Extraction form for project: Schedule of Visits and Televisits for Routine Antenatal Care

Design Details

1. Key Question this study addressed
Select all that apply.
Multiple Choice
KQ 1
KQ 2
KQ 3
2. Study DESIGN
Single Choice
RCT
NRCS: Nonrandomized parallel comparison
NRCS: Pre-Post
Single group study
3. Study DESIGN
Multiple Choice
Interviews
Focus groups
Ethnographic studies
Surveys with open-ended questions amenable to qualitative analysis
Others
NR
4. If an RCT, please choose the randomization level
Dependency
  • Question Position: 2
    • - Option: RCT
    Multiple Choice
    Individual randomization
    Cluster randomization (e.g., randomization by clinic)
    5. DIRECTIONALITY of the study
    Dependency
  • Question Position: 2
    • - Option: NRCS: Nonrandomized parallel comparison
    • - Option: NRCS: Pre-Post
    Single Choice
    Prospective
    Retrospective
    Both prospective and retrospective (i.e., ambidirectional)
    Can't tell/Not reported
    6. Registry record (e.g., NCT)
    Please provide details on ClinicalTrials.gov NCT number, other country's protocol registry, or journal article protocols
    7. Multiple publications
    List PMID (if available) or "file name" of ALL publications extracted under this record
    8. Study NAME or ACRONYM
    Leave blank if there wasn't any. Copy Registry information if available.
    9. COUNTRY the study was done
    Select all that apply.
    Multiple Choice
    US
    UK
    Canada
    Australia
    Sweden
    Japan
    Italy
    Netherlands
    Denmark
    Germany
    Belgium
    France
    Finland
    China
    Can't tell/Not reported
    10. LOCATION (e.g., Dallas, TX)
    11. HOSPITAL details: name and type
    Hospital name
    Hospital description
    Academic hospital
    Multiple Choice

    Non-academic hospital
    Multiple Choice

    NR
    Single Choice

    12. Number of CENTERS involved in the study
    Single Choice
    Single-center
    Multi-center
    Not reported
    13. FUNDING SOURCE
    Multiple Choice
    Not funded
    Non-Industry
    Industry
    NR
    14. STUDY PERIOD
    Leave blank if not specified.
    Start yearEnd year
    15. Study INCLUSION criteria
    Keep succinct. Use common abbreviations. Omit criteria of minor relevance (e.g., language, consent). Enter "NR" if not reported.
    16. Study EXCLUSION criteria
    Keep succinct. Use common abbreviations. Omit criteria of minor relevance (e.g., language, consent). Enter "NR" if not reported.
    17. Specific population
    Multiple Choice
    No (all comers or not specified)
    Population specified
    18. Specific population
    Multiple Choice
    Pregnant individuals
    Postpartum individuals
    Individuals considering or planning pregnancy
    Partners/family
    Providers of antenatal care (any profession or licensure)
    Others
    NR
    19. SAMPLE SIZE
    20. Power calculation reported (y/n)
    Single Choice
    Yes
    No
    21. Primary outcome (for which power calculation was performed)
    Dependency
  • Question Position: 20
    • - Option: Yes
    22. What was the EFFECT SIZE ESTIMATE used to calculate power
    Usually RD (although they will describe as change in %). Format: RD +2.5% (5.5% to 8.0%)
    23. Was primary outcome adequately powered (N≥ calculated N)
    On Sample Characteristics tab, we ask about calculated sample sizes (actual N's).
    Dependency
  • Question Position: 20
    • - Option: Yes
    Single Choice
    Yes
    No
    24. What METHOD was used to account for confounding
    Multiple Choice
    Randomization
    Propensity score method (or equivalent)
    Regression
    Matching
    Other
    None
    25. What FACTORS were adjusted for
    Dependency
  • Question Position: 24
    • - Option: Randomization
    • - Option: Propensity score method (or equivalent)
    • - Option: Regression
    • - Option: Matching
    • - Option: Other
    List all factors
    Did ALL important factors have been adjusted? (including 1)age, 2)race/ethnicity, 3)at least one of the following factors: pre-existing DM, pre-existing HTN, obesity, prior preterm birth history, prior high risk pregnancy)
    Multiple Choice
    Yes
    No
    26. NOTES regarding the design or any overall aspects of this study
    Leave blank if you don't have any notes.
    27. Unextracted outcomes not in protocol

    Arm Details

    1. BRIEF DESCRIPTION of the intervention
    Be specific, but concise.
    More intensive antenatal visits schedule:
    2. Any guidelines (e.g., ACOG) the prenatal visits followed
    More intensive antenatal visits schedule:
    3. Total number of visits
    More intensive antenatal visits schedule:
    4. Number of visits (In person vs. Tele)
    More intensive antenatal visits schedule:
    Number of in person visits
    Number of tele visits
    NR
    Single Choice

    5. Timing of visit (weeks)
    Please provide a list of time of prenatal visit reported by the paper; enter NR if not reported
    More intensive antenatal visits schedule:
    6. Setting of visits (Office vs. Home)
    More intensive antenatal visits schedule:
    Multiple Choice
    Office visits
    Home visits
    NR
    7. Format of visits (Group vs. Individual)
    More intensive antenatal visits schedule:
    Multiple Choice
    Group visits
    Individual visits
    NR
    8. Provider of visits (record % if more than one type of providers were mentioned)
    More intensive antenatal visits schedule:
    OB/GYN
    Multiple Choice

    Nurse
    Multiple Choice

    Nurse practitioner/registered nurse
    Multiple Choice

    Midwife
    Multiple Choice

    Family medicine clinician
    Multiple Choice

    Maternal fetal medicine (MFM)
    Multiple Choice

    Physician assistant (associate)
    Multiple Choice

    Other licensed personnel
    Multiple Choice

    NR
    Single Choice

    9. PARTICIPANTS of the visit
    More intensive antenatal visits schedule:
    Single Choice
    Patients only
    Patients and partners
    Others
    NR
    10. Amount of time per visit
    More intensive antenatal visits schedule:
    11. Tele visits details: TECHNOLOGY
    Enter NR if not reported.
    More intensive antenatal visits schedule:
    12. Tele visits details: Synchronous vs. asynchronous INTERACTIONS
    More intensive antenatal visits schedule:
    Single Choice
    Synchronous interactions (real-time visits such as video calls)
    Asynchronous interactions (e.g., email)
    NA
    NR
    13. Tele visits details: use of HOME MONITORING DEVICE
    More intensive antenatal visits schedule:
    Single Choice
    Yes
    No
    NA
    NR
    14. Tele visits details: Occurred during the pandemic
    More intensive antenatal visits schedule:
    Single Choice
    Yes
    No
    NA
    NR
    15. Other differences between groups
    Briefly summarize differences other than number/timing of visits (KQ 1) or use of televisits (KQ 2) between arms.
    More intensive antenatal visits schedule:
    16. NOTES regarding the arm details in this study
    More intensive antenatal visits schedule:

    Sample Characteristics

    1. How many people were ENROLLED in this study
    More intensive antenatal visits schedule:
    2. How many people were ANALYZED in this study
    More intensive antenatal visits schedule:
    3. Required N from Power Calculation
    Leave blank if no power calculation. Answer for each arm and total.
    More intensive antenatal visits schedule:
    4. AGE distribution (CONTINUOUS DATA) (in years)
    Select and enter data for all that apply.
    More intensive antenatal visits schedule:
    MeanStandard deviation (SD)Standard error (SE) 95% CIMedianIQRRangeNR
    Single Choice

    5. AGE distribution (CATEGORICAL DATA) (in years)
    More intensive antenatal visits schedule:
    Definition of category%
    Youngest category
    2nd category
    3rd category
    4th category
    5th category
    6th category
    NR
    Single Choice

    6. % of maternal age≥35
    Please calculate if possible, and enter NR if not reported.
    More intensive antenatal visits schedule:
    7. RACIAL distribution (%)
    More intensive antenatal visits schedule:
    %
    White (or Caucasian)
    Black (or African)
    Asian
    Hispanic (or Latino)
    Other 1
    Other 2
    NR
    Single Choice

    8. BMI distribution pre-pregnancy or at first encounter (CONTINUOUS DATA)
    Select and enter data for all that apply.
    More intensive antenatal visits schedule:
    MeanStandard deviation (SD)Standard error (SE) 95% CIMedianIQRRangeNR
    Single Choice

    9. BMI distribution pre-pregnancy or at first encounter (CATEGORICAL DATA)
    More intensive antenatal visits schedule:
    Definition of category%
    Lowest BMI category
    2nd BMI category
    3rd BMI category
    4th BMI category
    5th BMI category
    6th BMI category
    NR
    Single Choice

    10. INCOME distribution (CONTINUOUS DATA)
    Select and enter data for all that apply.
    More intensive antenatal visits schedule:
    MeanStandard deviation (SD)Standard error (SE) 95% CIMedianIQRRangeNR
    Single Choice
    Not reported
    11. INCOME distribution (CATEGORICAL DATA)
    More intensive antenatal visits schedule:
    Definition of category%
    Lowest INCOME category
    2nd INCOME category
    3rd INCOME category
    4th INCOME category
    5th INCOME category
    6th INCOME category
    NR
    Single Choice

    12. EDUCATION distribution (%) (CATEGORICAL DATA)
    More intensive antenatal visits schedule:
    %
    Primary school or below
    Junior high school
    High school
    College or above
    NR
    Single Choice

    13. Yeas of education completed (CONTINUOUS DATA)
    Select and enter data for all that apply.
    More intensive antenatal visits schedule:
    MeanStandard deviation (SD)Standard error (SE) 95% CIMedianIQRRangeNR
    Single Choice

    14. RELATIONSHIP status (%)
    More intensive antenatal visits schedule:
    %
    Married
    Unmarried but marriage-like relationships
    Single
    Other
    NR
    Single Choice

    15. EMPLOYMENT distribution
    More intensive antenatal visits schedule:
    %
    Full time
    Part time
    Unemployment
    Other
    NR
    Single Choice

    16. % of nulliparous women
    If answering "NR", enter NR under specific arms, NOT total
    More intensive antenatal visits schedule:
    17. % of unplanned pregnancy
    If answering "NR", enter NR under specific arms, NOT total
    More intensive antenatal visits schedule:
    18. % of multiple gestation
    If answering "NR", enter NR under specific arms, NOT total
    More intensive antenatal visits schedule:
    19. COMORBIDITIES
    Define the comorbidities. If % reported, enter % for total sample and each study arm. Skip those that are not reported.
    More intensive antenatal visits schedule:
    Definition%
    CVD (total)
    Pregestational HTN
    Gestational HTN
    DM, type 1
    Pregestational DM
    Gestational DM
    Depression
    Anxiety
    Intrauterine growth restriction (IUGR) or fetal growth restriction
    Prior history or risk factors for preterm delivery
    Tobacco, any
    Other pre-existing conditions
    NR
    Single Choice

    20. SOCIAL SUPPORT
    More intensive antenatal visits schedule:
    21. Pregnancy EDUCATION NEEDS
    More intensive antenatal visits schedule:
    22. Live in RURAL/URBAN areas (%)
    More intensive antenatal visits schedule:
    %
    Rural
    Urban
    Others
    NR
    Single Choice

    23. % of food insecurity
    If answering "NR", enter NR under specific arms, NOT total
    More intensive antenatal visits schedule:
    24. INSURANCE status (%)
    More intensive antenatal visits schedule:
    %
    Private
    Medicare
    Medicaid
    Others
    NR
    Single Choice

    25. TRANSPORTATION to health care facilities
    More intensive antenatal visits schedule:
    26. % of having internet access at home
    If answering "NR", enter NR under specific arms, NOT total
    More intensive antenatal visits schedule:
    %
    Have internet access
    27. Other information regarding ACCESS TO CARE
    More intensive antenatal visits schedule:
    28. Any baseline DIFFERENCES between two arms (p<0.05)?
    If answering yes, choose yes under "Total", and make a note on what factors
    More intensive antenatal visits schedule:
    Multiple Choice
    Yes
    No
    29. Experienced gestational age at first prenatal visit (weeks)
    More intensive antenatal visits schedule:
    30. NOTES regarding the sample characteristics in this study
    More intensive antenatal visits schedule:

    Outcome Details

    1. NOTES regarding the outcome
    Also indicate when an outcome is extracted from a co-publication (not the primary publication)
    Maternal Mortality:

    RCT Risk of Bias Assessment

    1. 1.1 Was the allocation sequence random?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    2. 1.2 Was the allocation sequence concealed until participants were enrolled and assigned to interventions?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    3. 1.3 Did baseline differences between intervention groups suggest a problem with the randomization process?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    4. Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:
    5. 2.1. (effect of assignment to intervention) Were participants aware of their assigned intervention during the trial?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    6. 2.2. (effect of assignment to intervention) Were carers and people delivering the interventions aware of participants' assigned intervention during the trial?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    7. 2.3. (effect of assignment to intervention) If Y/PY/NI to 2.1 or 2.2: Were there deviations from the intended intervention that arose because of the experimental context?
    Consider non-adherence or crossover
    Dependency
  • Question Position: 5
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    • - Cell: (Rating x )
    • - Option: NI
  • Question Position: 6
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    8. 2.4. (effect of assignment to intervention) If Y/PY to 2.3: Were these deviations from intended intervention balanced between groups?
    Dependency
  • Question Position: 7
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    9. 2.5. (effect of assignment to intervention) If N/PN/NI to 2.4: Were these deviations likely to have affected the outcome?
    Dependency
  • Question Position: 8
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    10. 2.6. (effect of assignment to intervention) Was an appropriate analysis used to estimate the effect of assignment to intervention?
    (modified) intention-to-treat analysis
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    11. 2.7. (effect of assignment to intervention) If N/PN/NI to 2.6: Was there potential for a substantial impact (on the result) of the failure to analyse participants in the group to which they were randomized?
    Should be <5% for crossover
    Dependency
  • Question Position: 10
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    12. Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:
    13. 2.1. (effect of adhering to intervention) Were participants aware of their assigned intervention during the trial?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    14. 2.2. (effect of adhering to intervention) Were carers and people delivering the interventions aware of participants' assigned intervention during the trial?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    15. 2.3. (effect of adhering to intervention) If Y/PY/NI to 2.1 or 2.2: Were important co-interventions balanced across intervention groups?
    Dependency
  • Question Position: 13
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    • - Cell: (Rating x )
    • - Option: NI
  • Question Position: 14
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    16. 2.4. (effect of adhering to intervention) Were there failures in implementing the intervention that could have affected the outcome?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    17. 2.5. (effect of adhering to intervention) Was there non-adherence to the assigned intervention regimen that could have affected participants’ outcomes?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    18. 2.6. (effect of adhering to intervention) If N/PN/NI to 2.3 or 2.5 or Y/PY/NI to 2.4: Was an appropriate analysis used to estimate the effect of adhering to the intervention?
    Dependency
  • Question Position: 15
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    • - Cell: (Rating x )
    • - Option: NI
  • Question Position: 16
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    • - Cell: (Rating x )
    • - Option: NI
  • Question Position: 17
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    19. Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:
    20. 3.1. Were data for this outcome available for all, or nearly all, participants randomized?
    Should be <10% lost to follow-up
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    21. 3.2. If N/PN/NI to 3.1: Is there evidence that the result was not biased by missing outcome data?
    Dependency
  • Question Position: 20
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N

    Notes/Comments:
    22. 3.3. If N/PN to 3.2: Could missingness in the outcome depend on its true value?
    Dependency
  • Question Position: 21
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    23. 3.4. If Y/PY/NI to 3.3: Is it likely that missingness in the outcome depended on its true value?
    Dependency
  • Question Position: 22
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    • - Cell: (Rating x )
    • - Option: NI
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    24. Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:
    25. 4.1. Was the method of measuring the outcome inappropriate?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    26. 4.2. Could measurement or ascertainment of the outcome have differed between intervention groups?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    27. 4.3. If N/PN/NI to 4.1 and 4.2: Were outcome assessors aware of the intervention received by study participants?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    28. 4.4. If Y/PY/NI to 4.3: Could assessment of the outcome have been influenced by knowledge of intervention received?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    29. 4.5. If Y/PY/NI to 4.4: Is it likely that assessment of the outcome was influenced by knowledge of intervention received?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    30. Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:
    31. 5.1. Were the data that produced this result analysed in accordance with a pre-specified analysis plan that was finalized before unblinded outcome data were available for analysis?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    32. 5.2. Is the numerical result being assessed likely to have been selected, on the basis of the results, from multiple outcome measurements (e.g. scales, definitions, time points) within the outcome domain?
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    33. 5.3. Is the numerical result being assessed likely to have been selected, on the basis of the results, from multiple analyses of the data
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    34. Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:
    35. Other potential bias
    36. Overall Risk-of-bias judgement
    Rating
    Single Choice
    Low
    High
    Some concerns
    Notes/Comments:

    NRCS Risk of Bias Assessment

    1. 1.1 Is there potential for confounding of the effect of intervention in this study?
    In rare situations, such as when studying harms that are very unlikely to be related to factors that influence treatment decisions, no confounding is expected and the study can be considered to be at low risk of bias due to confounding, equivalent to a fully randomized trial. There is no NI (No information) option for this signalling question. If N/PN to 1.1: the study can be considered to be at low risk of bias due to confounding and no further signalling questions need be considered. If Y/PY to 1.1: determine whether there is a need to assess time-varying confounding:
    Rating
    Single Choice
    Y
    PY
    PN
    N
    Notes/Comments:
    2. 1.2 Was the analysis based on splitting participants’ follow up time according to intervention received?
    If participants could switch between intervention groups then associations between intervention and outcome may be biased by time-varying confounding. This occurs when prognostic factors influence switches between intended interventions. If N/PN, answer questions relating to baseline confounding (1.4 to 1.6). If Y/PY, proceed to question 1.3.
    Dependency
  • Question Position: 1
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    3. 1.3 Were intervention discontinuations or switches likely to be related to factors that are prognostic for the outcome?
    If intervention switches are unrelated to the outcome, for example when the outcome is an unexpected harm, then time-varying confounding will not be present and only control for baseline confounding is required.
    Dependency
  • Question Position: 1
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
  • Question Position: 2
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    4. 1.4. Did the authors use an appropriate analysis method that controlled for all the important confounding domains?
    Appropriate methods to control for measured confounders include stratification, regression, matching, standardization, and inverse probability weighting. They may control for individual variables or for the estimated propensity score. Inverse probability weighting is based on a function of the propensity score. Each method depends on the assumption that there is no unmeasured or residual confounding. if Propensity score matching/adjustment or equivalent, answer Y. If regression or other matching answer PY with a note reporting method. IMPORTANT CONFOUNDING DOMAINS defined by our team include: 1)age, 2)race/ethnicity, 3)at least one of the following factors: pre-existing DM, pre-existing HTN, obesity, prior preterm birth history, prior high risk pregnancy.
    Dependency
  • Question Position: 2
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 3
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    5. 1.5. If Y/PY to 1.4: Were confounding domains that were controlled for measured validly and reliably by the variables available in this study?
    Appropriate control of confounding requires that the variables adjusted for are valid and reliable measures of the confounding domains. For some topics, a list of valid and reliable measures of confounding domains will be specified in the review protocol but for others such a list may not be available. Study authors may cite references to support the use of a particular measure. If authors control for confounding variables with no indication of their validity or reliability pay attention to the subjectivity of the measure. Subjective measures (e.g. based on self-report) may have lower validity and reliability than objective measures such as lab findings.
    Dependency
  • Question Position: 2
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 3
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 4
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    6. 1.6. Did the authors control for any post-intervention variables that could have been affected by the intervention?
    Controlling for post-intervention variables that are affected by intervention is not appropriate. Controlling for mediating variables estimates the direct effect of intervention and may introduce bias. Controlling for common effects of intervention and outcome introduces bias.
    Dependency
  • Question Position: 2
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 3
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    7. 1.7. Did the authors use an appropriate analysis method that adjusted for all the important confounding domains and for time-varying confounding?
    Adjustment for time-varying confounding is necessary to estimate the effect of starting and adhering to intervention, in both randomized trials and NRSI. Appropriate methods include those based on inverse probability weighting. Standard regression models that include time-updated confounders may be problematic if time-varying confounding is present.
    Dependency
  • Question Position: 3
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    8. 1.8. If Y/PY to 1.7: Were confounding domains that were adjusted for measured validly and reliably by the variables available in this study?
    See 1.5 above
    Dependency
  • Question Position: 3
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
  • Question Position: 7
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    9. 2.1. Was selection of participants into the study (or into the analysis) based on participant characteristics observed after the start of intervention?
    This domain is concerned only with selection into the study based on participant characteristics observed after the start of intervention. Selection based on characteristics observed before the start of intervention can be addressed by controlling for imbalances between experimental intervention and comparator groups in baseline characteristics that are prognostic for the outcome (baseline confounding). If N/PN to 2.1: go to 2.4
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    10. 2.2. If Y/PY to 2.1: Were the post-intervention variables that influenced selection likely to be associated with intervention?
    Selection bias occurs when selection is related to an effect of either intervention or a cause of intervention and an effect of either the outcome or a cause of the outcome. Therefore, the result is at risk of selection bias if selection into the study is related to both the intervention and the outcome.
    Dependency
  • Question Position: 9
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    11. 2.3 If Y/PY to 2.2: Were the post-intervention variables that influenced selection likely to be influenced by the outcome or a cause of the outcome?
    Dependency
  • Question Position: 10
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    12. 2.4. Do start of follow-up and start of intervention coincide for most participants?
    If participants are not followed from the start of the intervention then a period of follow up has been excluded, and individuals who experienced the outcome soon after intervention will be missing from analyses. This problem may occur when prevalent, rather than new (incident), users of the intervention are included in analyses.
    Dependency
  • Question Position: 9
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    13. 2.5. If Y/PY to 2.2 and 2.3, or N/PN to 2.4: Were adjustment techniques used that are likely to correct for the presence of selection biases?
    It is in principle possible to correct for selection biases, for example by using inverse probability weights to create a pseudo-population in which the selection bias has been removed, or by modelling the distributions of the missing participants or follow up times and outcome events and including them using missing data methodology. However such methods are rarely used and the answer to this question will usually be ‘No’.
    Dependency
  • Question Position: 10
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
  • Question Position: 11
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
  • Question Position: 12
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    14. 3.1 Were intervention groups clearly defined?
    A pre-requisite for an appropriate comparison of interventions is that the interventions are well defined. Ambiguity in the definition may lead to bias in the classification of participants. For individual-level interventions, criteria for considering individuals to have received each intervention should be clear and explicit, covering issues such as type, setting, dose, frequency, intensity and/or timing of intervention. For population-level interventions (e.g. measures to control air pollution), the question relates to whether the population is clearly defined, and the answer is likely to be ‘Yes’
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    15. 3.2 Was the information used to define intervention groups recorded at the start of the intervention?
    In general, if information about interventions received is available from sources that could not have been affected by subsequent outcomes, then differential misclassification of intervention status is unlikely. Collection of the information at the time of the intervention makes it easier to avoid such misclassification. For population-level interventions (e.g. measures to control air pollution), the answer to this question is likely to be ‘Yes’.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    16. 3.3 Could classification of intervention status have been affected by knowledge of the outcome or risk of the outcome?
    Collection of the information at the time of the intervention may not be sufficient to avoid bias. The way in which the data are collected for the purposes of the NRSI should also avoid misclassification.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    17. 4.1. Were there deviations from the intended intervention beyond what would be expected in usual practice?
    Deviations that happen in usual practice following the intervention (for example, cessation of a drug intervention because of acute toxicity) are part of the intended intervention and therefore do not lead to bias in the effect of assignment to intervention. Deviations may arise due to expectations of a difference between intervention and comparator (for example because participants feel unlucky to have been assigned to the comparator group and therefore seek the active intervention, or components of it, or other interventions). Such deviations are not part of usual practice, so may lead to biased effect estimates. However these are not expected in observational studies of individuals in routine care.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    18. 4.2. If Y/PY to 4.1: Were these deviations from intended intervention unbalanced between groups and likely to have affected the outcome?
    Deviations from intended interventions that do not reflect usual practice will be important if they affect the outcome, but not otherwise. Furthermore, bias will arise only if there is imbalance in the deviations across the two groups.
    Dependency
  • Question Position: 17
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    19. 4.3. Were important co-interventions balanced across intervention groups?
    Risk of bias will be higher if unplanned co-interventions were implemented in a way that would bias the estimated effect of intervention. Co-interventions will be important if they affect the outcome, but not otherwise. Bias will arise only if there is imbalance in such co-interventions between the intervention groups. Consider the co-interventions, including any pre-specified co-interventions, that are likely to affect the outcome and to have been administered in this study. Consider whether these co-interventions are balanced between intervention groups.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    20. 4.4. Was the intervention implemented successfully for most participants?
    Risk of bias will be higher if the intervention was not implemented as intended by, for example, the health care professionals delivering care during the trial. Consider whether implementation of the intervention was successful for most participants.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    21. 4.5. Did study participants adhere to the assigned intervention regimen?
    Risk of bias will be higher if participants did not adhere to the intervention as intended. Lack of adherence includes imperfect compliance, cessation of intervention, crossovers to the comparator intervention and switches to another active intervention. Consider available information on the proportion of study participants who continued with their assigned intervention throughout follow up, and answer ‘No’ or ‘Probably No’ if this proportion is high enough to raise concerns. Answer ‘Yes’ for studies of interventions that are administered once, so that imperfect adherence is not possible.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    22. 4.6. If N/PN to 4.3, 4.4 or 4.5: Was an appropriate analysis used to estimate the effect of starting and adhering to the intervention?
    It is possible to conduct an analysis that corrects for some types of deviation from the intended intervention. Examples of appropriate analysis strategies include inverse probability weighting or instrumental variable estimation. It is possible that a paper reports such an analysis without reporting information on the deviations from intended intervention, but it would be hard to judge such an analysis to be appropriate in the absence of such information. Specialist advice may be needed to assess studies that used these approaches. If everyone in one group received a co-intervention, adjustments cannot be made to overcome this.
    Dependency
  • Question Position: 19
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 20
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 21
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    23. 5.1 Were outcome data available for all, or nearly all, participants?
    ‘Nearly all’ should be interpreted as ‘enough to be confident of the findings’, and a suitable proportion depends on the context. In some situations, availability of data from 95% (or possibly 90%) of the participants may be sufficient, providing that events of interest are reasonably common in both intervention groups. One aspect of this is that review authors would ideally try and locate an analysis plan for the study.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    24. 5.2 Were participants excluded due to missing data on intervention status?
    Missing intervention status may be a problem. This requires that the intended study sample is clear, which it may not be in practice.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    25. 5.3 Were participants excluded due to missing data on other variables needed for the analysis?
    This question relates particularly to participants excluded from the analysis because of missing information on confounders that were controlled for in the analysis.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    26. 5.4 If PN/N to 5.1, orY/PY to 5.2 or 5.3: Are the proportion of participants and reasons for missing data similar across interventions?
    This aims to elicit whether either (i) differential proportion of missing observations or (ii) differences in reasons for missing observations could substantially impact on our ability to answer the question being addressed. ‘Similar’ includes some minor degree of discrepancy across intervention groups as expected by chance.
    Dependency
  • Question Position: 23
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 24
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
  • Question Position: 25
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    27. 5.5 If PN/N to 5.1, orY/PY to 5.2 or 5.3: Is there evidence that results were robust to the presence of missing data?
    Evidence for robustness may come from how missing data were handled in the analysis and whether sensitivity analyses were performed by the investigators, or occasionally from additional analyses performed by the systematic reviewers. It is important to assess whether assumptions employed in analyses are clear and plausible. Both content knowledge and statistical expertise will often be required for this. For instance, use of a statistical method such as multiple imputation does not guarantee an appropriate answer. Review authors should seek naïve (complete-case) analyses for comparison, and clear differences between complete-case and multiple imputation-based findings should lead to careful assessment of the validity of the methods used.
    Dependency
  • Question Position: 23
    • - Cell: (Rating x )
    • - Option: PN
    • - Cell: (Rating x )
    • - Option: N
  • Question Position: 24
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
  • Question Position: 25
    • - Cell: (Rating x )
    • - Option: Y
    • - Cell: (Rating x )
    • - Option: PY
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    NA
    Notes/Comments:
    28. 6.1 Could the outcome measure have been influenced by knowledge of the intervention received?
    Some outcome measures involve negligible assessor judgment, e.g. all-cause mortality or non-repeatable automated laboratory assessments. Risk of bias due to measurement of these outcomes would be expected to be low.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    29. 6.2 Were outcome assessors aware of the intervention received by study participants?
    If outcome assessors were blinded to intervention status, the answer to this question would be ‘No’. In other situations, outcome assessors may be unaware of the interventions being received by participants despite there being no active blinding by the study investigators; the answer this question would then also be‘No’. In studies where participants report their outcomes themselves, for example in a questionnaire, the outcome assessor is the study participant. In an observational study, the answer to this question will usually be ‘Yes’ when the participants report their outcomes themselves.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    30. 6.3 Were the methods of outcome assessment comparable across intervention groups?
    Comparable assessment methods (i.e. data collection) would involve the same outcome detection methods and thresholds, same time point, same definition, and same measurements.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    31. 6.4 Were any systematic errors in measurement of the outcome related to intervention received?
    This question refers to differential misclassification of outcomes. Systematic errors in measuring the outcome, if present, could cause bias if they are related to intervention or to a confounder of the intervention-outcome relationship. This will usually be due either to outcome assessors being aware of the intervention received or to non-comparability of outcome assessment methods, but there are examples of differential misclassification arising despite these controls being in place.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    32. 7.1. (Is the reported effect estimate likely to be selected, on the basis of the results, from) multiple outcome measurements within the outcome domain?
    For a specified outcome domain, it is possible to generate multiple effect estimates for different measurements. If multiple measurements were made, but only one or a subset is reported, there is a risk of selective reporting on the basis of results.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    33. 7.2. (Is the reported effect estimate likely to be selected, on the basis of the results, from) multiple analyses of the intervention-outcome relationship?
    Because of the limitations of using data from non-randomized studies for analyses of effectiveness (need to control confounding, substantial missing data, etc), analysts may implement different analytic methods to address these limitations. Examples include unadjusted and adjusted models; use of final value vs change from baseline vs analysis of covariance; different transformations of variables; a continuously scaled outcome converted to categorical data with different cut-points; different sets of covariates used for adjustment; and different analytic strategies for dealing with missing data. Application of such methods generates multiple estimates of the effect of the intervention versus the comparator on the outcome. If the analyst does not pre-specify the methods to be applied, and multiple estimates are generated but only one or a subset is reported, there is a risk of selective reporting on the basis of results.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:
    34. 7.3. (Is the reported effect estimate likely to be selected, on the basis of the results, from) different subgroups?
    Particularly with large cohorts often available from routine data sources, it is possible to generate multiple effect estimates for different subgroups or simply to omit varying proportions of the original cohort. If multiple estimates are generated but only one or a subset is reported, there is a risk of selective reporting on the basis of results.
    Rating
    Single Choice
    Y
    PY
    PN
    N
    NI
    Notes/Comments:

    Suggested Arms

    NameDescription
    Hybrid visits (in person visits and tele visits)
    In person visits
    Less intensive antenatal visits schedule
    More intensive antenatal visits schedule
    Tele visits
    Total All Arms combined

    Please see downloadable data for more

    Suggested Outcomes

    TypeDomainSpecific measurement (i.e., tool/definition/specific outcome)
    CategoricalAbnormal Apgar score (threshold, e.g. <7)* (including abnormal cord pH [e.g., <7])
    Categoricalanemia
    Categoricalanxiety
    CategoricalBreastfeeding
    CategoricalCesarean delivery
    CategoricalCompletion of ACOG recommended servicesACOG recommended services (potential alternative names or subcategories) 1. First trimester: Laboratory testing (CBC, Type & Screen, HIV, Hepatitis B, Syphilis, Rubella immunity, Urinalysis); Ultrasound (Dating ultrasound, viability ultrasound) 2. Second trimester: Anatomy ultrasound 3. Second/Third Trimester (24-28 weeks): Diabetic screen 4. Third Trimester: CBC; TDap vaccination; Group B Strep test; Assessment for fetal presentation (ultrasound vs. manual assessment, presentation scan)
    Categoricaldepression
    CategoricalFull-term delivery
    CategoricalGestational diabetes
    CategoricalGestational hypertension
    CategoricalHarms to marginalized groups / equity outcomes
    CategoricalHarms: delayed diagnosis
    CategoricalHarms: overdiagnosis
    CategoricalHemorrhage
    CategoricalHysterectomy
    CategoricalInduction of labor
    CategoricalIntrauterine growth restriction (IUGR)
    CategoricalLacerations
    CategoricalLow birth weight
    CategoricalMaternal full-term delivery
    CategoricalMaternal inappropriate weight gain
    CategoricalMaternal Mortality
    CategoricalMaternal quality of life
    CategoricalNeed for social services
    CategoricalNeonatal intensive care unit [NICU] admission
    CategoricalNeonatal mortality
    CategoricalOperative vaginal delivery: forceps and vacuum
    CategoricalOther mental health outcomes
    CategoricalPatient preferences
    CategoricalPatient satisfaction with antenatal care
    CategoricalPerinatal morbidity (e.g., birth trauma [e.g., shoulder dystocia])
    CategoricalPost-term delivery
    CategoricalPostpartum contraception
    CategoricalPre-eclampsia
    CategoricalPre-term labor
    CategoricalPreterm birth
    CategoricalProvider satisfaction with antenatal care
    CategoricalSmall for gestational age
    CategoricalStillbirth
    CategoricalTransfusion need
    CategoricalUrinary tract infections
    ContinuousAttendance at antenatal visit (adherence/compliance)
    ContinuousEmergency room/triage visits
    ContinuousGestational age at birth
    ContinuousLost work time (including used vacation/health days)
    ContinuousNeonatal intensive care unit [NICU] length of stay
    ContinuousNumber of contacts (eg, portal/phone messages)
    ContinuousNumber of referrals to other providers
    ContinuousNumber of unplanned visits
    ContinuousPatient financial costs
    ContinuousPatient travel (e.g., driving miles or costs)
    ContinuousUnplanned hospital admissions

    Please see downloadable data for more

    Suggested Single Group Risk of Bias Assessment

    N/A