Private Sector

To develop a guideline for reports of web-based interventions we broadly followed the standard methodology developed by the CONSORT group, reported in detail elsewhere [14 (link)]. We started the work on CONSORT-EHEALTH in October 2010 with writing a grant proposal requesting funding for a consensus workshop from the Canadian Institutes of Health Research (CIHR). Unfortunately, this funding request was turned down (with some rather odd explanations, such as “[it is unclear] why journal editors [private sector] need funding to complete this project.”) Without funding, our initial plan to use a 3-phase process of premeeting item generation, a meeting with invited stakeholders, and postmeeting consolidation, had to be modified, with only a very short face-to-face workshop in the context of a scientific meeting, and the bulk of the work being conducted through online consultations.
The core international group of CONSORT-EHEALTH contributors included researchers, funders, consumers, journal editors, and industry, listed under acknowledgments. This is (and remains) an open and dynamic group.
In the premeeting item-generation process, we used the current CONSORT guideline items as a framework, and generated additional items and subitems through literature searches, extracting reported items from published RCTs as well as relevant guidelines. We had access to the referee reports of JMIR, which helped us to evaluate which items are frequently pointed out by reviewers as “missing” in the original submissions of the authors. Additional input came from a face-to-face session hosted by the International Society for Research on Internet Interventions (ISRII), in Sydney, Australia on April 6-8, 2011.
A preliminary version of the CONSORT-EHEALTH checklist (V1.5) was published in April 2010. In a web-based Delphi process we gathered some data on the importance of the items [14 (link)]. The initial questionnaire with the list of items is shown in Multimedia Appendix 1. Participants were asked to suggest missing items (under each CONSORT subheading), and to rate each proposed subitem on a scale of 1-5 (where 1 was “subitem not at all important” and 5 was “essential”).
We kept items as “essential” in the CONSORT-EHEALTH when at least 50% of respondents rated an item as “5-essential”. We downgraded items as “highly recommended” when at least 50% of respondents rated an item as 4 or 5 (but less than 50% said it is “essential”). We eliminated items when less than 50% of respondents answered 4 or 5.

We used the NIH Research Portfolio Online Reporting Tool [38 ] to identify all projects that received funding during any US fiscal year between 2007 and 2014 through the D&I funding opportunity announcements (FOAs) Dissemination and Implementation Research in Health (PARs: 06-039, 06-071, 06-072, 06-520, 06-521, 07-086, 10-038, 10-039, 10-040, 13-054, 13-055, 13-056) or Dissemination and Implementation Research in Mental Health (PA 02-131). We selected 2007 as the starting point because it was the first year that projects supported by the Dissemination and Implementation Research in Health FOAs received funding. For each project, we extracted the title, abstract, project terms, award amount per fiscal year, funding mechanism, and Institute.
Our review was guided by Bogenschneider’s definition of policy as: “the development, enactment, and implementation of a plan or course of action carried out through law, rule, code, or other mechanism in the public or private sector” [5 ]. Accordingly, we identified potential policy D&I projects by searching the project title, abstract, and term fields for mentions of “policy,” “policies,” “law,” “legal,” “legislation,” “ordinance,” “statute,” “regulation,” “regulatory,” “code,” or “rule.” Two coders then independently reviewed project abstracts and developed preliminary coding categories to capture project characteristics. These categories reflected themes in that data, categories used in previous NIH D&I funding reviews [36 (link), 37 (link)], and policy D&I scholarship [3 (link)–6 , 8 (link), 9 (link)]. The coders then jointly developed a coding guide and independently re-reviewed and coded the projects. Because many projects were not exclusively focused on policy D&I, projects were coded according to their policy D&I features. Incongruent coding decisions were identified in <10 % of projects and resolved through discussions.
We classified a project as policy D&I if it explicitly proposed to conduct empirical research about the “content” of a policy (e.g., analysis of the text of clean indoor air laws), the “process” through which it was developed (e.g., assessment of how state legislators use research evidence when developing clean indoor air laws), or the “outcomes” it produced (e.g., evaluation of the impacts of clean indoor air laws on cardiovascular health outcomes). These inclusion criteria were informed by Bogenschneider’s definition of policy [6 ] and domains of health policy research proposed by Brownson and colleagues [3 (link)–5 ]. For projects classified as policy D&I, we searched for its NIH project number in ClinicalTrials.gov [39 ] and PubMed [40 ] to obtain additional information and validate coding decisions.
We calculated the total dollar amount awarded through all NIH research grants and through the D&I FOAs, stratified by funding mechanism and Institute. We also calculated the amount awarded for policy D&I projects, and the percentage of total D&I FOA funding they comprised, within strata. Data were managed and analyzed in Microsoft Excel.

Respondents were recruited from a wide range of sociodemographic settings to broadly represent the targets for the application of the questionnaire in the future. Individuals were included if they were above age 18 years and able to read or understand Danish. Potential respondents were randomly approached by trained interviewers in a variety of locations in the broader community, such as in libraries, private sector workplaces, a hospital, nursing homes, health centers, and an outpatient clinic. To ensure inclusion of people who may have low literacy, potential respondents were given the option of completing the questionnaire themselves or to have it read aloud in an interview. If respondents did not have time to finish the questionnaire, they were encouraged to complete it at home and were provided with a reply-paid envelope. They also had the option of completing a Web-based questionnaire.
Demographic data including age, sex, educational background, self-reported health condition, and presence of chronic conditions were also collected to evaluate whether the resulting scales were invariant to these exogenous factors and thus provided unbiased estimates of mean differences across these groups.
The administration of the questionnaires also included the administration of a validation version of the eHealth literacy assessment toolkit, which is reported elsewhere (personal communication, Karnoe 2017). Respondents did not receive any payment for filling out the questionnaire.

This report updates and expands CDC recommendations for hepatitis B screening of adults published in 2008 (14 (link)). CDC evaluated the addition of a universal screening recommendation among adults as well as testing persons expected to be at increased risk for HBV infection that were not included in the 2008 testing recommendations.
Members of the CDC Guidelines Work Group (hereafter referred to as the work group) followed CDC guideline development and reporting standards (28 (link)) to develop research questions needed to assess the proposed updates; conduct systematic reviews; assess the quality of the evidence; and review existing systematic reviews, meta-analyses, and cost-effectiveness analyses, when available (Supplementary Appendix 2; Supplementary Tables 1, 4, and 7, https://stacks.cdc.gov/view/cdc/124432). Comprehensive systematic literature reviews were conducted for recommendations on 1) expanding screening to all adults (i.e., universal screening), 2) periodic testing for HBV infection among persons with hepatitis C virus (HCV) infection, and 3) testing for HBV infection among persons with a history of incarceration.
For all three systematic reviews, literature searches were conducted by CDC librarians with direction from subject matter experts. Searches were conducted for English-language literature published worldwide in Medline (OVID), Embase (OVID), CINAHL (Ebsco), and Cochrane Library. Duplicates were identified and removed using Endnote (version 20; Clarivate Analytics) and DistillerSR systematic review software (version 2.35; Evidence Partners) automated “find duplicates” functions.
CDC’s Viral Hepatitis Steering Committee considered multiple methods to assess quality of evidence. The Mixed Methods Appraisal Tool (MMAT) was selected because it is a validated tool for assessing nonrandomized analytic and descriptive studies, which comprise most of the HBV infection prevalence literature (29 (link)). MMAT users rate each study on methodological quality criteria, indicating whether criteria were met with “Yes,” “No,” or “Can’t Tell.” Calculating a summary score is not recommended for the tool because presenting a single number is not informative about which aspects of the studies are problematic. Economic analyses were evaluated by assessing whether the study met the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) (30 (link)).
CDC determined that the new recommendations constituted influential scientific information that will have a clear and substantial impact on important public policies and private sector decisions. Therefore, the Information Quality Act required peer review by specialists in the field who were not involved in the development of these recommendations. CDC solicited nominations for reviewers from AASLD, the Infectious Disease Society of America, and the American College of Physicians (ACP). Five clinicians with expertise in hepatology, gastroenterology, internal medicine, or infectious diseases provided structured peer reviews and any edits made in response were documented (Supplementary Appendices 2 and 3, https://stacks.cdc.gov/view/cdc/124432). No CDC staff or external peer reviewers reported a conflict of interest. In addition, feedback from the public was solicited through a Federal Register notice announcing the availability of the draft recommendations for public comment from April 4 through June 3, 2022. CDC received 28 public comments on the draft document from nonprofit/advocacy groups, providers, industry groups, medical professional organizations, the public, academia, and a consulting group. Public comments were considered by the work group and any edits made in response were documented (Supplementary Appendix 4, https://stacks.cdc.gov/view/cdc/124432).
The work group also presented these guidelines to the CDC/Health Resources and Services Administration (HRSA) Advisory Committee on HIV, Viral Hepatitis and STD Prevention and Treatment, but did not seek consensus decision-making from this advisory committee. The steering committee considered results of the systematic reviews in conjunction with cost-effectiveness analyses, supplemental literature, practicality of implementing guidelines, public health benefits, subject matter expertise, and reviewer and public feedback.

Microfinance data comes from Microfinance Information Exchange (MIX), hosted by the World Bank Data Catalogue and the World Development Indicators. MIX Market data has been reported from 1999 to 2019, and covers the financial statements, outreach, and social performance data of microfinance institutions “targeting the unbanked” in developing economies [33 ]. MIX is targeted at achieving greater transparency in the microfinance industry, and while the data is self-reported, it follows predetermined formats, validation mechanisms, and standardisation for ease of comparison [28 ].
Gross loan portfolio (GLP)—the key component of our explanatory variable which is obtained from MIX—shows the total funds disbursed in loans by microfinance institutions (MFIs) and is adjusted for write-offs and inflation. We standardise this country-level GLP with respect to 3 alternative variables, yielding 3 different measures of microfinance intensity.
For the first measure we divide GLP by the total domestic credit to the private sector (GLP/credit), obtained from the World Development Indicators (WDI) [34 ]. To our knowledge, this is the first study to use the ratio of GLP to credit for measuring microfinance intensity. This yields a precise estimate of the salience of microfinance in the economy, as it normalises the GLP with respect to the size of the financial sector rather than a general country-wide measure. This gives us confidence that the results in this study are driven by microfinance, and not by overall financial development in the country.
For the second measure of microfinance intensity we divide GLP by the population of the country, which yields microfinance loans per capita (or simply GLP/capita) [28 ]. This measure is important in terms of indicating, potentially, the social quantum of microfinance in terms of its penetration within the life of the average person. The third measure is the same as the one used by Hermes [29 ] in which we take the GLP as a percentage of the GDP of a country. This indicates the significance of microfinance in relation to the size of a country’s economy.

The plant materials used in this study consisted of 304 winter wheat lines (Supplemental Table 1). Entries 1 to 301, 303 were Triticeae Coordinated Agricultural Project (TCAP) winter wheat lines from wheat breeding programs of public universities and private sectors across the United States of America. The origin information can be found at the T3 (The Triticeae Toolbox) site: https://wheat.triticeaetoolbox.org/. Entries 302 and 304 were from Serbia and Australia, respectively, and were previously reported to have capability of delayed chlorophyll degradation under terminal heat stress conditions (Alkhatib and Paulsen, 1990 (link); Ristic et al., 2007 (link)).