Evidence map›Paper›PMID 35254268›Full record

ArticleJMIR medical informatics2022

A Comparison of Census and Cohort Sampling Models for the Longitudinal Collection of User-Reported Data in the Maternity Care Pathway: Mixed Methods Study.

Kendall Jamieson Gilmore, Manila Bonciani, Milena Vainieri

Open access · goldAbstract read
In one paragraph

Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Kendall Jamieson GilmoreManagement and Healthcare Laboratory, Department of Economics and Management in the era of Data Science, Institute of Management, Sant'Anna Scuola Superiore, Pisa, Italy.ORCID https://orcid.org/0000-0002-8012-8391
Manila BoncianiManagement and Healthcare Laboratory, Department of Economics and Management in the era of Data Science, Institute of Management, Sant'Anna Scuola Superiore, Pisa, Italy.ORCID https://orcid.org/0000-0002-1594-4819
Milena VainieriManagement and Healthcare Laboratory, Department of Economics and Management in the era of Data Science, Institute of Management, Sant'Anna Scuola Superiore, Pisa, Italy.ORCID https://orcid.org/0000-0002-0914-4487
Scuola Superiore Sant'Anna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTypical measures of maternity performance remain focused on the technical elements of birth, especially pathological elements, with insufficient measurement of nontechnical measures and those collected pre- and postpartum. New technologies allow for patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) to be collected from large samples at multiple time points, which can be considered alongside existing administrative sources; however, such models are not widely implemented or evaluated. Since 2018, a longitudinal, personalized, and integrated user-reported data collection process for the maternal care pathway has been used in Tuscany, Italy. This model has been through two methodological iterations.

objectiveThe aim of this study was to compare and contrast two sampling models of longitudinal user-reported data for the maternity care pathway, exploring factors influencing participation, cost, and suitability of the models for different stakeholders.

methodsData were collected by two modes: (1) "cohort" recruitment at the birth hospital of a predetermined sample size and (2) continuous, ongoing "census" recruitment of women at the first midwife appointment. Surveys were used to collect experiential and outcome data related to existing services. Women were included who passed 12 months after initial enrollment, meaning that they either received the surveys issued after that interval or dropped out in the intervening period. Data were collected from women in Tuscany, Italy, between September 2018 and July 2020. The total sample included 7784 individuals with 38,656 observations. The two models of longitudinal collection of user-reported data were analyzed using descriptive statistics, survival analysis, cost comparison, and a qualitative review.

resultsCohort sampling provided lower initial participation than census sampling, although very high subsequent response rates (87%) were obtained 1 year after enrollment. Census sampling had higher initial participation, but greater dropout (up to 45% at 1 year). Both models showed high response rates for online surveys. There were nonproportional dropout hazards over time. There were higher rates of dropout for women with foreign nationality (hazard ratio [HR] 1.88, P<.001), and lower rates of dropout for those who had a higher level of education (HR 0.77 and 0.61 for women completing high school and college, respectively; P<.001), were employed (HR 0.87, P=.01), in a relationship (HR 0.84, P=.04), and with previous pregnancies (HR 0.86, P=.002). The census model was initially more expensive, albeit with lower repeat costs and could become cheaper if repeated more than six times.

conclusionsThe digital collection of user-reported data enables high response rates to targeted surveys in the maternity care pathway. The point at which pregnant women or mothers are recruited is relevant for response rates and sample bias. The census model of continuous enrollment and real-time data availability offers a wider set of potential benefits, but at an initially higher cost and with the requirement for more substantial data translation and managerial capacity to make use of such data.

Indexed as

digital collectiondigital healthlongitudinal studiesmaternitymothersonlinepatient-reported experience measurespatient-reported outcome measurespostpartumpregnancysurveyssurvival analysis

Identifiers

PMID35254268
PMCPMC8933795
OpenAlexW4220661785

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.