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Rainfall variability and adverse birth outcomes in Amazonia


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),; municipality-level covariates from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatistica, IBGE),; precipitation data from the Integrated Multi-satellite Retrievals for GPM (IMERG),; and river-level data from Brazil’s National Water Agency (Agência Nacional de Águas, ANA),

Code availability

All analyses were performed using the open-source platform R version 4.0.2. We used the mbsi package ( to compute the model-based SPI and the bamlss package ( to perform inference on the BAMLSS models. All the scripts for modelling can be found at and visualized at Additional code for data gathering, cleaning and processing as well as processed data can be provided upon request.


  1. 1.

    Barichivich, J. et al. Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4, eaat8785 (2018).

    Article  Google Scholar 

  2. 2.

    Yang, J. et al. Amazon drought and forest response: largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Glob. Change Biol. 24, 1919–1934 (2018).

    Article  Google Scholar 

  3. 3.

    Espinoza, J. C. et al. The extreme 2014 flood in south-western Amazon basin: the role of tropical–subtropical South Atlantic SST gradient. Environ. Res. Lett. 9, 124007 (2014).

    Article  Google Scholar 

  4. 4.

    Marengo, J. A. et al. Recent extremes of drought and flooding in Amazonia: vulnerabilities and human adaptation. Am. J. Clim. Change 2, 87–96 (2013).

    Article  Google Scholar 

  5. 5.

    Pinho, P., Marengo, J. & Smith, M. Complex socio-ecological dynamics driven by extreme events in the Amazon. Reg. Environ. Change 15, 643–655 (2015).

    Article  Google Scholar 

  6. 6.

    Marengo, J. A. & Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int. J. Climatol. 36, 1033–1050 (2016).

    Article  Google Scholar 

  7. 7.

    Parry, L. et al. Social vulnerability to climatic shocks is shaped by urban accessibility. Ann. Am. Assoc. Geogr. 108, 125–143 (2018).

    Google Scholar 

  8. 8.

    Brondízio, E. S., de Lima, A. C., Schramski, S. & Adams, C. Social and health dimensions of climate change in the Amazon. Ann. Hum. Biol. 43, 405–414 (2016).

    Article  Google Scholar 

  9. 9.

    Parry, L. et al. The (in)visible health risks of climate change. Soc. Sci. Med. 241, 112448 (2019).

    Article  Google Scholar 

  10. 10.

    Andalón, M., Azevedo, J. P., Rodríguez-Castelán, C., Sanfelice, V. & Valderrama-González, D. Weather shocks and health at birth in Colombia. World Dev. 82, 69–82 (2016).

    Article  Google Scholar 

  11. 11.

    Rocha, R. & Soares, R. R. Water scarcity and birth outcomes in the Brazilian semiarid. J. Dev. Econ. 112, 72–91 (2015).

    Article  Google Scholar 

  12. 12.

    Hilmert, C. J., Kvasnicka-Gates, L., Teoh, A. N., Bresin, K. & Fiebiger, S. Major flood related strains and pregnancy outcomes. Health Psychol. 35, 1189–1196 (2016).

    Article  Google Scholar 

  13. 13.

    Alderman, H. Safety nets can help address the risks to nutrition from increasing climate variability. J. Nutr. 140, 148S–152S (2010).

    CAS  Article  Google Scholar 

  14. 14.

    Rodriguez-Llanes, J. M., Ranjan-Dash, S., Degomme, O., Mukhopadhyay, A. & Guha-Sapir, D. Child malnutrition and recurrent flooding in rural eastern India: a community-based survey. BMJ Open 1, e000109 (2011).

    Article  Google Scholar 

  15. 15.

    Woldehanna, T. Do Pre-natal and Post-natal Economic Shocks Have a Long-Lasting Effect on the Height of 5-Year-Old Children? Evidence from 20 Sentinel Sites of Rural and Urban Ethiopia (Young Lives, 2010).

  16. 16.

    Aizer, A. & Currie, J. The intergenerational transmission of inequality: maternal disadvantage and health at birth. Science 344, 856–861 (2014).

    CAS  Article  Google Scholar 

  17. 17.

    Oreopoulos, P., Stabile, M., Walld, R. & Roos, L. L. Short-, medium-, and long-term consequences of poor infant health an analysis using siblings and twins. J. Hum. Resour. 43, 88–138 (2008).

    Google Scholar 

  18. 18.

    Kramer, M. S. The epidemiology of low birthweight. Matern. Child Nutr. 74, 1–10 (2013).

    Google Scholar 

  19. 19.

    Wang, S.-F. et al. Birth weight and risk of coronary heart disease in adults: a meta-analysis of prospective cohort studies. J. Dev. Orig. Health Dis. 5, 408–419 (2014).

    Article  Google Scholar 

  20. 20.

    Cooper, M., Brown, M. E., Azzarri, C. & Meinzen-Dick, R. Hunger, nutrition, and precipitation: evidence from Ghana and Bangladesh. Popul. Environ. 41, 151–208 (2019).

    Article  Google Scholar 

  21. 21.

    Grace, K., Brown, M. & McNally, A. Examining the link between food prices and food insecurity: a multi-level analysis of maize price and birthweight in Kenya. Food Policy 46, 56–65 (2014).

    Article  Google Scholar 

  22. 22.

    Beeson, J. G., Scoullar, M. J. L. & Boeuf, P. Combating low birth weight due to malaria infection in pregnancy. Sci. Transl. Med. 10, eaat1506 (2018).

    Article  CAS  Google Scholar 

  23. 23.

    Kramer, M. S. et al. Stress pathways to spontaneous preterm birth: the role of stressors, psychological distress, and stress hormones. Am. J. Epidemiol. 169, 1319–1326 (2009).

    Article  Google Scholar 

  24. 24.

    Graignic-Philippe, R., Dayan, J., Chokron, S., Jacquet, A.-Y. & Tordjman, S. Effects of prenatal stress on fetal and child development: a critical literature review. Neurosci. Biobehav. Rev. 43, 137–162 (2014).

    CAS  Article  Google Scholar 

  25. 25.

    Lindsay, K. L., Buss, C., Wadhwa, P. D. & Entringer, S. The interplay between maternal nutrition and stress during pregnancy: issues and considerations. Ann. Nutr. Metab. 70, 191–200 (2017).

    CAS  Article  Google Scholar 

  26. 26.

    Buffa, G. et al. Prenatal stress and child development: a scoping review of research in low- and middle-income countries. PLoS ONE 13, e0207235 (2018).

    CAS  Article  Google Scholar 

  27. 27.

    Sanguanklin, N. et al. Effects of the 2011 flood in Thailand on birth outcomes and perceived social support. J. Obstet. Gynecol. Neonatal Nurs. 43, 435–444 (2014).

    Article  Google Scholar 

  28. 28.

    Ramakrishnan, U., Grant, F., Goldenberg, T., Zongrone, A. & Martorell, R. Effect of women’s nutrition before and during early pregnancy on maternal and infant outcomes: a systematic review. Paediatr. Perinat. Epidemiol. 26, 285–301 (2012).

    Article  Google Scholar 

  29. 29.

    Kibret, K. T., Chojenta, C., Gresham, E., Tegegne, T. K. & Loxton, D. Maternal dietary patterns and risk of adverse pregnancy (hypertensive disorders of pregnancy and gestational diabetes mellitus) and birth (preterm birth and low birth weight) outcomes: a systematic review and meta-analysis. Public Health Nutr. 22, 506–520 (2019).

    Article  Google Scholar 

  30. 30.

    Bloomfield, F. H. How is maternal nutrition related to preterm birth? Annu. Rev. Nutr. 31, 235–261 (2011).

    CAS  Article  Google Scholar 

  31. 31.

    Rosinger, A. Y. Household water insecurity after a historic flood: diarrhea and dehydration in the Bolivian Amazon. Soc. Sci. Med. 197, 192–202 (2018).

    Article  Google Scholar 

  32. 32.

    Wolfarth-Couto, B. et al. Variabilidade dos casos de malária e sua relação com a precipitação e nível d’água dos rios no Estado do Amazonas, Brasil. Cad. Saúde Pública 35, e00020218 (2019).

    Article  Google Scholar 

  33. 33.

    Fonseca, P., Hacon, S. D. E. S. & Reis, V. O uso de dados de satelite para estudar a relação entre chuva e doenças diarreicas em uma bacia na Amazonia Sul-Ocidental. Ciên. Saúde Colet. 21, 731–742 (2016).

    Article  Google Scholar 

  34. 34.

    Chibnik, M. Risky Rivers: The Economics and Politics of Floodplain Farming in Amazonia (Univ. of Arizona Press, 1994).

  35. 35.

    Phalkey, R. K., Aranda-Jan, C., Marx, S., Höfle, B. & Sauerborn, R. Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc. Natl Acad. Sci. USA 112, E4522–E4529 (2015).

    CAS  Article  Google Scholar 

  36. 36.

    Haines, A., Kovats, R. S., Campbell-Lendrum, D. & Corvalan, C. Climate change and human health: impacts, vulnerability and public health. Public Health 120, 585–596 (2006).

    CAS  Article  Google Scholar 

  37. 37.

    Tregidgo, D. J., Barlow, J., Pompeu, P. S. & Parry, L. Tough fishing and severe seasonal food insecurity in Amazonian flooded forests. People Nat. 2, 468–482 (2020).

    Article  Google Scholar 

  38. 38.

    Hallegatte, S. & Rozenberg, J. Climate Change through a poverty lens. Nat. Clim. Change 7, 250–256 (2017).

    Article  Google Scholar 

  39. 39.

    Berry, H. L., Waite, T. D., Dear, K. B., Capon, A. G. & Murray, V. The case for systems thinking about climate change and mental health. Nat. Clim. Change 8, 282–290 (2018).

    Article  Google Scholar 

  40. 40.

    Espinoza, J. C., Ronchail, J., Marengo, J. A. & Segura, H. Contrasting north–south changes in Amazon wet-day and dry-day frequency and related atmospheric features (1981–2017). Clim. Dyn. 52, 5413–5430 (2019).

    Article  Google Scholar 

  41. 41.

    Langill, J. C. & Abizaid, C. What is a bad flood? Local perspectives of extreme floods in the Peruvian Amazon. Ambio 49, 1423–1436 (2020).

    Article  Google Scholar 

  42. 42.

    Espinoza Villar, J. C. et al. Spatio-temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador). Int. J. Climatol. 29, 1574–1594 (2009).

    Article  Google Scholar 

  43. 43.

    Maccini, S. & Yang, D. Under the weather: health, schooling, and economic consequences of early-life rainfall. Am. Econ. Rev. 99, 1006–1026 (2009).

    Article  Google Scholar 

  44. 44.

    Chacón-Montalván, E., Luke, P., Davies, G. & Taylor, B. A model-based general alternative to the standardised precipitation index. Preprint at (2019).

  45. 45.

    Ovando, A. et al. Extreme flood events in the Bolivian Amazon wetlands. J. Hydrol. Reg. Stud. 5, 293–308 (2016).

    Article  Google Scholar 

  46. 46.

    Parry, L., Peres, C. A., Day, B. & Amaral, S. Rural–urban migration brings conservation threats and opportunities to Amazonian watersheds. Conserv. Lett. 3, 251–259 (2010).

    Article  Google Scholar 

  47. 47.

    Brilhante, N. Cheia histórica: 42 municípios estão em situação de emergência no Amazonas. A Crítica (2015).

  48. 48.

    Skoufias, E. & Vinha, K. Climate variability and child height in rural Mexico. Econ. Hum. Biol. 10, 54–73 (2012).

    Article  Google Scholar 

  49. 49.

    Thai, T. Q. & Falaris, E. M. Child schooling, child health, and rainfall shocks: evidence from rural Vietnam. J. Dev. Stud. 50, 1025–1037 (2014).

    Article  Google Scholar 

  50. 50.

    Strand, L. B., Barnett, A. G. & Tong, S. The influence of season and ambient temperature on birth outcomes: a review of the epidemiological literature. Environ. Res. 111, 451–462 (2011).

    CAS  Article  Google Scholar 

  51. 51.

    Sherman, M., Ford, J., Llanos-Cuentas, A., Valdivia, M. J. & Bussalleu, A. Vulnerability and adaptive capacity of community food systems in the Peruvian Amazon: a case study from Panaillo. Nat. Hazards 77, 2049–2079 (2015).

    Article  Google Scholar 

  52. 52.

    Guanais, F. C. & Macinko, J. The health effects of decentralizing primary care in Brazil. Health Aff. 28, 1127–1135 (2009).

    Article  Google Scholar 

  53. 53.

    Harpham, T. Urban health in developing countries: what do we know and where do we go? Health Place 15, 107–116 (2009).

    Article  Google Scholar 

  54. 54.

    Anderson, B., Grove, K., Rickards, L. & Kearnes, M. Slow emergencies: temporality and the racialized biopolitics of emergency governance. Prog. Hum. Geogr. 44, 621–639 (2020).

    Article  Google Scholar 

  55. 55.

    Friel, S. Climate Change and the People’s Health (Oxford Univ. Press, 2019).

  56. 56.

    Dottori, F. et al. Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change 8, 781–786 (2018).

    Article  Google Scholar 

  57. 57.

    Hoffman, M. C., Mazzoni, S. E., Wagner, B. D., Laudenslager, M. L. & Ross, R. G. Measures of maternal stress and mood in relation to preterm birth. Obstet. Gynecol. 127, 545–552 (2016).

    Article  Google Scholar 

  58. 58.

    Mallett, L. H. & Etzel, R. A. Flooding: what is the impact on pregnancy and child health? Disasters 42, 432–458 (2018).

    Article  Google Scholar 

  59. 59.

    Lee, A. C. et al. Validity of newborn clinical assessment to determine gestational age in Bangladesh. Pediatrics 138, e20153303 (2016).

    Article  Google Scholar 

  60. 60.

    Lee, A. C. et al. Diagnostic accuracy of neonatal assessment for gestational age determination: a systematic review. Pediatrics 140, e20171423 (2017).

    Article  Google Scholar 

  61. 61.

    de Lima, A. C. et al. Climate hazards in small and medium cities in the Amazon delta and estuary: challenges for resilience. Environ. Urban. 32, 195–212 (2019).

    Article  Google Scholar 

  62. 62.

    Ibisch, P. L. et al. A global map of roadless areas and their conservation status. Science 354, 1423–1427 (2016).

    CAS  Article  Google Scholar 

  63. 63.

    Gadelha, A. N. et al. Grid box-level evaluation of IMERG over Brazil at various space and time scales. Atmos. Res. 218, 231–244 (2019).

    Article  Google Scholar 

  64. 64.

    Oliveira, R., Maggioni, V., Vila, D. & Morales, C. Characteristics and diurnal cycle of GPM rainfall estimates over the central Amazon region. Remote Sens. 8, 544 (2016).

    Article  Google Scholar 

  65. 65.

    Rozante, J. R., Vila, D. A., Barboza Chiquetto, J., Fernandes, A. D. A. & Souza Alvim, D. Evaluation of TRMM/GPM blended daily products over Brazil. Remote Sens. 10, 882 (2018).

    Article  Google Scholar 

  66. 66.

    Zubieta, R., Getirana, A., Espinoza, J. C., Lavado-Casimiro, W. & Aragon, L. Hydrological modeling of the Peruvian–Ecuadorian Amazon basin using GPM-IMERG satellite-based precipitation dataset. Hydrol. Earth Syst. Sci. 21, 3543–3555 (2017).

    Article  Google Scholar 

  67. 67.

    Molina-Carpio, J. et al. Hydroclimatology of the Upper Madeira River basin: spatio-temporal variability and trends. Hydrol. Sci. J. 62, 911–927 (2017).

    CAS  Article  Google Scholar 

  68. 68.

    Espinoza, J. C. et al. Regional hydro-climatic changes in the southern Amazon basin (Upper Madeira basin) during the 1982–2017 period. J. Hydrol. Reg. Stud. 26, 100637 (2019).

    Article  Google Scholar 

  69. 69.

    Du, J., Fang, J., Xu, W. & Shi, P. Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stoch. Environ. Res. Risk Assess. 27, 377–387 (2013).

    Article  Google Scholar 

  70. 70.

    Wang, Y., Chen, X., Chen, Y., Liu, M. & Gao, L. Flood/drought event identification using an effective indicator based on the correlations between multiple time scales of the standardized precipitation index and river discharge. Theor. Appl. Climatol. 128, 159–168 (2017).

    Article  Google Scholar 

  71. 71.

    Seiler, R. A., Hayes, M. & Bressan, L. Using the standardized precipitation index for flood risk monitoring. Int. J. Climatol. 22, 1365–1376 (2002).

    Article  Google Scholar 

  72. 72.

    Frederick, I. O., Williams, M. A., Sales, A. E., Martin, D. P. & Killien, M. Pre-pregnancy body mass index, gestational weight gain, and other maternal characteristics in relation to infant birth weight. Matern. Child Health J. 12, 557–567 (2008).

    Article  Google Scholar 

  73. 73.

    McKee, T. B., Doesken, N. J. & Kleist, J. The relationship of drought frequency and duration to time scales. In Proc. 8th Conference on Applied Climatology 179–183 (American Meteorological Society, 1993).

  74. 74.

    Umlauf, N., Klein, N. & Zeileis, A. BAMLSS: Bayesian additive models for location, scale, and shape (and beyond). J. Comput. Graph. Stat. 27, 612–627 (2018).

    Article  Google Scholar 

  75. 75.

    Censo Demográfico 2010 (IBGE, 2011);

  76. 76.

    Blanc, A. K. & Wardlaw, T. Monitoring low birth weight: an evaluation of international estimates and an updated estimation procedure. Bull. World Health Organ. 83, 178–185 (2005).

    Google Scholar 

  77. 77.

    Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall, 2006).

Download references


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.

Author information




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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Competing interests

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

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 (2021).

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