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Factors associated with timely treatment of malaria in the Brazilian Amazon: a 10-year population-based study/Factores asociados con el tratamiento oportuno de la malaria en la Amazonia brasilena: estudio de 10 anos basado en la poblacion/Fatores associados ao tratamento precoce da malaria na Amazonia brasileira: um estudo populacional de 10 anos.

Malaria is a treatable, mosquito-borne (genus Anopheles) disease; the lifecycle of its etiologic agents (Plasmodium sp.) includes humans and invertebrate hosts. The disease has proven difficult to control and persists as an important public health problem. The World Health Organization (WHO) estimates that 3.3 billion people are at risk of contracting the disease worldwide each year (1). In 2013, WHO reported approximately 198 million new cases of malaria and 584 000 related deaths globally. Of these, approximately 427 000 cases (0.2%) were in the Americas, 178 000 (0.09%) of which were in Brazil (1).

Over the last eight decades, malaria transmission in Brazil has shown marked cyclical variations and various large epidemic periods. In the early 1940s, more than 6 million cases were reported, accounting for 20% of the entire population of Brazil at that time (2). In the 1990s, there was another sharp increase--more than 637 000 cases by 1999--associated with migration to the Brazilian Amazon region (BAR).3 Since then, malaria transmission has been more concentrated in this area, accounting for 99.9% of the cases in Brazil [3]. A total of 266 348 new malaria cases and 69 malaria deaths were reported in Brazil in 2011, representing reductions of approximately 20% and 9% compared to 2010, respectively (4). Moreover, there was a marked increase in the extension of the malaria-free territory: from 15.6% of municipalities with no notified new cases in 2003-2004, to 31.7% malaria-free municipalities in 2008-2009 (5).

In view of the cyclical history of the disease, sustaining reduced malaria incidence and mortality rates continues to be a challenge. Timely diagnosis and adequate treatment of malaria are of particular relevance in settings like the BAR, which are not very amenable to vector control measures (6). Timely diagnosis and treatment do not only help to prevent hospitalizations and deaths, but also help to control disease transmission by preventing or reducing the appearance of the sexual stages of the parasite (gametocytes) in human hosts, the infective forms to the mosquito vectors (4, 6). Clearly, the effectiveness of malaria treatment depends on the parasite species involved in the infection and the time delay between symptoms onset and the appearance of the sexual stages of the parasite (6).

The Brazilian National Malaria Control Program (PNCM) has stipulated that an important indicator of malaria control is the percentage of cases starting treatment within 48 hours after symptoms onset (7). Nevertheless, based on the parasite's lifecycle, it is expected that the sooner treatment is begun, the more effective it will be, both for patients and for controlling the disease in the community (8, 9). The aim of this study was to identify factors associated with the timely treatment of malaria in the BAR states where the disease is most prevalent.

MATERIALS AND METHODS

This was a population-based, cross-sectional study using secondary data from all cases of malaria notified in selected states of the BAR in 2004-2013.

Study population

Patients with symptomatic malaria, living in any of the six states of interest to this study (Acre, Amapa, Amazonas, Para, Rondonia, and Roraima) comprised the study population. The states of Maranhao, Mato Grosso, and Tocantins, although part of the BAR, were not included in the study because they account for only 2.0% of all incident malaria cases reported in the country (10). Although each selected state has distinct economic activities, they share many similarities, such as low population density and a relatively high percentage of rural inhabitants (11).

Episode of malaria

This study considered all symptomatic and positive malaria tests reported in the states of interest. Additionally, follow-up visits with cure verification slides were excluded, since these were clearly not new. Therefore, the term "malaria incident case" or "episode of symptomatic malaria" was used in this study to mean a "positive malaria test from a symptomatic person."

Data source

Data were obtained from the Malaria Epidemiological Surveillance Information System (SIVEP-Malaria), a database managed by the PNCM that collects all malaria tests performed in public or private health services throughout the BAR. In Brazil, notification of malaria is mandatory; therefore, all events must be reported to this information system or to the Notifiable Diseases Information System (SINAN) when the case is present in other areas of Brazil. The data was analyzed according to the patient's place of residence.

Study variables

Dependent variable.

* Timely treatment. Considered to be any anti-malaria treatment started within 24 hours following the onset of symptoms.

Independent variables. Aggregated as demographics, socioeconomics, and malaria-related variables as follows:

* Demographics. (a) age group: "0-5 years of age," "6-14 years," "15-29 years," "30-59 years," or "60 years or more;" (b) sex: "female," "male," or "not informed;" (c) race/color: "white," "black/brown," "yellow," "indigenous," or "not informed;" (d) state of residence: "Acre," "Amapa," "Amazonas," "Para," "Rondonia," or "Roraima;" and (e) year of case notification (2004-2013).

* Socioeconomics. (a) level of schooling: "no schooling-incomplete 5th grade," "complete 5th grade-complete 9th grade," "partial high-school or beyond," "not applicable," or "not informed;" (b) type of occupation: "agriculture," "traveler/tourism," "livestock farming/crop production/hunting and fishing/bridge building/mining," "domestic service," "prospector," "others," or "not informed or not applicable."

* Malaria-rela ted. (a) type of malaria: "Falciparum" (Falciparum, F+FG, FG, F+M), "Vivax" (Vivax, Non-F), "Mixed" (F+V, V+FG), or "Other" (Malariae, Ovale); (b) parasite density (graded as number of + sign): "+/2" (< 5 parasites/[micro]l), "+" (5-9 parasites/ [micro]l), "++" (10-100/[micro]l), "+++ or more" (> 100 parasites/[micro]l) or "not informed." According to the plus system, the more plus signs (+), the higher the parasite density; (c) type of detection: "passive detection" or "active detection." Passive detection occurs when a patient comes to the facility for malaria testing; active detection occurs when health professionals search for people with malaria symptoms.

The reference categories were chosen considering the number of observations in the category (small categories were avoided) and the expected relationship with the outcome (positive effects, Odds Ratio [OR] > 1, were preferable).

The category not informed was created for missing data on "level of schooling," "parasite density," and "race/color" variables. All children less than 6 years of age (too young for school) were reclassified into not applicable for "level of schooling" and "type of occupation" to avoid any potential misclassification. The reclassification due to missing variables or misclassifications accounted for less than 10% of the malaria cases.

Data analysis

Analysis was performed on a 10-year (2004-2013) population database of all malaria cases in the BAR. Frequencies and percentages for each study variable were calculated. Correlation analysis was subsequently performed using Pearson's correlation to identify high correlation coefficients between independent variables. Multicollinearity between the outcome and the independent variables was also accessed by Variance Inflation Factor (VIF) and Tolerance (12, 13). Variables showing Tolerance [greater than or equal to] 0.4 were excluded (12).

In the univariate analysis, each variable previously selected was tested against the dependent variable (timely treatment) and crude odds ratios ([OR.sub.(crude)]); respective 95% confidence intervals (95%CI) and P values were estimated. All variables with a P < 0.2 were selected for the next stage of the analysis using multivariable logistic regression models (14). Stepwise was used in order to identify the final model. Adjusted odds ratios (AOR) and respective 95%CI were estimated. At this stage, the critical P value was set at < 0.05. This study had high statistical power (n), and as such most statistical tests were significant and the clinical/epidemiological significance will be discussed elsewhere. All analyses were performed using SAS version 9.3 (SAS Institute, Cary, North Carolina, United States).

Ethics

All ethical criteria regarding the Brazilian National Health Council Resolution No. 196/96 were respected, in particular with regard to confidentiality and non-disclosure of information. This study was approved by the Research Ethics Committees from the Faculty of Medicine, University of Brasilia (Brasilia, Brazil).

RESULTS

A total of 3 365 718 malaria tests were notified in 2004-2013. Of these, 420 were excluded because the date of symptoms onset was missing. Therefore, 3 365 298 cases were considered in the analysis, henceforth referred to simply as "malaria cases."

Except for the variables level of schooling, type of occupation, and race/color, the completeness of the records averaged over 99%. Around 67.2% of malaria cases were among individuals < 30 years of age; with 34.8% among children < 15 years of age. Most cases were males (62.2%), black/brown (10.3%), and residents of the state of Amazonas (36.4%). The highest percentage of notified malaria cases occurred in 2005 (16.0%), and the lowest, in 2013 (4.4%). Among socioeconomic characteristics, malaria cases occurred mainly among those with no formal education or those who had studied up to 9th grade (65.5%); agriculture was the main professional occupation (20.9%). Among malaria-related characteristics, cases were due mainly to Plasmodium vivax infections (80.0%), with very low parasite density ("+/2," 39.7%), and diagnosed by passive detection (76.5%) (Table 1).

Table 2 shows malaria cases distributed according to the time-to-treatment, classified into three categories: < 24 hours (timely treatment); 24-48 hours; and > 48 hours. Approximately 41.1% of malaria cases began treatment within 24 hours, 18.9% within 24-48 hours, and 40.0% after 48 hours. In percentage terms, children 5 years of age or younger and 6-14 years of age received timely treatment more frequently (46.2% and 45.9%, respectively), than older people (39.5% in the 15-29 year age group; around 37% for those 30+ years of age). Starting late treatment was more common among those 30-59 years of age (43.9%) and those 60+ years of age (44.1%), demonstrating a clear trend of timely treatment among the younger age groups. This trend, however, was not observed comparing the crude (unadjusted) malaria case distribution through the different categories of sex and year of notification.

In the multivariable analysis (Table 3), it was found that malaria cases with timely treatment (versus delayed treatment) were more likely to be in the age groups 6 years of age or less (Odds Ratio [OR] = 1.39; 95% Confidence Interval [95%CI]: 1.34-1.44); 6-14 years of age (OR = 1.34; 95%CI: 1.32-1.36); and 15-29 years of age (OR = 1.11; 95%CI: 1.11-1.12) than in the group 30-59 years. Significant likelihood of timely treatment was also found in the following situations: patient records with self-identification of indigenous race/color (OR = 1.41; 95%CI: 1.37- 1.45) compared to white; residents of Rondonia (OR = 1.50; 95%CI: 1.49-1.51), Acre (OR = 1.53; 95%CI: 1.55-1.57), or Roraima (OR = 1.26; 95%CI: 1.25-1.27) compared to Para (though residents of Amazonas and Amapa were less likely); and those notified in the years 2012 (OR = 1.44; 95%CI: 1.42-1.47) and 2013 (OR = 1.40; 95%CI: 1.37-1.42) compared to those notified in 2004.

Level of schooling was the only socioeconomic variable associated with timely treatment, particularly among people with no schooling or who had completed up to the 5th grade (OR = 1.20; 95%CI: 1.19-1.22) compared to those with partial high school education or beyond. Similarly, with regard to malaria-related variables, cases receiving timely treatment, compared to those that did not, were more likely to have been tested and diagnosed through active detection (OR = 1.39; 95% CI: 1.38-1.39), compared to passive detection. A sensitivity analysis using exclusively data from 2013 was carried out and all factors associated with timely treatment remained statistically significant. Therefore, these results are evidence that, despite the effect of time in the model, the factors related to timely treatment remain the same.

DISCUSSION

This is the first national study that identifies factors associated with the timely treatment of malaria in the BAR using a population-based analysis. Approximately 41.1% of cases began timely treatment (< 24 hours of symptoms onset). This result is potentially related to the continuous efforts to establish and maintain a broad network of malaria laboratories all over the BAR, even in the most remote areas. In 1999, there were just over 1 000 malaria laboratories in the area. In 2009, as a result of increased health care investment, the number of laboratories increased to more than 3 490, and the number health care professionals in malaria control and prevention reached 48 000 (15).

People receiving timely treatment were more likely to live in the states of Rondonia, Acre, and Roraima, to be less than 14 years of age, to be indigenous, to have a low level of schooling, and to be diagnosed via active detection. Approximately 65% of all cases reported during the complete time series (2004-2013) were notified in 2004-2008, while the last 2 years of study accounted for just 11% of all cases. Other studies have also pointed to recent reductions in malaria incidence in the BAR and the marked amplification of the areas with no malaria transmission (5, 16). International border areas where people live in vulnerable conditions and with poor access to health services (17-19) are exceptions.

Cases of P. falciparum showed the greatest reduction compared to P. vivax. Several factors may have contributed to its important decreasing trend, including climate changes, greater stabilization of urban conglomerations, increased distances between urban settings and the forest, changes and seasonal factors in the productive sector (e.g., mining and fish farming), and increased single crop production in the area (5, 20). In particular, the drop in the incidence of P. falciparum might be related to the introduction of the artemisnin-based combination therapy (21). Artemether-lumefantrine was shown to be an efficacious, safe, and convenient treatment for P. falciparum malaria in highly drug-resistant parts of South America (22). Collaborative efforts among municipalities, the states, and the Ministry of Health involving malaria prevention and control measures, including scaling up access to diagnosis and treatment, the distribution of insecticide-treated mosquito nets, and other vector control measures may also have been key to successful outcomes in malaria control (2, 5). In this regard, one of the important control measures adopted recently by the malaria program in Brazil is shortened time-to-treatment (23).

Residents of the states of Acre (OR = 1.56), Rondonia (OR = 1.50), and Roraima (OR = 1.26) had a greater likelihood of timely treatment than those in Para, while those in Amapa and Amazon had a lower likelihood of timely treatment. Nevertheless, this difference might be related to the complexity involving access to health care due to the expansive geographical areas of these states (730.6 [km.sup.2] and 395.1 [km.sup.2], respectively), compared to Acre (49.5 [km.sup.2]) and Roraima (40.6 [km.sup.2]) (24). Rondonia has achieved excellent results in combating the disease by means of malaria prevention and control policies based on rapid diagnosis and timely treatment, application of vector control measures (distribution of insecticide-treated mosquito nets), and rapid detection of epidemics (15, 25). Evaluation studies may be necessary to identify determinant factors associated with this positive outcome to help those with less successful programs.

With regard to demographic characteristics, young individuals (0-14 years) were associated with greater odds of timely treatment. A dose-response relationship can be seen for age, i.e., the younger the patient, the greater the odds of receiving timely treatment, and the older the patient, the lower the odds. Explanations for this finding may be associated with younger age groups having lower immunity owing to low lifetime exposure to malaria, and consequently, more severe symptoms, and thus seeking health services quickly. In addition, parents tend to take their children for care as soon as the first symptoms appear. On the other hand, the elderly may have a reduced immune response, asymptomatic or oligosymptomatic cases, and thus, difficulty in making differentiated clinical diagnoses for malaria, which may be a barrier to malaria elimination (26). These hypotheses need to be examined in greater depth in future studies.

Timely treatment was also associated with indigenous patients (OR = 1.41) and those with very low schooling (from no schooling to the 5th grade; OR = 1.20). These variables indicate vulnerable groups who are highly dependent on the Brazilian public health care system (SUS). SUS health professionals tend to be more alert to the malaria diagnostic than providers in the private sector (1), and are generally more widely available where there is greater socioeconomic vulnerability and exposure to malaria.

As expected, in this study, patients identified in active detection appear to be more associated with timely treatment (OR = 1.39; 95%CI: 1.38-1.39) than those identified via passive detection. This is because health workers who visit households are advised to offer immediate treatment for malaria to all patients with positive slide or rapid test results, both for symptomatic and asymptomatic cases. Another study found that active detection of malaria cases in endemic areas contributed to the sustainable control of the disease (27).

It is important to discuss the challenges to malaria control in the BAR as a result of the P. vivax recurrence (due to hypnozoite persistence) and due to asymptomatic persons, especially as related to P. vivax malaria. Routine, free malaria treatment in Brazil includes drugs to eradicate the latent forms of the parasite (hypnozoites). Even so, some relapse cases may occur. Additionally, the magnitude and transmission impact of the asymptomatic malaria cases in Brazil are controversial and may vary from very low prevalence to as high as 49% in remote BAR communities living with continuous iransmission (28, 29). In both scenarios--hypnozoite and asymptomatic carriers--early treatment as a single strategy will not be sufficient to control P. vivax malaria; effective, active identification and treatment of positive cases may be necessary. Other authors have discussed the challenges regarding asymptomatic cases as a barrier to eliminating malaria in endemic areas (30). This issue should be addressed along with strategies to improve time to treatment.

Limitations

Despite the robust structure of the SIVEP-Malaria and its recognized good data quality, there are still some limitations that may have impacted this study. Firstly, despite the thousands of laboratories and health professionals across endemic areas (15), a small number of malaria cases may not have been included in the database due to underreporting or misdiagnosis, a common issue for studies using secondary data from national databases. Asymptomatic cases could also be a source of underreporting, but for this study, these were not considered part of the target population. Secondly, each case notified in the database was considered to be a new episode of malaria. Consequently, an individual with more than one positive test could produce over-reporting; however, considering the geographic barriers in the BAR to health care access, over-reporting would be uncommon. Finally, although the race/color variable appears as a factor associated with timely treatment, race/color only began to be consistently reported in 2011, and its quality and coverage was improved afterwards. Therefore, analysis regarding this variable must be considered with caution.

Conclusions

Early diagnosis and timely treatment are extremely important in interrupting the malaria transmission cycle, in addition to being a secondary prevention measure that prevents malaria cases from progressing to serious forms of the disease and death (23). In this study, timely treatment (starting within 24 hours of symptoms onset) was identified in approximately 40% of all malaria cases notified in 2004-2013. Factors associated with timely treatment were: being of a young age or elderly, living in the states of Acre, Rondonia or Roraima, having 2012 and 2013 as the year of notification, low level of schooling, and being identified via active detection.

Stemming from the findings of this study, two recommendations are to raise awareness of the importance of timely treatment, especially among individuals of middle/working age, residents of Amapa, Amazon, and Para, and across the private health care sector where those with more schooling tend to seek health services; and to improve and increase active surveillance of malaria cases.

Identifying factors associated to timely treatment can strengthen the strategies for malaria control program, especially considering the expected impact on gametocyte availability for malaria vectors. This matter is particularly important because malaria-related hospitalization and death are highly avoidable through effective primary health care actions. Timely treatment provides hope for malaria control and for achieving the target of interrupting transmission in the BAR.

Acknowledgements. The authors wish to thank the National Malaria Control Program at the Ministry of Health of Brazil for providing access to the SIVEP-Malaria database.

Conflict of interests: None declared.

Disclaimer. Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the RPSP/ PAJPH and/or PAHO.

REFERENCES

(1.) World Health Organization. World malaria report 2014. Available from: http://apps. who.int/iris/bitstream/10665/144852/ 2/9789241564830_eng.pdf Accessed on 18 November 2016.

(2.) Griffing SM, Tauil PL, Udhayakumar V, Silva-Flannery L. A historical perspective on malaria control in Brazil. Mem Inst Oswaldo Cruz. 2015:110:1-18.

(3.) Ministry of Health, Brazil. Health Brazil 2009-Uma analise da situacao de saude. Brasilia: Ministry of Health; 2010. Pp. 223-40. Available from: http://bvsms. saude.gov.br/bvs/publicacoes/saude_ brasil_2009.pdf Accessed on 18 November 2016.

(4.) Health Surveillance Secretariat, Brazil. Bol Epidemiol. 2013;(1). Available from: http://bvsms.saude.gov.br/bvs/periodicos/boletim_epidemiologico_numero_1_2013.pdf Accessed on 18 November 2016.

(5.) Duarte EC, Ramalho WM, Tauil PL, Fontes CJF, Pang L. The changing distribution of malaria in the Brazilian Amazon, 2003-2004 and 2008-2009. Rev Soc Bras Med Trop. 2014;47(6):763-9.

(6.) Ministry of Health, Brazil. Manual de terapeutica da malaria. Brasilia: Ministry of Health; 2001. Available from: http:// bvsms.saude.gov.br/bvs/publicacoes/funasa/manu_terapeutica_malaria.pdf Accessed on 18 November 2016.

(7.) National Malaria Control Program. Acoes do Programa de Malaria. Brasilia: Ministry of Health; 2014. Pp. 1-3. Available from: http://portalsaude.saude.gov.br/index. php/o-ministerio/principal/leiamais-o-ministerio /662-secretaria-svs / vigilancia-de-a-a-z / malaria / 11344-orientacoes Accessed on 18 November 2016.

(8.) Oswaldo Cruz Foundation. Malaria. Agencia Fiocruz; 2013. Available from: www.agencia.fiocruz.br/malaria Accessed on 26 April 2017.

(9.) Silva TPTE, Ferreira IDLM. Doencas infecciosas e parasitarias: guia de bolso. Cadernos de Saude Publica. Health Surveillance Secretariat. Brasilia: Ministry of Health; 2006.

(10.) Interagency Network of Information for Health (RIPSA). Exames positivos de malaria por ano segundo unidade da federacao. Tabnet 2013. Brasilia: Ministry of Health; 2013. Available from: http://tabnet.datasus.gov.br/cgi/deftohtm.exe?idb2011/d04.def Accessed on 18 November 2016.

(11.) Instituto Brasileiro de Geografia e Estatistica. Sinopse do censo demografico, 2010. Brasilia: IBGE; 2010. Available from: https://sidra.ibge.gov. br/tabela/608 Accessed on 18 November 2016.

(12.) Belsley DA, Kuh E, Welsch RE. Detecting and assessing collinearity. Regression Diagnostic: Identifying influential data and sources of collnearity. New York: John Wiley & Sons; 1980. Pp. 85-191.

(13.) O'Brien RM. A caution regarding rules of thumb for variance inflation factors. Qual & Quant. 2007;41(5):673-90.

(14.) Hosmer DW, Lemeshow S. Applied logistic regression. New York: Wiley; 2000.

(15.) Oliveira-Ferreira J, Lacerda MVG, Brasil P, Ladislau JLB, Tauil PL, Daniel-Ribeiro CT. Malaria in Brazil: an overview. Malar J. 2010;9(1):115.

(16.) Lima ISFL, Lapouble MMO, Duarte EC. Time trends and changes in the distribution of malaria cases in the Brazilian Amazon Region, 2004 -2013. Rio de Janeiro: Mem Inst Oswaldo Cruz; 2016. Pp. 1-11.

(17.) Health Surveillance Secretariat, Brazil. Boletim Epidemiologico-Malaria: Monitoramento dos casos no Brasil em 2014. Brasilia: Ministry of Health; 2015. Available from: http://portalsaude.saude. gov.br/images/pdf/2015/ agosto/18/2015-009--Mal--ria-para-publica--o.pdf Accessed on 18 November 2016.

(18.) Peiter PC. Condiciones de vida, situacion de la salud y disponibilidad de servicios de salud en la frontera de Brasil: un enfoque geografico. Cad Saude Publica. 2007;23: S237-50.

(19.) Gracie R, Xavier DR, Suarez-mutis MC. Situacao da malaria na triplice fronteira. Cad Saude Publica. 2013;29(12):2497-512.

(20.) Vieira GDD, Gim KNM, Zaqueo GM, Alves TDC, Katsuragawa TH, et al. Reduction of incidence and relapse or recrudescence cases of malaria in the western region of the Brazilian Amazon. J Infect Dev Ctries. 2014;8(9):1181-7.

(21.) Teixeira C, Vale N, Perez B, Gomes A, Gomes JRB, Gomes P. "Recycling" classical drugs for malaria. Chem Rev. 2014;114(22):11164-220.

(22.) Alecrim Md, de Lacerda MV, Mourao MP, Alecrim WD, Padilha A, Cardoso B, et al. First Brazilian experience with the use of artemether-lumefantrine (Coartem) affixed-dosed ACT combination. Am J Trop Med Hyg. 2006;75(2):187.

(23.) Ministry of Health, Brazil. Guia para gestao local do controle da malaria. Diagnostico e Tratamento, 2008. Available from: http:// bvsms.saude.gov.br/bvs/publicacoes/ guia_gestao_local_controle_malaria.pdf Accessed on 18 November 2016.

(24.) Instituto Brasileiro de Geografia e Estatistica. Area Territorial Brasileira, 2016. Available from: http://archive.is/ Gekj7 Accessed on 18 November 2016.

(25.) Costa KMDM, Almeida WAF De, Magalhaes IB, Montoya R, Moura MS, Lacerda MVG De. Malaria em Cruzeiro do Sul (Amazonia Ocidental brasileira): analise da serie historica de 1998 a 2008. Rev Panam Salud Publica. 2010;28(5): 353-60.

(26.) Ferreira MU, Castro MC. Challenges for malaria elimination in Brazil. Malar J. 2016; 15:284

(27.) Macauley C. Aggressive active case detection: a malaria control strategy based on the Brazilian model. Soc Sci Med. 2005; 60(3):563-73.

(28.) Alves FP, Gil LHS, Marrelli MT, Ribolla PEM, Camargo EP, Silva LHP. Asymptomatic carriers of Plasmodium spp. as infection source for malaria vector mosquitoes in the Brazilian Amazon. J Med Ent. 2005;42:777-9.

(29.) Coura JR, Suarez-Mutis M, Ladeia-Andrade S. A new challenge for malaria control in Brazil: asymptomatic Plasmodium infection-a review. Mem Inst Oswaldo Cruz. 2006; 101(3):229 -37.

(30.) Lindblade KA, Steinhardt L, Samuels A, Kachur SP, Slutsker L. The silent threat: asymptomatic parasitemia and malaria transmission. Expert Rev Anti Infect Ther. 2013;11(6):623-39.

Manuscript received on 26 December 2016. Accepted for publication on 28 December 2016.

Isac da S. F. Lima [1] and Elisabeth C. Duarte [2]

[1] Postgraduate Tropical Medicine Program, Tropical Medicine Department, School of Medicine, University of Brasilia, Brasilia, Brazil. Send correspondence to Isac da Silva Ferreira Lima, lima.isac@gmail.com

[2] Tropical Medicine Department, School of Medicine, University of Brasilia, Brasilia, Brazil.

[3] Geographic area in northern Brazil comprising the states of Acre, Amazonas, Roraima, Amapa, Rondonia, Para, Tocantins, Mato Grosso, and part of the state of Maranhao.
TABLE 1. Malaria incidence in the states of the Brazilian Amazon
area, 2004-2013

                                             Number of   Percentage
                                               cases        (%)

Malaria incident cases                       3 365 298     100.0
Demographic variables
  Age group
    0-5 years                                 439 804       13.1
    6-14 years                                731 537       21.7
    15-29 years                              1 090 736      32.4
    30-59 years                               991 062       29.5
    60+ years                                 112159        3.3
  Sex
    Female                                   1 270 279      37.8
    Male                                     2 094 569      62.2
    Not informed                                450         0.0
  Race/color
    White                                     41 130        1.2
    Black/Brown                               347 331       10.3
    Yellow                                     7 339        0.2
    Indigenous                                56 570        1.7
    Not informed                              2912928       86.6
  State of residence
    Acre                                      338 708       10.1
    Amapa                                     179696        5.3
    Amazonas                                 1 224876       36.4
    Para                                      898511        26.7
    Rondonia                                  558 482       16.6
    Roraima                                   165025        4.9
  Year of case notification
    2004                                      410596        12.2
    2005                                      537 690       16.0
    2006                                      500 255       14.9
    2007                                      418767        12.4
    2008                                      287 083       8.5
    2009                                      284 271       8.5
    2010                                      311 446       9.3
    2011                                      246 383       7.3
    2012                                      221 869       6.6
    2013                                      146938        4.4
Socioeconomic variables
  Level of schooling
    No schooling-incomplete 5th grade        1 293 003      38.4
    Complete 5th grade-complete 9th grade    1 012232       30.1
    Partial high-school or beyond             147446        4.4
    Not applicable                            556 583       16.5
    Not informed                              356 034       10.6
  Type of occupation
    Agriculture                               703 674       20.9
    Tourism                                   49 868        1.5
    Livestock farming/crop                    146316        4.4
      production/hunting and
      fishing/bridge building/mining
    Domestic services                         285 005       8.5
    Prospector                                143 345       4.3
    Other                                     959 000       28.5
    Not informed/not applicable              1 078 090      32.0
  Malaria-related variables
   Type of malaria
    Falciparum                                629 363       18.7
    Vivax                                    2 692 900      80.0
    Mixed                                     41 749        1.2
    Other                                      1 286        0.0
  Parasite density (grade as number of "+" signs)
    +/2                                      1 337 308      39.7
    +                                         722 650       21.5
    ++                                       1 202109       35.7
    +++ or more                               95 474        2.8
    Not informed                               7 757        0.2
  Type of detection
    Passive detection                        2 574 840      76.5
    Active detection                          790 458       23.5

Source: Prepared by the authors from study data.

TABLE 2. Malaria incident cases by time between onset of symptoms
and treatment initiation in the states of the Brazilian Amazon
area, 2004-2013

                           Total         Time taken to start
                                          treatment (%) (a)
                                     < 24 hours    24-48 hours
                                      (timely)

Malaria incident cases   3 365 298      41.1          18.9
Age group
  0-5 years               439 804       46.2          19.4
  6-14 years              731 537       45.9          19.1
  15-29 years            1 090 736      39.5          18.9
  30-59 years             991 062       37.4          18.6
  60 years or over        112159        37.0          18.9
Sex
  Female                 1 270 279      41.8          19.0
  Male                   2 094 569      40.6          18.9
  Not informed              450         43.1          20.9
Year of case
notification
  2004                    410 596       39.2          17.2
  2005                    537 690       41.4          18.0
  2006                    500 255       43.4          18.2
  2007                    418 767       41.0          19.6
  2008                    287 083       40.3          20.4
  2009                    284 271       41.7          19.7
  2010                    311 446       41.7          19.3
  2011                    246 383       39.4          19.9
  2012                    221 869       40.9          19.4
  2013                    146 938       40.0          20.0

                         > 48 hours

Malaria incident cases      40.0
Age group
  0-5 years                 34.4
  6-14 years                35.0
  15-29 years               41.6
  30-59 years               43.9
  60 years or over          44.1
Sex
  Female                    39.2
  Male                      40.5
  Not informed              36.0
Year of case
notification
  2004                      43.6
  2005                      40.7
  2006                      38.3
  2007                      39.5
  2008                      39.3
  2009                      38.6
  2010                      39.0
  2011                      40.8
  2012                      39.7
  2013                      40.0

(a) Time between first symptoms onset and starting treatment.

Note: Row percentages within each category in the table.

Source: Prepared by the authors from study data.

TABLE 3. Factors associated with timely treatment of malaria in the
Brazilian Amazon, 2004-2013

Categories                                        Unadjusted
                                     Odds ratio (OR)   95% Confidence
                                                        Interval (CI)
Demographic variables
  Age group
    0-5 years                             1.44            1.43-1.45
    6-14 years                            1.42            1.41-1.43
    15-29 years                           1.09            1.09-1.10
    30-59 years                           1.00               --
    60+ years                             0.98            0.97-0.99
  Race/color
    White                                 1.00               --
    Black/Brown                           1.13            1.10-1.15
    Yellow                                1.09            1.03-1.15
    Indigenous                            1.40            1.36-1.43
    Not informed                          1.31            1.28-1.34
  State of residence
    Acre                                  1.96            1.94-1.97
    Amapa                                 0.78            0.77-0.79
    Amazonas                              0.88            0.87-0.89
  Para                                    1.00               --
    Roraima                               1.42            1.40-1.43
    Rondonia                              1.36            1.36-1.37
  Year of case notification
    2004                                  1.00               --
    2005                                  1.09            1.08-1.10
    2006                                  1.19            1.18-1.20
    2007                                  1.07            1.07-1.08
    2008                                  1.04            1.03-1.05
    2009                                  1.11            1.10-1.12
    2010                                  1.11            1.10-1.12
    2011                                  1.00            0.99-1.02
    2012                                  1.07            1.06-1.08
    2013                                  1.03            1.02-1.04
Socioeconomic variables
  Level of schooling
    No schooling-incomplete               1.31            1.30-1.32
      5th grade
    Completed 5th grade-9th grade         1.06            1.05-1.08
    Partial high-school to beyond         1.00               --
    Not applicable                        1.58            1.56-1.60
    Not informed                          1.67            1.64-1.69
  Type of occupation
    Agriculture                           1.11            1.10-1.12
    Tourism                               1.08            1.05-1.10
    Livestock farming/crop                1.00               --
      production/hunting and
      fishing/bridge building/mining
    Domestic                              1.02            1.00-1.03
    Prospector                            0.94            0.93-0.96
    Other                                 1.23            1.22-1.24
    Not informed/not applicable           1.42            1.41-1.44
Malaria-related variables
  Type of malaria
    Falciparum                            1.03            1.03-1.04
    Vivax                                 1.00               --
    Mixed                                 0.97            0.95-0.99
    Other                                 0.51            0.45-0.58
  Type detection
    Passive                               1.00               --
    Active                                1.50            1.49-1.51

Categories                           Unadjusted   Adjusted (a)
                                      P value     Adjusted OR

Demographic variables
  Age group
    0-5 years                          < 0.01        1.38
    6-14 years                         < 0.01        1.33
    15-29 years                        < 0.01        1.11
    30-59 years                          --          1.00
    60+ years                          < 0.01        0.93
  Race/color
    White                                --          1.00
    Black/Brown                        < 0.01        1.15
    Yellow                             < 0.01        1.12
    Indigenous                         < 0.01        1.41
    Not informed                       < 0.01        1.48
  State of residence
    Acre                               < 0.01        1.56
    Amapa                              < 0.01        0.86
    Amazonas                           < 0.01        0.79
  Para                                   --          1.00
    Roraima                            < 0.01        1.26
    Rondonia                           < 0.01        1.50
  Year of case notification
    2004                                 --          1.00
    2005                               < 0.01        1.06
    2006                               < 0.01        1.13
    2007                               < 0.01        1.11
    2008                               < 0.01        1.10
    2009                               < 0.01        1.14
    2010                               < 0.01        1.12
    2011                                0.41         1.19
    2012                               < 0.01        1.44
    2013                               < 0.01        1.40
Socioeconomic variables
  Level of schooling
    No schooling-incomplete            < 0.01        1.20
      5th grade
    Completed 5th grade-9th grade      < 0.01        0.96
    Partial high-school to beyond        --          1.00
    Not applicable                     < 0.01        1.17
    Not informed                       < 0.01        1.42
  Type of occupation
    Agriculture                        < 0.01        1.06
    Tourism                            < 0.01        1.14
    Livestock farming/crop               --          1.00
      production/hunting and
      fishing/bridge building/mining
    Domestic                            0.02         0.96
    Prospector                         < 0.01        1.03
    Other                              < 0.01        1.13
    Not informed/not applicable        < 0.01        1.10
Malaria-related variables
  Type of malaria
    Falciparum                         < 0.01        1.01
    Vivax                                --          1.00
    Mixed                              < 0.01        1.05
    Other                              < 0.01        0.67
  Type detection
    Passive                              --          1.00
    Active                             < 0.01        1.39

Categories                                  Adjusted (a)
                                     95% Confidence    P value
                                      Interval (CI)
Demographic variables
  Age group
    0-5 years                           1.36-1.40      < 0.01
    6-14 years                          1.32-1.34      < 0.01
    15-29 years                         1.11-1.12      < 0.01
    30-59 years                            --            --
    60+ years                           0.92-0.95      < 0.01
  Race/color
    White                                  --            --
    Black/Brown                         1.13-1.18      < 0.01
    Yellow                              1.06-1.18      < 0.01
    Indigenous                          1.37-1.45      < 0.01
    Not informed                        1.45-1.52      < 0.01
  State of residence
    Acre                                1.55-1.57      < 0.01
    Amapa                               0.85-0.87      < 0.01
    Amazonas                            0.79-0.80      < 0.01
  Para                                     --            --
    Roraima                             1.25-1.27      < 0.01
    Rondonia                            1.49-1.51      < 0.01
  Year of case notification
    2004                                   --            --
    2005                                1.05-1.07      < 0.01
    2006                                1.12-1.14      < 0.01
    2007                                1.10-1.12      < 0.01
    2008                                1.09-1.11      < 0.01
    2009                                1.13-1.15      < 0.01
    2010                                1.11-1.13      < 0.01
    2011                                1.18-1.21      < 0.01
    2012                                1.42-1.47      < 0.01
    2013                                1.37-1.42      < 0.01
Socioeconomic variables
  Level of schooling
    No schooling-incomplete             1.19-1.22      < 0.01
      5th grade
    Completed 5th grade-9th grade       0.95-0.97      < 0.01
    Partial high-school to beyond          --            --
    Not applicable                      1.15-1.19      < 0.01
    Not informed                        1.40-1.44      < 0.01
  Type of occupation
    Agriculture                         1.05-1.07      < 0.01
    Tourism                             1.11-1.16      < 0.01
    Livestock farming/crop                 --            --
      production/hunting and
      fishing/bridge building/mining
    Domestic                            0.94-0.97      < 0.01
    Prospector                          1.02-1.05      < 0.01
    Other                               1.12-1.15      < 0.01
    Not informed/not applicable         1.09-1.12      < 0.01
Malaria-related variables
  Type of malaria
    Falciparum                          1.01-1.02      < 0.01
    Vivax                                  --            --
    Mixed                               1.03-1.07      < 0.01
    Other                               0.59-0.76      < 0.01
  Type detection
    Passive                                --            --
    Active                              1.38-1.39      < 0.01

(a) Model adjusted for sex and parasite density, as well as for all
the variables shown in the table.

Source: Prepared by the authors from the study data.
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Title Annotation:Original research texto en ingles
Author:Lima, Isac da S.F.; Duarte, Elisabeth C.
Publication:Revista Panamericana de Salud Publica
Date:Dec 1, 2017
Words:5963
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