The use of probiotics for improving lipid profiles in dyslipidemic individuals: an overview protocol

Patricia M Fortes
Solomar M Marques
Karolline A Viana
Luciane R Costa
Alessandra V Naghettini
Paulo Sucasas Costa
The use of probiotics for improving lipid profiles in dyslipidemic individuals: an overview protocol
10.1186/s13643-018-0826-2P R O T O C O L Open Access
Background: Dyslipidemia is a major risk factor in triggering cardiovascular events, which can lead to the death of millions of people around the world. Thus, several pharmacological and non-pharmacological therapeutic strategies have been developed in recent decades with the objective of improving lipid profiles, including the use of probiotics. Therefore, the purpose of this protocol is to describe the steps that will guide the construction of an overview to demonstrate the scientific evidence of the efficacy of probiotics in improving the lipid profile of dyslipidemic individuals and to propose specific recommendations regarding their use. Methods: The search will be conducted in the following databases: MEDLINE/PubMed, EMBASE, PROSPERO, Cochrane Library, CINAHL, JBI Database of Systematic Reviews and Implementation Reports, Google Scholar, and CADTH. Reviewers will select systematic evaluations and data analyses from randomized clinical trials that evaluated the effects of probiotics on lipid profiles. The studies will be analyzed for methodology quality by the AMSTAR 2 tool and risk of bias by ROBIS. The data will be extracted by three independent reviewers based on a data extraction sheet, which will include the most relevant variables for the analysis and interpretation of the results. The variables will be categorized and described in narrative form or in tables. Discussion: There are some systematic reviews about the use of probiotics to prevent and/or treat dyslipidemia; however, their outcomes related to the ability of probiotics to improve lipid profiles are conflicting. So, an overview on this topic is needed to clarify this important issue. Systematic review registration: PROSPERO CRD42017080328

Background

Dyslipidemia is one of the most important cardiovascular risk factors (CRF) in triggering serious events. According to the World Health Organization (WHO), 17 million people died in 2015 from cardiovascular disease, accounting for about 31% of deaths worldwide in that period; acute myocardial infarction (AMI) and stroke were the main events diagnosed as a cause of death [1]. Therefore, dyslipidemia is still the most important modifiable risk factor associated with these events [2]. However, what makes controlling it challenging is that it has reached epidemic proportions, develops quietly, and is associated with other cardiovascular risk factors [3,4].

Considering that the control of dyslipidemia is an important factor in the prevention of AMI and stroke, there are many protocols and guidelines with therapeutic regimens related to pharmacological and non-pharmacological treatments. Yet, controlling this condition has become increasingly difficult probably due to its multifactorial origin [5,6]. Therefore, new treatment options have been proposed; among them, some scientific interest has focused on the use of probiotics, defined as "living microorganisms that in adequate quantities are able to bring benefits to host's health" [7].

In recent decades, probiotics have been used in the management of different health conditions, demonstrating their benefits and safety when administered in humans, including dyslipidemic individuals [8]. The mechanisms of action by which different probiotic strains act in the control of dyslipidemia have been suggested from randomized clinical trials, highlighting (1) deconjugation and precipitation of bile acids by enzymatic action; (2) incorporation of cholesterol into the cell membrane of microorganisms; (3) inhibition of cholesterol transmembrane transporter expression in enterocytes; (4) cholesterol conversion to coprostanol; and (5) inhibition of hepatic synthesis of cholesterol [9].

Several studies, mainly clinical trials, have demonstrated the ability of some bacterial strains to reduce total cholesterol, low-density lipoprotein cholesterol (LDL-c), and triglycerides [10][11][12][13][14][15]. On the other hand, no changes were observed in the lipid profile after the use of probiotics in other studies [16][17][18]. In a preliminary literature search, we found systematic reviews (SRs) that generated contradictory results of probiotic use in dyslipidemic individuals [19][20][21][22]. Thus, an overview of SRs is required to support the healthcare decision-making on probiotics to treat dyslipidemia, Therefore, the purpose of this protocol is to critically assess the scientific evidence presented in the systematic reviews currently available regarding the efficacy of probiotics in improving the lipid profile in dyslipidemic individuals. After this process, an overview will be drawn up showing the quality of such evidence that can enable health professionals to make specific recommendations for the use of probiotics in this context.

Methods/design

The term "overview" refers to a review of systematic reviews and/or meta-analyses that compares studies and promotes a critical analysis of the main viable evidence about a particular issue, thus facilitating appropriate decision making [23]. The development of this protocol followed the guidelines presented by the Joanna Briggs Institute (JBI) for reviewers [23] and the recommendations proposed by PRISMA-P (Preferential Reporting Items for Systematic Review and Meta-analysis-Protocol) [24] (Additional file 1). The elaboration of the overview will follow the steps described in this protocol, and any deviation in the steps proposed here will be reported and justified. This overview protocol was registered in the PROSPERO database CRD42017080328.

Research question

Does the use of probiotics improve the lipid profile in dyslipidemic individuals?

Eligibility criteria Types of study

Only systematic reviews which have been carried out from randomized clinical trials with or without meta-analyses, performed or not under Cochrane methodology will be included to allow a greater level of evidence [25]. Additionally, systematic reviews may have clearly demonstrated a research question that includes the acronym PICO (population, intervention, comparison/control, outcome(s)) [23,26]. The SRs should have been published since January 2000, because this is the period of the publication of one of the first SR summarizing evidence about efficacy of probiotic use in the control of dyslipidemia [27]. Narrative reviews, protocols, reviews of animal studies, reviews of observational studies, and case series will not be included.

Participants

The participants included in SR should be well described regarding characteristics such as age, gender, sample size, and clinical conditions associated with dyslipidemia. Systematic reviews will be excluded if more than 80% of the participants in the sample are carriers of type 1 diabetes, hypothyroidism, nephrotic syndrome, or if they are pregnant women or alcoholics, because they might require other therapeutic interventions that would confound the probiotics effect.

Intervention

The probiotic should be the only intervention used to control dyslipidemia. Systematic reviews will be included if they are detailed in the study design: administration of treatment, presentation form, probiotic strain identification, dose and time of probiotic use, whether the groups were conducted in parallel or cross-over, and if there were associations with other factors that could interfere with the action of the probiotic (e.g., prebiotic, symbiotic, phytotherapeutic, neutracetic, soy, drugs).

Comparator

The selected SRs should demonstrate that the interventions were compared with a control group composed of placebo or non-therapeutic substance.

Outcomes

We will include SRs whose outcomes were related to changes in lipid profiles (total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides).

Search strategy Electronic databases

Two investigators (PMF and PSC) will carry out the searches using the following databases: PubMed-MEDLINE (www.ncbi.nlm.nih.gov/pubmed/), EMBASE (www.embase.com), Cochrane Library (www.cochranelibrary.com), PROSPERO (www.crd.york.ac.uk), CINAHL, and JBI Database of Systematic Reviews and Implementation Reports. All electronic search tools are available for free, with the exception of EMBASE. Databases for gray literature such as Google Scholar (www.scholar.google. com) and CADTH "Gray Matters Light" (http:// www.cadth.ca//resources/finding-evidence/grey-matters) will be searched for non-indexed publications and government policies. The references cited in the selected studies will also be reviewed by manual search.

Search terms

For the overview, authors used the key elements of the research question to define the descriptors selection. The purpose of this selection is to define the keywords that best described the eligibility criteria of the SRs to be selected without significantly narrowing the search. After identification, the terms will be used in combination, including keywords (MeSH-Medical Subject Headings) and entry terms that are present in the title, abstract, and full text (Additional file 2). The combinations of keywords and search strategy are exemplified in Fig. 1 and will be repeated for all databases.

Selection of studies

Two independent reviewers (PMF and PSC) will select the studies. In case of disagreement between reviewers, a third reviewer will be consulted. Evaluation will begin with the exclusion of duplicate articles using the program Mendeley-Reference Management Software and Researcher Network, followed by application of the eligibility criteria (initially, the title and abstract). Selected articles from screening will undergo thorough reading for further examination of the eligibility criteria; eligible studies will be submitted for data extraction and quality assessment.

For a better visualization of the stages of study selection, a flow diagram will be set up as suggested by PRISMA (Preferential Reporting Items for Systematic Reviews and Meta-analyses) [28].

Extraction of data

Each of the systematic reviews will be submitted to data extraction by three separate reviewers (PMF, SMM, and KAV), thus ensuring that important data will not be lost. The reviewers will use a standard form that will be initially tested in three studies; if necessary, it will be adapted. In order to adequately meet the interests research question of this overview, the following data will be extracted: (1) author, country, year of publication, number of RCTs included, funding (if any), language, and database (including gray literature); (2) characteristics of the population (sex, age, baseline diseases) and sample size; (3) intervention-strains of probiotics, study design, duration of treatment, dose, presentation form, association (prebiotic, symbiotic); (4) comparators (placebo or yogurt); (5) outcomes-changes in serum levels of total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides; (6) main adverse events related to probiotic supplementation (e.g., systemic-bacteremia, sepsis; and gastrointestinal-diarrhea, flatulence, abdominal pain); and (7) results-qualitative and quantitative (means, standard deviations, effect size, confidence interval, p value, and meta-analysis, if the data allow). If there are questions and disagreements between the reviewers in this process, a fourth reviewer will be consulted. If necessary, the RCTs analyzed in SRs will be checked for completion of some important data.

Assessment of the risk of bias and methodological quality

The risk of bias will be evaluated using the ROBIS tool, according to specific guidelines for systematic reviews related to interventions. This tool is composed of three phases: phase 1-assessment of relevance (optional); phase 2-identification of concerns with the review process regarding the presence of bias, which is performed by four different domains and phase ~"((probiotic).ti,ab OR (probiotics).ti,ab OR (probiotic agent).ti,ab OR (ferment milk).ti,ab OR (yogurt).ti,ab OR (yoghurt).ti,ab OR (Lactobacillus).ti,ab OR (Bifidobacterium).ti,ab) AND ((dyslipidemia).ti,ab OR (hypercholesterolemia).ti,ab OR (hyperlipidemia).ti,ab OR (low density lipoprotein cholesterol).ti,ab OR (dyslipoproteinemia).ti,ab OR (high cholesterol levels).ti,ab OR (elevated cholesterol).ti,ab OR (lipid profile).ti,ab)) AND ((systematic review).ti,ab OR (systematic review AND meta-analysis).ti,ab OR (meta-analysis).ti,ab)" Fig. 1 Combination of keywords and entry terms for search in the database 3-judgment of the risk of bias through the summary of phase 2 findings. With the exception of phase 1, the other phases present answers that vary in (Y) "yes," (PY) "probably yes," (PN) "probably not," (N) "no," and (NI) "not informed" contained in each sub-area of the domains. The reason for concern is the risk of bias classified as high, low, or unclear [29].

The tool AMSTAR 2 (Additional file 3) (https:// amstar.ca/docs/AMSTAR-2.pdf ), which is composed of 16 domains, will be used in the evaluation of the methodological quality of the SRs. Of these 16 domains, 7 (2, 4, 7, 9, 11, 13, and 15) are considered critical for the reliability of SRs results, ranking the SR in high, moderate, low, and critically low [30]. For each domain, the answers will vary in "yes" (all criteria required by the domain have been checked), "partially yes" (only part of the criteria required in the domain have been checked), and "no" (the criteria required by the domain were not checked). Multiple flaws in non-critical domains (more than 3) will be considered capable of decreasing confidence in results; in this case, the classification will start from the moderate level. The domains will be analyzed independently, so that the response to a particular domain does not influence the evaluation of the others [30].

Both the bias risk assessment and the methodological quality evaluation of the reviews will be carried out by two independent reviewers (PMF, KAV). If disagreements occur, a third reviewer will be consulted (SMM).

Data synthesis

Results of reviews will be summarized, and the characteristics of the variables will be described in narrative form or in tables where each one will be categorized and, if appropriate, allocated as a statistical measure. This will allow the comparison of studies, visualization of effects and critical interpretation of results.

A meta-analysis will not be performed, given that pooling the results of meta-analyses could introduce significant overlap and biased results, as a RCT could be included in more than one SR [31].

The calculation of the corrected covered area (CCA) will be applied to estimate the overlap of RCTs among the reviews included [31]. In order to calculate the CCA, a citation matrix containing the primary articles and the revisions will be elaborated and arranged in the order of publication of the revisions. If there is a high overlap of RCTs, the most recent revisions will be maintained and the impact of such findings on the evidence will be discussed.

The discordant results presented among SRs will be analyzed based on the algorithm proposed by Jadad et al. [32], which seeks to identify the likely causes of contradictory results in reviews that presented the same research question. Subsequently, it will be discussed how these different causes of disagreement between the results impact on the evidence.

Discussion

The increase in the number of dyslipidemic individuals due to changes in life habits in all age groups has become a challenge for the control of this important cardiovascular risk factor [1]. Consequently, more and more investigations are carried out seeking therapeutic alternatives that can prevent the onset of cardiovascular events. Therefore, the purpose of this protocol is to present the search pathway for a critical analysis of current scientific evidence about the efficacy of probiotics use in the control of dyslipidemia. Furthermore, at the end of the execution of its stages, an overview can be produced to demonstrate the evidences found, contributing with greater clarity in the applicability of probiotics as a therapeutic resource against dyslipidemia, which may point out gaps and stimulate further research in this area. Authors' contributions All authors, PMF, SMM, KAV, AVN, LRC, and PSC, were involved in the intellectual production and protocol design. The steps of searching, extracting, and synthesizing data proposed in the protocol will be developed by the authors PMF, KAV, and SMM under the coordination of LRC, AVN, and PSC. At the end of the development of the steps described in the protocol, the authors PMF, SMM, KAV, LRC, AVN, and PSC will carry out the analysis, interpretation, and discussion of the results for the production of an overview. The reviewers PMF and PSC were responsible for writing the protocol. All authors read and approved the final manuscript.

Additional files

Example: PUBMED / MEDLINE -115 studies (search 29 Sept 2017).

Additional file 1 :
1PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist. Recommended items to address in a systematic review protocol. (DOCX 41 kb) Additional file 2: PubMed search. Combination of keywords and entry terms for search in the PUBMED (DOCX 14 kb) Additional file 3: AMSTAR 2. Tool that will be used for critically appraising the systematic reviews included (PDF 250 kb) Abbreviations AMI: Acute myocardial infraction; AMSTAR: A Measurement Tool to Assess Systematic Reviews; CA: Covered area; CCA: Corrected covered area; CRF: Cardiovascular risk factors; HDL-c: High-density lipoprotein cholesterol; JBI: Joanna Briggs Institute; LDL-c: Low-density lipoprotein cholesterol; MA: Meta-analyze; MeSH: Medical Subject Headings; PICO: Population, Intervention, Comparison, Outcome(s); PRISMA: Preferential Reporting Items for Systematic Reviews and Meta-analyses; PRISMA-P: Preferred Reporting Items for Systematic Review and Meta-analysis-Protocol; RCT: Randomized controlled trials; SR: Systematic review; TIAB: Title/Abstract; WHO: World Health Organization

Authors' information PMF-MD, MS, PhD Student. Pediatric, in Pediatric Nephrology. SMM-MD, PhD. Professor and author of papers based on systematic reviews. KAV-MS, PhD Student. Author of papers based on systematic reviews. LRC-MS, PhD. Professor, certified by the Joanna Briggs Institute on the Systematic Reviews methods. AVN-MD, PhD. Associate Professor in Pediatric Nephrology. PSC-MD, PhD. Associate Professor, certified by the Joanna Briggs Institute on the Systematic Reviews methods.
Ethics approval and consent to participate Not applicable.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.
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Computed Tomography for Lung Cancer Screening in the United States
2017. 2018

PhDJan M Eberth
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

MSParisa Bozorgi
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

Logan M Lebrón
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

Sarah E Bills
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

M A
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

PhD, MPHLinda J Hazlett
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

MDRuth C Carlos
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

PhDJennifer C King
Delaware, Florida, MassachusettsMaryland, New Jersey, New York, Pennsylvania

Computed Tomography for Lung Cancer Screening in the United States

Prev Chronic Dis
151802412017. 2018PREVENTING CHRONIC DISEASE PUBLIC HEALTH RESEARCH, PRACTICE, AND POLICY Volume 15, E119 OCTOBER 2018 GIS SNAPSHOTS Suggested citation for this article: Eberth JM, Bozorgi P, Lebrón LM, Bills SE, Hazlett LJ, Carlos RC, et al. Geographic Availability of The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. www.cdc.gov/pcd/issues/2018/18_0241.htm • Centers for Disease Control and Prevention 1
Panel A. Location of low-dose computed tomography (LDCT) screening centers in the United States and percentage of US population aged 55 to 79 who live without access to a screening center within 30 miles. Symbol indicates location of a LDCT screening center. Panel B. Lung cancer mortality per 100,000 persons and percentage of US population aged 55 to 79 without access to a screening center within 30 miles. Mortality and accessibility scores were classified into 3 groups (low, medium, high), each based on the natural breaks method, and combined for bivariate mapping: 1) high mortality/high access, 2) high mortality/medium access, 3) high mortality/low access, 4) medium mortality/high access, 5) medium mortality/medium access, 6) medium mortality/low access, 7) low mortality/high access, 8) low mortality/ medium access, and 9) low mortality/low access. These maps highlight state-level variation in LDCT screening availability and accessibility, as well as lung cancer mortality among persons of LDCT screening age. The maps help identify areas in need of LDCT screening program creation and/or expansion, particularly in rural areas.

Background

Lung cancer is the leading cause of cancer-related death among men and women in the United States, with more than 234,000 persons diagnosed yearly (1). Most diagnoses are made at a late stage when the cancer is more difficult to treat (1). Until 2011, there was not broad evidence that any one type of screening reduced lung cancer mortality. However, the National Lung Screening Trial clearly demonstrated that annual low-dose computed tomography (LDCT) can reduce lung cancer deaths by up to 20% in high-risk populations (ie, people aged 55-74 y who have a ≥30-pack-year smoking history and who, if former smokers, had quit within the previous 15 years) (2). Subsequently, in 2014, the US Preventive Services Task Force (USPSTF) (3) endorsed LDCT screening for high-risk persons, resulting in changes to the reimbursement policies of private and public insurers.

Despite endorsements and new reimbursement policies favoring LDCT screening, uptake is low among high-risk persons (3.9% based on the 2015 National Health Interview Survey) (4). In 2014, we showed that the United States had 203 active LDCT screening centers and that most were in the Northeast and the East North Central states (5). In 2015, the Centers for Medicare & Medicaid Services introduced insurance coverage and associated billing codes for LDCT screening. An updated assessment of the landscape of LDCT screening in the United States is now needed to determine the extent of geographic variation in LDCT screening availability and to identify regions for program expansion based on potential demand.

Methods

In January 2017, we obtained the names and locations of LDCT screening centers that meet/attest to quality standards set by the Lung Cancer Alliance (ie, Screening Centers of Excellence) and/or the American College of Radiology (ie, ACR Designated Lung Cancer Screening Centers). The business addresses of each LDCT screening center was geocoded using Esri's World Geocoding Service and ArcGIS Desktop version 10.2.

The primary geographic unit was the census block group. Block groups were classified as urban or rural areas according to 2010 Rural-Urban Commuting Area (RUCA) codes established by the US Department of Agriculture's Economic Research Service. Each block group was assigned an urban or rural designation according to whether its centroid (ie, geographic center of a polygon) was inside the census-tract boundaries in which it was nested. RUCA codes of 1, 2, and 3 were categorized as urban, and codes 4 through 10 were categorized as rural. We used population estimates for the number of persons aged 55 to 79 to approximate the USPSTF-recommended screening age (ie, 55-80 y). We obtained population estimates at the block group level from the US Census Bureau's Integrated Public Use Microdata Series, specifying data from the 2011-2015 American Community Survey (6).

Data on state lung cancer mortality rates (aggregated data from 2010 through 2014), extracted from the National Cancer Institute's State Cancer Profiles website (7), were overlaid with data on LD-CT screening center locations to determine whether areas of high need had equitable access to services. We measured spatial accessibility to screening by using a 30-mile Euclidean distance buffer (ie, a circle around a point 30 miles in any direction) to identify the number of persons aged 55 to 79 who reside within and outside the facility catchment area. A centroid was created for each block group and was assigned a corresponding block group population. To calculate the population residing within and outside of each buffer, we assumed that if a census block group's centroid was inside the 30-mile buffer, then the population in that block group would have access to that screening center. We aggregated block group estimates to compute accessibility for all 50 states and the District of Columbia and by level of rurality (urban or rural). For the first map, the proportion of persons aged 55 to 79 without access to a designated LDCT screening center within 30 miles was categorized into 4 groups in equal intervals; the breaks occurred at 22%, 43%, and 65%. For a second map, lung cancer mortality and accessibility scores were classified into 3 groups by using the natural breaks method. For lung cancer mortality, these groups were classified as low (20.0 to 40.3 deaths per 100,000), medium (40.6 to 50.7 deaths per 100,000), and high (51.1 to 69.6 deaths per 100,000). For accessibility, the groups were classified as low (51%-86%), medium (20%-48%), and high (0%-17%). These categories resulted in 9 combinations of lung cancer mortality and accessibility, presented in bivariate map format. Finally, we performed a sensitivity analysis to examine accessibility based on a 30-minute driving distance.

Main Findings

The number of designated LDCT screening centers increased from an estimated 203 in 2014 to 1,748 in early 2017. The mean number of designated LDCT screening centers per state was 34. Nine states (Alaska, Hawaii, Montana, New Mexico, North Dakota, South Dakota, Utah, Vermont, and Wyoming) and the District of Columbia had 5 or fewer designated centers.

Across all states, an average 14.9% of persons aged 55 to 79 did not have access to a designated screening center within 30 miles, and an average 28.1% did not have access within a 30-minute drive. In most states (27 states and the District of Columbia), 0% to 22% of the population aged 55 to 79 did not have access to a PREVENTING CHRONIC DISEASE designated screening center within 30 miles; 66% to 86% of this population did not have access to these services within 30 miles in 4 states (North Dakota, South Dakota, Montana, and Wyoming) clustered in the upper Midwest (Panel A). In 9 states (Connecticut, Delaware, Florida, Maryland, Massachusetts, New Jersey, New York, Pennsylvania, and Rhode Island) and the District of Columbia, 95% or more of the population aged 55 to 79 had access to a designated LDCT screening center within 30 miles. Rural residents were less likely than urban residents to have access to a designated LDCT screening center within 30 miles (47.5% rural vs 93.7% urban) or a 30-minute drive (22.2% rural vs 83.2% urban).

Lung cancer mortality was highest in the eastern interior of the United States. A cluster of high mortality stretches north to south from West Virginia to Louisiana and west to east from Oklahoma to West Virginia. Despite their high rate of lung cancer mortality, Alabama, Arkansas, Mississippi, Oklahoma, Tennessee, and West Virginia had low to medium levels of access to LDCT screening. All the states along the eastern seaboard and in the Northeast, except Maine, had high access to LDCT screening (Panel B).

Action

These maps update an assessment of the geographic variation of LDCT screening availability in the United States and extend research by using spatial proximity of persons aged 55 to 79 to designated screening centers to identify disparities in access (5,8). Findings indicate that although the number of designated LDCT screening centers increased by more than 8 times since 2014, pronounced disparities in the distribution of centers exist, particularly between rural and urban areas. This disparity in access is concerning, given the large proportion of high-risk persons in rural areas. We hope that the geographic patterns illustrated in these maps stimulate further research into ways to improve equitable access to high-quality screening services in high-need regions.

Charkhchi P, Kolenic GE, Carlos RC. Access to lung cancer screening services: preliminary analysis of geographic service distribution using the ACR lung cancer screening registry. J Am Coll Radiol 2017;14(11):1388-95.

8.

PREVENTING CHRONIC DISEASE

expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.

expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.
Centers for Disease Control and Prevention • www.cdc.gov/pcd/issues/2018/18_0241.htm
AcknowledgmentsThis publication was made possible in part by grant no. T32-GM081740 from the National Institutes of Health-National Institute of General Medical Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or the National Institute of General Medical Sciences. No copyrighted images, materials, surveys, instruments, or tools were used in this study.Author Information
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