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(TRIAMS) at Subdistrict Health Facilities in Tsunami-Affected Provinces in Thailand

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(TRIAMS) at Subdistrict Health Facilities in Tsunami-Affected Provinces in Thailand Monitoring of Key Health Indicators (TRIAMS) at Subdistrict Health Facilities in Tsunami-affected Provinces in Thailand Pratap Singhasivanon 420/6 Ratchawithi Rd., Bangkok 0400, Thailand Tel: 66 (0) 2644 7483; Fax: 66 (0) 2644 4436 Email: [email protected] Irwin F. Chavez Tanapat Chupraphawan Wanchai Maneebunyang Sutthiporn Prommongkol Samrerng Prommongkol Phiraphon Chusongsang Surapon Yimsamran Faculty of Tropical Medicine Mahidol University Faculty oF tropical Medicine, Mahidol university ...REPORT INTRODUCTION he South Asian tsunami of 2004 claimed thousands of lives and left thousands of families homeless. In the affected countries, the respective governments and local and Tinternational non-governmental organizations have initiated numerous programs for the reconstruction and rehabilitation of the affected areas. Recovery activities remain ongoing and several are still being planned. Tsunami-affected Villages : Thailand Figure 1. Location Map of Villages & Health Centers in the 6 Tsunami-affected Provinces in Southern Thailand. 2 REPORT... Monitoring oF Key health indicators (triaMs) at subdistrict health Facilities in tsunaMi-aFFected provinces in thailand Of major interest now is an evaluation of the recovery process in the affected areas. The Tsunami Recovery Impact Assessment and Monitoring Systems (TRIAMS) have been set up to initiate processes and strengthen regional and national mechanisms to facilitate planning for further recovery activities; hence, numerous core and country-specific impact assessment and monitoring indicators have been formulated under 4 major headings: ) vital needs, 2) basic social services, 3) infrastructure, and 4) livelihoods. But, in order to succeed in monitoring such indicators, the existence of a detailed and reliable database is critical. This need prompted the conduct of this survey. The contents of this report are based on a survey of health-related TRIAMS indicators carried out November 2006-January 2007 in the 6 tsunami-affected areas in southern Thailand (Fig ). SPECIFIC OBJECTIVES . To measure the impact of the tsunami on health, using key indicators derived from the TRIAMS. 2. To generate a baseline database of health-related indicators in the tsunami-affected villages. MATERIALS AND METHODS Study area and data collection he areas selected for this survey were confined to the tsunami-affected provinces of Phangnga, Krabi, Phuket, Ranong, Trang, and Satun, wherein 408 of the total ,946 Tvillages were affected. The proportions of the affected districts and subdistricts are listed in Table , below. Plates & 2 show the district and subdistrict boundaries of the tsunami- affected areas. Table 1. Distribution of Tsunami-affected Areas by Administrative Level1 District Subdistrict Village Province Total Affected % Total Affected % Total Affected % Krabi 8 5 62.5 6 22 36. 379 114 30. Phangnga 8 5 62.5 49 16 32.7 37 66 20.8 Phuket 3 3 100.0 17 11 64.7 96 59 6.5 Ranong 5 3 60.0 30 9 30.0 174 47 27.0 Satun 0 4 40.0 95 16 16.8 73 70 9.8 Trang 7 4 57. 36 13 36. 267 52 19.5 Total 4 24 58.5 288 87 30.2 1,946 408 2.0 Faculty oF tropical Medicine, Mahidol university ...REPORT 3 District Boundaries of Tsunami-affected Areas in Thailand ID DISTRICT 1 Muang Krabi 2 Ko Lanta 3 Khlong Thom 4 Ao Luk 5 Nua Khlong 6 Ko Yao 7 Takua Thung 8 Takua Pa 9 Khura Buri 10 Thai Muang 11 Muang Phuket 12 Kathu 13 Thalang 14 Muang Ranong 15 Kapoe 16 Suk Samran 17 Muang Satun 18 Tha Phae 19 Langu 20 Thung Wa 2 Kantrang 22 Palian 23 Sikao 24 Hat Samran 4 REPORT... Monitoring oF Key health indicators (triaMs) at subdistrict health Facilities in tsunaMi-aFFected provinces in thailand Subdistrict Boundaries of Tsunami-affected Areas in Thailand ID SUBDISTRICT ID SUBDISTRICT 1 Sai Daeng 45 Khlong Prasong 2 Pak Nam 46 Nua Khlong 3 Ngao 47 Khlong Khamot 4 Ko Phayam 48 Talingchan 5 Ratchakrut 49 Khlong Khanan 6 Kapoe 50 Ko Sriboya 7 Muang Kluang 5 Khlong Thom Tai 8 Bang Hin 52 Huai Nam Khao 9 Na Kha 53 Khlong Yang 10 Kamphuan 54 Ko Klang 11 Khura 55 Ko Lanta Noi 12 Ko Phra Thong 56 Saladan 13 Bang Wan 57 Ko Lanta Yai 14 Ko Kho Khao 58 Khlong Phon 15 Bang Muang 59 Sai Khao 16 Khukkhak 60 Khao Mai Kaeo 17 Lam Kaen 6 Bo Hin 18 Thung Maphrao 62 Mai Fat 19 Thai Muang 63 Bang Sak 20 Na Toei 64 Ko Libong 2 Khok Kloi 65 Na Klua 22 Lo Yung 66 Kan Tang Tai 23 Khlong Khian 67 Ban Na 24 Ko Yao Noi 68 Hat Samran 25 Ko Yao Yai 69 Tase 26 Phru Nai 70 Suso 27 Mai Khao 7 Ko Sukon 28 Sa Khu 72 Tha Kham 29 Choeng Tale 73 Na Thon 30 Kammala 74 Thung Bu Lang 3 Karon 75 Khon Khlan 32 Rawai 76 Laem Son 33 Chalong 77 Kamphaeng 34 Wichit 78 Pak Nam 35 Ratsada 79 La Ngu 36 Ko Kaeo 80 Ko Sarai 37 Pa Khlok 8 Sakhon 38 Laem Sak 82 Tha Phae 39 Ao Luk Noi 83 Chebilang 40 Khao Khram 84 Ban Khuan 4 Khao Thong 85 Tanyongpo 42 Nong Tale 86 Khlong Khut 43 Ao Nang 87 Puyu 44 Sai Thai - - Faculty oF tropical Medicine, Mahidol university ...REPORT 5 Data on specific key health indicators were collected from local health facilities/offices. As annexed in the TRIAMS workshop report2, data for country indicators vary in terms of their geographic scale (i.e. subdistrict or district levels). To address this, subdistrict data were collected from health centers, while district data were taken from each district’s respective health office and community hospital. Table 2 shows the list of identified indicators and their respective sources. Table 2. Sources of Data for Key Health-related Indicators Area of Indicator Health Community District Recovery Center Hospital Health Office Demographic indices Crude mortality rate + + Under 5 mortality rate + + Vital needs % population with access to water from + + an improved source % population without basic sanitation + + facilities Measles immunization coverage + + % of low birthweight (LBW) newborns + + % of children <5 who are underweight + + % children <5 who are wasting + + (moderate & severe) Access to basic No. of hospital beds per 0,000 + social services population (in-patient and maternity) No. of outpatient + consultations/person/year % children 2-23 months who are fully + + immunized against all antigens No. of health facilities with Emergency + Obstetric Care per 0,000 population Adequate antenatal coverage + + (at least 4 visits during a pregnancy) % subdistrict covered by outreach + psychological support by community workers % of births attended by a skilled + + birth attendant Infrastructure % destroyed/damaged health facilities + rebuilt or rehabilitated Data collection instruments tandardized checklists and record forms translated into the Thai language were used to extract the relevant information on the listed health indicators from health centers, Sdistrict health offices, and community hospitals (Annexes I-VI). 6 REPORT... Monitoring oF Key health indicators (triaMs) at subdistrict health Facilities in tsunaMi-aFFected provinces in thailand Data management and analysis he data collected were organized and processed using Microsoft® Access and Microsoft® Excel. Maps were created with ArcView® 3.3 using the ESRI shapefile Tspecification (*.shp). Spatial analysis was performed using GeoDa™ 0.95i. The results are presented through thematic maps and summary statistics. Spatial analyses of the various indicators were conducted, including identification of local clusters using Moran’s I for spatial autocorrelation at the subdistrict level. The analysis considers the attribute values of each subdistrict and compares it with the same attribute values of other adjacent areas considered as “neighbors”. A “neighbor” in this case is an affected subdistrict that shares a common border with one or more other affected subdistricts. This manner of identifying neighbors is called the rook contiguity. Evaluation of how each subdistrict is related to each other in terms of location and contiguity gives rise to numeric spatial weights essential for calculating the relevant statistics. The presence of global or overall clustering is given by Moran’s I with p-values calculated based on conditional permutation as implemented in GeoDa. The level of significance was set at 0.05 with 999 permutations. Moreover, significant local clustering was assessed using local Moran statistics calculated through randomization (using 999 permutations). Subdistricts were then classified into 5 cluster categories: not statistically significant, HIGH-HIGH, LOW-LOW, HIGH-LOW, and LOW-HIGH. In the analysis, a subdistrict with a high attribute value contiguous to one or more neighboring areas with similarly high values were classified as HIGH-HIGH clusters. Conversely, areas with low values in proximity to neighboring areas with similarly low values were classified as LOW-LOW clusters. HIGH-LOW clusters are areas with high values surrounded by areas with low values, while LOW-HIGH clusters are areas with low values surrounded by areas with high values. HIGH-LOW and LOW-HIGH clusters are also known as spatial outliers, since the neighboring values do not agree with the local values. HIGH-HIGH and LOW-LOW locations indicate positive local spatial autocorrelation (spatial clusters) while HIGH-LOW and LOW-HIGH locations indicate negative local spatial autocorrelation (spatial outliers)4. While spatial clusters may highlight areas where recovery activities need to be strengthened, spatial outliers may also provide insights where the levels of the specific indicators are not distributed homogeneously. Faculty oF tropical Medicine, Mahidol university ...REPORT 7 DEMOGRAPHIC INDICES ndicators, such as crude and under 5 death rates, give an overall picture of the current standards of health in the surveyed areas. Crude death rates in the tsunami-affected Idistricts ranged from .8 per ,000 population in Nua Khlong, Krabi to 7 per ,000 in Muang Ranong, Ranong. On average, the crude death rate in the affected district was 4.63 ± 2.95 per ,000 (Table 3).
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