The association between heat exposure and hospitalization for undernutrition in Brazil during 2000−2015: A nationwide case-crossover study

English version

Autoři: Rongbin Xu ^aff001; Qi Zhao ^aff002; Micheline S. Z. S. Coelho ^aff003; Paulo H. N. Saldiva ^aff003; Michael J. Abramson ^aff002; Shanshan Li ^aff002; Yuming Guo ^aff001
Působiště autorů: Department of Epidemiology, School of Public Health and Management, Binzhou Medical University, Yantai, Shandong, China ^aff001; Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia ^aff002; Institute of Advanced Studies, University of São Paulo, São Paulo, Brazil ^aff003
Vyšlo v časopise: The association between heat exposure and hospitalization for undernutrition in Brazil during 2000−2015: A nationwide case-crossover study. PLoS Med 16(10): e32767. doi:10.1371/journal.pmed.1002950
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1002950

Souhrn

Background

Global warming is predicted to indirectly result in more undernutrition by threatening crop production. Whether temperature rise could affect undernutrition directly is unknown. We aim to quantify the relationship between short-term heat exposure and risk of hospitalization due to undernutrition in Brazil.

Methods and findings

We collected hospitalization and weather data for the hot season (the 4 adjacent hottest months for each city) from 1,814 Brazilian cities during 1 January 2000−31 December 2015. We used a time-stratified case-crossover design to quantify the association between heat exposure and hospitalization due to undernutrition. Region-specific odds ratios (ORs) were used to calculate the attributable fractions (AFs). A total of 238,320 hospitalizations for undernutrition were recorded during the 2000−2015 hot seasons. Every 1°C increase in daily mean temperature was associated with a 2.5% (OR 1.025, 95% CI 1.020−1.030, p < 0.001) increase in hospitalizations for undernutrition across lag 0–7 days. The association was greatest for individuals aged ≥80 years (OR 1.046, 95% CI 1.034−1.059, p < 0.001), 0–4 years (OR 1.039, 95% CI 1.024–1.055, p < 0.001), and 5–19 years (OR 1.042, 95% CI 1.015–1.069, p = 0.002). Assuming a causal relationship, we estimate that 15.6% of undernutrition hospitalizations could be attributed to heat exposure during the study period. The AF grew from 14.1% to 17.5% with a 1.1°C increase in mean temperature from 2000 to 2015. The main limitations of this study are misclassification of different types of undernutrition, lack of individual temperature exposure data, and being unable to adjust for relative humidity.

Conclusions

Our study suggests that global warming might directly increase undernutrition morbidity, by a route other than by threatening food security. This short-term effect is increasingly important with global warming. Global strategies addressing the syndemic of climate change and undernutrition should focus not only on food systems, but also on the prevention of heat exposure.

Klíčová slova:

Geriatrics – Brazil – Seasons – Climate change – Malnutrition – Morbidity – Global warming – Humidity

Zdroje

1. Maleta K. Undernutrition. Malawi Med J. 2006;18(4):189–205. 27529011

2. Ezzati M, Bentham J, Di Cesare M, Bilano V, Bixby H, Zhou B, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42. doi: 10.1016/S0140-6736(17)32129-3 29029897

3. World Health Organization. 2018 global nutrition report. Geneva: World Health Organization; 2018 [cited 2019 Mar 20]. Available from: https://globalnutritionreport.org/reports/global-nutrition-report-2018/.

4. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382(9890):427–51. doi: 10.1016/S0140-6736(13)60937-X 23746772

5. Swinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, et al. The global syndemic of obesity, undernutrition, and climate change: the Lancet Commission report. Lancet. 2019;393(10173):791–846. doi: 10.1016/S0140-6736(18)32822-8 30700377

6. Watts N, Adger WN, Agnolucci P, Blackstock A, Byass P, Cai WJ, et al. Health and climate change: policy responses to protect public health. Lancet. 2015;386(10006):1861–914. doi: 10.1016/S0140-6736(15)60854-6 26111439

7. Watts N, Amann M, Ayeb-Karlsson S, Belesova K, Bouley T, Boykoff M, et al. The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. Lancet. 2018;391(10120):581–630. doi: 10.1016/S0140-6736(17)32464-9 29096948

8. Lloyd SJ, Kovats RS, Chalabi Z. Climate change, crop yields, and undernutrition: development of a model to quantify the impact of climate scenarios on child undernutrition. Environ Health Perspect. 2011;119(12):1817–23. doi: 10.1289/ehp.1003311 21844000

9. Phalkey RK, Aranda-Jan C, Marx S, Hofle B, Sauerborn R. Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc Natl Acad Sci U S A. 2015;112(33):E4522–9. doi: 10.1073/pnas.1409769112 26216952

10. Myers SS, Smith MR, Guth S, Golden CD, Vaitla B, Mueller ND, et al. Climate change and global food systems: potential impacts on food security and undernutrition. Annu Rev Public Health. 2017;38:259–77. doi: 10.1146/annurev-publhealth-031816-044356 28125383

11. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885 26440803

12. Zhao Q, Li S, Coelho MSZS, Saldiva PHN, Hu K, Huxley RR, et al. The association between heatwaves and risk of hospitalization in Brazil: a nationwide time series study between 2000 and 2015. PLoS Med. 2019;16(2):e1002753. doi: 10.1371/journal.pmed.1002753 30794537

13. Zhao Q, Li S, Coelho M, Saldiva PHN, Hu K, Arblaster JM, et al. Geographic, demographic, and temporal variations in the association between heat exposure and hospitalization in Brazil: a nationwide study between 2000 and 2015. Environ Health Perspect. 2019;127(1):17001. doi: 10.1289/EHP3889 30620212

14. Xavier AC, King CW, Scanlon BR. Daily gridded meteorological variables in Brazil (1980–2013). Int J Climatol. 2016;36(6):2644–59. doi: 10.1002/joc.4518

15. Levy D, Sheppard L, Checkoway H, Kaufman J, Lumley T, Koenig J, et al. A case-crossover analysis of particulate matter air pollution and out-of-hospital primary cardiac arrest. Epidemiology. 2001;12(2):193–9. 11246580

16. Li S, Guo Y, Williams G. Acute impact of hourly ambient air pollution on preterm birth. Environ Health Perspect. 2016;124(10):1623. doi: 10.1289/EHP200 27128028

17. Janes H, Sheppard L, Lumley T. Case-crossover analyses of air pollution exposure data—referent selection strategies and their implications for bias. Epidemiology. 2005;16(6):717–26. doi: 10.1097/01.ede.0000181315.18836.9d 16222160

18. Wang X, Wang S, Kindzierski W. Eliminating systematic bias from case-crossover designs. Stat Methods Med Res. 2019;28(10–11):3100–11. doi: 10.1177/0962280218797145 30189796

19. Barnett AG, Dobson AJ. Analysing seasonal health data: Springer; 2010.

20. Mittleman MA. Optimal referent selection strategies in case-crossover studies—a settled issue. Epidemiology. 2005;16(6):715–6. doi: 10.1097/01.ede.0000183170.92955.25 16222159

21. Zhao Q, Li S, Coelho MSZS, Saldiva PHN, Hu K, Huxley RR, et al. Temperature variability and hospitalization for ischaemic heart disease in Brazil: a nationwide case-crossover study during 2000–2015. Sci Total Environ. 2019;664:707–12. doi: 10.1016/j.scitotenv.2019.02.066 30763851

22. Breslow NE, Day NE, Halvorsen KT, Prentice RL, Sabai C. Estimation of multiple relative risk functions in matched case-control studies. Am J Epidemiol. 1978;108(4):299–307. doi: 10.1093/oxfordjournals.aje.a112623 727199

23. Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw. 2011;43(8):1–20. 22003319

24. Guo Y. Hourly associations between heat and ambulance calls. Environ Pollut. 2017;220:1424–8. doi: 10.1016/j.envpol.2016.10.091 27825842

25. Buckland ST, Burnham KP, Augustin NH. Model selection: an integral part of inference. Biometrics. 1997;53(2):603–18. doi: 10.2307/2533961

26. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97–111. doi: 10.1002/jrsm.12 26061376

27. Gasparrini A, Leone M. Attributable risk from distributed lag models. BMC Med Res Methodol. 2014;14:55. doi: 10.1186/1471-2288-14-55 24758509

28. Hu K, Guo Y, Hu D, Du R, Yang X, Zhong J, et al. Mortality burden attributable to PM1 in Zhejiang province, China. Environ Int. 2018;121(Pt 1):515–22. doi: 10.1016/j.envint.2018.09.033 30292144

29. Mason P. Under nutrition in hospital. Hospital Pharmacist. 2006;13:353–58.

30. Morral-Puigmal C, Martinez-Solanas E, Villanueva CM, Basagana X. Weather and gastrointestinal disease in Spain: a retrospective time series regression study. Environ Int. 2018;121:649–57. doi: 10.1016/j.envint.2018.10.003 30316180

31. Fellows I, Macdonald I, Bennett T, Allison S. The effect of undernutrition on thermoregulation in the elderly. Clin Sci. 1985;69(5):525–32. doi: 10.1042/cs0690525 4053508

32. Roman GC. Nutritional disorders in tropical neurology. Handb Clin Neurol. 2013;114:381–404. doi: 10.1016/B978-0-444-53490-3.00030-3 23829926

33. Smith SE, Ramos RA, Refinetti R, Farthing JP, Paterson PG. Protein-energy malnutrition induces an aberrant acute-phase response and modifies the circadian rhythm of core temperature. Appl Physiol Nutr Metab. 2013;38(8):844–53. doi: 10.1139/apnm-2012-0420 23855272

34. Hajat S, O’Connor M, Kosatsky T. Health effects of hot weather: from awareness of risk factors to effective health protection. Lancet. 2010;375(9717):856–63. doi: 10.1016/S0140-6736(09)61711-6 20153519

35. Cuevas MA, Karpowicz MI, Mulas-Granados MC, Soto M. Fiscal challenges of population aging in Brazil. Washington (DC): International Monetary Fund; 2017.

36. Hu K, Guo Y, Hochrainer-Stigler S, Liu W, See L, Yang X, et al. Evidence for urban–rural disparity in temperature–mortality relationships in Zhejiang Province, China. Environ Health Perspect. 2019;127(3):037001. doi: 10.1289/EHP3556 30822387

37. The Brazil Business. Brazilian regions. São Paulo: The Brazil Business; 2019 [cited 2019 Jul 18]. Available from: https://thebrazilbusiness.com/article/brazilian-regions.

38. Springmann M, Mason-D’Croz D, Robinson S, Garnett T, Godfray HC, Gollin D, et al. Global and regional health effects of future food production under climate change: a modelling study. Lancet. 2016;387(10031):1937–46. doi: 10.1016/S0140-6736(15)01156-3 26947322

39. Armstrong BG. Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med. 1998;55(10):651–6. doi: 10.1136/oem.55.10.651 9930084

40. Hyslop DR, Imbens GW. Bias from classical and other forms of measurement error. J Bus Econ Stat. 2001;19(4):475–81. doi: 10.1198/07350010152596727

41. Buckley JP, Samet JM, Richardson DB. Does air pollution confound studies of temperature? Epidemiology. 2014;25(2):242–5. doi: 10.1097/EDE.0000000000000051 24487206

42. Gasparrini A, Guo YM, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet. 2015;386(9991):369–75. doi: 10.1016/S0140-6736(14)62114-0 26003380

43. Li YH, Cheng YB, Cui GQ, Peng CQ, Xu Y, Wang YL, et al. Association between high temperature and mortality in metropolitan areas of four cities in various climatic zones in China: a time-series study. Environ Health. 2014;13:65. doi: 10.1186/1476-069X-13-65 25103276