DOI: 10.1590/1413-81232015213.20162015 731 artigo article artigo Usn s d sudy h nshp bwn nf nd dh dsss n Suhwsn amzn bsn

O uso de dados de satélite para estudar a relação entre chuva e doenças diarreicas em uma bacia na Amazônia Sul-Ocidental

Paula Andrea Morelli Fonseca 1 Sandra de Souza Hacon 2 Vera Lúcia Reis 3 Duarte Costa 4 Irving Foster Brown 5

abs The North region is the second region rsum A região Norte é a segunda no Brasil com in with the highest incidence rate o diar- a maior taxa de incidência de doenças diarreicas rheal diseases in children under 5 years old. The em crianças menores de 5 anos. O objetivo deste aim o this study was to investigate the relation- estudo oi investigar a relação entre chuva e nível ship between rainall and water level during the do rio, principalmente durante a estação chuvosa, rainy season principally with the incidence rate com a taxa de incidência da reerida doença em o this disease in a southwestern Amazon basin. uma bacia no sudoeste da Amazônia. Estimativas Rainall estimates and the water level were cor- de chuva e nível do rio oram correlacionadas e related and both o them were correlated with the ambos correlacionados com a taxa de incidência diarrheal incidence rate. For the Alto region, da diarreia. Para a região do Alto Acre, 2 a 3 dias 2 to 3 days’ time-lag is the best interval to observe de deasagem é o melhor intervalo para observar o the impact o the rainall in the water level (R = impacto da chuva no nível do rio (R = 0.35). Na 0.35). In the Lower Acre region this time-lag in- região do Baixo Acre essa deasagem aumentou (4 1 Programa de Pós- creased (4 days) with a reduction in the correla- dias) com redução na correlação. A correlação en- Graduação em Clima e Ambiente, Instituto tion value was ound. The correlation between tre chuva e doenças diarreicas oi melhor na região Nacional de Pesquisas da rainall and diarrheal disease was better in the do Baixo Acre (Acrelândia, R = 0.7) e a chuva rio Amazônia. Av. André Araújo Lower Acre region (Acrelândia, R = 0.7) and acima da cidade. Entre o nível do rio e as doenças 2936, Aleixo. 69060- 000 Manaus AM Brasil. rainall upstream o the city. Between water level diarreicas, os melhores resultados oram encontra- pamorellionseca@ and diarrheal disease, the best results were ound dos para a estação de Brasiléia (casos em Brasiléia, gmail.com or the Brasiléia gauging station (Brasiléia, R = R = 0.3 e Epitaciolândia, R = 0.5). Os resultados 2 Escola Nacional de Saúde Pública, Fiocruz. Rio de 0.3; Epitaciolândia, R = 0.5). This study’s results deste estudo podem dar apoio ao planejamento Janeiro RJ Brasil. may support planning and fnancial resources e alocação de recursos fnanceiros para priorizar 3 Secretaria de Estado de allocation to prioritize actions or local Civil De- ações para Deesa Civil e serviços de saúde antes, Meio Ambiente. Rio Branco AC Brasil. ense and health care services beore, during and durante e depois da estação chuvosa. 4 College o Engineering, ater the rainy season. Pvs-hv Diarreia inantil, Estação chu- Mathematics and Physical Ky wds Inant diarrhea, Rainy season, Re- vosa, Sensoriamento remoto, Vigilância em saúde Sciences, University o Exeter. Exeter United mote sensing, Environmental health surveillance, ambiental, Amazônia Brasileira Kingdom. Brazilian Amazon 5 Woods Hole Research Center. Falmonth Massachusetts United States. 732 etal.

indun ting any satellite rainall estimates. Thus, this FonsecaPAM work aims to a) evaluate the satellite rainall es- Under normal climate conditions, diarrheal dise- timates or the Acre Basin; b) analyze the corre- ases and pneumonia are the major cause o mor- lation between these estimates and: 1) water level tality among children under ve years, especially in three cities along river Acre, 2) Investigate the in poorer countries, which represent 29% o total incidence rates o inant diarrheal diseases across deceased in an annual base worldwide1. However, all municipalities in the river Acre Basin. these diseases have been widely reported ater geophysical disasters, and hydrometeorological events such as foods2. According to statistics ob- Ms nd mhds tained rom the Mortality Inormation System in Brazil3 under the Inormatics’ Department Sudy a o the Health System (DATASUS) rom 1996 to 2012, diarrhea and gastroenteritis arise as one o the main causes o children mortality in Brazil. The Acre State, which is located at the sou- In Northeast region, this disease is the 4th cause thwestern Amazon, is characterized by a seaso- o death (6.2 cases per 1,000 children). Never- nal rainall regime monsoon7,16, with an annual theless it must be considered the higher number rainall approx. 1,960 mm17. It is in the border o children in this specic region. In the North with the Peruvian and Bolivian departments o and Central-West regions diarrhea is the 8th main Madre de Dios and Pando respectively, as shown cause o inant mortality with an incidence rate in Figure 1 a. The Acre basin, which is in the eas- o 2.4 and 1.7 per 1,000 children respectively. tern side o Acre State, comprises approximately Several studies have been trying to establish 30,000km2,18. This basin is basically divided in a link between the occurrence o water borne in- and Riozinho do Rola sub-basins. It co- ectious diseases (as diarrhea) and climatic and vers 11 municipalities and they are distributed in hydrological patterns4,5. Some studies analyzed two geopolitical regions Alto (Upper) and Baixo data related to climate extremes and food epi- (Lower) Acre. Rio Branco is the Acre’s state capi- sodes to discuss the impacts on health6,7. Some tal localized downstream Riozinho do Rola River o the water diseases related to climatic extreme that has a crucial role on the food process in this events are cholera, leptospirosis, hepatitis, ba- city. Approximately 65% o the ~770,000 Acre cillary dysentery and typhoid ever. These extre- State population lives in these regions and 6% o mes episodes lead to a decrease in hygiene habits them are children under 5 years old, hal o this due to shortage o resh water and the damage percentage lives in the east o Acre19. caused by the lack o sewage system. In addition, the high number o people accommodated in th v w v d sus shelters during the food period increases altoge- ther the incidence o these diseases. Water level daily data series were obtained Since 2012, southwestern Amazon has been rom the National Water Agency20 or three gau- severely aected by extreme foods8,9. In 2014, ging station: in Brasiléia and Xapuri, both in the Rio Branco’s mayor, the capital o Acre State, Xapuri sub basin, and data series available to declared the emergency state alongside with Pe- 1998-2006 and 2001-2012 period, respectively, ruvian, Bolivian and other Brazilian cities. More and in Rio Branco, Riozinho do Rola sub basin, than 2.000 houses were aected in the capital o with data available or 1998-2012 period. Acre. Recently, during the 1st semester o 2015 Diculties regarding maintenance o the another food event aected this region and equipment (station or rain gauges) and logistic again exposed the Amazonian population to the problems are some o the reason why the data impact o waterborne diseases10. rom some o the stations are not updated. The number o meteorological stations and rain gauges is very limited in Amazonia conside- rnf bsvd d su ring the nature, scale, dynamic and microphysi- cs involved in the rainall events in the tropics, This study used daily records o the 12 rain usually caused by local convection11. Satellite data gauges rom AcreBioClima Project21 or the 2006- provides a useul alternative to allow lling the 2013 period (Table 1).The selection was based absence o locally measured data12-15. Thereore on the availability o data during the period o in Acre basin there is no previous work valida- November to April. This criteria was applied be- 733 Ciência & Saúde Coletiva, 21(3):731-742, 2016 21(3):731-742, Saúde& Coletiva, Ciência

Fu 1. A - Municipalities within the Acre River Basin using the code number available in the shape le provided by UCEGEO/AC. The municipalities are part o 2 Acre Regions: Alto Acre (Upper Basin):16 – , 17 – Brasiléia, 18 – Epitaciolândia, 19 – Xapuri; and Baixo Acre (Lower Basin):4 – Rio Branco, 20 - Capixaba, 8 – , 5 – , 2 – Senador Guiomard, 21 – Plácido de Castro, 22 – Acrelândia. Black dots represent the geographical locations where the water level series were obtained. Riozinho do Rola and Xapuri sub-basins are delimited in order to highlight the proportion and importance each one has when compared to the total extension o Acre Basin. B - Accumulated rainall or the NOV-APR semester rom 2006 to 2013 split between observed, estimated, and spatially between Upper and Lower Acre; C - Average accumulated rainall in Upper and Lower Acre regions considering the geographical locations o the rain gauges. Values rom NOV 2006 to APR 2013; D - Daily mean o the water level or each municipality, the values were calculated or specic year intervals, according to what was available or each location; E - Incidence rate or inant diarrhea diseases in Acre River basin municipalities rom Jan/2000 to Dec/2012. 734 etal.

tb 1. Rain gauges with data available or the NOV-APR semester to 2006-2013. FonsecaPAM

cd rn Sn Munpy ln l P1 Upper Acre Assis Brasil Assis Brasil -69.575 -10.943 P4 Oriente Rio Branco -68.746 -9.934 P10 Riozinho do Rola Rio Branco -67.900 -10.087 P3 Seringal Espalha Rio Branco -68.662 -10.191 P13 Vila Capixaba Capixaba -67.682 -10.579 P5 Xapuri Xapuri -68.489 -10.662 P12 Lower Acre Baixa Verde Rio Branco -67.557 -10.025 P6 Fazenda Alenas Rio Branco -68.165 -9.957 P14 Limoeiro Rio Branco -67.634 -9.867 P18 Porto Acre (66) Porto Acre -67.679 -9.683 P17 Tucandeira Acrelândia -66.877 -9.822 P11 UFAC Rio Branco -67.862 -9.954

Source: AcreBioClima (2013).

cause 83% o the total annual rainall is recorded which range rom cholera to gastroenteritis and during this semester17. colitis o inectious and unspecied origin. In These rain gauges were split into Upper and other words, it includes the three main causes o Lower Acre (six or each) with a balanced distri- the disease virus, protozoa and bacteria25. bution o the available stations in order to show the series behavior. S rnf Vdn

rnf smd d su Rainall observed data series were used to evaluate and validate the satellite data or this geographic location to which TRMM data had The Tropical Rainall Measuring Mission22 co- never been tested beore. vers the latitudes rom 50oN to 50oS and provides Rainall estimates rom TRMM were extrac- inormation on a 3-hour time through the 3B42RT ted using the AcreBioClima geographic coordi- product. In this work it was used a derived version nates or the stations available in the Acre basin o the 3B42 data provided in a daily basis available and both datasets were compared against each rom 1998-2015-Feb15,23. TRMM’s spatial resolu- other through a contingency table26,27, as can be tion is 0.25o x 0.25o degrees and it corresponds to ound in Wilks (2011, p. 261)26. This verication grid cells o 625 km2 approximately. procedure is essential to validate the quality and coherence o satellite-retrieved climate variables infn Dh Dsss indn css in the context o a specic spatial and seasonal/ temporal scale o analysis. Finally, validated esti- The statistic o inant diarrheal diseases rom mates are more comparable to other studies and the Acre State Health Secretary was obtained or products. the 2000-2012 period on a monthly basis. See From the contingency table it was possible to PULSE-Brazil website24 in order to access this calculate the ollowing indexes: Proportion Cor- data series. rect (PC), Critical Success Index (CSI), Bias, False Our ocus was children under 5 years old and Alarm Ratio (FAR), Hit rate (H) and Probabili- the data were extracted rom the Acute Diarrhea ty o False Detection (POFD). The ormulas or Daily Monitoring System also known by the each o them are described in Equation 1-6. acronym SIVEP-DDA. The dataset was used to (a + d) calculate the incidence rate or each municipality. PC = According to World Health Organization (WHO) n diarrheal diseases is classied in the International Equation 1: Proportion correct (PC) Classication o Diseases (ICD) under code 10, in Chapter I, and it covers the A00-A09 subtypes, Where, 735 Ciência & Saúde Coletiva, 21(3):731-742, 2016 21(3):731-742, Saúde& Coletiva, Ciência

a = rainall events both observed and estima- rsus ted d = rainall events neither observed nor es- tsn trMM ds timated n = number o days analyzed (n), or sample size. The results obtained through the analysis o contingency table have PC = 73% and CSI = a CSI = 67%, and the higher the result the more accura- a + b + c te are the estimates. The Critical Success Index Equation 2: Critical success index (CSI) value means that the total hits or the rainall es- timates are higher than all the times it missed or Where, generated a alse alarm (estimated a rainall event b = total rainall events observed and not es- that was not captured by the station). For bias it timated was obtained 1.07 which indicates that there is c = rainall events only observed. a balance and the satellite estimates are not sub/ overestimating the number o events. It also ac- a + b Bias = counts or estimation accuracy. To evaluate relia- a + c bility and resolution it was calculated FAR, 22%, Equation 3: Ratio o the estimated and preci- and H, 83%. Last index, POFD resulted in 49%. pitated rainall events. Figure 1, graphic b, presents the rainall data observed and estimated divided by region, b FAR = Upper and Lower Acre regions. Median values (a + b) or both data sources and or both regions vary Equation 4: False Alarm Ratio rom approx. 200 to 260 mm monthly. Maxi- mum values foats around 400 mm (estimated) a H = and 500 mm (observed). The minimum values a + c present dierences lower than 100 mm compa- Equation 5: Hit rate ring the two sources / two regions and they foat around 100 mm or the upper range limit. b POFD = In Figure 1 c the monthly estimated values ba- b + d sically ollow the observed ones which implies in a Equation 6: Probability o alse detection. good representation o the seasonal behavior. Cor- relation values obtained between rain gauges’ series Rainall estimates and water level were cor- (observed) and TRMM values were around 0.4. related assuming that the daily estimates would Figure 1 d introduces the water level explora- directly infuence the water level in the three gau- tory analysis showing the observed daily average ging station chosen and a shorter or longer time during the year, which was built using the time lag would be better to represent that infuence. series available. Characteristics as the location o For the correlation analysis between rainall and the city relative to the main rivers and the tribu- diseases, it was considered that the monthly ac- taries were taken into consideration to analyze cumulated rainall in each pixel within a muni- these results. cipality have an infuence in the incidence rate When a time-lag is applied it is observed a o diarrheal diseases in this municipality. For the spatial coherence between the rainall estimates correlation between river water level and diarrhea and the water level at Brasiléia gauging station. diseases, it was chosen three parameters combi- The higher correlation values obtained was or 2 nation: monthly water level mean versus diarrhea and 3 days lag, as shown in Figure 2, maps A and incidence, monthly maximum water level versus B (R ≈ 0.35). These values are the pixels in light diarrhea incidence and monthly minimum water grey on the let side o the white dot that indica- level versus diarrhea incidence. It was maintained tes the station. The three pixels in the let bottom all cities independent o its location in the basin correspond to Assis Brasil municipality that is (whether i it was up or downstream rom where less than 100 km rom Brasiléia. This conrms the water level was measured). that the water transit time is higher the urther the rainall is upstream. Xapuri gauging station presents the strongest signals during the 3 and 4 day lag as shown in 736 etal. FonsecaPAM

Fu 2. Best correlation values between rainall estimates and water level taken in Brasiléia, Xapuri and Rio Branco. Check the white dots to identiy where the river measurements where obtained.

Figure 2, maps C and D (R ≈ 0.35) and it repre- butary o the Riozinho do Rola River. This pixel sents the critical time-lags or the water level in maintains higher values until the nal time-lag Xapuri to respond to rainall events in the ups- is applied even though its neighbor’s pixels pre- tream basin. sents lower values as the lag increases. It is not Rio Branco gauging station shows the stron- clear why this pixel maintains a higher correla- gest signals or 4 and 5 day lag, as shown in Figure tion value despite o the decrease presented by 2, maps E and F (R ≈ 0.31). The pixels located the neighbor pixels. However, the act that it is upstream the measurement point have stronger next to northern limit o the basin an area that correlation coecients as the time-lag increa- probably has higher altitude values would ex- ses. Short time-lags have low correlation values plain why this area has a larger contribution to indicating that rainall relates with water level in the water level variation. this gauging station more requently ater a mini- mum time-lag o 3 days. cn: nf sms From Figure 2 e, a pixel in the northwest part vsus dh dsss ss o the basin highlights an area o higher infuen- ce on the water levels (light grey). This particular Box plots or the incidence rate o inant diar- pixel corresponds to Espalha River, a small tri- rheal diseases cases are shown in Figure 1 e or 737 Ciência & Saúde Coletiva, 21(3):731-742, 2016 21(3):731-742, Saúde& Coletiva, Ciência

the eleven municipalities. As the ratio numbers ging station (0.7 and 0.5 respectively, Figure 4 a). are low (incidence rate) or most o the cities it However, Xapuri and Rio Branco gauging station is dicult to analyze the data. The median values show low correlation values with the diseases or these cities are around 0.5 (less than one case case registered in the respectively cities. per 1,000 children). Maximum extreme values are or Assis Brasil (> 4) and Acrelândia (> 3). Figure 3 b and k show overlapping between Dsussn the rainall pixel and the cities, Epitaciolândia and Plácido de Castro, suggesting that the rainall rnf sms vdn in that period had an infuence in the diarrhea cases in these cities. However, the correlation is Rainall estimates obtained through remote not as higher than Senador Guiomard and Acre- sensing methods have been used due to its enor- lândia cities where the pixels with the highest mous advances in areas with dicult access and values are located upstream the city. logistic (such as Amazonia). The rainall estimates validation perormed cn: w v d or TRMM using the contingency table ound a vsus dh dsss ss bias next to a unit, and it is less than what was ound in Santos e Silva et al.28. The results presented in this section compri- For the FAR index, the value obtained is re- se the correlation values obtained rom the water asonable when compared to the 25% approx. level data and the diarrheal diseases cases (Figure obtained in Santos e Silva et al.28 but it was hi- 4 a-c). gher than 0.15 ound in Scheel et al.29 study whi- Diarrheal diseases recorded in the neighbo- ch perormed a similar validation process using ring cities, Epitaciolândia and Brasiléia, show 3B42-V6 in the Peruvian and Bolivian territory good correlation values with the Brasiléia gau- during the rainy season (DJF).

Fu 3. Correlation results obtained rom rainall estimates or all the Acre river basin and the inant diarrhea diseases cases or all municipalities in the Acre basin (A-G) or the Dec-Jan-Feb. 738 etal. FonsecaPAM

Fu 4. Correlation between Acre River water level or Brasiléia (a), Xapuri (b) and Rio Branco (c) and the inant diarrhea in children in cities that comprises Upper and Lower Acre regions. Correlation values where obtained using maximum, medium and minimum monthly water level or the DJF trimester o each dataset.

The value obtained or H means that the es- (3h) ound values o approximately 57% or rain timation achieves 4/5 rainall events and it is a gauges in the Ganges, Brahmaputra and Mehg- very good proxy in qualitative terms being higher na basins in Bangladesh. Using these values as a than the ~77% ound in Scheel et al.29. Mott threshold it is assumed that the value obtained et al.30 analyzing H, which authors named Pro- in this study is a reasonable match. Authors also bability o Detection index (POD), or the pre- ound that there are more hits during the rainy vious version o the TRMM product 3B42V6 season (monsoon) then on the dry season (no- 739 Ciência & Saúde Coletiva, 21(3):731-742, 2016 21(3):731-742, Saúde& Coletiva, Ciência

monsoon). Due to the data scarcity, it was not time intervals would smooth other actors that possible to test this hypothesis in our study. The might be infuencing the river seasonal behavior. value obtained or POFD index means that the- There is no correlation between instanta- re is a 50% percentage o a alse event actually neous values or the rainall estimates analysis being a non-occurrence o rainall. This value is whether it is with water level or diseases. However more than twice o what was ound in Mott et the results improve when it is applied 1 to 4 days al.30. Authors attributed it to an inherited uncer- time-lag, or the rainall and water level analysis. tainty o the satellite estimates. Improvements in Ater 5 day time-lag the correlation values tends the newest version o 3B42 (3h) algorithm are to be very low. analyzed in the Zulkafi et al.14 study as the re- Comparing the three gauging stations in the duction o the negative bias especially during the rst analysis perormed, rainall estimates and rainy season and probably due to the inclusion water level, the best correlation values was ound o more rain gauges in the validation process. It in Xapuri or 3 day time-lag (R = 0.35). was reassuring or our purposes the accuracy me- The results o the Brasiléia gauging station asures being above 60%, the act that it does not might be infuenced by the exclusion o the basin overestimate the number o events and a small area that does not belong to the Brazilian territory. alse alarm ratio (less than 30%). The rainall near the station infuences the This specic TRMM product used in our water level or a ew hours when it does so, be- analysis aggregates the best o all TRMM set o cause there are cases when these amounts o rain sensors and even data rom other satellites al- join the river downstream or even inltrates the though it is still a proxy rom the real data (in- soil. The eects o fash foods with rapid res- direct measurement) and it has limitations. The ponse on the water level are not registered in the convection processes in this region has characte- ocial records, because it usually lasts less than ristics as the weak horizontal fuxes and the act 24 hours. that most o the momentum is vertical and ha- ppens during one-hour time-space31 which are Dh dsss nyss not properly accounted by the sensors and it may lead to over/underestimates. However, TRMM It was ound an acceptable correlation betwe- estimates provide a good dataset to analyze rain- en the hydrologic variables (rainall and water all events in this area and its main advantages level) and diarrheal diseases. It can be due to the are due to their availability, spatial resolution, short time series available, data gaps and due to and the series length (15 yr. long). The estimated the temporal interval in which the analysis were rainall pattern in the Upper and Lower Acre is perormed (e.g. diarrheal cases are monthly grou- similar to the observed one, although the corre- ped). The result might improve i the diseases lation value between both datasets is low (~0.4). statistics were available in a higher requency (e.g. Our hypothesis to the low correlation is the act weekly) which was also suggest by Curriero et al.32. that the datasets were compared to each other This study was not able to show the rela- directly, instead o using mean or accumulated tionship during the rainy period between rainall values per municipality. No statistical technique and water level and the diarrheal diseases. Howe- or lters were applied in order to smooth pos- ver, or Epitaciolândia e Plácido de Castro the sible ailures in these series as the ones listed in correlation values obtained or rainall and diar- Collischonn et al.13. rheal disease suggest that the rainall around the Although the correlation values are statisti- city could infuence the disease series. Carlton et cally low, we classied them as relevant or high al.33 also suggested that the highest incidence o when compared with the results presented in diarrheal diseases occurs during the rainy season each map or graphic. and that the most aected cities are the ones with low sanitation coverage. Torres et al.34 assert that rnf esms vsus W lv the increase in population in urban areas leads to an increase in the pressure and demands or the The results or the correlation between rain- sanitation system. And although the improve- all estimates and water level presented a good ments in the basic Health Attention helped to di- correspondence despite the low values or the minish the number o deaths caused by diarrheal three cities analyzed. The seasonal analysis (not diseases, the lack o adequate sanitation systems shown) did not improve the results shown here. maintained the number o cases. According to A hypothesis that might explain this is that longer ACRE35, Rio Branco as the state largest city pro- 740 etal.

vides a median inrastructure and health services the projections o health impacts associated with FonsecaPAM in the state, and concentrates most o the state’s the fooding o the rivers o Amazonia, and con- population. A study perormed by Oliveira et al.36 sequently help government entities in the stra- or Minas Gerais State, in the Southeast Brazilian tegies o early actions to the local people. Such region, showed that the increase in the coverage analysis can improve the preparedness and help o the sanitation system combined with the ac- the government to adapt its strategies to help the tual coverage o the water system could reduce population. The location, strength and time-lag in almost 5% the raction o diarrheal disease in o the spatial correlation calculated here indicate children less than 5 years attributed to these two that rainall is not the only environmental varia- inrastructure characteristics. ble explaining the behavior o water level or the There were no coherent values between rain- occurrence o inant diarrheal diseases cases. all and Brasiléia diarrheal diseases cases however Finally, considering the possible relationship high correlation values were ound between the between diarrheal diseases and rainall behaviour water level data in Brasiléia and diarrheal diseases and the act that other states in the Brazil North cases in both Epitaciolândia (0.7) and Brasiléia region have worse diarrheal diseases ratios there (0.5). It can be due to the act that the analysis is a scope to replicate this methodology in other with rainall does not include the Bolivian part contexts within the Brazilian Amazon. o the basin and on the other hand the water level data indirect includes the runo provided by the excluded area or due to a dierent response time between the rainall event and a possible impact on inant’s health. Another possibility to explain these ndings may be the distance rom the pla- ce o residence and health services. However, we cbns could not get data in time or analysis. PAM Fonseca participated in all the phases o the study going through bibliographic revision, data cnusn gathering, analysis and the process o writing the manuscript. SS Hacon was responsible or the This study showed that the rainall satellite esti- project supervising and orientation, methodolo- mates is a good indicator o the rainall distribu- gical approach, data analysis, discussion and the tion in the Acre basin. It allows the use o this nal writing revision. VL Reis was responsible data series to support local public policies and or the project supervising, data gathering and actions o various groups such as civil deense, review process. D Costa and IF Brown partici- health proessionals to better plan and prioritize pated in the data gathering, analysis and writing the nancial and human resources available, and and review process. mitigate the impact o foods to the local popu- lation. Moreover, these estimate also allowed an analysis o the infuence o rainall upstream in aknwdmns the water level in three gauging stations in the Acre basin. The authors thank to the nancial support rom It is a rst approach to aggregate satellite in- the National Council or Scientic and Techno- ormation on water level or children ´s diarrheal logical Development (CNPq), FINEP project diseases analyses in the Amazon basin. It is ne- Rede CLIMA 2; Also to the Acre Government cessary to deepen statistical analysis, which could and PULSE-Brasil project NERC. We also would allow a better understanding o these processes. like to thank ENSP/FIOCRUZ (National School Knowing the response time between a rainall o Public Health / Oswaldo Cruz Foundation). event and its impact on the river and consequen- Special thanks to Dr. Alejandro Duarte Fonseca ces to the population’s health are important issues (AcreBioClima Project) or maintaining such an when it comes to planning response and early important project or many years and thus pro- warning in case o foods or even extreme rainall viding important contribution or the society. To events. Recent studies using climate projections Dr. Elisa Armijos rom PPG-CLIAMB/UEA-IN- indicate an increase in extreme rainall events in PA or the revision support. To Diego Viana rom Amazon37,38 and on the discharge o the Amazo- the Acre State Health Secretary or the technical nian rivers39. This type o study can contribute to support. 741 Ciência & Saúde Coletiva, 21(3):731-742, 2016 21(3):731-742, Saúde& Coletiva, Ciência

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Artigo apresentado em 15/08/2015 Aprovado em 04/12/2015 Versão nal apresentada em 14/12/2015