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Risk factors for dengue outbreaks in Odisha, India: A case-control study
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Risk factors for dengue outbreaks in Odisha, India: A case-control study Subhashisa Swain a,c,∗ , Minakshi Bhatt a , Debasish Biswal b , Sanghamitra Pati d , Ricardo J. Soares Magalhaes e,f a
Indian Institute of Public Health-Bhubaneswar, Public Health Foundation of India, Odisha, India Department of Biotechnology, Ravenshaw University, Cuttack, Odisha, India c School of Medicine, University of Nottingham, Nottingham, United Kingdom d Regional Medical Research Center, Indian Council of Medical Research, Odisha, India e UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton 4343 QLD, Australia f UQ Child Health Research Centre, Children’s Health and Environment Program, The University of Queensland, South Brisbane 4101 QLD, Australia b
a r t i c l e
i n f o
Article history: Received 21 May 2019 Received in revised form 13 August 2019 Accepted 25 August 2019 Keywords: Dengue Outbreak Epidemiology Odisha India
a b s t r a c t Background: Environmental and climatic risk factors of dengue outbreak has been studied in detail. However, the socio–epidemiological association with the disease is least explored. The study aims to identify the social and ecological factors associated with emerging dengue in Odisha, India. Methods: A population-based case-control study (age and sex matched at the ratio of 1:1) was conducted in six districts of the state in 2017. A structured validated questionnaire was used to collect information for each consenting participant. An ecological household survey was done using a checklist during the month of July–September. Along with the descriptive statistics, conditional logistic regression model was used to calculate the adjusted odds ratio using STATA. Results: Of 380 cases, nearly 55% were male and the median age was 33 years. The adjusted odds of having dengue was nearly three times higher among the people having occupation which demands long travel, presence of breeding sites (1.7; 95% CI 1.2–2.6), presence of swampy area near home (1.5; 95% CI 1.1–2.1) and having travel history close to the index date (1.6; 95% CI 1.1–2.4). People staying in thatched houses had three times higher risk of the disease, however, households keeping the swampy areas clean had 50% less risk for the disease (0.5; 95% CI 0.31–0.67). Nearly 22.2% of cases had a travel history during the index date. Of them, 36% had diagnosis before the travel, whereas, 64% developed the disease after the returning from the travel. Conclusion: Household factors such as occupation and ecological condition of households play important roles in dengue outbreaks in Odisha. However, our study suggests travel/commuting are also essential factors to be considered during disease prevention planning. © 2019 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Dengue, a neglected tropical vector born disease has become challenge to the public health system in the world. The estimated annual dengue burden in the globe is nearly 390 million cases from
∗ Corresponding author at: School of Medicine, University of Nottingham, United Kingdom. E-mail addresses: [email protected] (S. Swain), [email protected] (M. Bhatt), [email protected] (D. Biswal), [email protected] (S. Pati), [email protected] (R.J. Soares Magalhaes).
120 countries [1]. However, these estimates can be higher due to large number of under-reporting and non-symptomatic cases. Most of the notified cases are concentrated in the South East Asia (SEA) and Western Pacific Regions of the World Health Organization (WHO) [2,3]. In the SEA, countries like Thailand, Indonesia, India contribute in large extent to the burden [4]. Since the mid-1990s, dengue epidemic episodes in India have grown rapidly and have become more frequent [5]. Currently India is endemic for both dengue fever (DF) and dengue hemorrhagic fever (DHF). Initially the infection was geographically limited to a few states, which later expanded to most of the states in the country [6]. Odisha, an Eastern state of the country historically did not have any reported cases of the dengue till 2009, but now
https://doi.org/10.1016/j.jiph.2019.08.015 1876-0341/© 2019 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Swain S, et al. Risk factors for dengue outbreaks in Odisha, India: A case-control study. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.08.015
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Table 1 Sociodemographic description [Univariate analysis]. Variables
Category
Control N = 380% [95% CI]
Case N = 387% [95% CI]
Total N = 767% [95% CI]
Crude OR [95% CI]
Sex
Men Women
54.5 [49.4–59.4] 45.5 [40.6–50.6]
52.2 [47.2–57.1] 47.8 [42.9–52.8]
53.3 [49.8–56.8] 46.7 [43.2–50.2]
Matched
Age group
<= 15 years 16–25 years 26–35 years 36–45 years >= 46 years
12.6 [9.6–16.4] 20.0 [16.3–24.3] 23.9 [19.9–28.5] 21.3 [17.5–25.7] 22.1 [18.2–26.6]
12.7 [9.7–16.4] 23.3 [19.3–27.7] 22.2 [18.3–26.7] 18.6 [15.0–22.8] 23.3 [19.3–27.7]
12.6 [10.5–15.2] 21.6 [18.9–24.7] 23.1 [20.2–26.2] 19.9 [17.3–22.9] 22.7 [19.9–25.8]
Matched
Education
No schooling Primary Secondary High School and above
19.5 [15.8–23.8] 20.0 [16.3–24.3] 47.6 [42.6–52.7] 12.9 [9.9–16.7]
14.7 [11.5–18.6] 19.1 [15.5–23.4] 42.4 [37.5–47.4] 23.8 [19.8–28.3]
17.1 [14.6–19.9] 19.6 [16.9–22.5] 45 [41.5–48.5] 18.4 [15.8–21.3]
Reference 1.2 [0.7–1.9] 1.2 [0.8–1.8] 2.5 [1.5–4.2]*
Ethnicity
Non SC/ST SC/ST
22.6 [18.7–27.1] 77.4 [72.9–81.3]
15.5 [12.2–19.5] 84.5 [80.5–87.8]
19.1 [16.4–22.0] 81.0 [78.0–83.6]
Reference 1.6 [1.1–2.3]*
Occupation
Unemployed Agriculture/Daily worker Business Home maker/student Industry/Office
22.4 [18.4–26.8] 15.3 [11.9–19.2] 7.6 [5.3–10.8] 36.6 [31.9–41.6] 19.1 [14.6–22.4]
9.8 [7.2–13.2] 19.4 [15.7–23.6] 8.8 [6.3–12.1] 46.5[41.6–51.5] 15.5 [12.2–19.5]
16.1 [13.6–18.8] 17.3 [14.8–20.2] 8.2 [6.5–10.4] 41.6 [38.1–45.1] 16.8 [14.3–19.6]
Reference 3.9 [2.1–7.5]* 3.5 [1.7–7.4]* 4.6 [2.6–8.1]* 2.7 [1.4–5.2]*
Housing type
Asbestos roof Concrete roof Mixed roof Thatched roof
32.4 [27.8–37.2] 30.3 [25.8–35.1] 18.4 [14.8–22.6] 18.9 [15.3–23.2]
19.9 [16.2–24.2] 34.6 [30.1–39.5] 17.8 [14.3–21.9] 27.6 [23.4–32.3]
26.0 [23.1–29.3] 32.5 [29.2–35.9] 18.1 [15.5–21.0] 23.3 [20.5–26.5]
Reference 1.9 [1.3–2.9]* 1.6 [1.1–2.6]* 2.6 [1.7–4.1]*
SC- Schedule caste, ST- Schedule Tribe; OR- Odds Ratio; CI- Confidence Interval. * P value < 0.05.
there is wide reporting from all the districts. According to the surveillance data, the state now contributes nearly 10–15% total dengue cases of the country [7]. These cases are scattered in distribution and uneven with circulation of four serotypes of the virus [8]. The transmission of dengue is dependent on various macro and micro level factors such as temperature, humidity, rainfall, the population density, movement, immunity and virus load, urbanization, environmental factors and socio-demographic and economic factors [4,9,10]. These influence the spread of the disease through increased Aedes aegypti mosquito population, transmission of the vector, spreading of the disease and practices of protection mechanism. Vector dynamics [11], population movement [12] and their association with the dengue outbreak is well searched. Climatic and environmental factors create the most favorable conditions for dengue transmission and mostly studied. But investigating the socio-demographic and other individual factors is equally important for identifying the people at risk. Especially, with current shift in the disease pattern it has become essential to study the mentioned factors in detail. Studies say, dengue is no more a disease of women, children and urban problem [13–15]. Community and individual practices can prevent or increase the chances of the disease depending on the nature of practices. Potential socio–demographic and ecological factors can spread dengue to other regions due to increasing frequency of transportation and travel [16]. Even though environmental factors are crucial for the disease epidemic, a comprehensive prevention mechanism would require information at individual level. Especially, in country like India, where diversity and differences are predominant, studying these factors is essential. The recent spike of cases in the state Odisha supports the hypothesis of availability of favorable conditions for the disease spread [17]. Moreover, recurring dengue epidemics also creates hyperendemic areas, typically large, densely populated areas where several or all four serotypes dengue virus circulate in a sustained fashion. In the state, increased numbers of cases are reported among children and adults [8,18,19]. The available literature is limited to the epidemiological profile of the clinical cases, disease
burden and studying the serotypes [20,21]. Till date, there is scant of evidence on social epidemiology of the disease in the state of Odisha. We aimed to identify the socio–demographic and ecological factors associated with dengue outbreaks in the state of Odisha, India through population-based case-control study design. Methods Sample size calculation For estimating sample size and testing of the study tool, a pilot study was conducted among 30 subjects (10 cases and 20 controls) from non-study area. Findings from the pilot study was used for sample size calculation in the absence of relevant literature on risk of travel for dengue. To detect minimum 10% difference of travel exposure to endemic area among case (25%) and control (15%) of 1:1 ratio, with a power of 90% at a significant level of 0.05, the required sample size was calculated as 354 in each group. Considering 10% non-response rate, total required sample in each group was 393. Proportionate sampling method was used to recruit patients from five districts, based on the disease burden in the year 2016. Within each district, patients were selected randomly from the available list of cases obtained from the health department. The study was conducted during the rainy seasons from July to September 2017 with additional purpose of inspecting the environmental conditions (Table 4). Study setting The eastern state of India, Odisha covers about 155,707 sq km of the total area of the country and has nearly 48,903 km2 of forests spread over 31.41% of the state’s total area. The average rainfall in Odisha is measured as 1482 mm, of which 78% is received between the months of June and September of a year. The summer season in Odisha spans from March to June during which the maximum temperature at most of the places goes well above 40 ◦ C. The state projects distinct yet homogeneous features of topography and divided into five morphological units such as mountainous
Please cite this article in press as: Swain S, et al. Risk factors for dengue outbreaks in Odisha, India: A case-control study. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.08.015
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Fig. 1. Mapping of case and control samples. Red dots represent cases and green dot represents controls. These are only spot maps, does not represent the numbers exactly.
and highlands region, coastal plains, western rolling uplands, central plateaus and flood plains. According to the 2011 census of India, the total population of Odisha was 41,947,358, of which 21,201,678 (50.54%) are male and 20,745,680 (49.46%) are female with population density of 269 per km2 . The study was carried out in 5 of the 9 high dengue burden districts of the state, which contributes to more than 85% of the total cases. Details of the distribution of dengue has been provided elsewhere [17]. These districts represent nearly, 40% of the total population. Within each district, high burden blocks were selected, based on reported number of cases. This purposive selection was done after consulting with the state nodal officers. Study participants The list of confirmed dengue patients within last one year (2016) recorded at public & private hospitals of Odisha was used for selection of cases. The place of residence at the time of getting disease was chosen as the study areas. This information was obtained from National Vector Borne Disease Control Program (NVBDCP) cell, Odisha, India. We excluded cases, who had disease history of more than one year to avoid the recall bias. For the participant aged less than 18 years, parents were interviewed, preferably those who spend more time with them (for e.g. mother for single parent working and grandparents or elder siblings in case of both parents working). Cases were confirmed dengue fever patients tested through IgM method and hospitalized to any public or private health facilities of the state. Controls were from same living area of cases matched with sex and age (±2 years) with no history of dengue, chikungunya or acute febrile illness in last one year. People having any history of dengue in last one year but were not living in the study area in during that period were excluded from the study. The index date of the matched control was same that of (diagnosis date) cases. All the questions referred to the index date only. The distribution of the cases and controls are given in Fig. 1.
sociodemographic, travel history and exposure, environmental condition and cleanliness practice, disease status of the family. Socio–demographic and household profile section had questions on age, sex, ethnicity (defined as ‘schedule caste/tribe’ or not according to government classification), education (based on the schooling completed), occupation (current and during the disease) and housing conditions. The housing type was categorized according to the type of roof, like asbestos, concrete, thatched or mixed. Travel history section asked questions on travel history during the disease period, duration of travel, place of travel, onset of disease in relation to travel time and purpose of travel. Environmental and sanitation section constituted questions on the presence of mosquito breeding sites, current and past cleaning practices (during the disease period). Reported environmental conditions were validated with the visit made by investigators with a check list. Participant’s houses and gardens, including those of the neighbors (living within 10 ms radius of the house of the study participants) were inspected to check the sources for the stagnant water, one of the favorable breeding sites. The potential dengue breeding sites such as, water storage containers or tanks (whether the containers were tightly covered or not), gutters (collect rainwater), empty receptacles (bottles, cans, tires, plastic containers, coconut shells etc.), presence of any cattle shed was noted by the investigators. The heads of families (cases, controls and the respective neighbors) who were asked to participate in the study permitted inspection of their houses, courtyards and gardens and voluntarily gave all information requested. Final approved tool was created in epi collect+ software and installed in two tablets which was used during data collection. We used digital data collection methods to obtain accurate geocoordinates of the houses, easy data management and real time monitoring. Ten percent of the randomly picked data from total was cross checked by the principal investigator. Members of the research team had access to collected data through unique password. Ethical considerations
Data collection procedures For the data collection, cases and controls were interviewed with the help of a questionnaire designed for the study in vernacular language (Odia). The tool was tested for validity and reliability and reported Cronbach’s alpha (0.76) for all the items. Based on the responses from pilot study, necessary modification was done. The survey questionnaire comprised four sections namely;
The study was approved by state research and ethics committee, Department of Health and Family Welfare, Government of Odisha, India (letter number 141/SHRMU). Participants were explained about the nature and the purpose of the study. Anonymity and confidentiality of information provided were guaranteed. Before the commencement of the interview, their right to refuse or withdraw from the interview at any time during the process was clearly
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4 Table 2 Environmental practices [univariate analysis]. Environmental condition during the index date
Control N = 380% [95% CI]
Case N = 387% [95% CI]
Total N = 767% [95% CI]
Crude OR [95% CI]
Presence of swampy area surrounding home Participants clean the swampy Area Presence of breeding sites surrounding home Presence of container breeding surrounding home Participants clean the breeding sites Presence of the construction sites
71.0 [66.3–75.4]
78.6 [74.2–82.4]
74.8 [71.6–77.8]
1.5 [1.1–2.0]*
51.1 [46–56.1]
43.7 [38.8–48.7]
47.3 [43.8–50.9]
0.7 [0.5–0.9]
44.5 [39.5–49.5]
55.5 [50.5–60.4]
50.1 [46.5–53.6]
1.6 [1.2–2.1]*
18.4 [14.8–22.7]
29.5 [25.1–34.2]
24.0 [21.1–27.1]
1.8 [1.3–2.6]*
20.0 [16.3–24.3]
22.7 [18.8–27.2]
21.4 [18.6–24.4]
1.2 [0.8–1.8]
18.4 [14.8–22.7]
21.4 [17.6–25.8]
19.9 [17.3–22.9]
1.2 [0.8–1.7]
Reference group for all the variables was ‘No’; OR- Odds Ratio; CI- Confidence Interval. * P value < 0.05. Table 3 Travel history during the index date. Variables
Categories
Control N = 380 n[95% CI]
Case N = 387 n[95% CI]
Total N = 767 n[95% CI]
Crude OR [95% CI]
Travelled during the index datea Purposes of the travel
Yes
16.3 [12.9–20.4]
22.2 [18.3–26.7]
19.3 [16.7–22.3]
1.5 [1.1–2.1]*
Never Business Work Other
83.7 [79.6–87.1] 4.5 [2.8–7.1] 4.7 [3–7.4] 7.1 [4.9–10.2]
77.8 [73.3–81.7] 2.1 [1.0–4.1] 12.4 [9.5–16.1] 7.8 [5.5–10.9]
80.7 [77.7–83.3] 3.3 [2.2–4.8] 8.6 [6.8–10.8] 7.4 [5.8–9.5]
Reference 0.5 [0.2–1.2] 3.3 [1.8–6.1]* 1.2 [0.7–2.1]
Did not travel
83.7 [79.6–87.1]
77.8 [73.3–81.7]
80.7 [77.7–83.3]
Reference
Nearby town Cities within the state Out of the state
7.4 [5.1–10.5] 4.7 [3.0–7.4] 4.2 [2.6–6.8]
11.4 [8.6–14.9] 2.1 [1.0–4.1] 8.8 [6.3–12.1]
9.4 [7.5–11.7] 3.4 [2.3–4.9] 6.5 [5.0–8.5]
1.7 [1.1–2.7]* 0.5 [0.2–1.1] 2.4 [1.3–4.7]*
Did not travel
83.7 [79.6–87.1]
77.8 [73.3–81.7]
80.7 [77.7–83.3]
Reference
Within 7 days of getting the disease 7-30 days 1month to 6 months More than one year
8.7 [6.2–12]
14.2 [11.1–18.1]
11.5 [9.4–13.9]
1.8 [1.1–2.8]*
1.3 [0.5–3.1] 2.9 [1.6–5.2] 3.4 [2.0–5.8]
3.1 [1.8–5.4] 1.8 [0.9–3.8] 3.1 [1.8–5.4]
2.2 [1.4–3.5] 2.3 [1.5–3.7] 3.3 [2.2–4.8]
3.2 [1.02–10.1]* 0.80 [0.29–2.1] 1.1 [0.41–2.4]
Place visited during the travel
Period of travel with reference to index date
OR- Odds Ratio; CI- Confidence Interval. * P value < 0.05. a Reference group —‘did not travel’.
explained. Written consent was obtained from each adult participants and parental consent was obtained for study participants aged less than 18 years before the interview.
Results Dataset for analysis In total, 767 individuals (387 cases and 380 controls) from 5 districts participated in the study. Of 387 cases, 380 had age (±2 years) and sex matched controls. Proportion of males (53.3%) were higher than females but not statistically significant. Participants age varied from 3 to 91 year (median age 33 year, IQR 8). The response rate during the study was 97.54% (780 subjects were contacted but only 767 responded positively). Other sociodemographic characteristics is described in Table 1. Significant association was seen with occupation, education and type of housing. Compared to controls, cases (78.6%) had more swampy areas (including gutter) surrounding their home. Nearly, 51% of the control population clean their environment compared to 43.7% cases. Whereas, equal proportion of people in both the groups, were involved in cleaning of wastes surrounding their home. Case house-
hold had more mosquito breeding sites (55.5%) and container breeding empty receptacles (29.5%), which was statistically significant (Table 2). Travel history collected during the survey is provided in Table 3. Nearly 22.2% of cases had travel history during the index date. Of them, 36% had diagnosis before the travelling, whereas, 64% were diagnosed with dengue after returning from the travel. Describing the purposes of travel, 12.4% of the cases travelled for work, whereas, 7.8% travelled for other purposes (including visit to relative home). Of the cases, who travelled, 80% travelled within one month of the disease and 53% travelled to the nearby town (Fig. 2).
Risk factors for dengue outbreaks in Odisha Univariate regression models show ethnicity, occupation, higher education, housing types, environmental factors like presence of swampy area around home, travel history, travelling from outside of the state and having disease before the travelling had statistically significant unadjusted odds ratio (OR) at p value 0.05. Multivariate conditional logistic regression model explains the association with occupation (OR ranged from 3–5), housing (OR ranged from 2–3), travelling during the disease [1.6, 95% CI: 1.1–2.4]
Please cite this article in press as: Swain S, et al. Risk factors for dengue outbreaks in Odisha, India: A case-control study. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.08.015
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Fig. 2. Distribution of the travel details among cases.
5
Percentage in each cell has been calculated with respect to the total people travelled during the disease (86).
and presence of the breeding sites [1.7, 95% CI: 1.2–2.6] were statistically significant after adjusting for other variables.
Discussion Researchers have studied the risk factors of dengue transmission, especially the environmental and climatic associations. More advanced researches use the population dynamics of both human and vector to understand the outbreak. However, most of these population-based studies are based on simulative mathematical models [11]. Few in India have researched the epidemiological determinants for dengue. This is the first ever study done to identify the risk factors in the Eastern India. We found the leading associated factors are (i) occupation demanding travel has nearly three times higher risk; (ii) presence of breeding sites increases the risk by nearly two times and (iii) travel during the disease increases the risk by two times as well. The case-control study is matched for age, gender and small extent of area of living; thus, the identified individual and housing level factors making the estimates real and comparable. We found significant association of dengue with occupation and housing type. Which demonstrates a clear difference in the occupation and housing characteristics of cases compared to controls. The positive association with businesspersons and agriculture/daily worker indicates the jobs demanding travel to other places or nearby towns predisposes them more at risk towards the disease. There is inconsistent evidence on dengue association with occupation [16,22,23]. The reported age group of cases in our study is quite same as reported in other studies from the country [6]. However, the four times high association with students and homemakers needs further investigation. Also, the reporting of the cases within same families need to be studied in detail for better explanation. As there is no difference across the groups observed, our findings indicate the possible source of the disease could be the workplace.
Housing structure is proved to be linked with dengue outbreak. Studies reported that, staying in sheds/old flats creates higher chances of dengue [16]. Especially, densely and nearly located houses increase the dengue spread chances because of the crowding and environmental conditions [24]. I Peri–urban areas of India are crowded, and the houses are closely located. Thus, the outbreak is thought to have affected all the houses irrespective of the structure. But gradient association with the housing structure provides a proxy indication towards the socio–economic condition of the people. People living with thatched houses, may represent lower socio–economic strata compared to others. So, increased dengue from these houses could be because of the low protective measures in practice. Also, the housing characteristics such as windows, curtains, gardens and its relationship with dengue breeding sites cannot be ruled out. This specific aspect needs further investigation in an Indian context. Environmental factors as the risk factor of dengue is well established in different countries [16,25–27]. Interestingly, we found the presence of swampy area and breeding sites increases the dengue outbreak by two times but, the presence of container was not significant. Similar findings were reported other studies [28]. The non-association of the container breeding sites could have two possible explanations. One, there could be lack of knowledge in understanding the ‘container breeding’ sites. Even though the researchers explained the participants with examples, still the under-reporting could have been because of the knowledge gap [29,30]. Another factor could be unorganized and unstructured urbanization as most of the participants were from the peri-urban area, the environmental sanitation conditions are often neglected. Thus, the presence of container breeding sites in surrounding areas instead of the house of the participants which could have been overlooked and under reported. We did find similar responses among school children in our previous study [31]. Presence of breeding sites and their association necessitates a comprehensive prevention strategy for stopping the dengue spread, which is supported by the findings from our study. Dengue is being new to the state,
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Table 4 Conditional logistic regression for association of dengue. Variable
Categories
Adjusted OR [95% CI]
Ethnicity
SC/ST Non SC/ST
Reference 1.4 [0.9–2.1]
Education
Illiterate Primary Secondary High School and above
Reference 0.8 [0.47–1.5] 0.9 [0.54–1.5] 1.4 [0.76–2.8]
Occupation
Unemployed Agriculture/Daily worker Business Home maker/student Industry/Office
Reference 3.8 [1.8–7.9]*
Housing
Asbestos Concrete Mixed Thatched
Reference 1.9 [1.2–3.1]* 2.0 [1.1–3.5]* 2.9 [1.7–5.1]*
Presence of swampy area surrounding home
No
Reference
Yes
1.5 [1.1–2.1]*
No
Reference
Yes
0.5 [0.31–0.67]
No
Reference
Yes
1.5 [0.95–2.3]
Presence of any breeding site
No Yes
Reference 1.7 [1.2–2.6]*
Travelled during the index date
No Yes
Reference 1.6 [1.1–2.4]*
Participants clean the swampy area Presence of container breeding surrounding the home
4.6 [1.9–10.6]* 4.4 [2.3–8.4]* 3.0 [1.4–6.5]*
Variables with significant association in univariate analysis were included in multivariate analysis; OR- Odds Ratio; CI- Confidence Interval; SC- Schedule Caste; STSchedule Tribe. *P value < 0.05.
the lower knowledge about the disease and risk factors cannot be excluded. The travel history among cases during the index date was associated with more risk. This explains the link between the travel and dengue infection, which to some extent gives information on the source of the infection. There is a clear evidence on the travel history and dengue infections both nationally and internationally [32]. Studies from other cities of India also reported similar findings [11]. We found majority of the people got the disease within 7 days of the travel and of them nearly 70% travelled to the nearby cities. Even though we did not enquire about the reason of the travel, the association of occupation and dengue it is an attempt for it. The purposes of the travel could be for business, occupation and visit to other places/houses. A study mapped the epidemic dynamics of the vector borne diseases in villages and city with commuter network found positive association with travel to the cities and acquiring the infection [33]. Studying the age structure in detail we find, nearly 30% of the people are below 25 years and 45% of the cases were women. So, the higher chances of the travel could be to the schools, workplace or to relative’s houses. Stoddard et al. reported the positive association with house–house movement [34]. Similar findings are reported in Brazil [35]. Verma et al. too mentioned the travel history as an essential criterion for suspicion of dengue in India [36]. A mathematical model used similar concept to estimate the outbreak in the western India [37]. Our study has some inherent limitations of case-control study design. To minimize recall bias, we used a pre-tested standardized
questionnaire and limited the recall period to 12 weeks. Interviews were always performed by the same investigators to minimize the inter-observer bias. We reduced the non-response bias by active case recruitment through the health workers. These health workers live in the study areas and have most of the information about the patient. Because of financial constraints and logistic reasons, it was not possible to perform laboratory test for the control group. Some controls accidentally may have been asymptomatic cases, and misclassification bias cannot be excluded. We reduced this bias by excluding rigorously subjects from the control group who presented with fever or any of the symptoms associated with dengue fever. For the control group, who was not sure about the questions, help of other family members were taken. Our findings are based on cases reported in 2016 only. Further, expanding the analysis, we found nearly 2/3 of cases developed the dengue after returning from the travel. However, we did not have enough sample size to run any further regression model to estimate the risk. Thus, generalization of the risk factor needs caution because of the change in dengue virus circulation in the state. Conclusions Our study is the first in the Eastern India to explore the risk factors at individual and environmental level. Our findings corroborate the available evidence on dengue transmission and suggest a dynamic pathway. It can hypothesize that, the initial infection of the cases happened at other places than home. The travel history might help the infected person to carry the virus to home and the favorable environmental conditions and mosquitos could have spread the disease. At present, we do not have enough evidence to support our hypothesis, but the future research should explore this aspect. Authors’ contributions SS and RSM conceptualised the study. MB and DB did data collection. SS and MB did data analysis. SS and SP drafted the full paper. RSM and SP revised edited the manuscript. All authors contributed to final development of the article. Funding This study was financially supported by the PHRI-Research Grant awarded by Public Health Foundation of India with the financial support from Department of Science and Technology, Government of India (No. IN-DL220960833674480). Competing interests None declared. Ethical considerations The study was approved by state research and ethics committee, Department of Health and Family Welfare, Government of Odisha, India with letter number 141/SHRMU. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from National Vector Borne Disease Control Cell, Department
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of Health and Family Welfare, Government of Odisha, India but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Department of Health and Family Welfare, Government of Odisha, India. CRediT authorship contribution statement Subhashisa Swain: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Writing - original draft, Writing - review & editing. Minakshi Bhatt: Data curation, Formal analysis, Software. Debasish Biswal: Data curation. Sanghamitra Pati: Investigation, Methodology, Resources, Validation, Writing - review & editing. Ricardo J. Soares Magalhaes: Conceptualization, Methodology, Supervision, Writing - review & editing. Acknowledgments We are thankful to Dr Madan Mohan Pradhan, Joint Director of National Vector Borne Disease Control Cell, Odisha, India for his kind support. The authors acknowledge the support provided by the Directorate of Health Services and National Vector Borne Disease Control Cell, Government of Odisha for providing the data. We thank to all the health workers, medical officers, field investigators and participants for their valuable support. References [1] WHO | Epidemiology [Internet]. [Cited 2019 Mar 13]. Available from: https:// www.who.int/denguecontrol/epidemiology/en/. [2] Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis 2016;16:712–23. [3] Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature 2013;496:504–7. [4] Bhatia R, Dash A, Sunyoto T. Changing epidemiology of dengue in South-East Asia. WHO South-East Asia J Public Health 2013;2:23. [5] Chakravarti A, Arora R, Luxemburger C. Fifty years of dengue in India. Trans R Soc Trop Med Hyg 2012;106:273–82. [6] Ganeshkumar P, Murhekar MV, Poornima V, Saravanakumar V, Sukumaran K, Anandaselvasankar A, et al. Dengue infection in India: a systematic review and meta-analysis. PLoS Negl Trop Dis 2018;12:e0006618. [7] NVBDCP | National Vector Borne Disease Control Programme [Internet]. [Cited 2017 Dec 25]. Available from: http://nvbdcp.gov.in/den-cd.html. [8] Mishra B, Turuk J, Sahu SJ, Khajuria A, Kumar S, Dey A, et al. Co-circulation of all four dengue virus serotypes: first report from Odisha. Indian J Med Microbiol 2017;35:293–5. [9] Akter R, Hu W, Naish S, Banu S, Tong S. Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence. Trop Med Int Health 2017;22:656–69. [10] Mutheneni SR, Morse AP, Caminade C, Upadhyayula SM. Dengue burden in India: recent trends and importance of climatic parameters. Emerg Microbes Infect 2017;6:e70. [11] Enduri MK, Jolad S. Dynamics of dengue disease with human and vector mobility. Spatial Spatio-Temporal Epidemiol 2018;25:57–66. [12] Sirisena P, Noordeen F, Kurukulasuriya H, Romesh TA, Fernando L. Effect of climatic factors and population density on the distribution of dengue in Sri Lanka: a GIS based evaluation for prediction of outbreaks. PLoS One 2017;12:e0166806. [13] Arunachalam N, Murty US, Kabilan L, Balasubramanian A, Thenmozhi V, Narahari D, et al. Studies on dengue in rural areas of Kurnool District, Andhra Pradesh, India. J Am Mosq Control Assoc 2004;20:87–90. [14] Biswas D, Bhunia R, Basu M. Dengue fever in a rural area of West Bengal, India, 2012: an outbreak investigation. WHO South-East Asia J Public Health 2014;3:46. [15] Singru S, Bhosale S, Debnath D, Fernandez K, Pandve H. Study of knowledge, attitude and practices regarding dengue in the urban and rural field practice area of a tertiary care teaching hospital in Pune, India. Med J Dr Patil Univ 2013;6:374.
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[16] Chen B, Yang J, Luo L, Yang Z, Liu Q. Who is vulnerable to dengue fever? A community survey of the 2014 outbreak in Guangzhou, China. Int J Environ Res Public Health 2016;13:712. [17] Swain S, Bhatt M, Pati S, Soares Magalhaes RJ. Distribution of and associated factors for dengue burden in the state of Odisha, India during 2010–2016. Infect Dis Poverty 2019 [cited 2019 May 22];8. Available from: https://www.ncbi.nlm. nih.gov/pmc/articles/PMC6501402/. [18] Padhi S, Dash M, Panda P, Parida B, Mohanty I, Sahu S, et al. A three year retrospective study on the increasing trend in seroprevalence of dengue infection from southern Odisha, India. Indian J Med Res 2014;140:660–4. [19] Mishra S, Ramanathan R, Agarwalla SK. Clinical profile of dengue fever in children: a study from Southern Odisha, India. Scientifica 2016;2016 [cited 2017 Dec 25]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC4860230/. [20] Rao MRK, Padhy RN, Das MK. Episodes of the epidemiological factors correlated with prevailing viral infections with dengue virus and molecular characterization of serotype-specific dengue virus circulation in eastern India. Infect Genet Evol 2018;58:40–9. [21] Sahu SK, Pasupalak S, Mohanty I, Narasimham MV. Recent trends of seroprevalence of dengue in a tertiary care hospital in Southern Odisha. J Clin Diagn Res 2018;12(01) [cited 2018 Feb Available from: http://jcdr.net/article fulltext.asp?issn=097321]; 709x&year=2018&volume=12&issue=1&page=DC05&issn=0973709x&id=11138. [22] Harapan H, Rajamoorthy Y, Anwar S, Bustamam A, Radiansyah A, Angraini P, et al. Knowledge, attitude, and practice regarding dengue virus infection among inhabitants of Aceh, Indonesia: a cross-sectional study. BMC Infect Dis 2018;18 [cited 2019 Mar 11]. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-018-3006z. [23] Koyadun S, Butraporn P, Kittayapong P. Ecologic and sociodemographic risk determinants for dengue transmission in urban areas in Thailand. Interdiscip Perspect Infect Dis 2012;2012:1–12. [24] Seidahmed OME, Lu D, Chong CS, Ng LC, Eltahir EAB. Patterns of urban housing shape dengue distribution in Singapore at neighborhood and country scales: dengue, housing, and drainage in Singapore. GeoHealth 2018;2:54–67. [25] Schmidt W-P, Suzuki M, Thiem VD, White RG, Tsuzuki A, Yoshida L-M, et al. Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis. PLoS Med 2011;8:e1001082. [26] Kajeguka DC, Msonga M, Schiøler KL, Meyrowitsch DW, Syrianou P, Tenu F, et al. Individual and environmental risk factors for dengue and chikungunya seropositivity in North-Eastern Tanzania. Infect Dis Health 2017;22:65–76. [27] Sharmin S, Viennet E, Glass K, Harley D. The emergence of dengue in Bangladesh: epidemiology, challenges and future disease risk. Trans R Soc Trop Med Hyg 2015;109:619–27. [28] Nagpal BN, Gupta SK, Shamim A, Vikram K, Srivastava A, Tuli NR, et al. Control of Aedes aegypti breeding: a novel intervention for prevention and control of dengue in an endemic zone of Delhi, India. PLoS One 2016;11:e0166768. [29] Kohli C, Kumar R, Meena G, Singh M, Ingle G. A study on knowledge and preventive practices about mosquito borne diseases in Delhi. MAMC J Med Sci 2015;1:16. [30] Kumar V, Rathi A, Lal P, Goel S. Malaria and dengue: knowledge, attitude, practice, and effect of sensitization workshop among school teachers as health educators. J Fam Med Prim Care 2018;7:1368. [31] Swain S, Pati S, Pati S. ‘Health Promoting School’ model in prevention of vector-borne diseases in Odisha: a pilot intervention. J Trop Pediatr Jan. 21 2019; [cited 2019 Mar 11]; Available from: https://academic.oup.com/tropej/ advance-article/doi/10.1093/tropej/fmy077/5298566. [32] Wichmann O, Jelinek T. Dengue in travelers: a review. J Travel Med 2006;11:161–70. [33] Mpolya EA, Yashima K, Ohtsuki H, Sasaki A. Epidemic dynamics of a vectorborne disease on a villages-and-city star network with commuters. J Theor Biol 2014;343:120–6. [34] Stoddard ST, Forshey BM, Morrison AC, Paz-Soldan VA, Vazquez-Prokopec GM, Astete H, et al. House-to-house human movement drives dengue virus transmission. Proc Natl Acad Sci 2013;110:994–9. [35] da Silva-Nunes M, de Souza VAF, Pannuti CS, Speranc¸a MA, Terzian ACB, Nogueira ML, et al. Risk factors for dengue virus infection in rural Amazonia: population-based cross-sectional surveys. Am J Trop Med Hyg 2008;79:485–94. [36] Verma S, Kanga A, Singh D, Verma GK, Mokta K, Ganju SA, et al. Emergence of travel: associated dengue fever in a non-endemic, hilly state. Adv Biomed Res 2014;3 [cited 2018 Feb 21]. Available from: https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC4260275/. [37] Enduri MK, Jolad S. Estimation of reproduction number and non stationary spectral analysis of dengue epidemic. Math Biosci 2016;288:140–8.
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Journal of Infection and Public Health journal homepage: http://www.elsevier.com/locate/jiph
Risk factors for dengue outbreaks in Odisha, India: A case-control study Subhashisa Swain a,c,∗ , Minakshi Bhatt a , Debasish Biswal b , Sanghamitra Pati d , Ricardo J. Soares Magalhaes e,f a
Indian Institute of Public Health-Bhubaneswar, Public Health Foundation of India, Odisha, India Department of Biotechnology, Ravenshaw University, Cuttack, Odisha, India c School of Medicine, University of Nottingham, Nottingham, United Kingdom d Regional Medical Research Center, Indian Council of Medical Research, Odisha, India e UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton 4343 QLD, Australia f UQ Child Health Research Centre, Children’s Health and Environment Program, The University of Queensland, South Brisbane 4101 QLD, Australia b
a r t i c l e
i n f o
Article history: Received 21 May 2019 Received in revised form 13 August 2019 Accepted 25 August 2019 Keywords: Dengue Outbreak Epidemiology Odisha India
a b s t r a c t Background: Environmental and climatic risk factors of dengue outbreak has been studied in detail. However, the socio–epidemiological association with the disease is least explored. The study aims to identify the social and ecological factors associated with emerging dengue in Odisha, India. Methods: A population-based case-control study (age and sex matched at the ratio of 1:1) was conducted in six districts of the state in 2017. A structured validated questionnaire was used to collect information for each consenting participant. An ecological household survey was done using a checklist during the month of July–September. Along with the descriptive statistics, conditional logistic regression model was used to calculate the adjusted odds ratio using STATA. Results: Of 380 cases, nearly 55% were male and the median age was 33 years. The adjusted odds of having dengue was nearly three times higher among the people having occupation which demands long travel, presence of breeding sites (1.7; 95% CI 1.2–2.6), presence of swampy area near home (1.5; 95% CI 1.1–2.1) and having travel history close to the index date (1.6; 95% CI 1.1–2.4). People staying in thatched houses had three times higher risk of the disease, however, households keeping the swampy areas clean had 50% less risk for the disease (0.5; 95% CI 0.31–0.67). Nearly 22.2% of cases had a travel history during the index date. Of them, 36% had diagnosis before the travel, whereas, 64% developed the disease after the returning from the travel. Conclusion: Household factors such as occupation and ecological condition of households play important roles in dengue outbreaks in Odisha. However, our study suggests travel/commuting are also essential factors to be considered during disease prevention planning. © 2019 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Dengue, a neglected tropical vector born disease has become challenge to the public health system in the world. The estimated annual dengue burden in the globe is nearly 390 million cases from
∗ Corresponding author at: School of Medicine, University of Nottingham, United Kingdom. E-mail addresses: [email protected] (S. Swain), [email protected] (M. Bhatt), [email protected] (D. Biswal), [email protected] (S. Pati), [email protected] (R.J. Soares Magalhaes).
120 countries [1]. However, these estimates can be higher due to large number of under-reporting and non-symptomatic cases. Most of the notified cases are concentrated in the South East Asia (SEA) and Western Pacific Regions of the World Health Organization (WHO) [2,3]. In the SEA, countries like Thailand, Indonesia, India contribute in large extent to the burden [4]. Since the mid-1990s, dengue epidemic episodes in India have grown rapidly and have become more frequent [5]. Currently India is endemic for both dengue fever (DF) and dengue hemorrhagic fever (DHF). Initially the infection was geographically limited to a few states, which later expanded to most of the states in the country [6]. Odisha, an Eastern state of the country historically did not have any reported cases of the dengue till 2009, but now
https://doi.org/10.1016/j.jiph.2019.08.015 1876-0341/© 2019 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Table 1 Sociodemographic description [Univariate analysis]. Variables
Category
Control N = 380% [95% CI]
Case N = 387% [95% CI]
Total N = 767% [95% CI]
Crude OR [95% CI]
Sex
Men Women
54.5 [49.4–59.4] 45.5 [40.6–50.6]
52.2 [47.2–57.1] 47.8 [42.9–52.8]
53.3 [49.8–56.8] 46.7 [43.2–50.2]
Matched
Age group
<= 15 years 16–25 years 26–35 years 36–45 years >= 46 years
12.6 [9.6–16.4] 20.0 [16.3–24.3] 23.9 [19.9–28.5] 21.3 [17.5–25.7] 22.1 [18.2–26.6]
12.7 [9.7–16.4] 23.3 [19.3–27.7] 22.2 [18.3–26.7] 18.6 [15.0–22.8] 23.3 [19.3–27.7]
12.6 [10.5–15.2] 21.6 [18.9–24.7] 23.1 [20.2–26.2] 19.9 [17.3–22.9] 22.7 [19.9–25.8]
Matched
Education
No schooling Primary Secondary High School and above
19.5 [15.8–23.8] 20.0 [16.3–24.3] 47.6 [42.6–52.7] 12.9 [9.9–16.7]
14.7 [11.5–18.6] 19.1 [15.5–23.4] 42.4 [37.5–47.4] 23.8 [19.8–28.3]
17.1 [14.6–19.9] 19.6 [16.9–22.5] 45 [41.5–48.5] 18.4 [15.8–21.3]
Reference 1.2 [0.7–1.9] 1.2 [0.8–1.8] 2.5 [1.5–4.2]*
Ethnicity
Non SC/ST SC/ST
22.6 [18.7–27.1] 77.4 [72.9–81.3]
15.5 [12.2–19.5] 84.5 [80.5–87.8]
19.1 [16.4–22.0] 81.0 [78.0–83.6]
Reference 1.6 [1.1–2.3]*
Occupation
Unemployed Agriculture/Daily worker Business Home maker/student Industry/Office
22.4 [18.4–26.8] 15.3 [11.9–19.2] 7.6 [5.3–10.8] 36.6 [31.9–41.6] 19.1 [14.6–22.4]
9.8 [7.2–13.2] 19.4 [15.7–23.6] 8.8 [6.3–12.1] 46.5[41.6–51.5] 15.5 [12.2–19.5]
16.1 [13.6–18.8] 17.3 [14.8–20.2] 8.2 [6.5–10.4] 41.6 [38.1–45.1] 16.8 [14.3–19.6]
Reference 3.9 [2.1–7.5]* 3.5 [1.7–7.4]* 4.6 [2.6–8.1]* 2.7 [1.4–5.2]*
Housing type
Asbestos roof Concrete roof Mixed roof Thatched roof
32.4 [27.8–37.2] 30.3 [25.8–35.1] 18.4 [14.8–22.6] 18.9 [15.3–23.2]
19.9 [16.2–24.2] 34.6 [30.1–39.5] 17.8 [14.3–21.9] 27.6 [23.4–32.3]
26.0 [23.1–29.3] 32.5 [29.2–35.9] 18.1 [15.5–21.0] 23.3 [20.5–26.5]
Reference 1.9 [1.3–2.9]* 1.6 [1.1–2.6]* 2.6 [1.7–4.1]*
SC- Schedule caste, ST- Schedule Tribe; OR- Odds Ratio; CI- Confidence Interval. * P value < 0.05.
there is wide reporting from all the districts. According to the surveillance data, the state now contributes nearly 10–15% total dengue cases of the country [7]. These cases are scattered in distribution and uneven with circulation of four serotypes of the virus [8]. The transmission of dengue is dependent on various macro and micro level factors such as temperature, humidity, rainfall, the population density, movement, immunity and virus load, urbanization, environmental factors and socio-demographic and economic factors [4,9,10]. These influence the spread of the disease through increased Aedes aegypti mosquito population, transmission of the vector, spreading of the disease and practices of protection mechanism. Vector dynamics [11], population movement [12] and their association with the dengue outbreak is well searched. Climatic and environmental factors create the most favorable conditions for dengue transmission and mostly studied. But investigating the socio-demographic and other individual factors is equally important for identifying the people at risk. Especially, with current shift in the disease pattern it has become essential to study the mentioned factors in detail. Studies say, dengue is no more a disease of women, children and urban problem [13–15]. Community and individual practices can prevent or increase the chances of the disease depending on the nature of practices. Potential socio–demographic and ecological factors can spread dengue to other regions due to increasing frequency of transportation and travel [16]. Even though environmental factors are crucial for the disease epidemic, a comprehensive prevention mechanism would require information at individual level. Especially, in country like India, where diversity and differences are predominant, studying these factors is essential. The recent spike of cases in the state Odisha supports the hypothesis of availability of favorable conditions for the disease spread [17]. Moreover, recurring dengue epidemics also creates hyperendemic areas, typically large, densely populated areas where several or all four serotypes dengue virus circulate in a sustained fashion. In the state, increased numbers of cases are reported among children and adults [8,18,19]. The available literature is limited to the epidemiological profile of the clinical cases, disease
burden and studying the serotypes [20,21]. Till date, there is scant of evidence on social epidemiology of the disease in the state of Odisha. We aimed to identify the socio–demographic and ecological factors associated with dengue outbreaks in the state of Odisha, India through population-based case-control study design. Methods Sample size calculation For estimating sample size and testing of the study tool, a pilot study was conducted among 30 subjects (10 cases and 20 controls) from non-study area. Findings from the pilot study was used for sample size calculation in the absence of relevant literature on risk of travel for dengue. To detect minimum 10% difference of travel exposure to endemic area among case (25%) and control (15%) of 1:1 ratio, with a power of 90% at a significant level of 0.05, the required sample size was calculated as 354 in each group. Considering 10% non-response rate, total required sample in each group was 393. Proportionate sampling method was used to recruit patients from five districts, based on the disease burden in the year 2016. Within each district, patients were selected randomly from the available list of cases obtained from the health department. The study was conducted during the rainy seasons from July to September 2017 with additional purpose of inspecting the environmental conditions (Table 4). Study setting The eastern state of India, Odisha covers about 155,707 sq km of the total area of the country and has nearly 48,903 km2 of forests spread over 31.41% of the state’s total area. The average rainfall in Odisha is measured as 1482 mm, of which 78% is received between the months of June and September of a year. The summer season in Odisha spans from March to June during which the maximum temperature at most of the places goes well above 40 ◦ C. The state projects distinct yet homogeneous features of topography and divided into five morphological units such as mountainous
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Fig. 1. Mapping of case and control samples. Red dots represent cases and green dot represents controls. These are only spot maps, does not represent the numbers exactly.
and highlands region, coastal plains, western rolling uplands, central plateaus and flood plains. According to the 2011 census of India, the total population of Odisha was 41,947,358, of which 21,201,678 (50.54%) are male and 20,745,680 (49.46%) are female with population density of 269 per km2 . The study was carried out in 5 of the 9 high dengue burden districts of the state, which contributes to more than 85% of the total cases. Details of the distribution of dengue has been provided elsewhere [17]. These districts represent nearly, 40% of the total population. Within each district, high burden blocks were selected, based on reported number of cases. This purposive selection was done after consulting with the state nodal officers. Study participants The list of confirmed dengue patients within last one year (2016) recorded at public & private hospitals of Odisha was used for selection of cases. The place of residence at the time of getting disease was chosen as the study areas. This information was obtained from National Vector Borne Disease Control Program (NVBDCP) cell, Odisha, India. We excluded cases, who had disease history of more than one year to avoid the recall bias. For the participant aged less than 18 years, parents were interviewed, preferably those who spend more time with them (for e.g. mother for single parent working and grandparents or elder siblings in case of both parents working). Cases were confirmed dengue fever patients tested through IgM method and hospitalized to any public or private health facilities of the state. Controls were from same living area of cases matched with sex and age (±2 years) with no history of dengue, chikungunya or acute febrile illness in last one year. People having any history of dengue in last one year but were not living in the study area in during that period were excluded from the study. The index date of the matched control was same that of (diagnosis date) cases. All the questions referred to the index date only. The distribution of the cases and controls are given in Fig. 1.
sociodemographic, travel history and exposure, environmental condition and cleanliness practice, disease status of the family. Socio–demographic and household profile section had questions on age, sex, ethnicity (defined as ‘schedule caste/tribe’ or not according to government classification), education (based on the schooling completed), occupation (current and during the disease) and housing conditions. The housing type was categorized according to the type of roof, like asbestos, concrete, thatched or mixed. Travel history section asked questions on travel history during the disease period, duration of travel, place of travel, onset of disease in relation to travel time and purpose of travel. Environmental and sanitation section constituted questions on the presence of mosquito breeding sites, current and past cleaning practices (during the disease period). Reported environmental conditions were validated with the visit made by investigators with a check list. Participant’s houses and gardens, including those of the neighbors (living within 10 ms radius of the house of the study participants) were inspected to check the sources for the stagnant water, one of the favorable breeding sites. The potential dengue breeding sites such as, water storage containers or tanks (whether the containers were tightly covered or not), gutters (collect rainwater), empty receptacles (bottles, cans, tires, plastic containers, coconut shells etc.), presence of any cattle shed was noted by the investigators. The heads of families (cases, controls and the respective neighbors) who were asked to participate in the study permitted inspection of their houses, courtyards and gardens and voluntarily gave all information requested. Final approved tool was created in epi collect+ software and installed in two tablets which was used during data collection. We used digital data collection methods to obtain accurate geocoordinates of the houses, easy data management and real time monitoring. Ten percent of the randomly picked data from total was cross checked by the principal investigator. Members of the research team had access to collected data through unique password. Ethical considerations
Data collection procedures For the data collection, cases and controls were interviewed with the help of a questionnaire designed for the study in vernacular language (Odia). The tool was tested for validity and reliability and reported Cronbach’s alpha (0.76) for all the items. Based on the responses from pilot study, necessary modification was done. The survey questionnaire comprised four sections namely;
The study was approved by state research and ethics committee, Department of Health and Family Welfare, Government of Odisha, India (letter number 141/SHRMU). Participants were explained about the nature and the purpose of the study. Anonymity and confidentiality of information provided were guaranteed. Before the commencement of the interview, their right to refuse or withdraw from the interview at any time during the process was clearly
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4 Table 2 Environmental practices [univariate analysis]. Environmental condition during the index date
Control N = 380% [95% CI]
Case N = 387% [95% CI]
Total N = 767% [95% CI]
Crude OR [95% CI]
Presence of swampy area surrounding home Participants clean the swampy Area Presence of breeding sites surrounding home Presence of container breeding surrounding home Participants clean the breeding sites Presence of the construction sites
71.0 [66.3–75.4]
78.6 [74.2–82.4]
74.8 [71.6–77.8]
1.5 [1.1–2.0]*
51.1 [46–56.1]
43.7 [38.8–48.7]
47.3 [43.8–50.9]
0.7 [0.5–0.9]
44.5 [39.5–49.5]
55.5 [50.5–60.4]
50.1 [46.5–53.6]
1.6 [1.2–2.1]*
18.4 [14.8–22.7]
29.5 [25.1–34.2]
24.0 [21.1–27.1]
1.8 [1.3–2.6]*
20.0 [16.3–24.3]
22.7 [18.8–27.2]
21.4 [18.6–24.4]
1.2 [0.8–1.8]
18.4 [14.8–22.7]
21.4 [17.6–25.8]
19.9 [17.3–22.9]
1.2 [0.8–1.7]
Reference group for all the variables was ‘No’; OR- Odds Ratio; CI- Confidence Interval. * P value < 0.05. Table 3 Travel history during the index date. Variables
Categories
Control N = 380 n[95% CI]
Case N = 387 n[95% CI]
Total N = 767 n[95% CI]
Crude OR [95% CI]
Travelled during the index datea Purposes of the travel
Yes
16.3 [12.9–20.4]
22.2 [18.3–26.7]
19.3 [16.7–22.3]
1.5 [1.1–2.1]*
Never Business Work Other
83.7 [79.6–87.1] 4.5 [2.8–7.1] 4.7 [3–7.4] 7.1 [4.9–10.2]
77.8 [73.3–81.7] 2.1 [1.0–4.1] 12.4 [9.5–16.1] 7.8 [5.5–10.9]
80.7 [77.7–83.3] 3.3 [2.2–4.8] 8.6 [6.8–10.8] 7.4 [5.8–9.5]
Reference 0.5 [0.2–1.2] 3.3 [1.8–6.1]* 1.2 [0.7–2.1]
Did not travel
83.7 [79.6–87.1]
77.8 [73.3–81.7]
80.7 [77.7–83.3]
Reference
Nearby town Cities within the state Out of the state
7.4 [5.1–10.5] 4.7 [3.0–7.4] 4.2 [2.6–6.8]
11.4 [8.6–14.9] 2.1 [1.0–4.1] 8.8 [6.3–12.1]
9.4 [7.5–11.7] 3.4 [2.3–4.9] 6.5 [5.0–8.5]
1.7 [1.1–2.7]* 0.5 [0.2–1.1] 2.4 [1.3–4.7]*
Did not travel
83.7 [79.6–87.1]
77.8 [73.3–81.7]
80.7 [77.7–83.3]
Reference
Within 7 days of getting the disease 7-30 days 1month to 6 months More than one year
8.7 [6.2–12]
14.2 [11.1–18.1]
11.5 [9.4–13.9]
1.8 [1.1–2.8]*
1.3 [0.5–3.1] 2.9 [1.6–5.2] 3.4 [2.0–5.8]
3.1 [1.8–5.4] 1.8 [0.9–3.8] 3.1 [1.8–5.4]
2.2 [1.4–3.5] 2.3 [1.5–3.7] 3.3 [2.2–4.8]
3.2 [1.02–10.1]* 0.80 [0.29–2.1] 1.1 [0.41–2.4]
Place visited during the travel
Period of travel with reference to index date
OR- Odds Ratio; CI- Confidence Interval. * P value < 0.05. a Reference group —‘did not travel’.
explained. Written consent was obtained from each adult participants and parental consent was obtained for study participants aged less than 18 years before the interview.
Results Dataset for analysis In total, 767 individuals (387 cases and 380 controls) from 5 districts participated in the study. Of 387 cases, 380 had age (±2 years) and sex matched controls. Proportion of males (53.3%) were higher than females but not statistically significant. Participants age varied from 3 to 91 year (median age 33 year, IQR 8). The response rate during the study was 97.54% (780 subjects were contacted but only 767 responded positively). Other sociodemographic characteristics is described in Table 1. Significant association was seen with occupation, education and type of housing. Compared to controls, cases (78.6%) had more swampy areas (including gutter) surrounding their home. Nearly, 51% of the control population clean their environment compared to 43.7% cases. Whereas, equal proportion of people in both the groups, were involved in cleaning of wastes surrounding their home. Case house-
hold had more mosquito breeding sites (55.5%) and container breeding empty receptacles (29.5%), which was statistically significant (Table 2). Travel history collected during the survey is provided in Table 3. Nearly 22.2% of cases had travel history during the index date. Of them, 36% had diagnosis before the travelling, whereas, 64% were diagnosed with dengue after returning from the travel. Describing the purposes of travel, 12.4% of the cases travelled for work, whereas, 7.8% travelled for other purposes (including visit to relative home). Of the cases, who travelled, 80% travelled within one month of the disease and 53% travelled to the nearby town (Fig. 2).
Risk factors for dengue outbreaks in Odisha Univariate regression models show ethnicity, occupation, higher education, housing types, environmental factors like presence of swampy area around home, travel history, travelling from outside of the state and having disease before the travelling had statistically significant unadjusted odds ratio (OR) at p value 0.05. Multivariate conditional logistic regression model explains the association with occupation (OR ranged from 3–5), housing (OR ranged from 2–3), travelling during the disease [1.6, 95% CI: 1.1–2.4]
Please cite this article in press as: Swain S, et al. Risk factors for dengue outbreaks in Odisha, India: A case-control study. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.08.015
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Fig. 2. Distribution of the travel details among cases.
5
Percentage in each cell has been calculated with respect to the total people travelled during the disease (86).
and presence of the breeding sites [1.7, 95% CI: 1.2–2.6] were statistically significant after adjusting for other variables.
Discussion Researchers have studied the risk factors of dengue transmission, especially the environmental and climatic associations. More advanced researches use the population dynamics of both human and vector to understand the outbreak. However, most of these population-based studies are based on simulative mathematical models [11]. Few in India have researched the epidemiological determinants for dengue. This is the first ever study done to identify the risk factors in the Eastern India. We found the leading associated factors are (i) occupation demanding travel has nearly three times higher risk; (ii) presence of breeding sites increases the risk by nearly two times and (iii) travel during the disease increases the risk by two times as well. The case-control study is matched for age, gender and small extent of area of living; thus, the identified individual and housing level factors making the estimates real and comparable. We found significant association of dengue with occupation and housing type. Which demonstrates a clear difference in the occupation and housing characteristics of cases compared to controls. The positive association with businesspersons and agriculture/daily worker indicates the jobs demanding travel to other places or nearby towns predisposes them more at risk towards the disease. There is inconsistent evidence on dengue association with occupation [16,22,23]. The reported age group of cases in our study is quite same as reported in other studies from the country [6]. However, the four times high association with students and homemakers needs further investigation. Also, the reporting of the cases within same families need to be studied in detail for better explanation. As there is no difference across the groups observed, our findings indicate the possible source of the disease could be the workplace.
Housing structure is proved to be linked with dengue outbreak. Studies reported that, staying in sheds/old flats creates higher chances of dengue [16]. Especially, densely and nearly located houses increase the dengue spread chances because of the crowding and environmental conditions [24]. I Peri–urban areas of India are crowded, and the houses are closely located. Thus, the outbreak is thought to have affected all the houses irrespective of the structure. But gradient association with the housing structure provides a proxy indication towards the socio–economic condition of the people. People living with thatched houses, may represent lower socio–economic strata compared to others. So, increased dengue from these houses could be because of the low protective measures in practice. Also, the housing characteristics such as windows, curtains, gardens and its relationship with dengue breeding sites cannot be ruled out. This specific aspect needs further investigation in an Indian context. Environmental factors as the risk factor of dengue is well established in different countries [16,25–27]. Interestingly, we found the presence of swampy area and breeding sites increases the dengue outbreak by two times but, the presence of container was not significant. Similar findings were reported other studies [28]. The non-association of the container breeding sites could have two possible explanations. One, there could be lack of knowledge in understanding the ‘container breeding’ sites. Even though the researchers explained the participants with examples, still the under-reporting could have been because of the knowledge gap [29,30]. Another factor could be unorganized and unstructured urbanization as most of the participants were from the peri-urban area, the environmental sanitation conditions are often neglected. Thus, the presence of container breeding sites in surrounding areas instead of the house of the participants which could have been overlooked and under reported. We did find similar responses among school children in our previous study [31]. Presence of breeding sites and their association necessitates a comprehensive prevention strategy for stopping the dengue spread, which is supported by the findings from our study. Dengue is being new to the state,
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Table 4 Conditional logistic regression for association of dengue. Variable
Categories
Adjusted OR [95% CI]
Ethnicity
SC/ST Non SC/ST
Reference 1.4 [0.9–2.1]
Education
Illiterate Primary Secondary High School and above
Reference 0.8 [0.47–1.5] 0.9 [0.54–1.5] 1.4 [0.76–2.8]
Occupation
Unemployed Agriculture/Daily worker Business Home maker/student Industry/Office
Reference 3.8 [1.8–7.9]*
Housing
Asbestos Concrete Mixed Thatched
Reference 1.9 [1.2–3.1]* 2.0 [1.1–3.5]* 2.9 [1.7–5.1]*
Presence of swampy area surrounding home
No
Reference
Yes
1.5 [1.1–2.1]*
No
Reference
Yes
0.5 [0.31–0.67]
No
Reference
Yes
1.5 [0.95–2.3]
Presence of any breeding site
No Yes
Reference 1.7 [1.2–2.6]*
Travelled during the index date
No Yes
Reference 1.6 [1.1–2.4]*
Participants clean the swampy area Presence of container breeding surrounding the home
4.6 [1.9–10.6]* 4.4 [2.3–8.4]* 3.0 [1.4–6.5]*
Variables with significant association in univariate analysis were included in multivariate analysis; OR- Odds Ratio; CI- Confidence Interval; SC- Schedule Caste; STSchedule Tribe. *P value < 0.05.
the lower knowledge about the disease and risk factors cannot be excluded. The travel history among cases during the index date was associated with more risk. This explains the link between the travel and dengue infection, which to some extent gives information on the source of the infection. There is a clear evidence on the travel history and dengue infections both nationally and internationally [32]. Studies from other cities of India also reported similar findings [11]. We found majority of the people got the disease within 7 days of the travel and of them nearly 70% travelled to the nearby cities. Even though we did not enquire about the reason of the travel, the association of occupation and dengue it is an attempt for it. The purposes of the travel could be for business, occupation and visit to other places/houses. A study mapped the epidemic dynamics of the vector borne diseases in villages and city with commuter network found positive association with travel to the cities and acquiring the infection [33]. Studying the age structure in detail we find, nearly 30% of the people are below 25 years and 45% of the cases were women. So, the higher chances of the travel could be to the schools, workplace or to relative’s houses. Stoddard et al. reported the positive association with house–house movement [34]. Similar findings are reported in Brazil [35]. Verma et al. too mentioned the travel history as an essential criterion for suspicion of dengue in India [36]. A mathematical model used similar concept to estimate the outbreak in the western India [37]. Our study has some inherent limitations of case-control study design. To minimize recall bias, we used a pre-tested standardized
questionnaire and limited the recall period to 12 weeks. Interviews were always performed by the same investigators to minimize the inter-observer bias. We reduced the non-response bias by active case recruitment through the health workers. These health workers live in the study areas and have most of the information about the patient. Because of financial constraints and logistic reasons, it was not possible to perform laboratory test for the control group. Some controls accidentally may have been asymptomatic cases, and misclassification bias cannot be excluded. We reduced this bias by excluding rigorously subjects from the control group who presented with fever or any of the symptoms associated with dengue fever. For the control group, who was not sure about the questions, help of other family members were taken. Our findings are based on cases reported in 2016 only. Further, expanding the analysis, we found nearly 2/3 of cases developed the dengue after returning from the travel. However, we did not have enough sample size to run any further regression model to estimate the risk. Thus, generalization of the risk factor needs caution because of the change in dengue virus circulation in the state. Conclusions Our study is the first in the Eastern India to explore the risk factors at individual and environmental level. Our findings corroborate the available evidence on dengue transmission and suggest a dynamic pathway. It can hypothesize that, the initial infection of the cases happened at other places than home. The travel history might help the infected person to carry the virus to home and the favorable environmental conditions and mosquitos could have spread the disease. At present, we do not have enough evidence to support our hypothesis, but the future research should explore this aspect. Authors’ contributions SS and RSM conceptualised the study. MB and DB did data collection. SS and MB did data analysis. SS and SP drafted the full paper. RSM and SP revised edited the manuscript. All authors contributed to final development of the article. Funding This study was financially supported by the PHRI-Research Grant awarded by Public Health Foundation of India with the financial support from Department of Science and Technology, Government of India (No. IN-DL220960833674480). Competing interests None declared. Ethical considerations The study was approved by state research and ethics committee, Department of Health and Family Welfare, Government of Odisha, India with letter number 141/SHRMU. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from National Vector Borne Disease Control Cell, Department
Please cite this article in press as: Swain S, et al. Risk factors for dengue outbreaks in Odisha, India: A case-control study. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.08.015
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of Health and Family Welfare, Government of Odisha, India but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Department of Health and Family Welfare, Government of Odisha, India. CRediT authorship contribution statement Subhashisa Swain: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Writing - original draft, Writing - review & editing. Minakshi Bhatt: Data curation, Formal analysis, Software. Debasish Biswal: Data curation. Sanghamitra Pati: Investigation, Methodology, Resources, Validation, Writing - review & editing. Ricardo J. Soares Magalhaes: Conceptualization, Methodology, Supervision, Writing - review & editing. Acknowledgments We are thankful to Dr Madan Mohan Pradhan, Joint Director of National Vector Borne Disease Control Cell, Odisha, India for his kind support. The authors acknowledge the support provided by the Directorate of Health Services and National Vector Borne Disease Control Cell, Government of Odisha for providing the data. We thank to all the health workers, medical officers, field investigators and participants for their valuable support. References [1] WHO | Epidemiology [Internet]. [Cited 2019 Mar 13]. Available from: https:// www.who.int/denguecontrol/epidemiology/en/. [2] Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis 2016;16:712–23. [3] Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature 2013;496:504–7. [4] Bhatia R, Dash A, Sunyoto T. Changing epidemiology of dengue in South-East Asia. WHO South-East Asia J Public Health 2013;2:23. [5] Chakravarti A, Arora R, Luxemburger C. Fifty years of dengue in India. Trans R Soc Trop Med Hyg 2012;106:273–82. [6] Ganeshkumar P, Murhekar MV, Poornima V, Saravanakumar V, Sukumaran K, Anandaselvasankar A, et al. Dengue infection in India: a systematic review and meta-analysis. PLoS Negl Trop Dis 2018;12:e0006618. [7] NVBDCP | National Vector Borne Disease Control Programme [Internet]. [Cited 2017 Dec 25]. Available from: http://nvbdcp.gov.in/den-cd.html. [8] Mishra B, Turuk J, Sahu SJ, Khajuria A, Kumar S, Dey A, et al. Co-circulation of all four dengue virus serotypes: first report from Odisha. Indian J Med Microbiol 2017;35:293–5. [9] Akter R, Hu W, Naish S, Banu S, Tong S. Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence. Trop Med Int Health 2017;22:656–69. [10] Mutheneni SR, Morse AP, Caminade C, Upadhyayula SM. Dengue burden in India: recent trends and importance of climatic parameters. Emerg Microbes Infect 2017;6:e70. [11] Enduri MK, Jolad S. Dynamics of dengue disease with human and vector mobility. Spatial Spatio-Temporal Epidemiol 2018;25:57–66. [12] Sirisena P, Noordeen F, Kurukulasuriya H, Romesh TA, Fernando L. Effect of climatic factors and population density on the distribution of dengue in Sri Lanka: a GIS based evaluation for prediction of outbreaks. PLoS One 2017;12:e0166806. [13] Arunachalam N, Murty US, Kabilan L, Balasubramanian A, Thenmozhi V, Narahari D, et al. Studies on dengue in rural areas of Kurnool District, Andhra Pradesh, India. J Am Mosq Control Assoc 2004;20:87–90. [14] Biswas D, Bhunia R, Basu M. Dengue fever in a rural area of West Bengal, India, 2012: an outbreak investigation. WHO South-East Asia J Public Health 2014;3:46. [15] Singru S, Bhosale S, Debnath D, Fernandez K, Pandve H. Study of knowledge, attitude and practices regarding dengue in the urban and rural field practice area of a tertiary care teaching hospital in Pune, India. Med J Dr Patil Univ 2013;6:374.
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Please cite this article in press as: Swain S, et al. Risk factors for dengue outbreaks in Odisha, India: A case-control study. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.08.015