|Year : 2022 | Volume
| Issue : 1 | Page : 33-37
Spatiotemporal epidemiology of Japanese encephalitis cases of Dibrugarh district from 2014 to 2018: A hospital record-based descriptive study
Monuj Kurmi1, Jenita Baruah2
1 Senior Resident, Department of Community Medicine, Guwahati Medical College and Hospital, Guwahati, India
2 Associate Professor, Department of Community Medicine, Assam Medical College and Hospital, Dibrugarh, India
|Date of Submission||03-Jun-2021|
|Date of Decision||15-Nov-2021|
|Date of Acceptance||19-Nov-2021|
|Date of Web Publication||5-Apr-2022|
Assam Medical College and Hospital, Officer's Mess Hostel, Room No. 108, Dibrugarh - 786 002, Assam
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Since 1976, several JE outbreaks have been reported from most of the districts of Assam. Objectives: The present study aims to conduct a descriptive and spatial analysis to understand the spatiotemporal distribution of JE cases of Dibrugarh district. Methods: Spatiotemporal distribution of JE cases from 2014 to 2018 at village level was described in maps using Geographical Information System. Spatial association between JE reporting places in the study area has been analyzed using spatial statistics analytical techniques. Temporal distribution of JE cases from 2014 to 2018 for different variable were described in tables. Results: During the period 2014–2018, incidence rate of JE cases ranged between 2.7/100,000 and 5.9/100,000 population and highest case fatality rate was 18.3% in 2014. Highest numbers of JE cases were reported in the age group 0–10 years. Most of the JE cases were from rural areas (84.2%). There was a seasonal pattern of JE which peaked in July. There were hotspots around Dibrugarh Municipality area, Duliajan oil town, Hatiali gaon, Naharkatiya chah bagicha, Nagaon Dhadumia gaon, and Nahortoli Tea Estate. Conclusion: On the basis of the study, JE hotspots can be identified that would help health authorities to further investigate and identify the factors responsible for its occurrence.
Keywords: Dibrugarh, geographic information system, hotspots, Japanese encephalitis, spatiotemporal
|How to cite this article:|
Kurmi M, Baruah J. Spatiotemporal epidemiology of Japanese encephalitis cases of Dibrugarh district from 2014 to 2018: A hospital record-based descriptive study. Indian J Public Health 2022;66:33-7
|How to cite this URL:|
Kurmi M, Baruah J. Spatiotemporal epidemiology of Japanese encephalitis cases of Dibrugarh district from 2014 to 2018: A hospital record-based descriptive study. Indian J Public Health [serial online] 2022 [cited 2022 Aug 19];66:33-7. Available from: https://www.ijph.in/text.asp?2022/66/1/33/342586
| Introduction|| |
With the emergence of new diseases, epidemics, and pandemics throughout the globe, it has become increasingly necessary to screen, identify their cause, and monitor them. Observing distribution of a disease or health related characteristics in human population with respect to a certain area or time has proven an important step to begin an epidemiological investigation. Maps represent an ideal tool to visualize, interpret, and translate such data into meaningful evidence for informed decisionmaking. Features of a map in digital format integrated to databases of information can provide a broad range of functions at a single time which can be understood quickly that paper maps, tables, charts, and graphs take to explain in many. Since much of the data generated and used by epidemiologists and health researchers have a spatial dimension, Geographic Information System (GIS) is a perfect tool for the health professionals and administrators in planning and routine management. GIS has been applied for surveillance for many vectorborne diseases to identify the pattern of distribution and risk assessment. Japanese encephalitis (JE) is a vectorborne disease that is endemic in 24 countries of the WHO South East Asia and Western Pacific regions. Developing country reports approximately 50,000 JE cases and 10,000 deaths annually. In a country like India, several JE outbreaks have been reported from most of the districts of Assam since 1976. JE virus is a flavivirus that causes viral encephalitis in human. It is estimated that more than 3 billion people are exposed to the risks of infection and around 67,900 cases of JE occur annually with a case fatality rate (CFR) of 20%–30% and neuropsychiatric sequel in 30%–50% of survivors. Although JE is most commonly seen in children <15 years old, a lot of cases are also observed in middle and older age groups too. JE is endemic in India and is on an increasing trend of incidence. Outbreaks were reported in 15 states in India including Assam. Major JE vectors in India are Culex (Cx.) tritaeniorhynchus, Cx. vishnui, and Cx. pseudovishnui. These vectors are also prevalent in Assam.
Spatial and temporal distribution of an infectious disease like JE enables the exploration of a broad range of determinants that influence disease risk and transmission. So, the present study aims to determine the spatio-temporal distribution of JE cases in the district of Dibrugarh from 2014-2018 using GIS technology.
| Materials and Methods|| |
Dibrugarh district extends from 27°5' 38” N to 27°42' 30” N latitude and 94°33' 46” E to 95°29'8” E longitude. The area stretches from the north bank of the Brahmaputra, which flow a length of 95 km through the northern margin of the district, to the Patkai foothills on the south.
Dibrugarh district has 6 health blocks, namely Borboruah, Nahoroni, Panitola, Tengakhat, Khowang, and Tengakhat; 7 CHCs; 30 PHCs; and 231 SCs. It has 1 municipality board, 6 towns, 2 town committee, 144 tea-garden, and 1362 villages. It has a population of 13, 26, 335 according to census 2011.
Descriptive observational study design.
May 2019 to September 2019.
All the laboratory-confirmed cases of JE from Dibrugarh district that was registered under AES surveillance of Assam Medical College and Hospital (AMCH), Joint Director of Health Services, Dibrugarh and Individual Block level of Dibrugarh district during the period of 2014–2018 were included for analysis.
Data collection and management
JE case data were collected from AES surveillance of AMCH, District AES Surveillance in the Office of Joint Director of Health Services, Dibrugarh, along with Block level AES surveillance of Dibrugarh district from the period 2014–2018. Individual JE case location information in the form of coordinates (latitudes and longitudes) was collected by visiting the places and Dibrugarh district reference maps.
Shape files of the spatial polygons of all the blocks and villages of Dibrugarh District were obtained from Map of India website. The shape file included information of location coordinates of villages, population data of all the blocks and villages, village codes and household data.
Mid-year population of the blocks for last for 5 years were collected from the Health Blocks and the Office of the Joint Director of Health Services, Dibrugarh. Population of all the blocks were integrated yearly to get the mid-year population of Dibrugarh district yearly for 5 years.
Data including monthly mean rainfall of Dibrugarh district from 2014 to 2018 were collected from Regional Meteorological Department, Guwahati.
Annual incidence and mortality rate, age and sex distribution, and the cumulative cases from 2014 to 2018 were presented in tabular form to illustrate the trends and distribution of JE cases.
To observe differences in the JE epidemiological features of different age groups, the JE cases were grouped into seven age categories, namely: 0–10, 11–20, 21–30, 31–40, 41–50, 51–60, and above 60 years.
To describe the distribution of JE cases, individual case data along with coordinates were entered in Excel Sheet and converted to Comma Separated Value format and saved. These data were than integrated to the Quantum Geographical Information System by table joining method and were projected as polygon feature in Georeferenced maps of Dibrugarh district to describe the spatial distribution of JE from 2014 to 2018.
It is important to describe the pattern of spatial distribution of JE reporting villages. The JE cases can be related to the place of occurrence and can be observed for any kind of clustering patterns. The arrangement of the polygons or points can be clustered, dispersed, or random. The neighboring polygons may show similarity or dissimilarity between them and these measurements are summarized to calculate the spatial autocorrelation. A high autocorrelation implies the occurrence of places with higher number of JE cases, and the correlation is attributed to the geographic ordering of the places. Most commonly used spatial autocorrelation statistic is Local Moran's I coefficient.
A spatial interpolation analysis was performed using the centroid of each polygon as a point layer to create spatial weight matrix. An inverse distance weighting method (Spatial Analyst Tools; GeoDa) was used to interpolate the JE cases. A rook contiguity weight file was used to define the spatial relationships using JE cases as the variable of interest.
Spatial clusters of JE-reported cases at the district from 2014 to 2018 were evaluated by using Local Indicators of Spatial Association (LISA) analysis. Local Moran's I coefficient value ranges from –1 to 1. The spatial correlations between the data on a local area unit basis, and the average of neighboring values in the surrounding units, were described on LISA cluster maps.,
Z-score was used to assess the significance of observed spatial correlations, as calculated by Local Moran's I. The spatial correlation of the local area units is considered as significant (α = 0.05) when the Z-score is >1.96 or <–1.96. A highly positive Z-score indicates that the surrounding features have either similarly high values (High–High) or similarly low values (Low–Low), while a low negative Z-score indicates a significant (P < 0.05) spatial outlier (High–Low or Low–High). The clusters of high–high values were labeled as hotspots, the clusters with low-low values were labeled as cold spots, high–low or low–high were labeled as outliers. The spatial statistical analysis software GeoDa (version 1.8) was used to perform LISA analysis and identify the spatial clusters of JE cases in the JE-epidemic areas.
Since the present study is a hospital record-based study, it may not be the true representation of the community. Hospital records are sometimes incomplete or missing (in the present study, 1%–2% of the JE data were found missing). The study also shows limitations in determining the incidence and exact location of JE cases at individual village level.
Ethical clearance was obtained from the Institutional Ethics Committee (Human) of AMCH, Dibrugarh.
IEC ref No.-AMC/EC/PG2060.
Date of approval-March 22, 2019.
Patient consent declaration
The data were collected from AES surveillance of AMCH, Dibrugarh and Office of the Joint Director of Health Services, Dibrugarh, after obtaining necessary permission from the authorities. Patient identity was kept confidential in the study.
| Results|| |
Japanese encephalitis incidence and case fatality rate
From 2014 to 2018, 304 JE cases were reported from Dibrugarh district with incidence ranging between 2.7/100,000 and 5.9/100,000 population [Table 1]. The highest CFR was 18.3% [Table 1]. The highest Incidence rate was observed in 2018 and the lowest in 2016. There was a declining trend of CFR observed with fluctuation in between.
|Table 1: Distribution of incidence rate and case fatality rate of Japanese encephalitis cases from 2014 to 2018|
Click here to view
Age distribution of Japanese encephalitis cases
The highest number of JE cases were reported on the age group 0–10-year age group [Table 2] and the highest fatality was seen in 51–60-year age group [Table 2].
|Table 2: Age distribution and case fatality rate of Japanese encephalitis cases in different age groups from 2014 to 2018|
Click here to view
Sex distribution of Japanese encephalitis cases
Out of 304 JE cases during 2014–2018, 67.1% were males and 32.9% were females.
Temporal pattern of Japanese encephalitis cases
The distribution of JE cases in Dibrugarh district shows a seasonal pattern of occurrence. Although JE cases reported throughout the year, cases increase from June to August with peak in July followed by June and August and decline by September. Highest mean rainfall was observed in the month of August. On calculating Pearson's correlation coefficient (r) between monthly mean rainfall and number of JE cases, the value was found to be 0.643 with P = 0.024. A statistically significant positive correlation was observed between monthly mean rainfall and occurrence of JE cases.
Of the 1378 locations in Dibrugarh district, 191 have reported 304 JE cases during 2014–2018 [Figure 1]A. Single number of JE cases were reported in 68% of the places followed by 2 numbers of JE cases in 20.4% of the places, 3 numbers of JE cases in 6% of the places, 4 numbers of JE cases in 2% of the places, 7 numbers of JE cases in 1% of the places, and 5, 10 numbers of the JE cases in 0.5% of the places each [Table 3]. Out of total, 84.2% of the JE cases were from rural areas [Table 3].
|Figure 1: Panel A: Spatial distribution of cumulative cases of JE during 2014-2018, Panel B: Local Indicator of Spatial Autocorrelation (LISA) cluster map showing clustering pattern of JE cases at village level during the period 2014-2018|
Click here to view
|Table 3: Distribution of Japanese encephalitis cases according to places of origin and geographic distribution|
Click here to view
A high degree of clustering was noted around Dibrugarh Municipality area, Duliajan oil town, Hatiali gaon, Naharkatiya chah bagicha, Nagaon Dhadumia gaon, and Nahortoli Tea Estate [Figure 1]B. On LISA analysis, values were Moran's I = 0.4756, Z score = 4.8217, P = 0.003 at 95% confidence interval.
| Discussion|| |
The incidence rate of JE shows a declining trend till 2016 and then an increasing trend in subsequent years. The range of incidence rate was between 2.7/100,000 and 5.9/100,000 population. The initial decline may be attributed to the vaccination program launched in Assam during 2014 and the incline might be due to spread of disease in new areas, also more numbers of referred cases and increase testing for JE might serve the cause. Similar findings were observed by study done by Singh et al. and Kumari and Joshi.
The range of CFR was 6%–18.3%. This might be the result of early case detection, early referral to the treatment centers, and better case management. Similar trend was also observed in the study conducted by Kumar Pant D et al., Medhi et al., Kumari and Joshi. The highest CFR (34.6%) was found in 51–60-year age group. The reason might be due to gradual decrease in host immune system, increase viral load, and increase duration between onset of disease and medical intervention. The finding of the present study is similar to the study findings of Li et al. and Doi et al.
Most of the JE cases were reported in the less than 10 years age group. Similar findings were also observed in study conducted by Singh et al., Saha et al., Kumar et al. JE cases were mostly reported in males (67.1%) which is similar to the findings of the the study conducted by Mohan et al. and Medhi et al. This might be due to higher exposure of males than females to mosquito vectors and better covering of the body of females, which acted as protective barrier.
In the present study, the peak number of JE cases was observed in the month of July followed by June and August. This was due to highest monthly mean rainfall in the month of July and August which causes accumulation of water in the paddy fields that serves as good breeding environment for vector. The findings of the present study resemble the study findings of Singh et al., Medhi et al., Sharma et al., Handique et al.
The present study shows that JE cases were distributed throughout the district with 84.2% reported from rural areas. Most of the villages (68%) reported a single JE case. Hotspots were observed at Dibrugarh Municipality area and four adjacent places, Duliajan oil town and two adjacent areas, Hatiali gaon and two adjacent areas, Naharkatiya chah bagicha and Nagaon Dhadumia gaon, and Nahortoli Tea Estate. These might be attributed to high population density and conducive factors including agricultural practices, presence of wetlands like paddy fields, and pig farming at the outskirts of town. Similar findings were also reported by Handique et al.
| Conclusion|| |
The study highlights the capacity of geospatial and analytical tools to describe the distribution of JE occurrence spatially and temporally at village level. This type of study helps not only to identify the effectiveness of control and preventive measures being adopted by the health authorities over the time but also helps to prioritize their focus on those areas that show no improvement. Detailed studies of JE distribution and hotspots, its relation to weather parameters, vector dynamics, host population, etc., in the endemic areas, may reveal the critical factors responsible for disease transmission and outbreak.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Choi M. Book review: Spatial analysis in epidemiology. Healthc Inform Res 2013;19:148-9.
Colledge M, Maxwell H, Parker R, Morse DJ. Geographical Information System in general practice: A new tool for needs assesment. J. Informatics in Primary Care 1996;7-11.
Saxena V, Dhole TN. Preventive strategies for frequent outbreaks of Japanese encephalitis in Northern India. J Biosci 2008;33:505-14.
Vajpayee A, Dey PN, Chakraborty AK, Chakraborty MS. Study of the outbreak of Japanese encephalitis in Lakhimpur district of Assam in 1989. J Indian Med Assoc 1992;90:114-5.
Campbell GL, Hills SL, Fischer M, Jacobson JA, Hoke CH, Hombach JM, et al
. Estimated global incidence of Japanese Encephalitis: A systemic review. Bull World Health Organ 2011;89:766-74, 774A-E.
Kabilan L, Rajendra R, Arunachalam N, Ramesh S, Srinivasan S, Samuel PP. Japanese encephalitis in India: An overview. Indian J Pediatr 2004;71:609-15.
Kanojia PC. Ecological study on mosquito vectors of Japanese encephalitis virus in Bellary district, Karnataka. Indian J Med Res 2007;126:152-7.
] [Full text]
Reuben R, Tewari SC, Hiriyan J, Akiyama J. Illustrated keys to species of Culex(Culex) associated with Japanese Encephalitis in Southeast Asia (Diptera: Culicidae). Mosquito Systematics 1994;26:75-96.
Khan SA, Narain K, Handigue R, Dutta P, Mahanta J, Satyanarayana K, et al
. Role of some environmental factors in modulating seasonal abundance of potential Japanese encephalitis vectors in Assam, India. Southeast Asian J Trop Med Public Health 1996;27:382-91.
Lee J, Wong DW. Statistical analysis of geographic information with ArcView GIS and ArcGIS. John Wiley & Sons, Incorporated; 2005.
Impoinvil DE, Solomon T, Schluter WW, Rayamajhi A, Bichha RP, et al
. The spatial heterogeneity between Japanese encephalitis incidence distribution and environmental variables in Nepal. PLoS One 2011;6:e22192.
Anselin L. Local indicators of spatial association LISA. Geogr Anal 1995;27:3-115.
Singh LS, Singh HL, Thokchom N, Manojkumar Singh RK. A descriptive study on prevalence pattern of Japanese encephalitis in State of Manipur. Indian J Med Microbiol 2019;37:235-40.
] [Full text]
Kumari R, Joshi PL. A review of Japanes Encephalitis in Uttar Pradesh, India. WHO South East Asia J Public Health 2012;1:374-95.
Kumar Pant D, Tenzin T, Chand R, Kumar Sharma B, Raj Bist P. Spatio-temporal epidemiology of Japanese encephalitis in Nepal, 2007-2015. PLoS One 2017;12:e0180591.
Medhi M, Saikia L, Patgiri SJ, Lahkar V, Hussain E, Kakati S. Incidence of Japanese Encephalitis amongst acute encephalitis syndrome cases in upper Assam districts from 2012 to 2014: A report from a tertiary care hospital. Indian J Med Res 2015;146:267-71.
Li X, Cui S, Gao X, Wang H, Song M, Li M, et al
. The spatio-temporal distribution of Japanese encephalitis cases in different age groups in Mainland China, 2004-2014. PLoS Negl Trop Dis 2016;10:e0004611.
Doi Y, Adams J, Keefe A, Quereshi Z, Ewan L, Paterson DL. Japanese Encephalitis Outbreak, Yuncheng, China, 2006. Emerg Infect Dis 2007;13:1123-5.
Saha P, Mondal T, Mandal AR, Kundu PK. Trend of Japanese encephalitis in rural areas of West Bengal, India: A retrospective study. IOSR J Dent Med Sci 2018;17:170.
Kumar PD, Tenzin T, Chand R, Sharma BK, Bist PR. Spatio-temporal epidemiology of Japanese encephalitis in Nepal, 2007-2015. PLoS One 2017;12:e0180591.
Mohan DG, Gogoi M, Sarma A. Seroprevalence of Japanese encephalitis amongst cases of acute encephalitis syndrome in a tertiary care centre of north east India: a four year retrospective study. International Journal of Research in Medical Sciences 2019;7:3490-5.
Sharma J, Baruah MK, Pathak A. Epidemiology of Japanese encephalitis cases in Dhemaji district of Assam. South Indian J Biol Sci 2015;1:141-4.
Handique BK, Khan SA, Dutta P, Sarma KK, Chakraborty K, Goswami J, et al
. Forecasting Spatial Distribution of Japanese Encephalitis Occurrence-A Remote Sensing and GIS Based Study in Assam. International Journal of Geoinformatics 2014;10:1-7.
[Table 1], [Table 2], [Table 3]