Randomised controlled trials are increasingly conducted as embedded, nested, or using cohorts or routinely collected data, including registries, electronic health records, and administrative databases, to assess if participants are eligible for the trial and to facilitate recruitment, to deliver an embedded intervention, to collect trial outcome data, or a combination of these purposes. This report presents the Consolidated Standards of Reporting Trials (CONSORT) extension for randomised controlled trials conducted using cohorts and routinely collected data (CONSORT-ROUTINE). The extension was developed to look at the unique characteristics of trials conducted with these types of data with the goal of improving reporting quality in the long term by setting standards early in the process of uptake of these trial designs. The extension was developed with a sequential approach, including a Delphi survey, a consensus meeting, and piloting of the checklist. The checklist was informed by the CONSORT 2010 statement and two reporting guidelines for observational studies, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and the REporting of studies Conducted using Observational Routinely collected Data (RECORD) statement. The extension includes eight items modified from the CONSORT 2010 statement and five new items. Reporting items with explanations and examples are provided, including key aspects of trials conducted using cohorts or routinely collected data that require specific reporting considerations. Well designed and conducted randomised controlled trials are the so called gold standard of healthcare intervention research.Interest in randomised controlled trials conducted using cohortsThe use of cohorts and routinely collected data might make randomised controlled trials easier and more feasible to perform by reducing cost, time, and other resources, and could facilitate the conduct of trials that more closely replicate real world clinical practice by supporting recruitment of large and representative samples.Box 1Trials conducted using cohorts or routinely collected databases might differ from conventional trial designs because they use these sources of data to identify eligible participants; automate randomisation; deliver an intervention; collect data, including assessing outcomes; or a combination of these functions.Some trials might use a hybrid approach that integrates the use of these sources of data and trial specific methods for functions such as delivery of the intervention and assessing outcomes.Cohorts and routinely collected databases can vary substantially in the way they represent complete, random, or convenience samples. Because the cohort or routinely collected database could serve as the sampling frame for the trial, the representativeness of participants in the trial might depend on the characteristics of the database.The comprehensiveness, collection procedures, and type of demographic or outcome data available in a cohort or routinely collected database could influence the design of the trial, including the research question, eligibility criteria of the trial, and the choice of outcomes.The timing between identifying eligibility, delivering the intervention, and assessing the outcomes might be governed by the frequency of data collection in a cohort or routinely collected database, and is less controllable by trial investigators than in conventional trials.In trials using cohorts or routinely collected data, informed consent could be applied at different levels and in different ways compared with conventional trial designs. Consent might be sought and obtained to use the cohort or routinely collected database and for the trial, and consent that would typically be expected to occur in conventional trials might not be done because of features of the integrated cohort or database and trial design.Because cohorts, registries, electronic health records, and administrative databases vary in the way they are set up for research, clinical care, or financial and administrative purposes, the completeness and accuracy of the data might vary substantially between different databases and between variables within one database.Challenges could arise in linking routinely collected data to other sources of data, including linkage errors when records cannot be linked or are linked incorrectly.A unique feature of trials using cohorts and routinely collected data is that investigators can often access information on participants not enrolled in the trial. Differences in baseline characteristics of eligible people from the cohort or routinely collected database who do not participate in the trial can often be compared with trial participants to inform judgments on the representativeness of the participants in the trial and the generalisability of the results.Potential differences between the trial target population, people included in the cohort or routinely collected database, and participants in the trial, can influence the applicability of the trial results and should be considered when interpreting the findings.Limitations to the use of a cohort or routinely collected data for a trial include constraints on available outcome measures and issues with data linkage, data validation, and data quality that could influence eligibility for the trial and assessment of outcomes.Trials might use a cohort or routinely collected data to identify eligible participants, to determine outcomes, to implement an intervention, or for a combination of these purposes.These trial designs are relatively recent innovations, and published randomised controlled trial reports might not describe important aspects of their methodology in a standardised way.A CONSORT extension was developed for the reporting of randomised controlled trials conducted using cohorts and routinely collected data (CONSORT-ROUTINE)The project was registered with the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network,Consistent with other CONSORT statements, this extension describes a minimum set of information that should be reported and provides a checklist to facilitate compliance. The extension applies to randomised controlled trials conducted using one or more cohorts or routinely collected databases to: identify, recruit, or consent eligible participants; implement an intervention; collect trial data, including outcomes; or a combination of these purposes. For randomised controlled trials that use cohorts or routinely collected data to only assess outcomes, some extension items might not be relevant.The extension includes eight items from the CONSORT 2010 statement that were modified and five new items. No items were removed from the CONSORT 2010 checklist. Box 2ROUTINE-1 on the description of the cohort or routinely collected database(s)ROUTINE-2 on the eligibility criteria for participants in the cohort or routinely collected database(s)ROUTINE-3 on data linkage across two or more databasesROUTINE-4 on consent for use of cohort or routinely collected data and trial participationROUTINE-5 on codes and algorithms used to define or derive the outcomes from the cohort or routinely collected database(s)Item 1b on specifying that a cohort or routinely collected data were used in the abstractItem 3a on specifying that a cohort or routinely collected data were used in the trial designItem 4a on eligibility criteria for trial participantsItem 6a on outcome measuresItem 9 on implementation of the random allocation sequenceItem 13a on the participant flowItem 22 on the interpretation of resultsItem 25 on the sources of fundingwww.consort-statement.org/extensions), including those for cluster trials,For each modified and new item, this document describes the item, identifies whether the item was modified or new, provides examples of good reporting, explains the rationale for including the item, and elaborates on reporting considerations. For items that were not modified from the CONSORT 2010 statement, but for which reporting considerations exist for trials conducted using cohorts or routinely collected data, we have also provided an example and explanation. Examples of good reporting were retrieved from primary and secondary trial reports and, in some cases, trial protocols. For all items, the explanations provided supplement those in the CONSORT 2010 explanation and elaboration statement.Identification as a randomised trial in the title.“Bivalirudin versus heparin in non-ST and ST-segment elevation myocardial infarction-a registry-based randomized clinical trial in the SWEDEHEART registry .”“Clinical effectiveness and cost-effectiveness of a multifaceted podiatry intervention for falls prevention in older people: a multicentre cohort randomised controlled trial .”Item 1a is meant to aid in indexing and identifying randomised controlled trial reports in electronic databases. The title, at a minimum, should contain recognisable terminology identifying the study as a randomised trial. If word count permits, the type of trial  or the cohort or routinely collected database(s) used to conduct the trial  should be provided.CONSORT 2010 item: Structured summary of trial design, methods, results, and conclusions (for specific guidance see CONSORT for abstracts).Modified CONSORT extension item: Structured summary of trial design, methods, results, and conclusions (for specific guidance see CONSORT for abstracts). Specify that a cohort or routinely collected data were used to conduct the trial and, if applicable, provide the name of the cohort or routinely collected database(s).“The TIMING study is a national, investigator-led, registry-based, multicentre, open-label, randomised controlled study. The Swedish Stroke Register is used for enrolment, randomisation and follow-up.”“The Department of Veterans Affairs (VA) MI-Plus study was a cluster-randomized trial involving 168 community-based primary care clinics and 847 providers in 26 states, the Virgin Islands, and Puerto Rico . . . with the clinic as the randomization unit. We collected administrative data for 15,847 post-MI [myocardial infarction] patients and medical record data for 10,452 of these.”Abstracts are used for electronic database indexing and are the most commonly read sections of articles.Scientific background and explanation of rationale (see CONSORT 2010).Specific objectives or hypotheses (see CONSORT 2010).CONSORT 2010 item: Description of trial design  including allocation ratio.Modified CONSORT extension item: Description of trial design  including allocation ratio, that a cohort or routinely collected database(s) was used to conduct the trial  and how the data were used within the trial .“The Determination of the Role of Oxygen in Suspected Acute Myocardial Infarction (DETO2X-AMI) trial was a multicenter, parallel-group, open-label, registry-based, randomized, controlled trial in which routine supplemental oxygen therapy was compared with ambient air in the treatment of patients with suspected myocardial infarction who did not have hypoxemia at baseline. The trial used the national comprehensive Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) . . . for patient enrollment and data collection.”“PATIENT was a parallel arm, pragmatic clinical trial in which 21,752 adults were randomized to receive either UC [usual care] or 1 of 2 interventions designed to increase adherence to statins, angiotensin-converting enzyme inhibitors (ACEIs), and angiotensin receptor blockers (ARBs) . . . Using each region’s EMR [electronic medical record], we identified participants 40 years and older with diabetes mellitus and/or cardiovascular disease (CVD), suboptimally (<90%) adherent to a statin or ACEI/ARB during the previous 12 months, and due or overdue for a refill . . . Within each region, we randomly assigned a sample of eligible members to the 3 primary study arms  in a 1:1:1 ratio at the study outset . . . We used the EMR to capture age, race, gender, healthcare utilization for diabetes and CVD, and BP [blood pressure] and lipid levels.”According to CONSORT 2010, authors should describe the trial design , conceptual framework , and allocation ratio . Also, they should describe that one or more cohorts or routinely collected databases were used, how they were used , and whether their use influenced other methodological choices that might have implications for how the results of the trial are interpreted and apply to different populations. Examples include constraints on the eligibility criteria for the trial; timing between evaluating eligibility, delivery of the intervention, and assessing outcomes; and outcomes available.Important changes to methods after trial commencement (such as eligibility criteria), with reasons (see CONSORT 2010).Trial methods and procedures might depend on protocols . Changes to protocols that affect aspects of trial methods, such as identification of eligible participants, outcome variables collected, or timing of outcome assessments, should be described .Name, if applicable, and description of the cohort or routinely collected database(s) used to conduct the trial, including information on the setting (such as primary care), locations, and dates .“Family practices in England, Scotland, or Wales were eligible for the study if they were contributing data to the Clinical Practice Research Datalink (CPRD). The CPRD is a large database that includes the EHRs [electronic health records] of ≈ 7% of all UK general practices from 1987 to the present.”https://www.spinsclero.com/en/sites) during regular medical visits, and written informed consent is obtained. A medical data form is submitted online by the site to enrol participants. Cohort participants complete outcome measures via the internet upon enrolment and subsequently every 3 months. SPIN Cohort enrollment started in March 2014 and is ongoing.”“The [Scleroderma Patient-centered Intervention Network] SPIN Cohort is a convenience sample. Eligible SPIN Cohort patients are recruited at SPIN sites . Providing the name of the cohort or routinely collected database allows readers to identify other studies, including trials, conducted with the same cohort or database and consider if the results apply to their setting. A description of the cohort or routinely collected database, including geographical locations and clinical settings, enables readers to assess characteristics relevant to understanding the sampling frame for recruitment of participants to the trial and the potential validity of the data for the research question. The authors should provide references to any publications that have described the cohort or database methods, or characteristics of the included participants. A rationale for why the specific cohort or routinely collected database was used for the trial should be provided.Characteristics that could influence data quality should be reported and, if applicable, include the reason for data collection , and the time period and related procedures by which data are collected, among others. Information on surgical procedures, for example, might be complete and accurate for administrative data derived from physician billing because reimbursement depends on its accuracy. Associated diagnostic codes, however, might be less reliable if these codes are not essential for billing. For data collected with electronic health records in the UK, for example, data that relate to items detailed in the Quality Outcomes Framework are likely of better quality if captured after 2004.Eligibility criteria for participants in the cohort or routinely collected database(s).“Patients were eligible for inclusion in the cohort if they were 45 years or older; had a smoking history of at least 10 pack-years; had a clinical diagnosis of mild-to-severe COPD [chronic obstructive pulmonary disease], defined as a postbronchodilator forced expiratory volume in 1s (FEV1) to forced vital capacity ratio of 0.7 or lower and a postbronchodilator FEV1 of at least 30%, according to Global Initiative of Chronic Obstructive Lung Disease (GOLD) and American Thoracic Society and European Respiratory Society criteria (GOLD stage 1–3); and had at least one documented or self-reported exacerbation during the past 3 years, with the restriction that the last exacerbation had ended at least 4 weeks before inclusion and symptoms had returned to patients’ baseline levels. Exclusion criteria were poor mastery of the Dutch language, poor cognitive functioning, known allergy to doxycycline, pregnancy, and a life expectancy of shorter than 1 month.”“Baseline characteristics and clinical outcomes will be extracted from routinely recorded clinical data held in the NNRD [National Neonatal Research Database]. The NNRD holds data from all infants admitted to National Health Service (NHS) neonatal units in England, Scotland and Wales . Contributing neonatal units are known as the UK Neonatal Collaborative. Data are extracted from point-of-care neonatal electronic health records completed by health professionals during routine clinical care. A defined data extract, the Neonatal Dataset of ~450 data items, is transmitted quarterly to the Neonatal Data Analysis Unit at Imperial College London and Chelsea and Westminster NHS Foundation Trust where patient episodes across different hospitals are linked and data are cleaned .”Because the cohort or routinely collected database serves as the sampling frame for the trial, the representativeness of the participants in the trial depends on the eligibility criteria, and a clear description of criteria for entry into the cohort or routinely collected database should be provided.When a cohort or routinely collected database in which eligibility fluctuates over time is used , researchers should clearly specify how eligibility was defined and how changes in eligibility over the study period were managed. Also, changes in variable coding over time could result in differences in characteristics of participants considered eligible for enrolment in the randomised controlled trial. Therefore, coding changes relevant to characterising participants in the cohort or database, and eligibility criteria and enrolment in the randomised controlled trial, should be reported.State whether the study included person-level, institutional-level, or other data linkage across two or more databases and, if so, linkage techniques and methods used to evaluate completeness and accuracy of linkage.[Information in main text] “Individuals on the Oregon Experiment “reservation list” (N=100 407) were probabilistically matched to individual OCHIN [Oregon Community Health Information Network] patients (N=106 692), using Link Plus software and demographic variables common to both data sets. Two researchers independently performed a case-by-case review of uncertain matches using additional demographic variables. Appendix Table 1 provides more details.”[Information in appendix] “To identify individuals common to both the Medicaid reservation list and the OCHIN patient population, we used Link Plus software to probabilistically compare demographic variables contained in both datasets. Matching variables included first and last name, date of birth, gender, street address, city, Oregon Medicaid identification number, and preferred language. The software generates a “match score” indicating each pair’s likelihood of being a match. For pairs of uncertain match status based on match score, we conducted double clerical review by independent reviewers. We also completed several rounds of quality assurance analyses to verify the validity of our match results.”When databases are linked, investigators need to select a set of variables to use for linking, determine the best method for linking the databases and develop a linking algorithm, and evaluate the accuracy of linkages between the databases.v probabilistic, quality and type of variables used for linkage), how linkage validation was done, and the results of linkage validation with estimated rates of successful linkage. Details should be provided on blocking variables , completeness of linkage variables, linkage rules, thresholds, and manual review of potential matches, if undertaken.Authors should describe if linkage of records across multiple databases was conducted and, if so, the methods of linkage , if applicable.“Primary care physicians were eligible for the study if they practiced in a study clinic, provided care to at least 10 adults with type 2 diabetes, and provided written informed consent to participate. Patients were classified as having diabetes if they had 2 or more out-patient diabetes International Classification of Diseases, Ninth Revision (ICD-9) codes (250.xx) or used 1 or more diabetes-specific medications in the 1-year period before randomization. This diabetes identification method has estimated sensitivity of 0.91 and positive predictive value of 0.94.”“An EHR [electronic health record]-based algorithm to identify eligible patients was constructed based on International Classification of Diseases, 9th and 10th Revisions, Clinical Modification codes (67–69) (see Table E1 in the online supplement) that are present on admission. In addition, nurses complete a five-item electronic checklist during intake to denote the disease-specific eligibility criteria. To validate the algorithm, we reviewed 271 medical charts across the participating hospitals. The algorithm identified 171 of these patients as eligible and 100 as ineligible. Using manual chart review as the gold standard, the algorithm had a false-positive rate of 1% and a false-negative rate of 5%.”This section relates to entry into the trial . When eligible trial participants were identified from records in a cohort or routinely collected database, authors should report information necessary to evaluate or replicate this process. This information should include a clear and detailed description of all codes, algorithms, and free text field entries, or combinations of these, including any statistical code if possible. Ideally, a link to all material needed for replication should be provided in an appendix or posting to an accessible website.Use of routinely collected data could introduce some degree of misclassification bias, and information on the validity of participant classification must be specifically described, including reference to available validation studies and any methods used to directly assess the validity of the data used for classification of participants and the accuracy of the classification. Potential changes that could affect different settings and time points should be considered .To help readers assess the applicability of trial results, authors should clearly describe potential differences between the trial target population, people included in the cohort or health database, and actual participants in the trial. Filtering effects could occur, for instance, when data are more often incomplete in special situations, such as emergency visits (compared with routine visits) as a result of different processes for routine data collection, and if people with incomplete data are not screened for trial eligibility.Settings and locations where the data were collected.“The trial was conducted in the area of the Lille-Douai Health Insurance district (Northern France) during the institutional seasonal influenza vaccination campaign of 2014–2015 . . . In the intervention group, 25 GPs received and were supposed to expose in their waiting rooms 135 pamphlets and one poster  withdrawing all the other posters. In the control group, waiting rooms were kept in their usual state . . . Data were extracted between October 15, 2014 and February 28, 2015 from the SIAM-ERASME claim database of the Lille-Douai district Health Insurance Fund on patient level.”“The present study is one of three trials that took place in the context of the PRO-AGE  project in three locations. The present study was conducted in Hamburg, Germany, and was intended to test whether HRA-O [health risk appraisal for older persons], combined with personal reinforcement and supplemented . . . In Hamburg, general practitioners (GPs) registered in the entire metropolitan area (~500 GPs) were informed via the newsletter of their regional GP association (BDA-Landesverband Hamburg) . . . Survival, nursing home admission, and need for ambulatory nursing care as well as change of residence data were obtained from the GP records and completed with participant and proxy information. At year 1, the HRA-O questionnaire was used for collecting outcome information from all study participants. It was sent to surviving persons in combination with a short questionnaire on self-efficacy in the patient–physician interaction.”Information on the settings and locations where the trial was conducted is key to judge the applicability and generalisability of the trial.Describe whether and how consent was obtained.“At enrollment in the cohort, patients are asked to provide informed consent for prospective collection of clinical, survival and PROMs [patient-reported outcome measures] data . . . we ask patients’ consent to be randomly selected to receive offers on experimental interventions in the future and to use their data comparatively . . . Patients within the cohort who meet the inclusion criteria form a subcohort of eligible patients . . . From among this subcohort, a random sample is selected . . . Randomly selected patients are offered the experimental intervention (boost prior to sCRT [standardised chemoradiation therapy]) by their treating physician. If they accept the offer, they will sign an additional informed consent to receive the boost. Patients who refuse the boost will receive care as usual . Patients in the subcohort who will not be randomly selected will not be informed about the boost intervention, nor will they be informed about their participation in the control arm of this study.”In trials in cohorts and with routinely collected data, informed consent might be applied at different levels and at multiple stages for an individual participant, and in different ways than in conventional randomised controlled trial designs where consent is usually obtained once for treatment, randomisation, and data use.Authors should describe the different types of consent sought and obtained for the cohort or routinely collected database, and the trial. These might include: consent for use of health data for research from a cohort or routinely collected database; consent to be contacted for future research purposes; prior consent to future randomisation without explicit notice, which often occurs in trials that use the cohort multiple randomised controlled trial designThe interventions for each group with sufficient details to allow replication, including how and when they were actually given.“We developed a computer-based electronic alert system for identifying consecutive hospitalized OAC [oral-anticoagulation]-naïve patients with AF [arterial fibrillation] and tested the hypothesis that such an alert system would improve OAC prescription. The alert system automatically identified hospitalized patients with AF without an active OAC prescription in the electronic order entry system. The alert system was incorporated into the electronic medical chart and order entry system of the University Hospital in Bern, Switzerland. It recognized AF by permanently searching diagnosis lists and physician notes of the entire electronic patient chart database for free text entries of AF or its various abbreviations. Alerts were issued 24 hours after the onset of hospital stay if . . . 4 criteria for an individual patient were present . . . Once the criteria were fulfilled, the alert was issued in the electronic patient chart. The alert was visible to physicians and nurses, but only physicians were enabled to respond to the alert.”“Intervention included a single real-time notification by letter to the patient and by electronic message within the KPSC [Kaiser Permanente Southern California] electronic medical record system to each patient’s primary care provider and asthma specialist (if the patient had previously seen one). The patient letters and physician messages noted excessive SABA [short-acting β2-agonist] dispensing, suggestions for management, and facilitated allergy referral recommendation for those patients without prior asthma specialist care . . . Controls received KPSC standard asthma care management without research contact.”Interventions are sometimes delivered by electronic health record systems or with an administrative database. Examples provided here describe a clinical decision support toolCONSORT 2010 item: Completely defined pre-specified primary and secondary outcome measures, including how and when they were assessed.Modified CONSORT extension item: Completely defined pre-specified primary and secondary outcome measures, including how and when they were ascertained and the cohort or routinely collected database(s) used to ascertain each outcome.“A hard CVD [cardiovascular disease] event, the primary outcome, was defined as the occurrence of any of the following events in the medical record or Medicare/Medicaid data between IMPACT [Improving Mood-Promoting Access to Collaborative Treatment] enrollment date and December 31, 2008: a) fatal MI [myocardial infarction] , b) laboratory evidence of acute MI , c) acute MI diagnosis (ICD-9 code 410), d) fatal stroke , or e) hemorrhagic (ICD-9 codes 430Y432) or nonhemorrhagic  stroke diagnosis. Secondary outcomes were fatal/nonfatal MI (categories a-c), fatal/nonfatal MI-cardiac enzyme confirmed (categories a and b), fatal/nonfatal stroke (categories d and e), and all-cause mortality. Death dates were extracted from the Medicare data, and causes of death were obtained from death certificates provided by the Indiana State Department of Health . . . Patients were followed up for a maximum of 7.5 to 9.5 years (median = 8.1 years); however, for cause of death (categories a and d), patients were followed up for a maximum of 5.5 to 7.5 years (median = 6.2 years).”“The trial used the national comprehensive Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) . . . for patient enrollment and data collection . . . The primary end point was death from any cause within 365 days after randomization, assessed in the intention-to-treat population. Secondary end points included death from any cause within 30 days after randomization, rehospitalization with myocardial infarction, rehospitalization with heart failure, and cardiovascular death . . . as well as composites of these end points, assessed at 30 days and 365 days . . . Data on the end points of rehospitalization with heart failure and cardiovascular death are not available from SWEDEHEART and must be obtained from the Swedish National Inpatient and Outpatient Registries. Mortality data were obtained from the Swedish National Population Registry, which includes the vital status of all Swedish citizens. All other variables were obtained from SWEDEHEART, which is monitored on a regular basis. Diagnoses at discharge are listed according to codes from the International Classification of Diseases, 10th Revision (ICD-10). The end of follow-up was December 30, 2016, which was 365 days after the last patient underwent randomization. To allow for any lag in registry reporting, the final database was extracted from SWEDEHEART on February 28, 2017, including data on any linked deaths that occurred through December 30, 2016, and reported in the population registry as of February 14, 2017 . . . No central adjudication or trial-specific patient follow-up was performed.”All primary and secondary outcomes should be identified and defined, including how and when they were measured, and the cohort(s) or routinely collected database(s) used to ascertain the outcome. The use of routinely collected data might introduce some degree of misclassification. Details on the accuracy and validity of outcome data  must be described, including reference to available validation studies and any methods used to directly assess the validity of data used as primary or secondary outcomes and the accuracy of the data collected. If different databases are used in some sites in the trial, authors should note if outcomes are determined consistently across trial sites.Because follow-up periods might be considerably longer than recruitment periods, sometimes lasting decades, special attention should be given to potential changes that occur over time that might affect the collection, quality, and completeness of the data. Authors could consider using flow diagrams or special tables to describe these circumstances. A crucial aspect to consider and carefully report is any connection between collection of outcomes and trial arms . For example, a comparison of surgery versus non-surgical care should consider that special diagnostic procedures that are routinely done in surgical follow-up visits might not be done in the control group.Information on how to access the list of codes and algorithms used to define or derive the outcomes from the cohort or routinely collected database(s) used to conduct the trial, information on accuracy and completeness of outcome variables, and methods used to validate accuracy and completeness , if applicable.https://ars.els-cdn.com/content/image/1-s2.0-S0749379715004237-mmc1.pdf).”“The primary outcomes were whether or not the patient received preventive care services in the post-period: screenings for cervical, breast, and colorectal cancer ; screenings for diabetes (glucose and hemoglobin A1c [HbA1c]), hypertension, obesity, and smoking; lipid screening; chlamydia testing; and receipt of influenza vaccination. Codes were used based on EHR [electronic health record] Meaningful Use Stage 1 measures. These included ICD-9-CM  diagnosis and procedure codes, Current Procedural Terminology and Healthcare Common Procedure Coding System codes, Logical Observation Identifiers Names and Codes, and medication codes. The authors also used relevant code groupings and codes specific to the OCHIN [Oregon Community Health Information Network] EHR, used for Meaningful Use reporting and internal quality improvement initiatives. Appendix Table 2 provides detailed technical specifications and patient eligibility criteria for each measure  agreement.”“If a patient was suspected to have had a clinical end-point event , the patient’s health care records were subjected to central blinded adjudication to determine the cause of the event according to prespecified criteria.”Trials using cohorts or routinely collected data might require specific codes or algorithms, such as diagnostic codes, to identify and define outcomes. An electronic health record query can be performed, for example, with a list of diagnostic codes to identify all patients who have experienced a specific adverse event. An algorithm, or sequence of steps necessary to score or grade an outcome, could also be used. To assess validity and to facilitate reproducibility, the list of codes and algorithms should be provided or linked to an external source within the text or in supplementary material, ideally with the computer code used to reproduce this step.Cohorts and routinely collected data are often collected and entered by staff involved in routine patient care or by non-clinical staff, based on medical records, and the level of completeness varies. Also, procedures for entering data for clinical care or billing might introduce certain biases, and concerns about data completeness and accuracy could arise.Outcome definitions might vary between cohorts and routinely collected data, and standards commonly used in clinical trials and data fields might be missing. The authors should describe any adjudication of outcomes, if adjudication was blinded to trial allocation, and which outcome definitions were used .Any changes to trial outcomes after the trial commenced, with reasons (see CONSORT 2010).How sample size was determined (see CONSORT 2010).When applicable, explanation of any interim analyses and stopping guidelines (see CONSORT 2010).Method used to generate the random allocation sequence (see CONSORT 2010).Type of randomisation; details of any restrictions .CONSORT 2010 item: Mechanism used to implement the random allocation sequence , describing any steps taken to conceal the sequence until interventions were assigned.Modified CONSORT extension item: Mechanism used to implement the random allocation sequence ), describing any steps taken to conceal the sequence until interventions were assigned.“The [WithHolding Enteral feeds Around packed red cell Transfusion] WHEAT trial is a randomised controlled, unblinded, multicentre, pilot trial comparing two care pathways . . . Infants will be randomised with a 1:1 allocation ratio (using permuted blocks of variable size), stratified within neonatal unit by gestational age at birth and infant sex. Trial processes will be embedded within neonatal EPR [electronic patient record] systems and all outcome data will be extracted from data that are routinely recorded within the existing neonatal EPR systems (BadgerNet and BadgerEPR), and held in the NNRD [National Neonatal Research Database] . . . Infants will be randomised using an online secure central randomisation system which will be embedded into the existing neonatal EPR systems (BadgerNet and BadgerEPR). Randomisation will occur within the EPR to ensure allocation concealment.”“Randomization to be offered versus not offered, the SPIN-HAND [Scleroderma Patient-centered Intervention Network hand exercise program] intervention will occur at the time of Cohort participants’ regular SPIN Cohort assessments. Eligible Cohort participants, based on questionnaire responses, will be randomized automatically as they complete their regular SPIN Cohort assessments using a feature in the SPIN Cohort platform, which provides immediate centralized randomization and, thus, complete allocation sequence concealment.”The use of cohorts or routinely collected data to conduct trials might provide opportunities to embed automated randomisation or selection and allocation algorithms into the cohort or database system to allocate participants to trial arms. This process could be automated or software embedded within the system could communicate with an external randomisation system. If such processes are used, authors should provide enough details for readers to understand the randomisation and allocation concealment processes and to assess how they could influence internal validity.Who generated the random allocation sequence, who enrolled participants, and who assigned participants to interventions (see CONSORT 2010).If done, who was blinded after assignment to interventions  and how (see CONSORT 2010).If relevant, description of the similarity of interventions (see CONSORT 2010).Statistical methods to compare groups for primary and secondary outcomes (see CONSORT 2010).Methods for additional analyses, such as subgroup analyses and adjusted analyses (see CONSORT 2010).CONSORT 2010 item: For each group, the numbers of participants who were randomly assigned, received intended treatment, and were analysed for the primary outcome.Modified CONSORT extension item: For each group, the number of participants in the cohort or routinely collected database(s) used to conduct the trial and the numbers screened for eligibility, randomly assigned, offered and accepted interventions , received intended treatment, and analysed for the primary outcome.“We identified the primary care physicians with the highest antibiotic prescription rates in Switzerland using routinely collected claims data of prescriptions of antibiotics and outpatient consultations collected by SASIS, a data warehouse company of an umbrella organization of Swiss statutory health insurers (Santésuisse). These data are collected by over 60 statutory health insurers covering 64% of the Swiss population (5.1 million residents).We included among all board certified primary care physicians the 2900 top antibiotic prescribers  opted out later. We used data from 2814 physicians for the intention-to-treat analysis”“Upon receiving permission to contact participants from their respective registry site, FHPP [Family Health Promotion Project] staff at the University of Colorado Cancer Center contacted participants to recruit them into the study . Of the 1,068 subjects contacted, 156 were deemed ineligible and 280 refused participation for an overall response rate of 69% (632 of 912 eligible . . .). The 632 consenting participants, representing 533 families, completed the baseline survey and were randomized to receive either the tailored telephone counseling intervention (N=322) or the general mailed intervention (N=310) . . . A total of 632 participants were enrolled in the FHPP trial. Of the 322 participants randomized to the telephone intervention, 306 (95%) received the intervention , and 309 of 310 (>99%) participants in the mailed group received the mailed packet. Retention of participants over 24 months was greater than 90% overall: 87% in the telephone and 94% in the mailed intervention group”The number of participants in a cohort or routinely collected database(s) and the numbers who were screened for eligibility, randomly assigned, offered and accepted interventions , received the intended treatment, and analysed for the primary and secondary outcomes should be described. When multiple sources of data were linked, potential exclusions because of data linkage should be specifically described. If people in a cohort or routinely collected database who are not included in the trial are observed and their data are reported, this should be clearly reported and included in the flowchart.For each group, losses and exclusions after randomisation, together with reasons (see CONSORT 2010).Also, for trials using cohorts or routinely collected data, losses and exclusions based on data quality or linkage problems should be specifically described.Dates defining the periods of recruitment and follow-up.“A parallel group randomised controlled trial (RCT) with 878 participants in the intervention and 1,702 in the control group was performed between 2001-2002 . . . Briefly, 14 general practitioners with solo practices recruited participants for the RCT over a nine-month period starting in October 2000. Potential participants were identified using complete GP’s patient lists. At baseline (2000/2001), eligible study participants were at least 60 years old . . . Eligible individuals received the study information letter from their GPs, the PRA questionnaire (Probability for Repeated Admission) measuring six items of baseline risk status for health service use, i.e., person’s age, gender, hospital admissions, visits to GP, health status (heart disease and diabetes status), and caregiver availability, one question on B-ADL [basic activities of daily living] and the informed consent form.”Participants in a cohort or routinely collected database are typically followed for an extended period, and the starting date of trial recruitment will often differ from the start date of data collection in the cohort or database. Trials with these types of data might be uniquely positioned to obtain long term follow-up data. The length of follow-up could be a fixed period after randomisation, but in randomised controlled trials when the outcome is time to an event, follow-up of all participants ends on a specific date. Start and end dates for the trial should be given, and the minimum, maximum, and median duration of follow-up for trials for which the outcome is time to an event should be reported. Longer term follow-up subsequent to a trial in an ongoing cohort or database, if expected, should be explained.Why the trial ended or stopped (see CONSORT 2010).A table showing baseline demographic and clinical characteristics for each group.“During the study period, 11,709 patients with STEMI [ST-segment elevation myocardial infarction] in Sweden and Iceland underwent PCI [percutaneous coronary intervention] and were registered in SCAAR [Swedish Coronary Angiography and Angioplasty Registry]. Of these, 7012 were enrolled in the trial. An additional 247 patients were enrolled from the center in Denmark, for a total of 7259 patients…. Fifteen erroneous enrollments  were excluded from the database, leaving 7244 patients who underwent randomization. The baseline clinical characteristics of all the patients who underwent randomization  and all the patients who did not undergo randomization  are listed in Table 1.”A feature of randomised controlled trials using cohorts and routinely collected data is that baseline data for participants not enrolled in the trial are usually more likely to be available. For each group, number of participants (denominator) included in each analysis and whether the analysis was by original assigned groups (see CONSORT 2010).For each primary and secondary outcome, results for each group, and the estimated effect size and its precision  (see CONSORT 2010).For binary outcomes, presentation of both absolute and relative effect sizes is recommended (see CONSORT 2010).Results of any other analyses performed, including subgroup analyses and adjusted analyses, distinguishing pre-specified from exploratory (see CONSORT 2010).All important harms or unintended effects in each group .Trial limitations, addressing sources of potential bias, imprecision, and, if relevant, multiplicity of analyses.“A number of limitations of the TASTE [Thrombus Aspiration in ST-Elevation Myocardial Infarction in Scandinavia] trial should be noted. First, the treating physician was aware of the group to which the patient had been assigned, and that physician entered the angiographic variables into the registry; therefore, these variables were susceptible to bias. Second, we did not adjudicate events and did not review angiograms in a blinded fashion. We used all-cause death as the primary end point as it is the most stringent end point and because of the completeness of the national death registries in each participating country. We chose not to perform separate adjudication of secondary end points both to limit expense and because of the high reliability of the SWEDEHEART [Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies] registry . . . A comparison of the clinical characteristics and outcomes between the patients who underwent randomization and those who did not indicates that the two cohorts differed significantly in a number of respects . . . Even when a trial uses a population-based registry for enrollment, the trial participants cannot be fully representative of the complete range of patients.”“Awareness of the trial might have itself promoted better data recording [in the EHR]. Nevertheless, we observed several limitations of the data including, for example, a high proportion of patients with unspecified subtype of stroke and a smaller number with BP [blood pressure] values not recorded during the intervention period. From an explanatory perspective, these limitations of the data reduce the capacity of the study to provide an accurate assessment of intervention efficacy.”According to CONSORT 2010, identifying and discussing the potential limitations of a trial is crucial to appropriately interpretating the trial results, including issues such as potential bias, imprecision, and multiplicity of comparisons. Unique characteristics of trials using cohorts or routinely collected data might be linked to risk of bias and associated problems and, therefore, need specific attention in the discussion, including issues such as data availability, problems with data linkage, data validation, and data quality.Generalisability  of the trial findings.“A comparison of the clinical characteristics and outcomes between the patients who underwent randomization and those who did not indicates that the two cohorts differed significantly in a number of respects, most notably in mortality at 30 days (2.9% among patients who underwent randomization vs. 10.6% among those who did not). In many cases, these differences reflect the exclusion from the trial of patients who were ineligible because they were unable to provide oral consent. Even when a trial uses a population-based registry for enrollment, the trial participants cannot be fully representative of the complete range of patients.”Careful attention should be paid to how participants in an ongoing cohort or with records in a routinely collected database might differ from the population targeted by the trial, and these differences and their relevance for interpretating the findings of the trial should be discussed. Also, any trial design decisions related to delivery of the intervention or collection of outcomes that were influenced by the use of a cohort or routinely collected database should be considered. An advantage of many trials conducted in cohorts or with routinely collected data is that information on participants not included in the trial is available. Assessing the degree to which trial participants differ from non-participants by reason of non-participation can provide readers with insight on representativeness. Possible risks to generalisability that are identified, and their potential implications, should be discussed.CONSORT 2010 item: Interpretation consistent with results, balancing benefits and harms, and considering other relevant evidence.Modified CONSORT extension item: Interpretation consistent with results, balancing benefits and harms, and considering other relevant evidence, including the implications of using data that were not collected to answer the specific research question.“Using the EHR [electronic health record] as a sole source of patient data is a limitation. For example, the EHR did not capture the patient experience of the intervention, including its potential impact on pain control, function, and disability. Furthermore, EHR data do not provide accurate substance use and mental health diagnoses. We did not have prescription or visit data from outside health systems.”Authors should report whether and how the use of cohort or routinely collected data might be a limitation of the trial. These limitations could include, among others, the choice of outcome measures based on availability in the cohort or routinely collected database, and the quality and accuracy of the outcome data. Where possible, results should be compared with evidence from similar randomised controlled trials with a conventional design, and differences that might be related to the use of a cohort or routinely collected data should be discussed.Registration number and name of trial registry.Where the full trial protocol can be accessed, if available.“This trial used the platform of preexisting health care registries for enrollment, randomization, collection of data, and follow-up .”According to CONSORT 2010, trials should be registered, and their protocol should be accessible. When a trial is being conducted in a cohort or routinely collected database, in addition to the trial protocol, the authors should ideally provide a link to the protocol for the cohort or routinely collected database, if separate. This information allows interested readers to better understand the characteristics of the participants in the cohort or database and the data collection methods.CONSORT 2010 item: Sources of funding and other support (such as supply of drugs), role of funders.Modified CONSORT extension item: Sources of funding and other support for both the trial and the cohort or routinely collected database(s), role of funders.“The registry is financed by the Swedish government and the Association of Local Authorities and Regions , and is supported by the Swedish Heart Association, the National Board of Health and Welfare and the Swedish Heart and Lung Foundation. Participating hospitals are not reimbursed by the registry and costs of local data entry are borne by their internal budget . The trial sponsor was the Karolinska Institutet.”In addition to providing the funding source for the trial, authors should also report any funding sources of the cohort or routinely collected data, and if they were involved in the use of the cohort or dataset in the trial, or in the trial itself.www.consort-statement.org/extensions. Authors are also encouraged to report any extra information, specific to their trial, that would assist readers to more easily evaluate the results of the trial or to replicate the methods of the trial.This extension of the CONSORT reporting guideline for trials conducted using cohorts and routinely collected data are a minimum set of items to inform readers about the trial design and its findings, and to support informed decisions about the validity of the results of the trial and applicability to readers’ research questions. The extension only deals with aspects of trial reporting specific to trials conducted using cohorts and routinely collected data. When reporting a trial using cohorts or routinely collected data, authors should look at all items on the CONSORT checklist and use this document together with the main CONSORT 2010 guidelines. Authors should also consult other CONSORT extensions that are relevant to their trial design, such as extensions for cluster trials,In addition to assisting authors of trial reports, this CONSORT extension aims to promote transparency and clarity, and to reduce research waste caused by poor reporting. We encourage journal editors to direct authors of trials conducted using cohorts and routinely collected data to use this checklist and to document adherence to reporting recommendations as a condition of manuscript submission.


With the development of science and technology, mankind’s exploration of outer space has increased tremendously. Settling in outer space or on other planets could help solve the Earth’s resource crisis, but such settlement will first face the problem of reproduction. There are considerable differences between outer space and the Earth’s environment, with the effects of gravity being one of the most significant. Studying the possible effects and underlying mechanisms of microgravity on embryonic stem cell (ESC) differentiation and embryonic development could help provide solutions to healthy living and reproduction in deep space. This article summarizes recent research progress on the effects of microgravity on ESCs and early embryonic development and proposes hypotheses regarding the potential mechanisms. In addition, we discuss the controversies and key questions in the field and indicate directions for future research. All creatures on Earth are impacted by gravity. When the human body is weightless in space, many physiological functions change, including bone loss, muscle atrophy, decreased cardiovascular capacity, decreased immune function, delayed wound healing, and delayed fracture healing . With exin vitro and maintain the ability to differentiate into different types of cells in the body, i.e., pluripotency, and are therefore widely used to study reproduction and development in mammals (Embryonic stem cells (ESCs) are derived from the inner cell mass of preimplantation embryos . They ex mammals . Various mammals . StudyinIn this article, we review recent advances in studies on the effects of microgravity on ESCs and early embryonic development, as well as the potential underlying mechanisms.Oct4 expression compared to the ground-based control group  of mouse ESCs carried on the SJ10 recoverable satellite found down-regulation of genes related to DNA repair in the space environment, suggesting a negative impact of space on the maintenance of genomic stability . In addiReactive oxygen species (ROS) are necessary for DNA repair pathways to maintain genome stability; however, excessive ROS can cause cell damage and apoptosis . Rad9 isTnnt2, Rbp4, Tnni1, Csrp3, Nppb, and Mybpc3 are suppressed, indicating that simulated microgravity may promote the differentiation of ESCs into myocardium CDC42 through Rap1GDS1, thereby promoting the occurrence of vascular branch morphology , suggesting that microgravity may regulate the activities of those pathways to control ESC differentiation. The expression of p21/Cdkn1a increases during spaceflight in osteoprogenitors  plays an important role in the development of embryos before implantation, with a critical concentration required for normal embryonic development . Severalkinase C . Cao et Xenopus embryos increase under microgravity conditions, and that the glutathione system is an important mechanism against oxidative stress. Thus, these results suggest that microgravity can cause oxidative stress and affect the development of mouse preimplantation embryos.Oxidative stress is involved in many embryonic developmental processes. Oxidative stress induced by excessive ROS or insufficient antioxidant protection can detrimentally affect embryonic development Recent research by Studying the effects and underlying mechanisms of microgravity on ESC differentiation and early embryonic development will help us better understand the impact of space on human reproduction and development and provide a foundation for our exploration and settlement of deep space. Previous studies have shown that microgravity can affect the differentiation of ESCs into the three germ layers and their cells. Optimizing the differentiation of ESCs into various types of cells and organoids under a microgravity environment could help promote their clinical application. Microgravity exposure affects early embryonic development of animals, including mice. Studies on the effects of microgravity on embryonic development will help lay a foundation for the long-term survival and reproduction of humans in outer space. However, due to various technical and experimental limitations, microgravity-related research on ESCs and early embryonic development is still in its infancy. Several exploratory mechanistic studies have shown that signaling pathways, transcription factors, stress response, and epigenetics are involved in the influence of microgravity on ESC differentiation and early embryonic development . HoweverIt should be noted that current simulation devices can only partially simulate the effects of space microgravity. Therefore, the influence of hydrostatic pressure and fluid shear should be considered during microgravity simulation. In addition, as the space environment includes microgravity and radiation, their combined impact should also be considered. Due to the scarcity and high cost of space-based research opportunities, ground-based simulation equipment is an important tool for studying the impact of microgravity. Nevertheless, the results achieved with simulation devices need to be carefully interpreted and verified in space in the future.


The Care Around Birth approach provides an integrated implementation framework to improve the quality, equity, and dignity of care during the intrapartum and immediate postpartum periods, thereby addressing key drivers of maternal and newborn mortality. Adopting an integrated implementation framework that combined training, mentoring, and quality improvement processes to improve the quality, equity, and dignity of care during the intrapartum and immediate postpartum periods, the Care Around Birth approach addressed key drivers of maternal and newborn mortality.The approach refocused energy on facility-based quality processes that are currently central to efforts in reducing mortality and achieving Sustainable Development Goal targets.Integrated implementation frameworks need to be aligned to the resources available and tailored to the ecosystem in which health programs operate.Facility-level ownership and accountability by optimizing the engagement of health staff remain the fulcrum for any improvement effort. This however needs to be adequately supported by district and state-level health systems. With the highest risk of maternal and newborn mortality occurring during the period around birth, quality of care during the intrapartum and immediate postpartum periods is critical for maternal and neonatal survival.The United States Agency for International Development’s Scaling Up Reproductive, Maternal, Newborn, Child, and Adolescent Health Interventions project, also known as the Vriddhi project, collaborated with the national and 6 state governments to design and implement the Care Around Birth approach in 141 high caseload facilities across 26 high-priority districts of India from January 2016 to December 2017. The approach aimed to synergize evidence-based technical interventions with quality improvement (QI) processes, respectful maternity care, and health system strengthening efforts. The approach was designed using experiential training, mentoring, and a QI model. A baseline assessment measured the care ecosystem, staff competencies, and labor room practices. At endline, the approach was externally evaluated.Availability of logistics, recording and reporting formats, and display of protocols improved across the intervention facilities. At endline (October–December 2017), delivery and newborn trays were available in 98% of facilities compared to 66% and 55% during baseline (October–December 2015), respectively. Competency scores (> 80%) for essential newborn care and newborn resuscitation improved from 7% to 70% and from 5% to 82% among health care providers, respectively. The use of partograph in monitoring labor improved from 29% at the baseline to 61%; administration of oxytocin within 1 minute of delivery from 35% to 93%; newborns successfully resuscitated from 71% to 96%; and postnatal monitoring of mothers from 52% to 94%.The approach successfully demonstrated an operational design to improve the provision and experience of care during the intrapartum and immediate postpartum periods, thereby augmenting efforts aimed at ending preventable child and maternal deaths. Though the maternal mortality ratio and the neonatal mortality rate have seen an appreciable decline over the past 2 decades,The Government of India has made efforts to improve the quality of care through various national policy initiatives, but the challenge of translating operational guidelines and plans to on-the-ground implementation remains.Prioritizing the need to improve access, utilization, and quality of care, in 2013, the Government of India launched the Reproductive, Maternal, Newborn, Child, and Adolescent Health (RMNCH+A) Strategy.Aligning with global priorities, WHO’s Quality of Care framework for maternal and newborn health, and national guidelines,The intervention, which was externally evaluated, included building a robust and scalable model and catalyzing and measuring the change .19Because implementation was planned across 6 states that varied considerably in sociodemographic and health parameters, adequate time was spent on designing and conceptualizing the approach .The project team engaged in multiple rounds of discussions to comprehensively define the scope and spectrum of the intervention including the geographical reach, the identification of facilities, the care practices to be supported, and the measurement indicators to be tracked. The team developed a robust and comprehensive assessment framework for establishing baselines across the facilities. Then, we finalized the care practices to be strengthened, tailored the technical intervention package to be delivered using adult learning principles, adapted the quality improvement (QI) processes for implementation at scale, and envisioned an actionable management information system. The preparatory phase also included training of the project team on all the components. The design phase culminated in advocacy workshops across the 6 states led by the respective state governments to disseminate the baseline findings, develop facility improvement plans, and initiate the implementation phase of the approach.The Care Around Birth approach integrated training, mentoring, and QI aspects to potentiate change. Identifying staff competencies as a major barrier to implementation of quality, high-impact interventions at scale, the approach included capacity building to ensure that health workers were not only well-trained on clinical, programmatic, and managerial aspects but also competent to provide high-quality services.The Care Around Birth approach included capacity building to ensure that health workers were not only well-trained on clinical, programmatic, and managerial aspects but also competent to provide high-quality services.A participant-centric, experiential training package based on adult learning principles was designed to address the knowledge and practice gap and help facility staff overcome barriers in translating their knowledge into action.Establishing a learning need: Sessions always started with establishing a learning need by allowing participants to realize the gap in knowledge or practice. This was done through structured questions and answers, writing cards, and demonstrating existing practices.Building on existing knowledge: Participants’ responses were valued, and facilitators provided additional new information only after summing up the group’s inputs. This helped to build upon existing knowledge and further highlighted the learning need.Learning by observation: The correct practice was demonstrated by the facilitator; the participants observed the demonstration and were encouraged to note differences from their current practice.Doing, observing, and critiquing: All participants were given an opportunity to practice individually under supervision in small groups. In addition to the group and facilitator, all co-participants observed the practice. This further added to the learning experience.Sharing problems, challenges, and solutions: This step allowed participants to reflect on what they had learned and visualize challenges and issues in implementing the new practice, the process facilitated by peer-to-peer learning. At this stage, problems related to larger systemic issues such as supplies, infrastructure, etc., which were beyond the scope of the training, were also listed to be discussed subsequently with district and state authorities.Performing the new skill: At the end of each session, participants demonstrated their newly learned skills and made commitments to continue the practice.The training methods included simulations, demonstrations, case studies, role playing, and other participatory methods. The choice of methods was guided by factors enabling spontaneous participation of all participants; building on participants’ experiences, views, and beliefs; encouraging peer learning; and allowing for self-assessment of knowledge and skills. The training session flow was adapted to fulfill these criteria and included the following:The trainings were centralized, held at the district level over 2 days, and were followed by planned, structured, and low-density, high-frequency on-site training sessions at health facilities to ensure saturation of all health care providers at the intervention facilities. The trainings were facilitated by the project team, which included professionals with relevant experience in maternal and child health.The project team conducted post-training follow-up facility visits to mentor and support health care providers, resolve doubts, and facilitate practice to boost confidence and enable local problem solving.The momentum for change was sustained by introducing QI processes at the facility level.QI efforts were built into the model and were optimized as the fulcrum for maintaining and sustaining changes over the intervention period.The facility-level QI efforts were complemented by district- and state-level experience-sharing platforms, which brought together facility staff at the district or state levels to share learnings, challenges, and solutions. This further facilitated peer-to-peer engagement and also served as a way to acknowledge good performers.To prioritize RMC, the project team ensured a provider-centric RMC framework that included institutionalizing RMC practices during various components of care. The project team identified RMC as integral to its outcomes, both in project design and implementation, and undertook a situational analysis in select public health facilities to understand their readiness to provide RMC. The project team aimed to improve the experience of care at the intervention facilities by including RMC practices within the training curriculum, engaging with health care providers on it during mentoring visits and QI meetings, and developing job aids for display at the intervention facilities .Data management and usage were built on USAID’s Collaborating Learning and Adapting framework, which ensured that the intervention was coordinated with other parallel efforts, grounded in a strong evidence base, and iteratively adapted.Identification of intervention facilities was based on the line-listing of public health facilities that had reported at least 1 delivery through the health management information system for the year 2013–2014. More than 1,400 health facilities in the 26 districts had reported deliveries during the year. These facilities were then enlisted by the district. An average of 5–7 high caseload facilities—accounting for close to 70% of the institutional delivery load in public health facilities in the district—were identified for the intervention. Except for 2 primary health centers in Himachal Pradesh, all of the intervention facilities were either district hospitals, first referral unit community health centers, non-first referral unit community health centers, referral hospitals, or subdivisional hospitals. Operationally, 69 intervention facilities were L2 and 72 L3 level facilitiesSupplement 1). The results of the baseline assessment were subsequently collated and analyzed using an Excel-based tool.From January to February 2016, a baseline assessment was conducted across all intervention facilities. The assessment included 748 data parameters on the labor room environment, staff competencies, and practices. While the labor room environment was assessed through a structured checklist based on MOHFW’s maternal and newborn health toolkit, competency assessment was conducted using a mix of vignette, knowledge, and simulation-based tools. Of the 1,410 staff nurses and auxiliary nurse-midwives (ANMs) posted at the intervention facilities during the assessment, 427 providers, identified based on 8-hourly duty shifts, were included in the assessment. Practices for a set of predetermined indicators at the intervention facilities were reviewed through facility-level labor room registers and case sheets with October–December 2015 serving as the reference period. While the labor room records were reviewed fully, case sheet-based records were assessed through a random sample of case sheets with 10 sheets for each of the 3 months at district hospitals and 5 at community health centers and other facilities  involved identifying a set of standardized indicators to track the progress of the intervention through an Excel-based data entry tool.The implementation was staggered with the first set of interventions rolled out through a series of district and on-site low-density, high-frequency training sessions between March and May 2016. Post-training monthly mentoring visits by district-level project officers were initiated from June 2016 to provide on-job handholding and supportive supervision. Facility QI platforms were established beginning October 2016. Similarly, trainings for the second set of interventions were conducted between February and April 2017.Supplement 2).The monthly MIS capturing key indicators was introduced at the facilities with efforts initially focused on streamlining data capture at facilities followed by ensuring regularity and completeness in data maintenance (Supplement 3).An independent external evaluation of the first set of interventions in the “Care Around Birth” approach was conducted by the Center for Operations Research and Training between November 2017 and February 2018. The assessment was conducted across a sample of 51 intervention facilities  and included facility assessment; competency assessment of staff nurses/ANMs; in-depth interviews with staff nurses/ANMs, medical officers, district and state officials, and beneficiaries; and direct observation, of deliveries. Overall competency assessments and in-depth interviews were conducted with 195 staff nurses/ANMs, 77 medical officers, and 33 district and 8 state officials. Direct observation of 399 deliveries was conducted to determine essential practices, and 392 beneficiaries were interviewed to assess their experience with care and provision of services. Weights were then applied to the sample to generalize the findings to the intervention universe of 139 facilities, as 2 facilities  had closed during the intervention period. Quantitative data were collated on CSPro and analyzed on SPSS, while the qualitative data were audio-recorded, transcribed, and coded thematically  labor room amenities and competency scores during the baseline assessment and external evaluation; (2) trends in essential maternal and newborn practices during the intervention period cumulatively and with facility and state-level differentials; and (3) provision and experience of care during the external evaluation. While aggregate level alterations over time were undertaken from the cumulative data of the 6 states, monthly records at each facility level were used to distinguish the variation in improvement over time. To compare the difference in the performance of the indicators at baseline and different phases of the intervention, Chi-square tests were performed. Each phase of the intervention was compared with its previous phase to examine whether the change in percent observed was significant or not. To compare the performance among levels of the facility and states scores were calculated for the pre and during intervention periods for each indicator. As the distribution of observations was skewed during both periods, median values were calculated to provide a better representation of the shift in improvement. Furthermore, provision and experience of care were estimated in percentage improvement.As the model included facilitating the on-the-ground implementation of interventions included in the national service delivery package for maternal and newborn health, ethical clearance was not sought before implementation. However, Institutional Review Board clearance was obtained for conducting the endline assessment (No.EC –CORT/1730), and each respondent involved in the in-depth interviews provided their oral or written consent.We present the results from the first set of interventions.The labor room environment was assessed across a set of indicators during the baseline and endline assessments, which included the availability of amenities, recording formats and protocols, infection control practices, and biomedical waste management. Improvement was observed across almost all of the indicators during the intervention .While most health care providers scored less than 50% for all the components during the baseline, after the intervention, competency improved across all of the components with maximum improvement observed for the use of the partograph, active management of the third stage of labor, essential newborn care, resuscitation, and newborn vaccination .Health care provider competency improved across all of the components.The good aspect of this project was skill enhancement. Skills have been enhanced which will stay with us. Staff who have been trained and posted at facilities will remain there and will train the new staff. —Former Chief Medical Health Officer, Mandi, Himachal PradeshMaternal care during delivery: oxytocin administered within 1 minute of delivery, filled partograph, postpartum monitoring within 6 hours and at discharge, and average number of times mothers monitored during their stay at the facility.Essential newborn care and resuscitation: temperature recorded at birth, breastfeeding initiated within 1 hour of birth, delayed cord clamp, administration of vitamin K1, monitoring within 1 hour and within 6 hours, average number of times monitored after birth, and rates for birth dose vaccination.Practices in the labor rooms were monitored through on-site mentoring visits and collection of monthly maternal and newborn health-related datasets. These include the following:Changes across these indicators were analyzed across 5 time periods: Period 1: October–December 2015 (baseline), Period 2: January–May 2016 (the baseline data collection and training period), Period 3: June–September 2016 (post-training mentorship period), Period 4: October–December 2016 (post-initiation of QI efforts by establishing QI teams and continued mentorship), Period 5: January–September 2017 (implementation with on-site mentoring and concurrent QI efforts) and Period 6: October–December 2017 (implementation and endline assessment).An upward trend was seen across all maternal and newborn indicators over the intervention period indicating improvement in care practices . StatistMoreover, apart from a few indicators like resuscitation and newborn vitals monitoring that had fluctuations during the intervention period, other indicators had an improved trajectory all through the intervention period.Comparison of care practices of facility levels reveals that while district hospitals had satisfactory performance of certain indicators like oxytocin administration, vitals monitoring, vitamin K1 administration, breastfeeding and resuscitation at base period, first referral unit community health centers, non-first referral unit community health centers, 24x7 primary health centers had negligible practice of oxytocin administration or resuscitation and therefore, improvements seen in the indicators among these facilities were comparatively higher. There was hardly any practice of temperature recorded at birth or delayed cord clamping in most facilities but after the intervention, these practices rose above 90% . Delhi, While monitoring the progress of labor using a partograph was done in 75% of deliveries observed during the endline assessment, 100% of women were administered oxytocin after birth with 99% receiving it within 1 minute of delivery. After delivery, newborns were kept in skin-to-skin contact with mothers in 86% of observations; breastfeeding was initiated within 1 hour of birth in 96% of cases; injection of vitamin K1 was administered to 97% of newborns; 99% of newborns were weighed at birth; and temperature was recorded for 88% of newborns observed. Health care providers used sterile disposable gloves during all the deliveries that were observed, 85% of health care providers practiced handwashing before examining the newborns, and protocols for safe disposal of placenta were followed in 99% of observations. Privacy was maintained in 84% of observations, and birth companions were allowed inside labor rooms in 78% of cases.Almost all the respondents reported the delivery room to be clean, and 84% reported the toilets to be clean. A majority of respondents (93%) reported availability of running water in the facility, 81% reported drinking water to be available; and 72% received free meals at the facilities. Regarding breastfeeding, 86% had initiated breastfeeding within the first hour after birth and 81% reported receiving necessary support for initiating breastfeeding. A majority (87%) reported that the nurse and/or doctor had checked them and/or the newborn during the postnatal period. Clients also reported that they had received information/counseling from providers, which included information on postpartum family planning (51%), hygiene and handwashing (44%), and the continuation of exclusive breastfeeding (41%) for the first 6 months. Clients were also provided with counseling information that applied to mothers, such as eating regular meals (23%) and drinking fluids regularly (18%), and to newborns, such as not applying anything to the cord stump (17%) and monitoring for danger signs. Nearly half (49%) of clients stated that they were able to ask health care providers questions about their own and their newborn’s health when needed. Regarding services provided at the facilities, 67% of clients were “satisfied” and 31% were “very satisfied,” with 97% of clients stating that the health care providers were supportive.I am very happy with the services here and the nurses talk very nicely to me. They also explain everything and make me feel comfortable. —Client, district hospital, Muktsar, PunjabAlthough a preliminary analysis of facility data indicated a reduction in perinatal mortality, the duration of the intervention limited any definitive statistical analysis.,,–With the global transition to the Sustainable Development Goals framework, multiple efforts have been undertaken for improving and optimizing the quality of care across the health care delivery system.The Care Around Birth approach adds to the existing body of evidence by potentiating efforts to improve the quality of health care delivery systems using a training, mentoring, and QI model in resource-constrained and poor-performing districts in India.The Care Around Birth approach adds to the existing body of evidence by potentiating efforts to improve the quality of health care delivery systems using a training, mentoring, and QI model in resource-constrained and poor-performing districts in India.–In a relatively short period of 2 years, this approach has been able to demonstrate improvements. Possible factors include addressing and streamlining implementation challenges through an inclusive problem-solving approach with attention to detail, comprehensive planning for end-to-end solutions, clear messaging, and role clarity. The approach ensured a high level of engagement from the government counterparts at all levels that generated ownership, which is in line with available literature.,By designing the training package according to the sequence of events in the labor room and postnatal wards, the approach integrated maternal and newborn care and helped health care providers recognize the importance of managing both the mother and the newborn together as 1 unit and ensured competencies for both maternal and neonatal care. The integration of interventions and operational strategies in the approach is in line with recent evidence that suggests while single interventions are unlikely to achieve reductions in maternal, newborn, and child mortality, combining interventions is more effective in improving health care practices.By designing the training package according to the sequence of events in the labor room and postnatal wards, the approach helped health care providers recognize the importance of managing both the mother and the newborn together as 1 unit.The approach adopted a staggered implementation framework where different capacity-building measures were initiated and implemented over 2 years. While the baseline assessment helped provide a thorough understanding of the intervention facilities, the initial round of short, centralized, and facility-based low-density, high-frequency trainings not only refreshed provider competency but also helped saturate facilities with trained health care providers thereby negating the potential impact of variable service provider competencies. The mentorship visits immediately after the trainings helped sustain the gains, improved the availability of equipment and logistics at the facilities, and engendered trust and mutual respect between the project staff and health care providers at the intervention facilities. The subsequent introduction of QI approaches through facility teams then transferred ownership to health care providers, which ensured accountability and local problem solving. It was also evident that this staggered implementation not only built the capacity of the health system to absorb and inculcate change but also ensured facility readiness to adopt QI approaches, which usually tend to work better in areas with increased levels of resources than in low-resource settings.Data-driven decision making and facility-based QI teams formed the fulcrum of the approach. While robust measurement, collation, and triangulation of data enabled the intervention facilities to adopt a comprehensive approach to improvement, the QI efforts utilized this data to address issues at the facility level. The dual approach of combining quantitative data through the MIS and qualitative data through the QI meetings demystified various implementation challenges and issues. The approach also differed from the traditional plan-do-study-act approach by using predefined data elements across a set of indicators to foster improvements rather than addressing 1 improvement aim at a time. This possibly helped faster improvement across the facilities and countered the narrative of QI approaches failing due to lack of leadership, financial, organizational, and system support.The approach differed from the traditional plan-do-study-act approach by using predefined data elements across a set of indicators to foster improvements rather than addressing 1 improvement aim at a time.Though overall improvements were noted inter-state and inter-facility, variations in results were observed during the implementation. These variations are attributable to the overall performance of health systems in the states and point toward the need for comprehensive improvements at the systems level for sustained gains.The approach prioritized high caseload facilities, however, engagement of the L1 facilities through a pared-down version of basic obstetric and newborn care could have led to improvements in quality of care across all levels, as well as possibly a more optimal distribution of caseload. Similarly, the interventions targeting maternal and newborn complication management including management of postpartum hemorrhage and pre-eclampsia/eclampsia, kangaroo mother care, assisted feeding of low birth weight babies, use of antenatal corticosteroids for preterm labor, management of maternal and newborn sepsis, and the prevention of parent to child transmission were implemented for less than a year and did not provide enough time for measurement and evaluation, moreover the relatively short implementation period might have limited sustainability of efforts. The approach also did not strengthen referral pathways.Although the approach did not measure motivation among staff, it can be presumed that improving the experience of care for beneficiaries is directly proportional to not only the competency but also the motivation level of health care providers. As there was hardly any increase in staff strength at the intervention facilities during the implementation period, improved results are a marker of both better competency and accountability of the facility staff. Hence, all interventions should identify health care providers as important stakeholders and ensure sustained capacity building through a multipronged approach to build competencies and enhance motivation and self-confidence among them. Also, while most of the training packages focus on improving clinical competencies, health care providers should acquire skills in time management, team building, and interpersonal communication. These skills are essential to health care providers, especially those in high-caseload facilities with low human resource capacity. Though the approach had these aspects incorporated in the clinical training modules, soft skills trainings should be designed and implemented for health care providers.––It is increasingly recognized that all women need and deserve respectful care and that RMC should be promoted as a critical element of strategies to improve the utilization and quality of maternity care.Sustained advocacy at the state level helped to mobilize financial resources to address larger systemic issues through annual program implementation plans with states providing funds for labor room strengthening, setting up skill labs, and training health care providers. In addition, efforts were made to build ownership by ensuring that government staff and program officers were identified to serve as focal points for reviewing progress and addressing challenges at the state, district, and facility levels and developing a pool of state-level trainers to ensure continued scale up to other facilities and districts across the states. Moreover, the partnership with the state governments helped to sustain and ensure a contextualized scale up of the approach. Some states like Punjab and Delhi scaled up the initiative statewide, but others undertook both intra- and inter-district scale-ups. Learnings from the approach, specifically those related to facility-level QI initiatives and RMC, were then integrated into the national labor room and maternity operation theater QI initiative, LaQshya, with the project supporting a national program management unit based out of the MOHFW for program rollout and implementation across the country and focused support to LaQshya implementation across 7 states through dedicated state units. A substantial number of the Care Around Birth facilities were also designated as LaQshya facilities and received state and national certifications in due course.While the “Care Around Birth” approach demonstrated impact at scale, it did have certain limitations. The difference in the methodology for the baseline assessment and external evaluation might have a bearing on the overall impact, moreover, the absence of control districts did not allow for comparative assessment. Both the provision and experience of care were measured only during the endline and hence results need to be interpreted cautiously as there might have been a Hawthorne effect.Overall, the Care Around Birth approach has demonstrated impact at scale. It is envisaged that subsequent efforts in this field will gain insights from the approach to comprehensively address key drivers of maternal and newborn mortality.