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SEAJPH Vol 3(2) Access this article online Original research Website: www.searo.who.int/ publications/journals/seajph Clustered tuberculosis incidence Quick Response Code: in Bandar Lampung, Indonesia Dyah WSR Wardani,1 Lutfan Lazuardi,2 Yodi Mahendradhata,2,3 Hari Kusnanto2 ABSTRACT 1Department of Public Health, Background: The incidence of tuberculosis (TB) in the city of Bandar Lampung, Faculty of Medicine, University of Indonesia, increased during the period 2009–2011, although the cure rate for TB Lampung, Jl. S. Brojonegoro No. 1 cases treated under the directly observed treatment, short course (DOTS) strategy Bandar Lampung, Indonesia, in the city has been maintained at more than 85%. Cluster analysis is recognized 2Department of Public Health, as an interactive tool that can be used to identify the significance of spatially Faculty of Medicine, Gadjah Mada grouping sites of TB incidence. This study aimed to identify space–time clusters of University, Jl. Farmako Sekip Utara Yogyakarta, Indonesia, TB during January to July 2012 in Bandar Lampung, and assess whether clustering 3 co-occurred with locations of high population density and poverty. Centre for Tropical Medicine, Faculty of Medicine, Gadjah Mada Methods: Medical records were obtained of smear-positive TB patients who were University, Yogyakarta, Indonesia receiving treatment at DOTS facilities, located at 27 primary health centres and one hospital, during the period January to July 2012. Data on home addresses Address for correspondence: from all cases were geocoded into latitude and longitude coordinates, using global Dr Dyah WSR Wardani, Department of Public Health, positioning system (GPS) tools. The coordinate data were then analysed using Faculty of Medicine, SaTScan. University of Lampung, Indonesia Results: Two significant clusters were identified with P value of 0.05 for the Email: [email protected] primary cluster and 0.1 for the secondary cluster. Clusters occurred in areas with high population density and a high proportion of poor families and poor housing conditions. The short radius of the clusters also indicated the possibility of local transmission of TB. Conclusions: The incidence of TB in Bandar Lampung was not randomly distributed, but significantly concentrated in two clusters. Identification of clusters of TB, together with its etiological factors such as social determinants, and risk factors, can be used to support TB control programmes, particularly those aiming to reach vulnerable populations, and intensified case-finding. Key words: Cluster, housing condition, poor family, population density, tuberculosis INTRODUCTION Development Goals (MDGs), are to halve the prevalence and mortality of TB by 2015 in comparison to their levels in Tuberculosis (TB) is an infectious disease caused by 1990.2–4 Mycobacterium tuberculosis. About one third of the world’s population is infected with M. tuberculosis and the incidence TB control averted up to 6 million deaths and cured 36 million of TB has a great potential to increase.1 Since 1947, the people in 1995–2008. Unfortunately, control programmes World Health Organization (WHO) has been conducting TB had less success in reducing the incidence of TB, which only control through various control programmes, such as mass declined by 0.7% per year during 2004–2008. Moreover, BCG vaccination and improved chemotherapy, management the decline occurred only in some American and European and service programmes, as well as implementation of the countries, none of which are among the 13 WHO high-burden directly observed treatment, short course (DOTS) strategy. countries, which are mainly in sub-Saharan Africa and South- In 2000, WHO initiated the Stop-TB Partnership, in order to East Asia.3,5–7 The incidence of TB globally in 2010 was improve the effectiveness of TB-control programmes globally. estimated to be 8.8 million, which is equivalent to 128 cases The Stop-TB Partnership targets, included in the Millennium per 100 000 in the population. Indonesia was one of the five WHO South-East Asia Journal of Public Health | April–June 2014 | 3 (2) 179 Wardani et al.: Tuberculosis clustering in Indonesia countries with the highest TB incidence in 2010 (0.37–0.54 SaTScan is free software, developed by Martin Kulldorff, that million cases), which was also an increase compared with the can be used to analyse spatial, temporal and space–time data, incidence in 2009 (0.35–0.52 million cases).3,6 using scan statistics to detect disease clusters and determine whether they are statistically significant.14–17 The space–time There is growing consensus that there is a need to “move out spatial scan statistics examine the null hypothesis that the data of the TB box” by supplementing traditional control methods are randomly distributed, against the alternative hypothesis with innovative interventions, including those that target the that the probability of a case being inside a specific zone is social determinants of the disease.8 Social determinants include greater than that of it being outside the zone. This type of weak health systems and poor access to health care, poverty, cluster analysis can be used not only to map TB distribution low education, and inappropriate health-seeking and unhealthy but also to identify potential underlying etiological factors. behaviours. These determinants in turn affect the risk factors: Spatiotemporal statistical analyses therefore have wide (i) active TB cases in the community and overcrowding, applications in TB surveillance and control.14 In this study, which affects high-level contact with infectious droplets; and space–time scan statistics were used to identify statistically (ii) risk factors such as HIV and malnutrition, which impair significant TB cluster locations in Bandar Lampung during host defence.9,10 Socioeconomic status (measured indirectly via January to July 2012 and assess whether clustering showed an assets score) was consistently found to be inversely related any co-occurrence with population density and poverty levels. to TB prevalence in surveys conducted in Bangladesh, Kenya, Philippines and Viet Nam.11 METHODS Bandar Lampung is the capital city of Lampung Province in Indonesia. Based on the Bandar Lampung Municipality Health Bandar Lampung is located in southern part of the Sumatra Office TB reports of 2010 and 2011, although the TB cure rate Island, about 200 km west of the Indonesian capital city of in 2009 and 2010 reached 80–85%, TB case notification in the Jakarta (see Figure 1). Medical records of smear-positive TB city during that period increased, from 112/100 000 population patients who were receiving TB treatment during January in 2009 to 114/100 000 population in 2010.12,13 Spatially, TB to July 2012 were obtained from 27 primary health centres case notification within the city was not equally distributed: and one hospital across the 13 submunicipalities of Bandar some submunicipalities had TB case notification of more than Lampung City. These have been implementing the DOTS 100/100 000 population, but there were also submunicipalities strategy. The time-aggregation period used in this study was with TB case notification below 50/100 000 population. 12,13 3 months, based on the fact that smear-positive TB patients Figure 1: Location of Bandar Lampung 180 WHO South-East Asia Journal of Public Health | April–June 2014 | 3 (2) Wardani et al.: Tuberculosis clustering in Indonesia became smear-negative after 2 months, and the median delay for food, clothing and housing.22 Data on family poverty were for patients with suspected TB (i.e. with a cough for at least taken from Bandar Lampung City in figures 2012.23 2 weeks) presenting at a lung clinic in urban Indonesia is 14 days.18,19 In addition to analysis by population density and proportion of poor families, TB clustering was also analysed in relation to The home addresses of all smear-positive TB patients were housing conditions. checked then geocoded into latitude and longitude coordinates, using global positioning system (GPS) tools. Defined clusters were then layered over a thematic map of identified social Ethical clearance determinants and TB risk factors (population density and proportion of poor families) using geographical information Ethical clearance was approved by the Medical and Health system (GIS) tools. Research Ethics Committee, Faculty of Medicine, Gadjah Mada University. Respondents in this research received written Population density was classified as low (1–50 people/km2), informed consent and signed the form as a confirmation of middle–low (51–250 people/km2), middle–high (251–400 approval. There is no declined from the respondents. people/km2) or high (>400 people/km2).20 However, as there is no submunicipality in Bandar Lampung City with a population density of 251–400 km2 (middle–high), only three RESULTS classifications of population density were used in this research. Distribution of TB in Bandar Lampung The proportion of poor families in this research is the proportion of poor families in each submunicipality level, There were 682 smear-positive TB cases in Bandar Lampung which is classified into four categories: low (<10%), middle– during the period of January to July 2012, distributed among 21 low (10–25%), middle–high (25–40%) and high (>40%). A 13 submunicipalities. Figure 2 shows that the incidence of poor family is defined as one that cannot fulfil its basic needs smear-positive TB cases in Bandar Lampung was not equally Figure 2: Distribution and clustering of TB incidence in Bandar
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