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gh-2018_1.qxp_Hrev_master 16/05/18 10:11 Pagina 127 Geospatial Health 2018; volume 13:642 Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia Gary K.K. Low,1 Panayotis Papapreponis,2 Ridzuan M. Isa,3 Seng Chiew Gan,1 Hui Yee Chee,4 Kian Keong Te,1 Nadia M. Hatta1 1Department of Population Medicine, Faculty of Medicine and Health Sciences (FMHS), University Tunku Abdul Rahman (UTAR), Selangor, Malaysia; 2Laboratory of Microbiology and Immunology, Cancer Hospital of Thessaloniki, Greece; 3Emergency and Trauma Department, Hospital Ampang, Ampang, Selangor, Malaysia; 4Faculty of Medicine and Health Sciences, University Putra Malaysia, Selangor, Malaysia ondary objective to describe the spatio-temporal pattern of all Abstract dengue cases admitted to this hospital. The dengue status of the Increasing numbers of dengue infection worldwide have led to patients was confirmed serologically with the geographic location a rise in deaths due to complications caused by this disease. We of the patients determined by residency, but not more specific than present here a cross-sectional study of dengue patients who the street level. A total of 1165 dengue patients were included in attended the Emergency and Trauma Department of Ampang the analysis using SaTScan software. The mean age of these Hospital, one of Malaysia’s leading specialist hospitals. The patients was 27.8 years,only with a standard deviation of 14.2 years objective was to search for potential clustering of severe dengue, and an age range from 1 to 77 years, among whom 54 (4.6%) were in space and/or time, among the annual admissions with the sec- cases of severe dengue. A cluster of general dengue cases was identified occurring from October to December in the study year of 2015 butuse the inclusion of severe dengue in that cluster was not statistically significant (P=0.862). The standardized incidence Correspondence: Gary K.K. Low, University Tunku Abdul Rahman ratio was 1.51. General presence of dengue cases was, however, (UTAR), Jalan Sungai Long, Bandar Sungai Long, Cheras 43000 detected to be concentrated at the end of the year, which should be Kajang, Selangor, Malaysia. useful for hospital planning and management if this pattern holds. Tel.: 0060123150115 - Fax: 00603.9019.8868. E-mail: [email protected] Key words: Severe dengue; Clusters; Mapping; Outbreak; Epidemics; Introduction Malaysia. An estimated 390 million dengue infections occur annually all See online Appendix for an additional Table. over the world (Bhatt et al., 2013). The World Health Organization (WHO) gives the figure of 500 000 people with Acknowledgements: the authors would like to thank the General severe dengue requiring hospitalization each year, about 2.5% of Director of Health Malaysia for his permission to publish this article. whom die (WHO, 2016). Various studies have investigated the Tok Zhen Yu and Daniel Lee for data collection. geographical distribution of dengue outbreaks in countries where the disease is endemic. (Chang et al., 2009; Anno et al., 2015; Contributions: the authors contributed equally. Rotela et al., 2017; Hafeez et al., 2017). A few recent studies have Non-commercialalso been conducted in Malaysia to evaluate the spatial pattern of Conflict of interest: the authors declare no potential conflict of interest. the disease (Er et al., 2010; Hassan et al., 2012; Ling et al., 2014). Funding: this study was supported by the University Tunku Abdul However, none of the studies focused on potential clustering of Rahman Strategic Research Fund (UTARSRF) programme 2014- severe dengue cases as a way of limiting transmission. C1/010. The epidemiological pattern of dengue transmission has been studied for a long time and statistical modelling has been devel- Received for publication: 25 October 2017. oped to predict future outbreaks (Louis et al., 2014). Yet, public Revision received: 19 January 2018. health interventions have not succeeded in interrupting transmis- Accepted for publication: 19 January 2018. sion of the disease. Interventions such as fogging and larvae inspection in large areas, e.g., all endemic parts of Malaysia, ©Copyright G.K.K. Low et al., 2018 would require enormous manpower. However, the identification Licensee PAGEPress, Italy Geospatial Health 2018; 13:642 of clusters of severe dengue cases may help focusing public health doi:10.4081/gh.2018.642 interventions and thus make them more cost-effective. This approach may also provide predictive information on the specific This article is distributed under the terms of the Creative Commons risk factors, which depend on variations of the geographical land- Attribution Noncommercial License (CC BY-NC 4.0) which permits any scape. Insights on severe dengue cluster patterns could be gained noncommercial use, distribution, and reproduction in any medium, pro- through the study of hospital admissions of dengue patients. vided the original author(s) and source are credited. Understanding the development of dengue outbreaks [Geospatial Health 2018; 13:642] [page 127] gh-2018_1.qxp_Hrev_master 16/05/18 10:11 Pagina 128 Article throughout the year is important for hospital planning and man- agement, as appropriate resources are usually budgeted for and Materials and Methods allocated annually from the central governing body, i.e. the Ministry of Health. Hence, the primary objective of this study Study area and population investigated was to investigate the occurrence of severe dengue clustering, in This is a cross-sectional study of dengue patients attending the space and/or time during one year. The secondary objective was Emergency and Trauma Department of Ampang Hospital between to describe the spatio-temporal pattern of all dengue cases admit- February and December 2015. All data regarding dengue patients ted to the hospital in the same year. It was felt to be useful to were obtained via the department’s census record, which is the carry out the work at Ampang Hospital, a leading specialist hos- most reliable and complete source of data because all patients have pital located in an area endemic for dengue near Kuala Lumpur, to be registered in this department before being admitted or dis- the capital of Malaysia. charged. The Ampang Hospital provides health care services in only use Non-commercial Figure 1. Ampang Jaya. Study area in pink with boundaries in red. [page 128] [Geospatial Health 2018; 13:642] gh-2018_1.qxp_Hrev_master 16/05/18 10:11 Pagina 129 Article Ampang Jaya, a sub-district of Hulu Langat. The Ampang Jaya infections (both severe and non-severe) was compared to what boundary and location is depicted in Figure 1. All cases beyond would have been expected if the spatial and temporal distribution this boundary were excluded. Sporadic cases found outside were independent to each other (i.e. without space-time interac- Ampang Jaya were excluded because they are usually treated by tion). This model included covariates such as age, gender and race other hospitals in surrounding subdistricts. Other excluded cases that were used for adjustments. were people with addresses in other states of Malaysia or coming In the spatio-temporal analyses, we set the range of 200 m as from other countries. All patients included in this study were con- the maximum size of the spatial window radius and 14 days as the firmed positive for dengue infection, either due to the presence of maximum temporal size. These parameters were set in accordance dengue IgM antibodies or NS1 antigen or both. The enzyme-linked with the definition of dengue clusters in Malaysia (Malaysian immunosorbent assay (ELISA) was used by the hospital laboratory Remote Sensing Agency, 2014). The mapping software of the epi- to detect dengue IgM in the blood of patients suspected to be demiological package of Epi Info 7 (Dean et al., 2011) was used to infected. The immunochromatographic NS1 Antigen rapid kit was display results in the form of maps. Google map was used to dis- employed in the Emergency Department and Medical Ward. play the boundaries of the regions under investigation. The level of (WHO/TDR, 2012) Patients were excluded if there was no positive significance for all analyses was set at <0.05. dengue IgM or NS1 antigen test, or an equivocal result of the dengue IgM test had been obtained indicating uncertainty of status. The final diagnosis accepted was based on the clinical practice guidelines recommended by WHO in 2009 (WHO/TDR, 2012). Results The WHO 2009 classification sorts the disease into three sub- groups according to the severity of the infection: dengue without The total number of dengue patients recorded by the Ampang warning sign, dengue with warning signs and severe dengue, Hospital during 2015 were 2379. Among them, 105 cases (4.4%) which is further subcategorised into severe plasma leakage, severe were severe dengue cases.only There were 340 (14.3%) missing data. bleeding and severe organ involvement. Only two diagnoses were After excluding 874 cases originating outside the study area and utilized in this study: severe dengue and non-severe dengue (with the 340 missing data, our study group consisted of 1165 dengue and without warning signs). patients, among whom 54 (4.6%) were classified as severe. Demographic data were extracted along with the correspond- The mean age of the patients (ranging in age from 1 to 77 ing home addresses, which indicated the geographic location of the years) wasuse 27.8 years with a standard deviation (SD) of 14.16. patients. The longitude and latitude of these addresses were deter- Upon stratification into severe and non-severe dengue, the mean mined using Google Maps (2016 version). However, to ensure age was 31.7 years (SD=14.33) and 27.6 years (SD=14.13), confidentiality, the addresses were not followed further than the respectively. Information concerning gender, nationality and race, street level.