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A Latent Markov Model with Covariates to Study Unobserved Heterogeneity among Fertility Patterns of Couples Employing Natural Family Planning Methods

Overview of attention for article published in Frontiers in Public Health, August 2017
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Title
A Latent Markov Model with Covariates to Study Unobserved Heterogeneity among Fertility Patterns of Couples Employing Natural Family Planning Methods
Published in
Frontiers in Public Health, August 2017
DOI 10.3389/fpubh.2017.00186
Pubmed ID
Authors

Fulvia Pennoni, Michele Barbato, Serena Del Zoppo

Abstract

We use the historical data from the European Study of Daily Fecundability and we develop an algorithm to determine the fertile window in a woman's cycle according to the rules of the C.A.Me.N. symptothermal method proposed by the Centro Ambrosiano Metodi Naturali. Our aim is to identify variables acting on the probability of conception by considering the fertile window and factors that cannot be explained by employing the observed covariates of individuals and couples. We adopt the latent Markov model with covariates tailored for data collected at times when a latent process detects the dependence across fertile periods of each woman's cycle. We consider measurement errors, transitions between conception and non-conception, and the prediction of conception rate over the fertile windows. We find that the conception pattern is mainly related to sexual intercourse behavior during the fertile window and to previous pregnancies. For the cohort under study, we predict a steep decline in the average conception rate across fertile windows.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Student > Postgraduate 4 19%
Student > Bachelor 3 14%
Researcher 3 14%
Student > Master 2 10%
Other 3 14%
Unknown 2 10%
Readers by discipline Count As %
Medicine and Dentistry 7 33%
Social Sciences 5 24%
Engineering 2 10%
Psychology 2 10%
Unspecified 1 5%
Other 3 14%
Unknown 1 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 February 2019.
All research outputs
#14,361,016
of 22,997,544 outputs
Outputs from Frontiers in Public Health
#3,639
of 10,208 outputs
Outputs of similar age
#176,080
of 316,580 outputs
Outputs of similar age from Frontiers in Public Health
#64
of 110 outputs
Altmetric has tracked 22,997,544 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,208 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 60% of its peers.
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We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.