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Rainfall variability and adverse birth outcomes in Amazonia

Abstract

Amazonian populations are increasingly exposed to climatic shocks, yet knowledge of related health impacts is limited. Understanding how health risks are coproduced by local climatic variability, place and social inequities is vital for improving decision-making, particularly in decentralized contexts. We assess the impacts of rainfall variability and multiscale vulnerabilities on birth weight, which has lifelong health consequences. We focus on highly river-dependent areas in Amazonia, using urban and rural birth registrations during 2006–2017. We find a strong but spatially differentiated relationship between local rainfall and subsequent river-level anomalies. Using Bayesian models, we disentangle the impacts of rainfall shocks of different magnitudes, municipal characteristics, social inequities and seasonality. Prenatal exposure to extremely intense rainfall is associated with preterm birth, restricted intra-uterine growth and lower mean birth weight (≤−183 g). Adverse birth outcomes also follow non-extreme intense rainfall (40% higher odds of low birth weight), drier conditions than seasonal averages (−39 g mean birth weight) and conception in the rising-water season (−13 g mean birth weight). Babies experience penalties totalling 646 g when born to adolescent, Amerindian, unmarried mothers that received no formal education or antenatal or obstetric health care. Rainfall variability confers intergenerational disadvantage, especially for socially marginalized Amazonians in forgotten places. Structural changes are required to reduce inequities, foster citizen empowerment and improve the social accountability of public institutions.

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Fig. 1: Municipality-scale exposure to extreme and non-extreme rainfall events between 2002 and 2017.
Fig. 2: Hydrological sub-basins in western/central Amazonia and maximum correlations between rainfall and river-level anomalies in highly river-dependent municipalities.
Fig. 3: Illustrative examples of the spatial distributions of extremely intense rainfall events and extremely deficient rainfall events.
Fig. 4: Effects and 95% CIs of prenatal exposure (including the pre-pregnancy trimester) to rainfall variability on birth outcomes.
Fig. 5: Nonlinear effects of maternal age and hydrological seasonality on birth outcomes in highly river-dependent Amazonian municipalities.

Data availability

The data that support the findings of this study are publicly available as follows: birth data from the Brazilian Information System for Live Births (SINASC), http://www2.datasus.gov.br/DATASUS/index.php?area=0901&item=1&acao=28&pad=31655; municipality-level covariates from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatistica, IBGE), ftp://ftp.ibge.gov.br/Censos/Censo_Demografico_2010/Sinopse/Agregados_por_Setores_Censitarios/; precipitation data from the Integrated Multi-satellite Retrievals for GPM (IMERG), https://pmm.nasa.gov/data-access/downloads/gpm; and river-level data from Brazil’s National Water Agency (Agência Nacional de Águas, ANA), https://www.snirh.gov.br/hidroweb/publico/apresentacao.jsf.

Code availability

All analyses were performed using the open-source platform R version 4.0.2. We used the mbsi package (https://github.com/ErickChacon/mbsi) to compute the model-based SPI and the bamlss package (https://cran.r-project.org/web/packages/bamlss/index.html) to perform inference on the BAMLSS models. All the scripts for modelling can be found at https://gitlab.com/ErickChacon/birthweight and visualized at https://erickchacon.gitlab.io/birthweight/. Additional code for data gathering, cleaning and processing as well as processed data can be provided upon request.

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Acknowledgements

This work was supported by grants from the United Kingdom (nos ESRC ES/K010018/1 and Newton ES/M011542/1), Brazil (no. CNPq PVE 313742/2013-8) and the European Commission (no. H2020 RISE 691053 ODYSSEA). Useful discussion and support was provided by Fiocruz colleagues A. Cardoso, M. Theme and A. A. da Silva and comments from N. Graham and J. Barlow.

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E.A.C.-M., L.P., B.M.T. and M.G.C. designed the research with additional input from J.D.Y.O. G.D. developed the network analysis for the spatial remoteness measures. E.A.C.-M. analysed the data with input from B.M.T., M.G.C. and L.P. J.D.Y.O. supported the data interpretation. L.P. and E.A.C.-M. wrote the manuscript, with input from all authors.

Corresponding author

Correspondence to Luke Parry.

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The authors declare no competing interests.

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Peer review information Nature Sustainability thanks Evan Kresch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Highly river-dependent municipalities in Amazonas State, Brazil included in the study.

Black lines indicate roads and blue lines indicate rivers.

Extended Data Fig. 2 Organizational diagram of analysis.

Chacón-Montalván et al. pre-print available from https://arxiv.org/abs/1906.07505.

Extended Data Fig. 3 Counts of newborn’s prenatal exposure to (a) seasonal deviations in rainfall, (b) non-extreme rainfall events; (c) extreme rainfall events.

Location A reflects a newborn’s typical prenatal exposure during the study period, and constitute the baselines for comparison. Evidently, some level of prenatal exposure to deviations and non-extreme variability is relatively normal, including weeks of deficient or intense rainfall. Exposure to extreme events was relatively uncommon during the study period but would be expected to become more common under climate change. Other locations refer to the main effects (Fig. 4), including intense (C); deficient (D); intense and deficient (B), and moderate intense (E).

Extended Data Fig. 4 Map of hydrological seasonality in Amazonas State based on long-term data from river monitoring stations.

see Methods.

Extended Data Fig. 5 Correlation and lag between rainfall and subsequent river-level anomalies in highly river-dependent municipalities.

(analagous to a US County) (n=43) in Amazonas State, Brazil between 2004 and 2014. (a) Relationship between maximum overall correlation of anomalies (blue line) and optimal lag time in weeks (red line); spatial variation in (b) maximum correlation, (c) optimal lag time (weeks).

Extended Data Fig. 6 Detailed maps of sub-basins and river networks for groups of study municipalities.

(a) River Negro sub-basin. There is a wide network of local sub-tributaries in the geographically large municipalities on the River Negro [sub-basin 4](e.g. Barcelos [BAR] is 122,476 km2) and hence local rainfall strongly determines river-level variation. (b) Eastern part of study area. Much of the Madeira sub-basin [13] is upstream of Amazonas State, in Rondônia State, and Bolivia and Peru (Fig. 2). However, rainfall anomalies are still reasonably well correlated with river-level anomalies for several of the Madeira municipalities in our study (Manicoré [MAI; r = 0.34], Borba [BOR; 0.36] and Nova Olinda do Norte [sub-basin 14][NLN; r = 0.30]) apparently because these they contain sizeable local sub-tributaries (e.g. the Manicoré River in Manicoré [MAI]). (c) South-western part of study area. Correlations on the Rivers Purús [sub-basin 11] and Juruá [sub-basin 9] tend to be higher in municipalities further up these rivers (e.g. Eirunepé [EIR; r = 0.44] on the Juruá) and are lower downstream (e.g. Berurí [BER] on the Purús [r = 0.29][Extended Data Fig. 5b]). Correlations are generally much lower in municipalities located along the main river-stem (e.g. Anori [ANO; r = 0.14] and the Içá River ([Santo Antônio do Içá [SAI; sub-basin 2][r = 0.20]. Nonetheless, local rainfall is highly correlated with river-level anomalies in several large municipalities which are next to the main stem, yet also contain major third-order tributaries (e.g. Javarí River [sub-basin 7] in Atalaia do Norte [ADN; r = 0.46] or Jutaí River in Jutaí [JUT; sub-basin 8][r = 0.45]).

Extended Data Fig. 7 Effects of combinations of exposure to rainfall variability on mean birth-weight (grams)(a-c); mean birth-weight controlling for gestational age (d-f); low birth-weight odds (h-j); low birth-weight odds controlling for gestational age (k-m); preterm birth odds (n-p).

Magnitudes of exposure include extreme intense (wet; y-axes) and deficient (dry; x-axes) rainfall events (left panels); non-extreme events (centre panels); deviation from long-term seasonal averages (right panels). Coloured areas indicate significant effects (95% confidence).

Extended Data Fig. 8 Effects of remoteness (a-c) and municipal-scale sanitation coverage (d-f) on mean birth-weight (grams) (a,d); low birth-weight (<2500g) odds (b,e); preterm birth (<37 weeks) odds (c,f).

Birth-weight is modelled without controlling for gestational age (red lines) and controlling for it (green lines). Remoteness is based on a index capturing travel distances from larger cities. Household sanitation is defined as having an inside toilet and a private tap with running water. Shading shows 95% credible intervals. These are marginal effects, having controlled for maternal and healthcare characteristics and random municipality effects.

Extended Data Fig. 9 Non-linear effects of conception date on (a) mean birth-weight (grams),(b) low birth-weight (LBW) odds; (e) preterm birth odds.

Birth-weight is modelled without controlling for gestational age (red lines) and controlling for it (turquoise lines). Shading shows 95% credible intervals. Effects are modelled by splines. Results show a study-region wide 44g drop (CI: −34 to −55) in mean birth-weight from 2006 to 2016, and 55% increase (1.55, from CI: 1.37 to 1.77) in LBW odds, unexplained by other predictors. Controlling for GA reduces these changes to −30 g, (CI: −17 to −44 g), and 1.17 OR (CI 1.02 to 1.33) and model fit becomes poor (evidence of over-fitting of the linear term). PTB odds increased 7.44 (CI: 6.1 to 8.99) during this period. Results indicate increasing PTB risks and no clear evidence of poorer growth. These trends could relate to changes in access to quality healthcare (e.g. leading to fewer miscarriages), methodological changes in GA and birth-weight assessment in provincial hospitals, or broader societal transitions.

Extended Data Fig. 10 Unexplained municipality-scale variation in (a) mean birth-weight (grams), and (b) low birth-weight odds, (c) preterm birth odds in Amazonas State, Brazil.

These effects are marginal to effects of exposure to rainfall variability, selected municipal or maternal socio-demographic characteristics and antenatal or obstetric healthcare. Birth-weight is modelled without controlling for gestational age (red) and controlling for it (turquoise).

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Chacón-Montalván, E.A., Taylor, B.M., Cunha, M.G. et al. Rainfall variability and adverse birth outcomes in Amazonia. Nat Sustain 4, 583–594 (2021). https://doi.org/10.1038/s41893-021-00684-9

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