Unintended pregnancy and low birthweight in Ecuador

Eggleston, Elizabeth; Tsui, Amy O.; Kotelchuck, Milton

doi:10.2105/ajph.91.5.808

Cited by 46 publications

(15 citation statements)

References 14 publications

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“…Cross-sectional studies generally produce findings that align with longitudinal evidence that children from unintended pregnancies experience worse subsequent health (Eggleston, Tsui and Kotelchuck 2001, Jensen and Ahlburg 1999, Jensen and Ahlburg 2002, Joyce, Kaestner and Korenman 2000b, Kost, Landry and Darroch 1998, Marston and Cleland 2003, Mohllajee et al 2007, Sable et al 1997). However, this is not the case for cross-sectional studies of child mortality, which have shown associations in the opposite direction.…”

Section: Unintended Pregnancy and Child Outcomesmentioning

confidence: 65%

Death and Desirability: Retrospective Reporting of Unintended Pregnancy After a Child’s Death

Smith‐Greenaway

Sennott

2016

Demography

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Social scientists have long debated how to best measure pregnancy intentions. The standard measure relies on mothers’ retrospective reports of their intentions at the time of their conception. Because women have already given birth at the time of this report, the resulting children’s health—including their vital status—may influence their mothers’ responses. We hypothesize that women are less likely to report deceased children were from unintended pregnancies, and this may explain why some longitudinal studies have shown that children from unintended pregnancies have lower survival, but cross-sectional studies produce counter findings. Using Demographic and Health Survey data from 31 sub-Saharan African countries, we confirm that mothers are less likely to report deceased children resulted from unintended pregnancies compared to surviving children, although the opposite is true for unhealthy children, who mothers more commonly report were from unintended pregnancies compared to healthier children. The results suggest that mothers (1) revise their recall of intentions after the traumatic experience of child death and/or (2) alter their reports in the face-to-face interview. The study challenges the reliability of retrospective reports of pregnancy intentions in high mortality settings, and thus our current knowledge of the levels and consequences of unintended pregnancies in these contexts.

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Section: Unintended Pregnancy and Child Outcomesmentioning

confidence: 65%

Death and Desirability: Retrospective Reporting of Unintended Pregnancy After a Child’s Death

Smith‐Greenaway

Sennott

2016

Demography

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“…Notwithstanding the fact that every unintended pregnancy represents a failure to meet the reproductive health needs of women and their partners, unintended pregnancies unnecessarily expose women to the risks associated with pregnancy, unsafe abortion and childbirth, thereby contributing to maternal mortality and morbidity. Unintended pregnancies have also been associated with adverse outcomes such as low birth weight, preterm birth, postnatal depression and neonatal mortality [2–11]. …”

Section: Introductionmentioning

confidence: 99%

Prevalence and Determinants of Unintended Pregnancy in Mchinji District, Malawi; Using a Conceptual Hierarchy to Inform Analysis

Hall

Barrett

Phiri³

et al. 2016

PLoS ONE

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BackgroundIn 2012 there were around 85 million unintended pregnancies globally. Unintended pregnancies unnecessarily expose women to the risks associated with pregnancy, unsafe abortion and childbirth, thereby contributing to maternal mortality and morbidity. Studies have identified a range of potential determinants of unplanned pregnancy but have used varying methodologies, measures of pregnancy intention and analysis techniques. Consequently there are many contradictions in their findings. Identifying women at risk of unplanned pregnancy is important as this information can be used to help with designing and targeting interventions and developing preventative policies.Methods4,244 pregnant women from Mchinji District, Malawi were interviewed at home between March and December 2013. They were asked about their pregnancy intention using the validated Chichewa version of the London Measure of Unplanned Pregnancy, as well as their socio-demographics and obstetric and psychiatric history. A conceptual hierarchical model of the determinants of pregnancy intention was developed and used to inform the analysis. Multiple random effects linear regression was used to explore the ways in which factors determine pregnancy intention leading to the identification of women at risk of unplanned pregnancies.Results44.4% of pregnancies were planned. On univariate analyses pregnancy intention was associated with mother and father’s age and education, marital status, number of live children, birth interval, socio-economic status, intimate partner violence and previous depression all at p<0.001. Multiple linear regression analysis found that increasing socio-economic status is associated with increasing pregnancy intention but its effect is mediated through other factors in the model. Socio-demographic factors of importance were marital status, which was the factor in the model that had the largest effect on pregnancy intention, partner’s age and mother’s education level. The effect of mother’s education level was mediated by maternal reproductive characteristics. Previous depression, abuse in the last year or sexual abuse, younger age, increasing number of children and short birth intervals were all associated with lower pregnancy intention having controlled for all other factors in the model. This suggests that women in Mchinji District who are either young, unmarried women having their first pregnancy, or older, married women who have completed their desired family size or recently given birth, or women who have experienced depression, abuse in the last year or sexual abuse are at higher risk of unintended pregnancies.ConclusionA simple measure of pregnancy intention with well-established psychometric properties was used to show the distribution of pregnancy planning among women from a poor rural population and to identify those women at higher risk of unintended pregnancy. An analysis informed by a conceptual hierarchical model shed light on the pathways that lead from socio-demographic determinants to pregnancy intention. This ...

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“…Roma women are poorer, according to a well-described situation of that ethnic minority, which is particularly exposed to higher rates of poverty [35];  Marital status of women, with benefits showed for married women. In fact, the decline in marriage is a well-known and well-documented phenomenon with major consequences for poverty, inequality and the use of welfare programs [36,37];  Educational level of women, which is inversely associated with poverty, i.e., the lower the educational level, the greater the poverty condition [8][9][10][11]33,34];  Differences based on socioeconomic status for entry in parenting, which is related with how the planning of children and willingness of couples to have, or have not, children occurs at the time that the pregnancy happens [14,38];  Most women did not think to become pregnant when they had the last child, described in Portugal [14] and internationally [38], where at least one child after the first one was not planned;  Patterns of utilization of pre-conception and postpar-tum consultations of the last child, with the very poor and poor women presenting lower rates of utilization [26][27][28];  Economic access and especially the results related to the incapacity to access certain health care and/or payment of medication, especially for women, corroborates the results from studies that have shown that the burden of payment for health services constitutes a cause of poverty [21]. According to the above associations extreme poverty, of the type that allows access to special social inclusion subsidies in Portugal, seems associated with: ethnicity [35]; household characteristics, such as size, with each extra person in a household increasing the chance of being poor [10] and income [10,33,34]; personal characteristics, namely the association between low levels of education and higher odds of being poor [10,33,39]; and marital status, with marriage appearing as a protective factor of poverty [36,37].…”

Section: Discussionmentioning

confidence: 99%

Healthcare access and the patterns of maternal health care utilization among poor and non-poor women living in urban areas in Portugal

Craveiro¹,

Ferrinho²,

Sousa³

et al. 2013

Health

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Unintended pregnancy and low birthweight in Ecuador

Cited by 46 publications

References 14 publications

Death and Desirability: Retrospective Reporting of Unintended Pregnancy After a Child’s Death

Death and Desirability: Retrospective Reporting of Unintended Pregnancy After a Child’s Death

Prevalence and Determinants of Unintended Pregnancy in Mchinji District, Malawi; Using a Conceptual Hierarchy to Inform Analysis

Healthcare access and the patterns of maternal health care utilization among poor and non-poor women living in urban areas in Portugal

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