Early postnatal discharge from hospital for healthy mothers and term infants.

Length of postnatal hospital stay has declined dramatically in the past 50 years. There is ongoing controversy about whether staying less time in hospital is harmful or beneficial. This is an update of a Cochrane Review first published in 2002, and previously updated in 2009. To assess the effects of a policy of early postnatal discharge from hospital for healthy mothers and term infants in terms of important maternal, infant and paternal health and related outcomes. We searched the Cochrane Pregnancy and Childbirth Group's Trials Register, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP) (21 May 2021) and the reference lists of retrieved articles. Randomised controlled trials comparing early discharge from hospital of healthy mothers and term infants (at least 37 weeks' gestation and greater than or equal to 2500 g), with the standard care in the respective settings in which trials were conducted. Trials using allocation methods that were not truly random (e.g. based on patient number or day of the week), trials with a cluster-randomisation design and trials published only in abstract form were also eligible for inclusion. Two review authors independently assessed trials for inclusion and risk of bias, extracted and checked data for accuracy, and assessed the certainty of evidence using the GRADE approach. We contacted authors of ongoing trials for additional information. We identified 17 trials (involving 9409 women) that met our inclusion criteria. We did not identify any trials from low-income countries. There was substantial variation in the definition of 'early discharge', ranging from six hours to four to five days. The extent of antenatal preparation and midwifery home care offered to women following discharge in intervention and control groups also varied considerably among trials. Nine trials recruited and randomised women in pregnancy, seven trials randomised women following childbirth and one did not report whether randomisation took place before or after childbirth. Risk of bias was generally unclear in most domains due to insufficient reporting of trial methods. The certainty of evidence is moderate to low and the reasons for downgrading were high or unclear risk of bias, imprecision (low numbers of events or wide 95% confidence intervals (CI)), and inconsistency (heterogeneity in direction and size of effect). Infant outcomes Early discharge probably slightly increases the number of infants readmitted within 28 days for neonatal morbidity (including jaundice, dehydration, infections) (risk ratio (RR) 1.59, 95% CI 1.27 to 1.98; 6918 infants; 10 studies; moderate-certainty evidence). In the early discharge group, the risk of infant readmission was 69 per 1000 infants compared to 43 per 1000 infants in the standard care group. It is uncertain whether early discharge has any effect on the risk of infant mortality within 28 days (RR 0.39, 95% CI 0.04 to 3.74; 4882 infants; two studies; low-certainty evidence). Early postnatal discharge probably makes little to no difference in the number of infants having at least one unscheduled medical consultation or contact with health professionals within the first four weeks after birth (RR 0.88, 95% CI 0.67 to 1.16; 639 infants; four studies; moderate-certainty evidence). Maternal outcomes Early discharge probably results in little to no difference in women readmitted within six weeks postpartum for complications related to childbirth (RR 1.12, 95% CI 0.82 to 1.54; 6992 women; 11 studies; moderate-certainty evidence) but the wide 95% CI indicates the possibility that the true effect is either an increase or a reduction in risk. Similarly, early discharge may result in little to no difference in the risk of depression within six months postpartum (RR 0.80, 95% CI 0.46 to 1.42; 4333 women; five studies; low-certainty evidence) but the wide 95% CI suggests the possibility that the true effect is either an increase or a reduction in risk. Early discharge probably results in little to no difference in women breastfeeding at six weeks postpartum (RR 1.04, 95% CI 0.96 to 1.13; 7156 women; 10 studies; moderate-certainty evidence) or in the number of women having at least one unscheduled medical consultation or contact with health professionals (RR 0.72, 95% CI 0.43 to 1.20; 464 women; two studies; moderate-certainty evidence). Maternal mortality within six weeks postpartum was not reported in any of the studies. Costs Early discharge may slightly reduce the costs of hospital care in the period immediately following the birth up to the time of discharge (low-certainty evidence; data not pooled) but it may result in little to no difference in costs of postnatal care following discharge from hospital, in the period up to six weeks after the birth (low-certainty evidence; data not pooled). The definition of 'early discharge' varied considerably among trials, which made interpretation of results challenging. Early discharge probably leads to a higher risk of infant readmission within 28 days of birth, but probably makes little to no difference to the risk of maternal readmission within six weeks postpartum. We are uncertain if early discharge has any effect on the risk of infant or maternal mortality. With regard to maternal depression, breastfeeding, the number of contacts with health professionals, and costs of care, there may be little to no difference between early discharge and standard discharge but further trials measuring these outcomes are needed in order to enhance the level of certainty of the evidence. Large well-designed trials of early discharge policies, incorporating process evaluation and using standardized approaches to outcome assessment, are needed to assess the uptake of co-interventions. Since none of the evidence presented here comes from low-income countries, where infant and maternal mortality may be higher, it is important to conduct future trials in low-income settings.

Jones E ，Stewart F ，Taylor B ，Davis PG ，Brown SJ ... -  《Cochrane Database of Systematic Reviews》

Schedules for home visits in the early postpartum period.

Maternal complications, including psychological/mental health problems and neonatal morbidity, have commonly been observed in the postpartum period. Home visits by health professionals or lay supporters in the weeks following birth may prevent health problems from becoming chronic, with long-term effects. This is an update of a review last published in 2017. The primary objective of this review is to assess the effects of different home-visiting schedules on maternal and newborn mortality during the early postpartum period. The review focuses on the frequency of home visits (how many home visits in total), the timing (when visits started, e.g. within 48 hours of the birth), duration (when visits ended), intensity (how many visits per week), and different types of home-visiting interventions. For this update, we searched the Cochrane Pregnancy and Childbirth Group's Trials Register, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP) (19 May 2021), and checked reference lists of retrieved studies. Randomised controlled trials (RCTs) (including cluster-, quasi-RCTs and studies available only as abstracts) comparing different home-visiting interventions that enrolled participants in the early postpartum period (up to 42 days after birth) were eligible for inclusion. We excluded studies in which women were enrolled and received an intervention during the antenatal period (even if the intervention continued into the postnatal period), and studies recruiting only women from specific high-risk groups (e.g. women with alcohol or drug problems). Two review authors independently assessed trials for inclusion and risk of bias, extracted data and checked them for accuracy. We used the GRADE approach to assess the certainty of the evidence. We included 16 randomised trials with data for 12,080 women. The trials were carried out in countries across the world, in both high- and low-resource settings. In low-resource settings, women receiving usual care may have received no additional postnatal care after early hospital discharge. The interventions and controls varied considerably across studies. Trials focused on three broad types of comparisons, as detailed below. In all but four of the included studies, postnatal care at home was delivered by healthcare professionals. The aim of all interventions was broadly to assess the well-being of mothers and babies, and to provide education and support. However, some interventions had more specific aims, such as to encourage breastfeeding, or to provide practical support. For most of our outcomes, only one or two studies provided data, and results were inconsistent overall. All studies had several domains with high or unclear risk of bias. More versus fewer home visits (five studies, 2102 women) The evidence is very uncertain about whether home visits have any effect on maternal and neonatal mortality (very low-certainty evidence). Mean postnatal depression scores as measured with the Edinburgh Postnatal Depression Scale (EPDS) may be slightly higher (worse) with more home visits, though the difference in scores was not clinically meaningful (mean difference (MD) 1.02, 95% confidence interval (CI) 0.25 to 1.79; two studies, 767 women; low-certainty evidence). Two separate analyses indicated conflicting results for maternal satisfaction (both low-certainty evidence); one indicated that there may be benefit with fewer visits, though the 95% CI just crossed the line of no effect (risk ratio (RR) 0.96, 95% CI 0.90 to 1.02; two studies, 862 women). However, in another study, the additional support provided by health visitors was associated with increased mean satisfaction scores (MD 14.70, 95% CI 8.43 to 20.97; one study, 280 women; low-certainty evidence). Infant healthcare utilisation may be decreased with more home visits (RR 0.48, 95% CI 0.36 to 0.64; four studies, 1365 infants) and exclusive breastfeeding at six weeks may be increased (RR 1.17, 95% CI 1.01 to 1.36; three studies, 960 women; low-certainty evidence). Serious neonatal morbidity up to six months was not reported in any trial. Different models of postnatal care (three studies, 4394 women) In a cluster-RCT comparing usual care with individualised care by midwives, extended up to three months after the birth, there may be little or no difference in neonatal mortality (RR 0.97, 95% CI 0.85 to 1.12; one study, 696 infants). The proportion of women with EPDS scores ≥ 13 at four months is probably reduced with individualised care (RR 0.68, 95% CI 0.53 to 0.86; one study, 1295 women). One study suggests there may be little to no difference between home visits and telephone screening in neonatal morbidity up to 28 days (RR 0.97, 95% CI 0.85 to 1.12; one study, 696 women). In a different study, there was no difference between breastfeeding promotion and routine visits in exclusive breastfeeding rates at six months (RR 1.47, 95% CI 0.81 to 2.69; one study, 656 women). Home versus facility-based postnatal care (eight studies, 5179 women) The evidence suggests there may be little to no difference in postnatal depression rates at 42 days postpartum and also as measured on an EPDS scale at 60 days. Maternal satisfaction with postnatal care may be better with home visits (RR 1.36, 95% CI 1.14 to 1.62; three studies, 2368 women). There may be little to no difference in infant emergency health care visits or infant hospital readmissions (RR 1.15, 95% CI 0.95 to 1.38; three studies, 3257 women) or in exclusive breastfeeding at two weeks (RR 1.05, 95% CI 0.93 to 1.18; 1 study, 513 women). The evidence is very uncertain about the effect of home visits on maternal and neonatal mortality. Individualised care as part of a package of home visits probably improves depression scores at four months and increasing the frequency of home visits may improve exclusive breastfeeding rates and infant healthcare utilisation. Maternal satisfaction may also be better with home visits compared to hospital check-ups. Overall, the certainty of evidence was found to be low and findings were not consistent among studies and comparisons. Further well designed RCTs evaluating this complex intervention will be required to formulate the optimal package.

Yonemoto N ，Nagai S ，Mori R 《Cochrane Database of Systematic Reviews》

Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.

Description of the condition Malaria, an infectious disease transmitted by the bite of female mosquitoes from several Anopheles species, occurs in 87 countries with ongoing transmission (WHO 2020). The World Health Organization (WHO) estimated that, in 2019, approximately 229 million cases of malaria occurred worldwide, with 94% occurring in the WHO's African region (WHO 2020). Of these malaria cases, an estimated 409,000 deaths occurred globally, with 67% occurring in children under five years of age (WHO 2020). Malaria also negatively impacts the health of women during pregnancy, childbirth, and the postnatal period (WHO 2020). Sulfadoxine/pyrimethamine (SP), an antifolate antimalarial, has been widely used across sub-Saharan Africa as the first-line treatment for uncomplicated malaria since it was first introduced in Malawi in 1993 (Filler 2006). Due to increasing resistance to SP, in 2000 the WHO recommended that one of several artemisinin-based combination therapies (ACTs) be used instead of SP for the treatment of uncomplicated malaria caused by Plasmodium falciparum (Global Partnership to Roll Back Malaria 2001). However, despite these recommendations, SP continues to be advised for intermittent preventive treatment in pregnancy (IPTp) and intermittent preventive treatment in infants (IPTi), whether the person has malaria or not (WHO 2013). Description of the intervention Folate (vitamin B9) includes both naturally occurring folates and folic acid, the fully oxidized monoglutamic form of the vitamin, used in dietary supplements and fortified food. Folate deficiency (e.g. red blood cell (RBC) folate concentrations of less than 305 nanomoles per litre (nmol/L); serum or plasma concentrations of less than 7 nmol/L) is common in many parts of the world and often presents as megaloblastic anaemia, resulting from inadequate intake, increased requirements, reduced absorption, or abnormal metabolism of folate (Bailey 2015; WHO 2015a). Pregnant women have greater folate requirements; inadequate folate intake (evidenced by RBC folate concentrations of less than 400 nanograms per millilitre (ng/mL), or 906 nmol/L) prior to and during the first month of pregnancy increases the risk of neural tube defects, preterm delivery, low birthweight, and fetal growth restriction (Bourassa 2019). The WHO recommends that all women who are trying to conceive consume 400 micrograms (µg) of folic acid daily from the time they begin trying to conceive through to 12 weeks of gestation (WHO 2017). In 2015, the WHO added the dosage of 0.4 mg of folic acid to the essential drug list (WHO 2015c). Alongside daily oral iron (30 mg to 60 mg elemental iron), folic acid supplementation is recommended for pregnant women to prevent neural tube defects, maternal anaemia, puerperal sepsis, low birthweight, and preterm birth in settings where anaemia in pregnant women is a severe public health problem (i.e. where at least 40% of pregnant women have a blood haemoglobin (Hb) concentration of less than 110 g/L). How the intervention might work Potential interactions between folate status and malaria infection The malaria parasite requires folate for survival and growth; this has led to the hypothesis that folate status may influence malaria risk and severity. In rhesus monkeys, folate deficiency has been found to be protective against Plasmodium cynomolgi malaria infection, compared to folate-replete animals (Metz 2007). Alternatively, malaria may induce or exacerbate folate deficiency due to increased folate utilization from haemolysis and fever. Further, folate status measured via RBC folate is not an appropriate biomarker of folate status in malaria-infected individuals since RBC folate values in these individuals are indicative of both the person's stores and the parasite's folate synthesis. A study in Nigeria found that children with malaria infection had significantly higher RBC folate concentrations compared to children without malaria infection, but plasma folate levels were similar (Bradley-Moore 1985). Why it is important to do this review The malaria parasite needs folate for survival and growth in humans. For individuals, adequate folate levels are critical for health and well-being, and for the prevention of anaemia and neural tube defects. Many countries rely on folic acid supplementation to ensure adequate folate status in at-risk populations. Different formulations for folic acid supplements are available in many international settings, with dosages ranging from 400 µg to 5 mg. Evaluating folic acid dosage levels used in supplementation efforts may increase public health understanding of its potential impacts on malaria risk and severity and on treatment failures. Examining folic acid interactions with antifolate antimalarial medications and with malaria disease progression may help countries in malaria-endemic areas determine what are the most appropriate lower dose folic acid formulations for at-risk populations. The WHO has highlighted the limited evidence available and has indicated the need for further research on biomarkers of folate status, particularly interactions between RBC folate concentrations and tuberculosis, human immunodeficiency virus (HIV), and antifolate antimalarial drugs (WHO 2015b). An earlier Cochrane Review assessed the effects and safety of iron supplementation, with or without folic acid, in children living in hyperendemic or holoendemic malaria areas; it demonstrated that iron supplementation did not increase the risk of malaria, as indicated by fever and the presence of parasites in the blood (Neuberger 2016). Further, this review stated that folic acid may interfere with the efficacy of SP; however, the efficacy and safety of folic acid supplementation on these outcomes has not been established. This review will provide evidence on the effectiveness of daily folic acid supplementation in healthy and malaria-infected individuals living in malaria-endemic areas. Additionally, it will contribute to achieving both the WHO Global Technical Strategy for Malaria 2016-2030 (WHO 2015d), and United Nations Sustainable Development Goal 3 (to ensure healthy lives and to promote well-being for all of all ages) (United Nations 2021), and evaluating whether the potential effects of folic acid supplementation, at different doses (e.g. 0.4 mg, 1 mg, 5 mg daily), interferes with the effect of drugs used for prevention or treatment of malaria. To examine the effects of folic acid supplementation, at various doses, on malaria susceptibility (risk of infection) and severity among people living in areas with various degrees of malaria endemicity. We will examine the interaction between folic acid supplements and antifolate antimalarial drugs. Specifically, we will aim to answer the following. Among uninfected people living in malaria endemic areas, who are taking or not taking antifolate antimalarials for malaria prophylaxis, does taking a folic acid-containing supplement increase susceptibility to or severity of malaria infection? Among people with malaria infection who are being treated with antifolate antimalarials, does folic acid supplementation increase the risk of treatment failure? Criteria for considering studies for this review Types of studies Inclusion criteria Randomized controlled trials (RCTs) Quasi-RCTs with randomization at the individual or cluster level conducted in malaria-endemic areas (areas with ongoing, local malaria transmission, including areas approaching elimination, as listed in the World Malaria Report 2020) (WHO 2020) Exclusion criteria Ecological studies Observational studies In vivo/in vitro studies Economic studies Systematic literature reviews and meta-analyses (relevant systematic literature reviews and meta-analyses will be excluded but flagged for grey literature screening) Types of participants Inclusion criteria Individuals of any age or gender, living in a malaria endemic area, who are taking antifolate antimalarial medications (including but not limited to sulfadoxine/pyrimethamine (SP), pyrimethamine-dapsone, pyrimethamine, chloroquine and proguanil, cotrimoxazole) for the prevention or treatment of malaria (studies will be included if more than 70% of the participants live in malaria-endemic regions) Studies assessing participants with or without anaemia and with or without malaria parasitaemia at baseline will be included Exclusion criteria Individuals not taking antifolate antimalarial medications for prevention or treatment of malaria Individuals living in non-malaria endemic areas Types of interventions Inclusion criteria Folic acid supplementation Form: in tablet, capsule, dispersible tablet at any dose, during administration, or periodically Timing: during, before, or after (within a period of four to six weeks) administration of antifolate antimalarials Iron-folic acid supplementation Folic acid supplementation in combination with co-interventions that are identical between the intervention and control groups. Co-interventions include: anthelminthic treatment; multivitamin or multiple micronutrient supplementation; 5-methyltetrahydrofolate supplementation. Exclusion criteria Folate through folate-fortified water Folic acid administered through large-scale fortification of rice, wheat, or maize Comparators Placebo No treatment No folic acid/different doses of folic acid Iron Types of outcome measures Primary outcomes Uncomplicated malaria (defined as a history of fever with parasitological confirmation; acceptable parasitological confirmation will include rapid diagnostic tests (RDTs), malaria smears, or nucleic acid detection (i.e. polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), etc.)) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Severe malaria (defined as any case with cerebral malaria or acute P. falciparum malaria, with signs of severity or evidence of vital organ dysfunction, or both) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Parasite clearance (any Plasmodium species), defined as the time it takes for a patient who tests positive at enrolment and is treated to become smear-negative or PCR negative. This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Treatment failure (defined as the inability to clear malaria parasitaemia or prevent recrudescence after administration of antimalarial medicine, regardless of whether clinical symptoms are resolved) (WHO 2019). This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Secondary outcomes Duration of parasitaemia Parasite density Haemoglobin (Hb) concentrations (g/L) Anaemia: severe anaemia (defined as Hb less than 70 g/L in pregnant women and children aged six to 59 months; and Hb less than 80 g/L in other populations); moderate anaemia (defined as Hb less than 100 g/L in pregnant women and children aged six to 59 months; and less than 110 g/L in others) Death from any cause Among pregnant women: stillbirth (at less than 28 weeks gestation); low birthweight (less than 2500 g); active placental malaria (defined as Plasmodium detected in placental blood by smear or PCR, or by Plasmodium detected on impression smear or placental histology). Search methods for identification of studies A search will be conducted to identify completed and ongoing studies, without date or language restrictions. Electronic searches A search strategy will be designed to include the appropriate subject headings and text word terms related to each intervention of interest and study design of interest (see Appendix 1). Searches will be broken down by these two criteria (intervention of interest and study design of interest) to allow for ease of prioritization, if necessary. The study design filters recommended by the Scottish Intercollegiate Guidelines Network (SIGN), and those designed by Cochrane for identifying clinical trials for MEDLINE and Embase, will be used (SIGN 2020). There will be no date or language restrictions. Non-English articles identified for inclusion will be translated into English. If translations are not possible, advice will be requested from the Cochrane Infectious Diseases Group and the record will be stored in the "Awaiting assessment" section of the review until a translation is available. The following electronic databases will be searched for primary studies. Cochrane Central Register of Controlled Trials. Cumulative Index to Nursing and Allied Health Literature (CINAHL). Embase. MEDLINE. Scopus. Web of Science (both the Social Science Citation Index and the Science Citation Index). We will conduct manual searches of ClinicalTrials.gov, the International Clinical Trials Registry Platform (ICTRP), and the United Nations Children's Fund (UNICEF) Evaluation and Research Database (ERD), in order to identify relevant ongoing or planned trials, abstracts, and full-text reports of evaluations, studies, and surveys related to programmes on folic acid supplementation in malaria-endemic areas. Additionally, manual searches of grey literature to identify RCTs that have not yet been published but are potentially eligible for inclusion will be conducted in the following sources. Global Index Medicus (GIM). African Index Medicus (AIM). Index Medicus for the Eastern Mediterranean Region (IMEMR). Latin American & Caribbean Health Sciences Literature (LILACS). Pan American Health Organization (PAHO). Western Pacific Region Index Medicus (WPRO). Index Medicus for the South-East Asian Region (IMSEAR). The Spanish Bibliographic Index in Health Sciences (IBECS) (ibecs.isciii.es/). Indian Journal of Medical Research (IJMR) (journals.lww.com/ijmr/pages/default.aspx). Native Health Database (nativehealthdatabase.net/). Scielo (www.scielo.br/). Searching other resources Handsearches of the five journals with the highest number of included studies in the last 12 months will be conducted to capture any relevant articles that may not have been indexed in the databases at the time of the search. We will contact the authors of included studies and will check reference lists of included papers for the identification of additional records. For assistance in identifying ongoing or unpublished studies, we will contact the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and the Division of Parasitic Diseases and Malaria (DPDM) of the CDC, the United Nations World Food Programme (WFP), Nutrition International (NI), Global Alliance for Improved Nutrition (GAIN), and Hellen Keller International (HKI). Data collection and analysis Selection of studies Two review authors will independently screen the titles and abstracts of articles retrieved by each search to assess eligibility, as determined by the inclusion and exclusion criteria. Studies deemed eligible for inclusion by both review authors in the abstract screening phase will advance to the full-text screening phase, and full-text copies of all eligible papers will be retrieved. If full articles cannot be obtained, we will attempt to contact the authors to obtain further details of the studies. If such information is not obtained, we will classify the study as "awaiting assessment" until further information is published or made available to us. The same two review authors will independently assess the eligibility of full-text articles for inclusion in the systematic review. If any discrepancies occur between the studies selected by the two review authors, a third review author will provide arbitration. Each trial will be scrutinized to identify multiple publications from the same data set, and the justification for excluded trials will be documented. A PRISMA flow diagram of the study selection process will be presented to provide information on the number of records identified in the literature searches, the number of studies included and excluded, and the reasons for exclusion (Moher 2009). The list of excluded studies, along with their reasons for exclusion at the full-text screening phase, will also be created. Data extraction and management Two review authors will independently extract data for the final list of included studies using a standardized data specification form. Discrepancies observed between the data extracted by the two authors will be resolved by involving a third review author and reaching a consensus. Information will be extracted on study design components, baseline participant characteristics, intervention characteristics, and outcomes. For individually randomized trials, we will record the number of participants experiencing the event and the number analyzed in each treatment group or the effect estimate reported (e.g. risk ratio (RR)) for dichotomous outcome measures. For count data, we will record the number of events and the number of person-months of follow-up in each group. If the number of person-months is not reported, the product of the duration of follow-up and the number of children evaluated will be used to estimate this figure. We will calculate the rate ratio and standard error (SE) for each study. Zero events will be replaced by 0.5. We will extract both adjusted and unadjusted covariate incidence rate ratios if they are reported in the original studies. For continuous data, we will extract means (arithmetic or geometric) and a measure of variance (standard deviation (SD), SE, or confidence interval (CI)), percentage or mean change from baseline, and the numbers analyzed in each group. SDs will be computed from SEs or 95% CIs, assuming a normal distribution of the values. Haemoglobin values in g/dL will be calculated by multiplying haematocrit or packed cell volume values by 0.34, and studies reporting haemoglobin values in g/dL will be converted to g/L. In cluster-randomized trials, we will record the unit of randomization (e.g. household, compound, sector, or village), the number of clusters in the trial, and the average cluster size. The statistical methods used to analyze the trials will be documented, along with details describing whether these methods adjusted for clustering or other covariates. We plan to extract estimates of the intra-cluster correlation coefficient (ICC) for each outcome. Where results are adjusted for clustering, we will extract the treatment effect estimate and the SD or CI. If the results are not adjusted for clustering, we will extract the data reported. Assessment of risk of bias in included studies Two review authors (KSC, LFY) will independently assess the risk of bias for each included trial using the Cochrane 'Risk of bias 2' tool (RoB 2) for randomized studies (Sterne 2019). Judgements about the risk of bias of included studies will be made according to the recommendations outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Disagreements will be resolved by discussion, or by involving a third review author. The interest of our review will be to assess the effect of assignment to the interventions at baseline. We will evaluate each primary outcome using the RoB2 tool. The five domains of the Cochrane RoB2 tool include the following. Bias arising from the randomization process. Bias due to deviations from intended interventions. Bias due to missing outcome data. Bias in measurement of the outcome. Bias in selection of the reported result. Each domain of the RoB2 tool comprises the following. A series of 'signalling' questions. A judgement about the risk of bias for the domain, facilitated by an algorithm that maps responses to the signalling questions to a proposed judgement. Free-text boxes to justify responses to the signalling questions and 'Risk of bias' judgements. An option to predict (and explain) the likely direction of bias. Responses to signalling questions elicit information relevant to an assessment of the risk of bias. These response options are as follows. Yes (may indicate either low or high risk of bias, depending on the most natural way to ask the question). Probably yes. Probably no. No. No information (may indicate no evidence of that problem or an absence of information leading to concerns about there being a problem). Based on the answer to the signalling question, a 'Risk of bias' judgement is assigned to each domain. These judgements include one of the following. High risk of bias Low risk of bias Some concerns To generate the risk of bias judgement for each domain in the randomized studies, we will use the Excel template, available at www.riskofbias.info/welcome/rob-2-0-tool/current-version-of-rob-2. This file will be stored on a scientific data website, available to readers. Risk of bias in cluster randomized controlled trials For the cluster randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 1b (bias arising from the timing of identification or recruitment of participants) and its related signalling questions. To generate the risk of bias judgement for each domain in the cluster RCTs, we will use the Excel template available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-cluster-randomized-trials. This file will be stored on a scientific data website, available to readers. Risk of bias in cross-over randomized controlled trials For cross-over randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 2 (bias due to deviations from intended interventions), and Domain 3 (bias due to missing outcome data), and their respective signalling questions. To generate the risk of bias judgement for each domain in the cross-over RCTs, we will use the Excel template, available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-crossover-trials, for each risk of bias judgement of cross-over randomized studies. This file will be stored on a scientific data website, available to readers. Overall risk of bias The overall 'Risk of bias' judgement for each specific trial being assessed will be based on each domain-level judgement. The overall judgements include the following. Low risk of bias (the trial is judged to be at low risk of bias for all domains). Some concerns (the trial is judged to raise some concerns in at least one domain but is not judged to be at high risk of bias for any domain). High risk of bias (the trial is judged to be at high risk of bias in at least one domain, or is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result). The 'risk of bias' assessments will inform our GRADE evaluations of the certainty of evidence for our primary outcomes presented in the 'Summary of findings' tables and will also be used to inform the sensitivity analyses; (see Sensitivity analysis). If there is insufficient information in study reports to enable an assessment of the risk of bias, studies will be classified as "awaiting assessment" until further information is published or made available to us. Measures of treatment effect Dichotomous data For dichotomous data, we will present proportions and, for two-group comparisons, results as average RR or odds ratio (OR) with 95% CIs. Ordered categorical data Continuous data We will report results for continuous outcomes as the mean difference (MD) with 95% CIs, if outcomes are measured in the same way between trials. Where some studies have reported endpoint data and others have reported change-from-baseline data (with errors), we will combine these in the meta-analysis, if the outcomes were reported using the same scale. We will use the standardized mean difference (SMD), with 95% CIs, to combine trials that measured the same outcome but used different methods. If we do not find three or more studies for a pooled analysis, we will summarize the results in a narrative form. Unit of analysis issues Cluster-randomized trials We plan to combine results from both cluster-randomized and individually randomized studies, providing there is little heterogeneity between the studies. If the authors of cluster-randomized trials conducted their analyses at a different level from that of allocation, and they have not appropriately accounted for the cluster design in their analyses, we will calculate the trials' effective sample sizes to account for the effect of clustering in data. When one or more cluster-RCT reports RRs adjusted for clustering, we will compute cluster-adjusted SEs for the other trials. When none of the cluster-RCTs provide cluster-adjusted RRs, we will adjust the sample size for clustering. We will divide, by the estimated design effects (DE), the number of events and number evaluated for dichotomous outcomes and the number evaluated for continuous outcomes, where DE = 1 + ((average cluster size 1) * ICC). The derivation of the estimated ICCs and DEs will be reported. We will utilize the intra-cluster correlation coefficient (ICC), derived from the trial (if available), or from another source (e.g., using the ICCs derived from other, similar trials) and then calculate the design effect with the formula provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). If this approach is used, we will report it and undertake sensitivity analysis to investigate the effect of variations in ICC. Studies with more than two treatment groups If we identify studies with more than two intervention groups (multi-arm studies), where possible we will combine groups to create a single pair-wise comparison or use the methods set out in the Cochrane Handbook to avoid double counting study participants (Higgins 2021). For the subgroup analyses, when the control group was shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double counting the participants. Trials with several study arms can be included more than once for different comparisons. Cross-over trials From cross-over trials, we will consider the first period of measurement only and will analyze the results together with parallel-group studies. Multiple outcome events In several outcomes, a participant might experience more than one outcome event during the trial period. For all outcomes, we will extract the number of participants with at least one event. Dealing with missing data We will contact the trial authors if the available data are unclear, missing, or reported in a format that is different from the format needed. We aim to perform a 'per protocol' or 'as observed' analysis; otherwise, we will perform a complete case analysis. This means that for treatment failure, we will base the analyses on the participants who received treatment and the number of participants for which there was an inability to clear malarial parasitaemia or prevent recrudescence after administration of an antimalarial medicine reported in the studies. Assessment of heterogeneity Heterogeneity in the results of the trials will be assessed by visually examining the forest plot to detect non-overlapping CIs, using the Chi2 test of heterogeneity (where a P value of less than 0.1 indicates statistical significance) and the I2 statistic of inconsistency (with a value of greater than 50% denoting moderate levels of heterogeneity). When statistical heterogeneity is present, we will investigate the reasons for it, using subgroup analysis. Assessment of reporting biases We will construct a funnel plot to assess the effect of small studies for the main outcome (when including more than 10 trials). Data synthesis The primary analysis will include all eligible studies that provide data regardless of the overall risk of bias as assessed by the RoB2 tool. Analyses will be conducted using Review Manager 5.4 (Review Manager 2020). Cluster-RCTs will be included in the main analysis after adjustment for clustering (see the previous section on cluster-RCTs). The meta-analysis will be performed using the Mantel-Haenszel random-effects model or the generic inverse variance method (when adjustment for clustering is performed by adjusting SEs), as appropriate. Subgroup analysis and investigation of heterogeneity The overall risk of bias will not be used as the basis in conducting our subgroup analyses. However, where data are available, we plan to conduct the following subgroup analyses, independent of heterogeneity. Dose of folic acid supplementation: higher doses (4 mg or more, daily) versus lower doses (less than 4 mg, daily). Moderate-severe anaemia at baseline (mean haemoglobin of participants in a trial at baseline below 100 g/L for pregnant women and children aged six to 59 months, and below 110 g/L for other populations) versus normal at baseline (mean haemoglobin above 100 g/L for pregnant women and children aged six to 59 months, and above 110 g/L for other populations). Antimalarial drug resistance to parasite: known resistance versus no resistance versus unknown/mixed/unreported parasite resistance. Folate status at baseline: Deficient (e.g. RBC folate concentration of less than 305 nmol/L, or serum folate concentration of less than 7nmol/L) and Insufficient (e.g. RBC folate concentration from 305 to less than 906 nmol/L, or serum folate concentration from 7 to less than 25 nmol/L) versus Sufficient (e.g. RBC folate concentration above 906 nmol/L, or serum folate concentration above 25 nmol/L). Presence of anaemia at baseline: yes versus no. Mandatory fortification status: yes, versus no (voluntary or none). We will only use the primary outcomes in any subgroup analyses, and we will limit subgroup analyses to those outcomes for which three or more trials contributed data. Comparisons between subgroups will be performed using Review Manager 5.4 (Review Manager 2020). Sensitivity analysis We will perform a sensitivity analysis, using the risk of bias as a variable to explore the robustness of the findings in our primary outcomes. We will verify the behaviour of our estimators by adding and removing studies with a high risk of bias overall from the analysis. That is, studies with a low risk of bias versus studies with a high risk of bias. Summary of findings and assessment of the certainty of the evidence For the assessment across studies, we will use the GRADE approach, as outlined in (Schünemann 2021). We will use the five GRADE considerations (study limitations based on RoB2 judgements, consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of the body of evidence as it relates to the studies which contribute data to the meta-analyses for the primary outcomes. The GRADEpro Guideline Development Tool (GRADEpro) will be used to import data from Review Manager 5.4 (Review Manager 2020) to create 'Summary of Findings' tables. The primary outcomes for the main comparison will be listed with estimates of relative effects, along with the number of participants and studies contributing data for those outcomes. These tables will provide outcome-specific information concerning the overall certainty of evidence from studies included in the comparison, the magnitude of the effect of the interventions examined, and the sum of available data on the outcomes we considered. We will include only primary outcomes in the summary of findings tables. For each individual outcome, two review authors (KSC, LFY) will independently assess the certainty of the evidence using the GRADE approach (Balshem 2011). For assessments of the overall certainty of evidence for each outcome that includes pooled data from included trials, we will downgrade the evidence from 'high certainty' by one level for serious (or by two for very serious) study limitations (risk of bias, indirectness of evidence, serious inconsistency, imprecision of effect estimates, or potential publication bias).

Crider K ，Williams J ，Qi YP ，Gutman J ，Yeung L ，Mai C ，Finkelstain J ，Mehta S ，Pons-Duran C ，Menéndez C ，Moraleda C ，Rogers L ，Daniels K ，Green P ... -  《Cochrane Database of Systematic Reviews》

Induction of labour at or beyond 37 weeks' gestation.

Risks of stillbirth or neonatal death increase as gestation continues beyond term (around 40 weeks' gestation). It is unclear whether a policy of labour induction can reduce these risks. This Cochrane Review is an update of a review that was originally published in 2006 and subsequently updated in 2012 and 2018. To assess the effects of a policy of labour induction at or beyond 37 weeks' gestation compared with a policy of awaiting spontaneous labour indefinitely (or until a later gestational age, or until a maternal or fetal indication for induction of labour arises) on pregnancy outcomes for the infant and the mother. For this update, we searched Cochrane Pregnancy and Childbirth's Trials Register, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP) (17 July 2019), and reference lists of retrieved studies. Randomised controlled trials (RCTs) conducted in pregnant women at or beyond 37 weeks, comparing a policy of labour induction with a policy of awaiting spontaneous onset of labour (expectant management). We also included trials published in abstract form only. Cluster-RCTs, quasi-RCTs and trials using a cross-over design were not eligible for inclusion in this review. We included pregnant women at or beyond 37 weeks' gestation. Since risk factors at this stage of pregnancy would normally require intervention, only trials including women at low risk for complications, as defined by trialists, were eligible. The trials of induction of labour in women with prelabour rupture of membranes at or beyond term were not considered in this review but are considered in a separate Cochrane Review. Two review authors independently assessed trials for inclusion, assessed risk of bias and extracted data. Data were checked for accuracy. We assessed the certainty of evidence using the GRADE approach. In this updated review, we included 34 RCTs (reporting on over 21,000 women and infants) mostly conducted in high-income settings. The trials compared a policy to induce labour usually after 41 completed weeks of gestation (> 287 days) with waiting for labour to start and/or waiting for a period before inducing labour. The trials were generally at low to moderate risk of bias. Compared with a policy of expectant management, a policy of labour induction was associated with fewer (all-cause) perinatal deaths (risk ratio (RR) 0.31, 95% confidence interval (CI) 0.15 to 0.64; 22 trials, 18,795 infants; high-certainty evidence). There were four perinatal deaths in the labour induction policy group compared with 25 perinatal deaths in the expectant management group. The number needed to treat for an additional beneficial outcome (NNTB) with induction of labour, in order to prevent one perinatal death, was 544 (95% CI 441 to 1042). There were also fewer stillbirths in the induction group (RR 0.30, 95% CI 0.12 to 0.75; 22 trials, 18,795 infants; high-certainty evidence); two in the induction policy group and 16 in the expectant management group. For women in the policy of induction arms of trials, there were probably fewer caesarean sections compared with expectant management (RR 0.90, 95% CI 0.85 to 0.95; 31 trials, 21,030 women; moderate-certainty evidence); and probably little or no difference in operative vaginal births with induction (RR 1.03, 95% CI 0.96 to 1.10; 22 trials, 18,584 women; moderate-certainty evidence). Induction may make little or difference to perineal trauma (severe perineal tear: RR 1.04, 95% CI 0.85 to 1.26; 5 trials; 11,589 women; low-certainty evidence). Induction probably makes little or no difference to postpartum haemorrhage (RR 1.02, 95% CI 0.91 to 1.15, 9 trials; 12,609 women; moderate-certainty evidence), or breastfeeding at discharge (RR 1.00, 95% CI 0.96 to 1.04; 2 trials, 7487 women; moderate-certainty evidence). Very low certainty evidence means that we are uncertain about the effect of induction or expectant management on the length of maternal hospital stay (average mean difference (MD) -0.19 days, 95% CI -0.56 to 0.18; 7 trials; 4120 women; Tau² = 0.20; I² = 94%). Rates of neonatal intensive care unit (NICU) admission were lower (RR 0.88, 95% CI 0.80 to 0.96; 17 trials, 17,826 infants; high-certainty evidence), and probably fewer babies had Apgar scores less than seven at five minutes in the induction groups compared with expectant management (RR 0.73, 95% CI 0.56 to 0.96; 20 trials, 18,345 infants; moderate-certainty evidence). Induction or expectant management may make little or no difference for neonatal encephalopathy (RR 0.69, 95% CI 0.37 to 1.31; 2 trials, 8851 infants; low-certainty evidence, and probably makes little or no difference for neonatal trauma (RR 0.97, 95% CI 0.63 to 1.49; 5 trials, 13,106 infants; moderate-certainty evidence) for induction compared with expectant management. Neurodevelopment at childhood follow-up and postnatal depression were not reported by any trials. In subgroup analyses, no differences were seen for timing of induction (< 40 versus 40-41 versus > 41 weeks' gestation), by parity (primiparous versus multiparous) or state of cervix for any of the main outcomes (perinatal death, stillbirth, NICU admission, caesarean section, operative vaginal birth, or perineal trauma). There is a clear reduction in perinatal death with a policy of labour induction at or beyond 37 weeks compared with expectant management, though absolute rates are small (0.4 versus 3 deaths per 1000). There were also lower caesarean rates without increasing rates of operative vaginal births and there were fewer NICU admissions with a policy of induction. Most of the important outcomes assessed using GRADE had high- or moderate-certainty ratings. While existing trials have not yet reported on childhood neurodevelopment, this is an important area for future research. The optimal timing of offering induction of labour to women at or beyond 37 weeks' gestation needs further investigation, as does further exploration of risk profiles of women and their values and preferences. Offering women tailored counselling may help them make an informed choice between induction of labour for pregnancies, particularly those continuing beyond 41 weeks - or waiting for labour to start and/or waiting before inducing labour.

Middleton P ，Shepherd E ，Morris J ，Crowther CA ，Gomersall JC ... -  《Cochrane Database of Systematic Reviews》

Different corticosteroids and regimens for accelerating fetal lung maturation for babies at risk of preterm birth.

Despite the widespread use of antenatal corticosteroids to prevent respiratory distress syndrome (RDS) in preterm infants, there is currently no consensus as to the type of corticosteroid to use, dose, frequency, timing of use or the route of administration.  OBJECTIVES: To assess the effects on fetal and neonatal morbidity and mortality, on maternal morbidity and mortality, and on the child and adult in later life, of administering different types of corticosteroids (dexamethasone or betamethasone), or different corticosteroid dose regimens, including timing, frequency and mode of administration. For this update, we searched Cochrane Pregnancy and Childbirth Group's Trials Register, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP) (9 May 2022) and reference lists of retrieved studies. We included all identified published and unpublished randomised controlled trials or quasi-randomised controlled trials comparing any two corticosteroids (dexamethasone or betamethasone or any other corticosteroid that can cross the placenta), comparing different dose regimens (including frequency and timing of administration) in women at risk of preterm birth. We planned to exclude cross-over trials and cluster-randomised trials. We planned to include studies published as abstracts only along with studies published as full-text manuscripts. At least two review authors independently assessed study eligibility, extracted data and assessed the risk of bias of included studies. Data were checked for accuracy. We assessed the certainty of the evidence using GRADE. We included 11 trials (2494 women and 2762 infants) in this update, all of which recruited women who were at increased risk of preterm birth or had a medical indication for preterm birth. All trials were conducted in high-income countries. Dexamethasone versus betamethasone Nine trials (2096 women and 2319 infants) compared dexamethasone versus betamethasone. All trials administered both drugs intramuscularly, and the total dose in the course was consistent (22.8 mg or 24 mg), but the regimen varied. We assessed one new study to have no serious risk of bias concerns for most outcomes, but other studies were at moderate (six trials) or high (two trials) risk of bias due to selection, detection and attrition bias. Our GRADE assessments ranged between high- and low-certainty, with downgrades due to risk of bias and imprecision.  Maternal outcomes The only maternal primary outcome reported was chorioamnionitis (death and puerperal sepsis were not reported). Although the rate of chorioamnionitis was lower with dexamethasone, we did not find conclusive evidence of a difference between the two drugs (risk ratio (RR) 0.71, 95% confidence interval (CI) 0.48 to 1.06; 1 trial, 1346 women; moderate-certainty evidence). The proportion of women experiencing maternal adverse effects of therapy was lower with dexamethasone; however, there was not conclusive evidence of a difference between interventions (RR 0.63, 95% CI 0.35 to 1.13; 2 trials, 1705 women; moderate-certainty evidence). Infant outcomes We are unsure whether the choice of drug makes a difference to the risk of any known death after randomisation, because the 95% CI was compatible with both appreciable benefit and harm with dexamethasone (RR 1.03, 95% CI 0.66 to 1.63; 5 trials, 2105 infants; moderate-certainty evidence). The choice of drug may make little or no difference to the risk of RDS (RR 1.06, 95% CI 0.91 to 1.22; 5 trials, 2105 infants; high-certainty evidence). While there may be little or no difference in the risk of intraventricular haemorrhage (IVH), there was substantial unexplained statistical heterogeneity in this result (average (a) RR 0.71, 95% CI 0.28 to 1.81; 4 trials, 1902 infants; I² = 62%; low-certainty evidence). We found no evidence of a difference between the two drugs for chronic lung disease (RR 0.92, 95% CI 0.64 to 1.34; 1 trial, 1509 infants; moderate-certainty evidence), and we are unsure of the effects on necrotising enterocolitis, because there were few events in the studies reporting this outcome (RR 5.08, 95% CI 0.25 to 105.15; 2 studies, 441 infants; low-certainty evidence). Longer-term child outcomes Only one trial consistently followed up children longer term, reporting  at two years' adjusted age. There is probably little or no difference between dexamethasone and betamethasone in the risk of neurodevelopmental disability at follow-up (RR 1.02, 95% CI 0.85 to 1.22; 2 trials, 1151 infants; moderate-certainty evidence). It is unclear whether the choice of drug makes a difference to the risk of visual impairment (RR 0.33, 95% CI 0.01 to 8.15; 1 trial, 1227 children; low-certainty evidence). There may be little or no difference between the drugs for hearing impairment (RR 1.16, 95% CI 0.63 to 2.16; 1 trial, 1227 children; moderate-certainty evidence), motor developmental delay (RR 0.89, 95% CI 0.66 to 1.20; 1 trial, 1166 children; moderate-certainty evidence) or intellectual impairment (RR 0.97, 95% CI 0.79 to 1.20; 1 trial, 1161 children; moderate-certainty evidence). However, the effect estimate for cerebral palsy is compatible with both an important increase in risk with dexamethasone, and no difference between interventions (RR 2.50, 95% CI 0.97 to 6.39; 1 trial, 1223 children; low-certainty evidence). No trials followed the children beyond early childhood. Comparisons of different preparations and regimens of corticosteroids We found three studies that included a comparison of a different regimen or preparation of either dexamethasone or betamethasone (oral dexamethasone 32 mg versus intramuscular dexamethasone 24 mg; betamethasone acetate plus phosphate versus betamethasone phosphate; 12-hourly betamethasone versus 24-hourly betamethasone). The certainty of the evidence for the main outcomes from all three studies was very low, due to  small sample size and  risk of bias. Therefore, we were limited in our ability to draw conclusions from any of these studies. Overall, it remains unclear whether there are important differences between dexamethasone and betamethasone, or between one regimen and another.  Most trials compared dexamethasone versus betamethasone. While for most infant and early childhood outcomes there may be no difference between these drugs, for several important outcomes for the mother, infant and child the evidence was inconclusive and did not rule out significant benefits or harms. The evidence on different antenatal corticosteroid regimens was sparse, and does not support the use of one particular corticosteroid regimen over another.

Williams MJ ，Ramson JA ，Brownfoot FC 《Cochrane Database of Systematic Reviews》