Geospatial Health 9(1), 2014, pp. 131-140 Spatio-temporal patterns of dengue in Malaysia: combining address and sub-district level Cheong Y. Ling1,2, Oliver Gruebner3, Alexander Krämer4, Tobia Lakes1 1Geoinformation Science Lab, Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany; 2Medical Research Resource Centre, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, Malaysia; 3Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA; 4Department of Public Health Medicine, University of Bielefeld, Bielefeld, Germany Abstract. Spatio-temporal patterns of dengue risk in Malaysia were studied both at the address and the sub-district level in the province of Selangor and the Federal Territory of Kuala Lumpur. We geocoded laboratory-confirmed dengue cases from the years 2008 to 2010 at the address level and further aggregated the cases in proportion to the population at risk at the sub-district level. Kulldorff’s spatial scan statistic was applied for the investigation that identified changing spatial patterns of dengue cases at both levels. At the address level, spatio-temporal clusters of dengue cases were concentrated at the central and south-eastern part of the study area in the early part of the years studied. Analyses at the sub-district level revealed a consistent spatial clustering of a high number of cases proportional to the population at risk. Linking both levels assisted in the identification of differences and confirmed the presence of areas at high risk for dengue infection. Our results suggest that the observed dengue cases had both a spatial and a temporal epidemiological component, which needs to be acknowl- edged and addressed to develop efficient control measures, including spatially explicit vector control. Our findings highlight the importance of detailed geographical analysis of disease cases in heterogeneous environments with a focus on clustered populations at different spatial and temporal scales. We conclude that bringing together information on the spatio-temporal distribution of dengue cases with a deeper insight of linkages between dengue risk, climate factors and land use constitutes an important step towards the development of an effective risk management strategy. Keywords: dengue, spatial and space-time scan statistics, relative risk, address and aggregation level, Malaysia. Introduction influence of complex climatic issues that vary over space and time. Main determinants include rainfall Dengue fever (DF) and dengue haemorrhagic fever and temperature (Chadee et al., 2007), presence of (DHF) are arguably the most important infectious dis- suitable larval development sites (Vanwambeke et al., eases in tropical areas (Gubler, 2006). Mainly distrib- 2011), behaviour and mobility of humans (Gubler, uted between latitudes 35° N and 35° S (WHO, 1998b) and herd immunity (Halstead, 2008). The dis- 2009a), almost two fifths of the world’s population tribution patterns of dengue cases reflect the multifac- are at risk with about 50 million new cases emerging eted interaction of all these risk factors. The risk of each year (Gubler, 1998a,b; WHO, 2009b). Dengue this disease is expected to significantly increase in the outbreaks commonly occurred in many parts of near future, and the absence of effective treatment Africa, Australia, Latin America, Southeast Asia and makes the development of adequate vector control throughout the Pacific Islands (Rogers et al., 2006). strategies more important than ever (Vanwambeke et Vector control has proven difficult due to the biology al., 2007; Barrera et al., 2011). of the mosquito and its ability to breed in domestic In Malaysia, dengue disease has been endemic since habitats (Eisen and Lozano-Fuentes, 2009). Moreover, the first case was found in Penang Island in 1901 little is known about the true determinants of high (Skae, 1902). Since then, peaks have been reported dengue rates and research gaps include the detailed many times; for example in 1974, 1978, 1982 and 1990, while the total number of infections has increased as well (Lam, 1993). Out of the four Corresponding author: Cheong Y. Ling serotypes, DEN-3 was predominant from 1992 to Geoinformation Science Lab 1995, while DEN-1, DEN-2 and DEN-3 have been Geography Department Humboldt-Universität zu Berlin alternating in recent years (WHO, 2008; Arima and Unter den Linden 6, 10099 Berlin, Germany Tel. +49 30 2093 6827; Fax +49 30 2093 6848 Matsui, 2011). In 2008, a Malaysian study that E-mail: [email protected] included 1,000 individuals aged between 35 and 74 132 C.Y. Ling et al. - Geospatial Health 9(1), 2014, pp. 131-140 years found a seropositive rate for dengue of 91.6% Territory of Kuala Lumpur to study patterns of dengue (Muhammad Azami et al., 2011). The incidence rate since a large number of dengue cases (69,702 cases) per 100,000 was the second highest in the Western occurred there between 2008 and 2010; indeed Pacific region in 2010 (Arima and Matsui, 2011). For accounting for 50.9% of all reported cases in the year 2010, the Malaysia Ministry of Health Malaysia according to the Department of Statistics in (MoH) reported a DF incidence rate of 148.7 per Malaysia (DoSM, 2011a). In addition, the area is geo- 100,000 population (MoH, 2011), which far exceeds graphically heterogeneous and includes the strongly the national target of not more than 50 cases per urbanised Kuala Lumpur with 100% urban popula- 100,000 population (MoH, 2010). To the best of our tion as well as surrounding suburban sub-districts and knowledge, the great majority of dengue studies in rural areas with great variations in population density. Malaysia have focused solely on spatial clustering on We aimed to investigate if a combination of two differ- a yearly basis rather than spatial and temporal distri- ent geographical levels would provide new insights on bution of the disease on a daily basis. The latest dengue disease patterns. To our knowledge, this is the national dengue studies were purely spatial studies in first spatio-temporal study combining address and Hulu Langat, a sub-district of Selangor in 2003 (Er et sub-district level with respect to dengue. We mapped al., 2010), in Kuala Lumpur in 2009 (Aziz et al., 2012) the spatio-temporal distribution of dengue risk in and in both Selangor and Kuala Lumpur in 2010 Selangor and Kuala Lumpur by quantifying high-risk (Hassan et al., 2012). clusters of serologically confirmed dengue cases. Effective vector control relies critically on the deter- Specifically, our research goals were: (i) to assess the mination of the proper scale to address dengue virus spatio-temporal patterns of dengue cases at the transmission (Getis et al., 2003; Suaya et al., 2009). address level; (ii) to assess the spatio-temporal patterns Different geographical scales have been suggested, of dengue cases at the sub-district level; and (iii) to ranging from finer scales based on the vector’s flight combine insights from both spatial levels to provide range (Getis et al., 2003; Yoon et al., 2012) to larger better documentation assisting health interventions. scales due to the relative importance the closeness of neighbouring communities and human movement Materials and methods (Shepard et al., 2012). Fine-scale studies, i.e. at the address level, allow for detailed spatio-temporal Study area analysis in linking the incidence to the local environ- ment as the complex process of viral transmission is As seen in Fig. 1, Selangor comprises nine districts: spatially continuous (Morrison et al., 1998; Siqueira- Klang, Petaling, Sepang, Gombak, Sabak Bernam, Junior et al., 2008). However, they do not account for Hulu Langat, Hulu Selangor, Kuala Langat and Kuala the background population at risk of contracting a dis- Selangor, which are further divided into 54 “Mukims” ease and therefore standard risk ratios cannot be cal- or sub-districts, the smallest local governing units. culated. In contrast, aggregation-level studies include Kuala Lumpur is enclosed by the state of Selangor and the baseline risk population (Jeffery et al., 2009) but has eight sub-districts, namely Ampang, Bandar Kuala encounter the “modifiable area unit problem” Lumpur, Batu, Cheras, Kuala Lumpur, Petaling, (MAUP) (Openshaw, 1983) that causes differences in Setapak and Ulu Klang. Both states cover an area of the analytical results of the same dataset compiled 8,222 km2 with a location between 2° 35' N to under different, spatial aggregation levels. Both levels 3° 60' N latitude and 100° 43' E to 102° 5' E longi- provide significant advantages and disadvantages. tude. The study area is thus geographically heteroge- Furthermore, previous studies that explicitly deal with neous varying from Kuala Lumpur to the surrounding comparisons of spatial clustering at two scales have suburban sub-districts and rural areas along the been conducted for various diseases, e.g. cancer at the boundaries of Selangor. The population density varies level of town, zip code and census tract (Sheehan et al., considerably from Kuala Lumpur at 6,891 persons per 2000) or the level of exact coordinates, census block km2 to Selangor at 674 persons per km2 in 2010 group, census tract and town (Gregorio et al., 2005). (DoSM, 2011a). However, these studies did not combine results of the different levels, e.g., the address and aggregation level, Case data and pre-processing where the latter can vary between administrative limi- tations such as province, district, subdistrict, etc. We obtained dengue case data from the Vector Borne We selected the state of Selangor and the Federal Disease Control Division (MoH) that were based on C.Y. Ling et al. - Geospatial Health 9(1), 2014, pp. 131-140 133 Fig. 1. Geographical distribution and population density (2010) in the 62 sub-districts of Selangor and Kuala Lumpur, Malaysia. passive surveillance, i.e. the routine notification of dis- Population and map data eases by the state or local health departments to the health department using standardised reporting forms We used population data at the sub-district level (WHO, 2009a).