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Demography

Erschienen in:

01.09.2020

Brazil’s Missing Infants: Zika Risk Changes Reproductive Behavior

verfasst von: Marcos A. Rangel, Jenna Nobles, Amar Hamoudi

Erschienen in: Demography | Ausgabe 5/2020

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Abstract

Zika virus epidemics have potential large-scale population effects. Controlled studies of mice and nonhuman primates indicate that Zika affects fecundity, raising concerns about miscarriage in human populations. In regions of Brazil, Zika risk peaked months before residents learned about the epidemic and its relation to congenital anomalies. This spatiotemporal variation supports analysis of both biological effects of Zika infection on fertility and the effects of learning about Zika risk on reproductive behavior. Causal inference techniques used with vital statistics indicate that the epidemic caused reductions in birth cohort size of approximately one-quarter 18 months after Zika infection risk peaked but 10 months after public health messages advocated childbearing delay. The evidence is consistent with small but not statistically detectable biological reductions in fecundity, as well as large strategic changes in reproductive behavior to temporally align childbearing with reduced risk to infant health. The behavioral effects are larger for more-educated and older women, which may reflect facilitated access to information and to family planning services within high-risk, mosquito-infested urban locations as well as perceptions about the opportunity costs of risks to pregnancy and infant survival.

Vorheriger Artikel New Partner, New Order? Multipartnered Fertility and Birth Order Effects on Educational Achievement

Nächster Artikel How Does Deprivation Affect Early-Age Mortality? Patterns of Socioeconomic Determinants of Neonatal and Postneonatal Mortality in Bolivia

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Reports of a new disease appeared as early as the third trimester of 2014. Laboratory tests later confirmed cases in Pernambuco in December 2014; and in Maranhao, Rio Grande do Norte, and Bahia, in February and March 2015.

Zika was then added to the list of microcephaly-correlated factors, such as severe malnutrition, substance abuse, infections with rubella or toxoplasmosis, and some conditions that interrupt blood supply to the fetus.

See the Pan American Health Organization website (https://www.paho.org/hq/index.php?option=com_topics&view=rdmore&cid=8095&item=zika-virus-infection&type=statistics&lang=en).

See the online appendix, Figs. A1 and A2, for a map of the Northeast region and of its microregional subdivisions.

Aaronson et al. (2014), used similar reasoning and argued that these “cheaper-quality” substitution effects operate among those who already have children, whereas fertility is stimulated among the childless.

Social media is an increasingly important conduit for health risk information (Fung et al. 2014; Panagiotopoulos et al. 2016). Media representations often inflate infectious illness threats relative to their contribution to population morbidity and mortality (Frost et al. 1997; McComas 2006).

In a recent review in the Annual Review of Virology, Dudley et al. (2018) argued that macaque studies could provide an important source of information on Zika-associated fetal death given how difficult it has been to study in humans.

See http://g1.globo.com/economia/noticia/2014/12/o-que-esperar-da-economia-em-2015.html and http://g1.globo.com/economia/noticia/2015/12/o-que-esperar-para-economia-em-2016.html.

A large literature spanning the social sciences suggests that fertility co-moves with the economic business cycles, with improvements in the latter generating increased fertility (see Adsera 2004, 2011), for example. A recent contribution by Buckles et al. (2018) has even uncovered evidence that fertility decisions are a leading economic indicator, with conceptions moving in anticipation of bad and good future economic tides.

Because we employ numerous data sets, to help readers, we include in Table A1 (online appendix) a list of all information and the respective sources.

See Sistema de Informacao de Nascidos Vivos (SINASC-DATASUS) for live births; see Sistema de Informacao de Mortalidade (SIM-DATASUS) for a registry of fetal deaths (http://www2.datasus.gov.br/DATASUS/index.php?area=0901&item=1).

We last updated our analysis files with data uploaded on May 23, 2019, by Brazilian authorities.

Retrieved from https://www.br.undp.org/.

See http://www.ipeadata.gov.br/Default.aspx.

Despite the high coverage, we expect more-educated, high-income, and privately insured individuals to be underrepresented within the SUS’s hospitalization records.

Collected under the Sistema de Informacoes Hospitalares (SIH-DATASUS) and the Sistema de Informacoes Ambulatoriais (SIA-DATASUS). Data can be found online at http://www2.datasus.gov.br/DATASUS/index.php?area=0901&item=1.

See http://www2.datasus.gov.br/DATASUS/.

This includes codes for vaginal and C-section deliveries (O80-O82) and deliveries with complications (O60–O69).

In practice, there is only one treated unit, which does not provide cross-sectional variation needed for parameter identification. This method heavily relies on longer time series variation, which we accommodate by including data from as early as 2011.

We use CBR values versus TFR and explicitly adjust for variation in the population age composition.

There is some discretion on the implementation of the method, including which attributes to use in the prediction and which period to use in order to fit the time series of interest. We make our choices clear in this section and examine robustness of findings with respect to those choices when discussing results.

We use as a cutoff for urbanization rates, measured from the 2010 census data, the rate for the least urbanized of the state capitals: approximately 77%.

We match the municipality-level reports to the microregions in our data by taking the maximum value of the index across municipalities within a given microregion. The latter is red-flagged if at least one of the municipalities within it is red-flagged.

Effect estimates are presented for the entire period of study in panel b of Fig. A4 and in Table A3 of the online appendix.

They are also robust to the analysis, which includes recorded miscarriages to the counts of births (online appendix, Table A6, columns 3 and 4), suggesting that fetal mortality accounted for in official records cannot explain variation in fertility we observe, particularly for the 2016 effects.

This reasoning is consistent with our finding that higher-parity births are also less frequent in Pernambuco as a result of the epidemics. See Fig. A5 in the online appendix.

Our full investigation of additional health records reveals interesting patterns of increase (although not statistically significantly different from 0) in use of intrauterine device and diaphragm insertion in health posts. We also see a significant reduction in abortion-related procedures (curettage), which we interpret as representing reductions in conception rates among portions of the population that were more prone to interrupting pregnancies. Finally, we see no change in counts of tubal ligation and vasectomies. See Table A11 in the online appendix for summary of these findings. See also Lautharte and Rasul (2020) for an interesting and complementary analysis.

For an interesting empirical exercise on long-term effects of aggregate fertility changes, see Pop-Eleches (2006).

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Titel: Brazil’s Missing Infants: Zika Risk Changes Reproductive Behavior
verfasst von: Marcos A. Rangel
Jenna Nobles
Amar Hamoudi
Publikationsdatum: 01.09.2020
Verlag: Springer US
Erschienen in: Demography / Ausgabe 5/2020
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-020-00900-9

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