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Table of Contents
RESEARCH ARTICLE
Year : 2021  |  Volume : 58  |  Issue : 1  |  Page : 39-46

Analysis of dengue cases and severity classifications in Cavite Province, Philippines


1 College of Business Administration and Accountancy, De La Salle University, Dasmarinas City, Cavite, Philippines
2 College of Science and Computer Studies, De La Salle University, Dasmarinas City, Cavite, Philippines

Date of Submission31-Mar-2019
Date of Acceptance24-Dec-2019
Date of Web Publication18-Nov-2021

Correspondence Address:
Willington Onuh
De La Salle University Dasmarinas City, Cavite 4115
Philippines
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0972-9062.321742

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  Abstract 

Background & objectives: Dengue is considered endemic in many countries in the world including the Philippines, and factors associated with dengue infections have not been adequately explored in the Philippines. The aim of this study was to assess demographic and location-related factors associated with different classifications of dengue: mild dengue, dengue fever, and hemorrhage dengue fever.
Methods: This study used consolidated dengue reports of 18482 individual cases from the Provincial Hospital (PH) of Cavite province from 2009–2014 and clinical classifications of dengue used by the Philippine Integrated Disease Surveillance and Response (PIDSR). Multinomial logistic regression and marginal effects were used to analyze factors associated with different dengue classifications.
Results: Living in densely populated cities and municipalities, individuals aged 19 years and below, and being female were closely associated with severe dengue (DHF) type, while being male and older (above 19 years old) decreased the risk of contracting severe dengue.
Interpretation & conclusion: Our study provides a preliminary assessment of association between demographic factors (gender and age-group), locations (municipalities and cities); and three classifications of dengue (mild, moderate, and severe) in Cavite province in the Philippines. To establish whether dengue is linked to populated areas, age and gender will require further assessments.

Keywords: Dengue; clinical classifications; demographic; prevention; management


How to cite this article:
Onuh W, Cabanacan-Salibay C. Analysis of dengue cases and severity classifications in Cavite Province, Philippines. J Vector Borne Dis 2021;58:39-46

How to cite this URL:
Onuh W, Cabanacan-Salibay C. Analysis of dengue cases and severity classifications in Cavite Province, Philippines. J Vector Borne Dis [serial online] 2021 [cited 2021 Dec 9];58:39-46. Available from: https://www.jvbd.org/text.asp?2021/58/1/39/321742


  Introduction Top


One of the most prevalent viral diseases transmitted by Aedes mosquitoes is dengue fever. The ability of this species of mosquitoes to breed in an environment even with small amount of water and to withstand tropical and subtropical conditions make survival of this insect high[1]. Hence, where there are these mosquitoes, transmission of viral diseases, such as dengue fever becomes inevitable.

Until recently, dengue was believed to have four viral serotypes namely; DENV-1, DENV-2, DENV-3, and DENV-4. The fifth dengue virus (DENV-5), though transmitted through non-human primates was recognized as distinct from the four serotypes, DENV1-4, in 2013[2]. In the same research, while it is believed that primary infection with a single serotype may provide immunity, one serotype does not provide immunity to another serotype; hence, an individual can be infected with the 4 serotypes (DENV1-4) and can still manifest the disease.

According to World Health Organisation (WHO)[3] dengue is listed as one of the neglected tropical diseases affecting about 128 countries worldwide. It is especially clustered in the tropical areas of Asia and Africa and commonly known to affect the poor. Southeast Asia is among the regions in the world that is highly affected by dengue fever[4],[5],[6],[7]. In addition, for the last decades, the region had experienced rapid urbanization and environmental degradation vis-à-vis the lack of potable water supply and solid waste disposal problems which could have influenced the breeding behavior of the mosquito vector[1],[6],[7].

In the Philippines, the occurrence of dengue is year round, although the peak of dengue cases coincides with the breeding time of mosquitoes during rainy days. With high number of dengue cases reported annually, coupled with the presence of all DENV1-4 dengue virus serotypes in the Philippines[8],[9] and the possibility of transmission of DENV-5 from non-human primates to human[2], dengue becomes a major health concern that imposes large economic burden. The economic cost and loss due to dengue has been revealed to be high[10]. However, it is believed that attempts to quantify economic costs and burden are grossly underestimated due to under-reporting[11]. Although promising efforts are currently underway to develop a safe vaccine, the reality is that no vaccine has been approved for extensive use to combat dengue. With safe vaccine considered to be many years into the future, a parallel option is to intensify vector control, disease prevention, management and care. Proper management of vector control would require better understanding of clinical classifications and level of severity including characteristics associated with dengue cases. Dengue infections can be described as a continuum in a clinical spectrum ranging from mild fever to moderate dengue fever (DF) and progresses to dengue hemorrhage fever (DHF) which may result in organ failure or death[9],[12],[13].

Dengue outbreaks are generally reported because dengue is considered as a notifiable disease in the Philippines. However, there has been a lack of consistent monitoring of dengue severity and thus it is difficult to discern any distribution pattern[14]. Consequently, little distribution information on dengue cases is available on sex and age groups for different municipalities and cities in the Philippines. Using data collected from hospitals in the province of Cavite from 2009 to 2014, we estimate a multinomial logistic regression model to describe demographic characteristics associated with different levels of dengue severity classifications, namely, undifferentiated (mild dengue), moderate dengue fever (DF), and dengue hemorrhage fever (DHF) [Table 1]. Such information may provide important data in setting priorities in the implementation of disease prevention and control strategies.
Table 1: Description of variables, 2009–2014

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  Material & Methods Top


Study area and data

The data used for this study covered 21 out of 23 municipalities and cities in Cavite province as shown in [Figure 1]: Bacoor, Imus, Rosario, Kawit, Gen. Trias, Dasmarinas, Naic, Noveleta, Tanza, Silang, Alfonso, Amadeo, GMA, Ternate, Indang, Mendez, Cavite, Tagaytay, Trece, Maragondon, and Magallanes. Dengue has been reported to be associated with the most populated areas in the Philippines[14] and Cavite province is an ideal setting to understand dengue reported cases due to rapid population increase and urbanization. Cavite province is one of 81 provinces in the Philippines and generally regarded as the fastest growing province in terms of population growth. Among the 81 provinces, Cavite was ranked second (2410 persons per square kilometer) out of the 10 most populated provinces in the Philippines[15]. Cavite belongs to an aggregation of five provinces classified as Region4-A and also known as CALABARZON (CAvite, LAguna, BAtangas, Rizal and QueZON). Philippines Department of Health (DOH) National Epidemiology Center ranked CALABARZON second in 2013 and 2018 in terms of dengue cases by region, and first in 2018 in terms of number of confirmed dengue deaths[3],[16].
Figure 1: Cities and Municipalities in Cavite Province, Philippines

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Under the Law of Reporting Notifiable Diseases Act 3573, Private and Public Hospitals including all Municipal Health Offices and Centers are required to report all diseases to the Provincial Hospital in Cavite as part of DOH Disease Surveillance and Unified Health Management Information System[17],[18]. The unique datasets used for this study came from consolidated reports of Provincial Hospital (PH) of Cavite of 18482 dengue cases from 2009–2014. The datasets contained the following variables: age, gender, number of days of hospitalization, municipality or city of dengue patients, type of hospital (private or public) and name of the hospital where patients were cared for, outcome (death or survived), hospitalized or ambulatory (outpatient) treatment and type of dengue [dengue (without warning signs), DF, and HDF]. To access these unique datasets, a letter of request was sent to the director of PH, stating the title of the study, objectives and a brief description of the study including possible policy interventions that may result from the study. After series of meetings and ethical review board approval, the data was released for this study.

Description of outcome variable

The clinical classifications: mild dengue (without warning signs), dengue fever (DF or moderate dengue), and hemorrhage dengue fever (DHF or severe dengue) used for this study was based on Philippine Integrated Disease Surveillance and Response (PIDSR) adopted by Epidemiology Bureau of Public Health Surveillance Bureau[3]. This definition of dengue disease combines elements of clinical classification published in the World Health Organization (WHO) guideline in 1975[19] and 1997[20] and the revised version of WHO in 2009[17].

Mild dengue (without warning signs)

A previously well person experiencing acute febrile illness of 2–7days duration plus two of the following: (a) headache, (b) retro-orbital pain, (c) myalgia, (d) rash (e) arthralgia (f) body malaise (g) anorexia (h) nausea (i) vomiting (j) diarrhea, and (k) flushed skin.

Moderate dengue fever or DF (with warning signs)

A previously well person experiencing acute febrile illness of 2–7days duration plus any of the following: (a) abdominal pain or tenderness, (b) persistent vomiting, (c) clinical signs of fluid accumulation, (d)mucosal bleeding, (e) lethargy, restlessness, (f) liver enlargement, and (g) laboratory: increase in Hct and/or decrease in platelet count.

Severe dengue or dengue hemorrhage fever (DHF)

A previously well person experiencing acute febrile illness of 2–7days duration plus any of the clinical manifestations of dengue (with or without warning signs, plus any of the following: (a) severe plasma leakage including shock and respiratory distress, (b) severe bleeding, and (c) severe organ impairment.

Covariates

We used the following socio-demographic factors as explanatory variables: age, gender, and place of residence (municipality or city) of dengue patients.

Model - Multinomial Logistic Regression

We modeled the dependent variable as dengue classifications: Mild dengue (k = 1), Moderate dengue (k = 2), and Severe dengue (k = 3); and independent variables as demographic factors: age with three categories (0–4 years, 5–19 years, and above 19 years), gender (male and female), and “location in the province where dengue patients resided” assessed using twenty-one categories or municipalities and cities in the province. The multinomial analysis described the probability that (Pij) of the ith dengue case reported being in each category, j (k =1, 2, or 3 and n = 1, …, k):



where xi is a set of explanatory variables: demographic characteristics (age and gender) and location dummies (consisting of 21 areas where dengue patients resided) for each person, i, and is the effect of the . variables on the probability of being in each dengue classification category (k = 1, 2, 3). The coefficients of multinomial regression were reported in [Table 2] as relative-risk ratios (RRR) and interpreted as the probability of classifying a dengue case reported to a dengue classification category given xi divided by the probability of being a reference group (mild dengue) given xi, meaning the coefficients of other alternatives (categories) are interpreted in reference to it. When compared to the reference group, positive coefficients would indicate an increase or more likely outcome, while negative coefficients would suggest negative or less likely outcome. Unlike OLS, the multinomial coefficients cannot provide direct interpretation with respect to the degree of effect. To investigate degree of effect, the marginal effect of an independent variable xi on the probability of classification into a particular category, j, can be calculated as:
Table 2: Summary of relative risk ratio (rrr), marginal effect and probability of dengue contraction based on 2010 census.

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Given an independent variable, xi, αj is the coefficient of being in a particular category j, while refers to the mean value of all the coefficients to different categories of dengue classification for a particular person, i.

Limitation of the study

Analysis of data was based on the past cases of dengue fever covering a five-year period. Hence, no other factors have been considered other than cases distribution from 2009 to 2014 with emphasis on the age, gender and residence area of the patients.

Ethical statement

This study was approved by the Ethics Review Board of De la Salle University Dasmarinas Cavite and data release was granted by the Director of Provincial Hospital (PH) of Cavite Province under agreement to keep personal information confidential following PH Ethics Review Board recommendation.


  Results Top


[Table 3] shows the most important descriptive statistics for dengue reported cases from 2009–2014. Compared to mild and moderate dengue, 78.07% of dengue reported cases were classified as severe (DHF). Persons between the ages of 5 to 19 years old had more severe dengue (44.08%) than ages 19 years and above (43.28%), and ages 5 years and below (9.7%). The same pattern of distribution of reported dengue cases was revealed in [Table 3] for moderate dengue (DF). About 47.77% of persons between the ages of 5 to 19 years old had moderate dengue compared to ages 19 years and above (38.46%), and ages 5 years old and below (13.76%). About 93% of reported dengue cases were hospitalized and 78% of hospitalized cases were classified as severe dengue (DHF) cases. More male (54%) had dengue than female (46%). Only 146 persons or 0.08% died of dengue from 2009 to 2014.
Table 3: Sociodemographic of dengue reported cases (2009–2014)

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Population densities calculated from population based on 2010 census population and housing[21] of 21 municipalities and cities in Cavite province are shown in [Table 4]. Bacoor and G.M.A stand out as densely populated areas easily surpassing the average population density of Cavite province.
Table 4: Number of dengue cases and population density (2009–2014)*

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The first set of parameter estimates of multinomial regression are shown in [Table 2]. It shows that living in Imus relative to Bacoor was associated with 86% increase in the probability or the relative risk of contracting severe dengue than mild dengue. On the other hand, living in Imus made the same individual 26% less likely to contract moderate dengue than mild dengue. The difference between a person living in Dasmarinas city with population of 575,817 and population density of 6388 persons per square kilometer) and one living in Cavite city (with population of 101,120 and population density of 9285 persons per square kilometer) is quite substantial. Living in Cavite city increased the probability of contracting severe dengue by 111% relative to the probability of contracting mild dengue. This is suggestive of secondary dengue infection which may lead to severe dengue in the Province of Cavite. Living specifically in the city of Dasmarinas increased the probability of severe dengue by only 59% relative to contracting mild dengue given that other variables are constant.

On the other hand, living in Indang decreases the probability of contracting severe dengue by 25% relative to mild dengue. For the same individual, the probability of contracting moderate dengue decreased by 87% relative to mild dengue. Both Dasmarinas and G.M.A have population densities higher than Indang. Being a female versus male increased the probability of contracting severe dengue by 7% relative to mild dengue. For the same individual, the probability of contracting moderate dengue relative to mild dengue increased by 4%, however, the coefficient for moderate dengue was statistically insignificant.

Being older (19 years old and above) decreased the probability of contracting severe dengue (32%) and moderate dengue (42%) relative to mild dengue. The incidence of dengue in children may indicate that children often stay in areas were mosquitoes thrived such as open grounds, school premises, playground; and even in their own unprotected home[22].

While multinomial coefficients provide relative probabilities, actual probabilities were obtained through marginal effects [Table 2]. The discrete marginal effects estimate for the most part agree with multinomial coefficients. It shows that on average contracting severe dengue, is about four percentage points higher in Imus than in Bacoor.

[Table 2]. also shows that on average, the probability of contracting severe dengue is 76% if the individual lived in Bacoor and 79% if the individual lived in Imus. The difference between the two probabilities reflects an actual four percentage point. For the same individual, on average, the probability of contracting moderate dengue is 5% if the individual lived in Bacoor, and 3% if the individual lived in Imus. The difference between the two probabilities reflects an actual two percentage point less in Imus.

Being female raised the probability of contracting severe than male. On average, the probability of contracting severe dengue for female is 78% and 77% for male [Table 2], and marginal effect revealed that contracting severe dengue for female is one percentage point higher than for male. However, the pattern reverses in the case of moderate dengue. Being male raised the probability by one percentage point, however, the coefficient was insignificant. Differences in age levels were associated with contracting both severe dengue and moderate dengue relative to mild dengue. Persons who were 19 years old and above were four percentage point less likely compared to persons 5 years old and below in contracting severe dengue. Contracting severe dengue for persons who were between the ages of 5 and 19 years old were two percentage points higher than persons 19 years and older. On the other hand, persons 5 years old and younger had two percentage points higher probability than persons aged between 5 to 19 years old in contracting severe dengue.


  Discussion Top


We used multinomial logistic regression to examine the associations between the three classifications of dengue and demographic factors using consolidated data of reported dengue cases from private and public hospitals in Cavite province from 2009 to 2014. Overall, the results from the model show that those living in densely populated cities and municipalities, persons below the age of 19 years, and being female was closely associated with severe dengue (DHF) type, while being a male and older age (above 19 years old) decreased the risk of contracting severe dengue. Municipalities or cities with higher population densities than the average in Cavite province (2410 persons per square kilometer) shown in [Table 4] were associated with severe dengue type (DFH) as shown in [Table 2], except Tanza with population density of 1974 persons per square kilometer. Earlier studies in Latin America and the Caribbean[23] and the Philippines[14] identified rapid growth and urbanization of populations as one of the main factors contributing to the emergence of dengue.

Elsewhere other associations between population density and dengue have been established[24],[25],[26]. On the other hand, there is evidence based on a study in Vietnam that areas with high population density and adequate water supply did not experience severe outbreak[27]. In the Vietnamese study, it was infrastructure characteristics such as the lack of water supply, rather than population density that had the greatest effect on the transmission of dengue. Because individual risk of dengue infection is largely determined by immune status, vaccine remains the most viable option to prevent dengue outbreaks and infections, but lack of widely approved vaccine has made vector control and care management as practical approaches to combat widespread dengue infections.

Our results indicate that persons aged 5 years and below had 81% average probability of contracting dengue (DHF), followed by persons between the ages 5> to 19 years old (79%), and 19 years old and above 77% [Table 2]. In South East Asia, dengue remains a disease predominantly associated with children and adolescents[23]. For instance, in the Philippines, age distribution of dengue reported cases for 1–14 years ranged from 15–50%[17],[18]. Further evidence provided estimates on the proportions of confirmed infections tended to be highest in the 6- to 10-year-old age group and tend to decrease in the older age groups[9].

Gender related exposure to dengue has been of interest in combating dengue. In a consolidated study involving six Asian countries including the Philippines, a study[28] used dengue surveillance data stratified by both age and sex to assess how males and females differ in terms of dengue infections across countries. They found a consistent pattern of male predominance in the reported number of incident dengue cases among persons 15 years or older in several Asian countries. Our study focused not only on gender and age group differences; we assessed dengue type-specific differences. Our results indicate that females were more likely than male to contract severe dengue (DHF). On the other hand, males were one percentage more likely to contract moderate dengue (DF). This difference between male and female is interesting, because more studies[29],[30], tended to find greater male predominance dengue incidence. There is no known biological or clinical explanation for the differences, but females might show more inclination for health precaution than male and females show more solicitous care for themselves in wearing clothes that relatively cover their body. Such habit prevents them from being bitten by mosquitoes. Once mosquitoes are closer to crowded areas, they can easily detect body heat and carbon dioxide gas exhaled by humans; and sense the potential blood source’s skin[31]. However, earlier belief that male predominant dengue infection may be linked to male exposure outside to mosquitoes capable of transmitting dengue during daytime has not been established scientifically[32].

In the absence of approved vaccine, this suggests that increased emphasis needs to be placed on dengue vector control, prevention and care management. This is because the proximity of mosquito vector breeding sites to human habitation is a significant risk factor for dengue. In addition, individuals who have chronic diseases such as hypertension and diabetes are likely to develop severe dengue thereby these individuals must follow preventive measures against mosquito bites[33]. In other cases, severe secondary dengue infection occurs when other kinds of dengue viruses infect a person who already had a dengue fever in the past associated with shock, low platelets and higher grade fever[34]. Against this premise, it is important to emphasize more preventive measures for individuals who already had primary dengue infection.


  Conclusion Top


We have assessed the association between demographic factors (gender and age-group), locations (municipalities and cities); and three classifications of dengue (mild, moderate, and severe) using multinomial logistic model. The findings provide the possibility that dengue could be linked to populated areas vis-à-vis age- and gender-related.

Conflict of interest: None


  Acknowledgements Top


Funding for this research was provided by University Research Office of De La Salle University Dasmarinas City, Cavite, Philippines.

 
  References Top

1.
Oncog AS, Pondoc J. Clinical and demographic profile of pediatric patients with dengue fever admitted in three hospitals in Tagbilaran City. Pediatr Ther 2017; 7(3): 2–6.  Back to cited text no. 1
    
2.
Mustafa MS, Rasotgi V, Jain S, Gupta V. Discovery of fifth serotype of dengue virus (DENV-5): A new public health dilemma in dengue control. Med J Armed Forces India 2014; 71(1): 67–70.  Back to cited text no. 2
    
3.
World Health Organization. List of Neglected Tropical Diseases. 2018. Available from: https://www.verywellhealth.com/ neglected-tropical-diseases-list-4171834#dengue. (Accessed on December 20, 2018)  Back to cited text no. 3
    
4.
Hung TM, Clapham HE, Bettis AA, Cuong HQ, Thwaites GE, Wills BA, et al. 2018. The Estimates of the Health and Economic Burden of Dengue in Vietnam. Trends in Parasitology 2018; 34(10): 905–918.  Back to cited text no. 4
    
5.
Onuh W, Cabanacan-Salibay C, Manaig P. Economic costs and burden of dengue disease in Cavite Province, Philippines Scientia Medica 2016; 26(2): 1–11.  Back to cited text no. 5
    
6.
Eng-Eong O, Gubler DJ. Dengue in Southeast Asia: epidemiological characteristics and strategic challenges in disease prevention. Cad Saúde Pública 2008; 25(1): 115–124.  Back to cited text no. 6
    
7.
Buczak AL, Baugher B, Babin SM, Ramac-Thomas LC, Guven E, Elbert Y, et al. Prediction of High Incidence of Dengue in the Philippines. PLoS Negl Trop Dis 2014; 8(4): 1–13.  Back to cited text no. 7
    
8.
L’Azou M, Moureau A, Sarti E, Nealon J, Zambrano B, Wartel A, et al. Symptomatic dengue in children in 10 Asian and Latin American Countries. N Engl J Med 2016; 374: 1155–66.  Back to cited text no. 8
    
9.
Capeding MRZ, L’Azou M, Manalaysay MCR. Vince-Woo CR, Rivera RG, et al. Laboratory-confirmed dengue in children in three regional hospitals in the Philippines in 2009-2010. Pediatr Infect Dis J 2015; 34(11): 1145–1151.  Back to cited text no. 9
    
10.
Edillo FE, Sarcos JR, Sayson SL. Natural vertical transmission of dengue viruses in Aedes aegypti in selected sites in Cebu City, Philippines. J Vector Ecol 2015; 40(2): 282–91.  Back to cited text no. 10
    
11.
Undurraga EA, Edillo FE, Erasmo JN, Alera MT, Yoon In-Kyu, Largo FM, Shepard DS. Disease Burden of Dengue in the Philippines: Adjusting for Underreporting by Comparing Active and Passive Dengue Surveillance in Punta Princesa, Cebu City. Am J Trop Med Hyg 2017; 96(4) 887–898.  Back to cited text no. 11
    
12.
World Health Organization. Dengue, guidelines for diagnosis, treatment, prevention and control. 2009. Geneva. Available from: https://www.who.int/tdr/publications/documents/dengue-diagnosis.pdf (Accessed on November 20, 2018)  Back to cited text no. 12
    
13.
Tantawichien T, Thisyakorn U. Dengue: Overview. Southeast Asian J Trop Med Public Health 2017; 48(1): 1–18.  Back to cited text no. 13
    
14.
Bravo L, Roque VG, Brett J, Dizon R, L’Azou M. Epidemiology of dengue disease in the Philippines (2000–2011): A systematic literature review. PLoS Negl Trop Dis 2014; 8(11): 1–12.  Back to cited text no. 14
    
15.
Philippine Statistics Authority. Philippine Population Density. 2015. Available from: https://psa.gov.ph/content/philippine-population-density-based-2015-census-population (Accessed on December 20, 2018)  Back to cited text no. 15
    
16.
Department of Health. National Epidemiology Center. Disease Surveillance Report. Morbidity Week 27, June 30 – July 6, 2013.  Back to cited text no. 16
    
17.
Department of Health. National Epidemiolgy Center. The 2009 Health Statistics. 2010. Available from: http://www.doh.gov.ph/ sites/default/files/. (Accessed on November 20, 2018)  Back to cited text no. 17
    
18.
Department of Health, National Epidemiology Center Public Health Surveillance and Informatics Division. Disease surveillance report. Morbidity Week 36 (September 4–10). 2011. (cited 2018 Oct 25). Available from: http://www.doh.gov.ph/sites/default/ files/2011Den36WMR_draft.pdf. (Accessed on October 25, 2018)  Back to cited text no. 18
    
19.
World Health Organization. Technical guides for diagnosis, treatment, surveillance, prevention and control of Dengue Hemorrhagic Fever. Geneva: World Health Organization; 1975. p. 47.  Back to cited text no. 19
    
20.
World health Organization. Dengue hemorrhagic fever: diagnosis, treatment, prevention and control. 2nd ed. 1997. Geneva: WHO. Available from: https://www.who.int/csr/resources/publications/dengue/Denguepublication/en/ (Accessed on November 13, 2018)  Back to cited text no. 20
    
21.
Philippines Statistics Authority. 2010 Census of population and housing. 2013. Available from: https://psa.gov.ph/content/ cavite-population-had-increased-one-million-results-2010-census-population-and-housing. (Accessed on December 20, 2018)  Back to cited text no. 21
    
22.
World Health Organization. Philippines Situation Report 9 - Dengue Outbreak. 2019. https://reliefweb.int/report/philippines/philippines-situation-report-9-dengue-outbreak-5-november-2019  Back to cited text no. 22
    
23.
Pinheiro FP, Corber SJ. Global situation of dengue and dengue haemorrhagic fever, and its emergence in the Americas. World Health Stat Q 1997; 50(3-4): 161–9.  Back to cited text no. 23
    
24.
Almeida ASD, Medronho RDA, Valencia LIO. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43: 666–73.  Back to cited text no. 24
    
25.
Padmanabha H, Durham D, Correa F, Diuk Wasser M, Galvani A. The interactive roles of Aedes aegypti super production and human density in dengue transmission. PLoS Negl Trop dis 2012; 6: 1–11  Back to cited text no. 25
    
26.
Rodrigues M, Marques G, Serpa L, Arduino M, Voltolini J, Barbosa G, et al. Density of Aedes aegypti and Aedes albopictus and its association with number of residents and meteorological variables in the home environment of dengue endemic area, São Paulo, Brazil. Parasit Vectors 2015; 8(115): 1–9.  Back to cited text no. 26
    
27.
Schmidt WP, Suzuki M, Dinh Thiem V, White RG, Tsuzuki A, Yoshida LM, et al. Population Density, Water Supply, and the Risk of Dengue Fever in Vietnam: Cohort Study and Spatial Analysis. PLoS Med 2011; 8(8): e1001082.  Back to cited text no. 27
    
28.
Anker M, Arima Y. Male-female differences in the number of reported incident dengue fever cases in six Asian countries. WPSAR 2011; 2(2): 17–23.  Back to cited text no. 28
    
29.
Ray G, Kumar V, Kapoor AK, Dutta AK, Batra S. Status of antioxidants and other biochemical abnormalities in children with dengue fever. J Trop Pediatr 1997; 45: 1–6.  Back to cited text no. 29
    
30.
Eng-Eong O. Changing pattern of dengue transmission in Singapore. Dengue Bull 2001; 25: 40–44.  Back to cited text no. 30
    
31.
Rebollar Téllez EA.. Human body odor, mosquito bites and the risk of disease transmission. Folia Entomologica Mexicana 2005; 44 (2):247–265.  Back to cited text no. 31
    
32.
Yew YW, Ye T, Ang LW, Ng LC, Yap G, James L, et al. Seroepidemiology of dengue virus infection among adults in Singapore. Annals Acad Med 2009; 38(8): 667–75.  Back to cited text no. 32
    
33.
Pang J, Salim A, Lee VJ, Hibberd ML, Chia KS, Leo YS, Lye DC. Diabetes with hypertension as risk factors for adult dengue hemorrhagic fever in a predominantly dengue serotype 2 epidemic: a case control study. PLoS Negl Trop Dis. 2012; 6(5): e1641.  Back to cited text no. 33
    
34.
Changal KH, Raina AH, Adnan R, Manzoor R, Bashir R, Latief M, Changal QH. Differentiating secondary from primary dengue using IgG to IgM ratio in early dengue: an observational hospital based clinico-serological study from North India. BMC Infect Dis 2016; 16: 715.  Back to cited text no. 34
    


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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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