 103 (30%) met criteria for acute GBS (annual rate 2.1/100,000 population), 14 (4%) were out-of-state residents, 114 (33%) were nonacute cases that occurred before the study period and patients were readmitted for other reasons, 90 (26%) had no documentation of GBS in the medical record, 17 (5%) were duplicate reports, and 6 (2%) had insufficient information for further investigation. The predictive-value positive of a GBS diagnostic code from the statewide hospital discharge database representing acute GBS was only 30%. Of the 103 confirmed cases, 26 (25%) would have been missed if only the state hospital discharge database was used to identify potential cases.

Of 103 cases, all were identified with ICD-9-CM diagnosis code 357.0; in 91 (88%) cases, this was the primary diagnosis. Other combinations of codes did not identify additional cases. Of cases of acute GBS identified, 32 (30%) met only clinical criteria (Brighton Level 1), 40 (39%) had either laboratory or electrophysiologic evidence (Brighton Level 2), and 32 (31%) had both (Brighton Level 3).

Because the 2 surveillance systems we compared both relied on medical record discharge diagnoses, they were not independent, and we could not perform a capture/recapture analysis. Because GBS is a diagnosis for which the great majority of patients are hospitalized, and our overall incidence rate is within the range identified in other studies, it is likely that the combination of these methods is reasonably sensitive. The administrative hospital discharge database could not be relied on to confirm that all coded GBS cases were acute. Even if the 114 nonacute cases could easily have been identified and excluded from the initial list of 344 records, only 103 (45%) of the remaining 230 reports were identified as confirmed acute cases.

Although the use of large hospital discharge databases may be useful as an adjunct for identification of GBS cases as part of public health surveillance, they lack sufficient sensitivity or specificity to be relied upon exclusively. The poor specificity of the system is particularly problematic for public health surveillance. A large investment of time and resources was necessary to perform manual chart reviews to confirm possible cases, two-thirds of which were ultimately found not to be cases at all. Statewide administrative hospital discharge diagnosis databases should not be solely relied on for GBS surveillance. Additional methods of reliable and efficient ascertainment and verification of cases are crucial to ensure valid data. Obtaining reliable methods is particularly important for urgent situations such as current surveillance for adverse events after pandemic (H1N1) 2009 virus vaccination, in which the detection of problems will have immediate public health effects.

Hospital discharge data for Guillain-Barre syndrome and influenza A (H1N1) vaccine adverse events.

Hemolytic uremic syndrome (HUS) is a severe sequela of Shiga toxin–producing Escherichia coli (STEC) O157 infection characterized by hemolytic anemia, thrombocytopenia, and kidney failure. More than 90% of HUS cases follow a diarrheal illness and most are attributable to STEC O157 infection [1]. HUS is the most common cause of acute renal failure among US children <5 years of age [2, 3]; of the estimated 93 000 domestically acquired STEC O157 infections in the United States each year [4], approximately 6% will develop HUS [2]. The mortality rate is approximately 5% [2]. HUS may result in major long-term complications, including chronic renal failure, neurologic dysfunction, and hypertension [5]. Providing appropriate supportive care in a timely manner improves outcomes [6].

Surveillance for HUS provides a way to monitor trends in STEC O157 infections, can help to assess which strains of STEC are associated with severe outcomes, and can evaluate whether improvements in medical care are leading to less frequent development of HUS in children infected with STEC. For example, trends in STEC O157 infections can be influenced by changes in diagnostic practices. By providing a stable marker of STEC incidence, HUS surveillance can be used to determine the effectiveness of preventive measures. HUS surveillance is complicated by the lack of a single diagnostic test and the need for multiple data elements to validate each reported case. The significance of HUS as a public health issue was reflected in the Healthy People 2010 goal to reduce the incidence of postdiarrheal HUS among children <5 years of age by 50% (to 0.9 cases per 100 000) [7]. The Foodborne Diseases Active Surveillance Network (FoodNet) conducts active surveillance for HUS in children <18 years using 2 complementary case ascertainment methods. We compare the finding of these 2 methods of case ascertainment and the impact of different case definitions on surveillance data from 2000 to 2007.

/pdf/strategies-for-surveillance-of-pediatric-hemolytic-uremic-19vm01hh3n.pdf

Strategies for Surveillance of Pediatric Hemolytic Uremic Syndrome: Foodborne Diseases Active Surveillance Network (FoodNet), 2000–2007

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Effie Boothe

Author Tools

Chat about Author

Papers

Incidence of Cronobacter spp. infections, United States, 2003-2009.

Postdiarrheal hemolytic uremic syndrome in United States children: clinical spectrum and predictors of in-hospital death.

Hospital discharge data for Guillain-Barre syndrome and influenza A (H1N1) vaccine adverse events.

Strategies for Surveillance of Pediatric Hemolytic Uremic Syndrome: Foodborne Diseases Active Surveillance Network (FoodNet), 2000–2007