Monitoring of Key Health Indicators (TRIAMS) at Subdistrict Health Facilities Level in Tsunami- Affected Provinces in Thailand
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REPORT Monitoring of Key Health Indicators (TRIAMS) at Subdistrict Health Facilities Level in Tsunami- affected Provinces in Thailand Prepared by Pratap Singhasivanon Irwin F. Chavez Tanapat Chupraphawan Wanchai Maneebunyang Sutthiporn Prommongkol Sumrerng Prommongkol Phiraphon Chusongsang Surapon Yimsamran Faculty of Tropical Medicine Mahidol University INTRODUCTION The South Asian tsunami of 2004 has claimed thousands of lives and left thousands of families homeless. Affected countries and their respective governments alongside local and international non-governmental organizations have initiated numerous programs for the reconstruction and rehabilitation of the affected areas. Recovery activities are still ongoing and several are still being planned. Figure 1. Tsunami-affected areas in Thailand Of major interest now is to evaluate the progress of recovery 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, the formulation of numerous core and country-specific impact assessment and monitoring indicators under 4 major headings: 1) vital needs, 2) basic social services, 3) infrastructure, and 4) livelihoods. But in order to succeed in monitoring such indicators, a detailed and reliable database is critical which necessitated the conduct of this survey. The contents of this report are based on the survey of health-related TRIAMS indicators. The survey was carried out from Nov 2006 to January 2007 in the 6 tsunami- affected areas in Southern Thailand (Fig 1). SPECIFIC OBJECTIVES: 1. 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 Areas for this survey were confined to the tsunami affected of provinces of Phangnga, Krabi, Phuket, Ranong, Trang and Satun wherein 408 out of the total 1,946 villages were affected1. The proportion of affected districts and subdistricts are listed on the table below. Plates 1 & 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 62.561 22 36.1 379 11430.1 Phangnga 8 5 62.5 49 16 32.7 317 66 20.8 Phuket 3 3 100.0 17 11 64.7 96 59 61.5 Ranong 5 3 60.030 9 30.0 174 47 27.0 Satun 10 4 40.095 16 16.8 713 70 9.8 Trang 7 4 57.136 13 36.1 267 52 19.5 Total 41 24 58.5288 87 30.2 1,946 408 21.0 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 21 Kantrang 22 Palian 23 Sikao 24 Hat Samran Plate 1 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 51 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 61 Bo Hin 18 Thung Maphrao 62 Mai Fat 19 Thai Muang 63 Bang Sak 20 Na Toei 64 Ko Libong 21 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 71 Ko Sukon 28 Sa Khu 72 Tha Kham 29 Choeng Tale 73 Na Thon 30 Kammala 74 Thung Bu Lang 31 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 81 Sakhon 38 Laem Sak 82 Tha Phae 39 Ao Luk Noi 83 Chebilang 40 Khao Khram 84 Ban Khuan 41 Khao Thong 85 Tanyongpo 42 Nong Tale 86 Khlong Khut 43 Ao Nang 87 Puyu 44 Sai Thai - - Plate 2 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. The table below shows the list of identified indicators and their respective sources. Table 2. Sources of Data for Key Health-related Indicators DISTRICT AREA of HEALTH COMMUNITY INDICATOR HEALTH RECOVERY CENTER HOSPITAL OFFICE Demographic Crude mortality rate + + indices Under 5 mortality rate + + % population with access to water from + + an improved source % population without basic sanitation + + facilities Vital needs Measles immunization coverage + + % of low birth weight (LBW) newborns + + % of children <5 who are underweight + + % children <5 who are wasting + + (moderate & severe) No. of hospital beds per 10,000 + population (in-patient and maternity) No. of outpatient + consultations/person/year % children 12-23 months who are fully + + immunized against all antigens No. of health facilities with Emergency Access to basic + Obstetric Care per 10,000 population social services Adequate antenatal coverage (at least 4 + + visits during a pregnancy) % subdistrict covered by outreach psychological support by community + workers % birth attended by skilled birth + + attendant % destroyed/damaged health facilities Infrastructure + rebuilt or rehabilitated Data collection instruments Standardized checklists and record forms translated into Thai language were used to extract the relevant information on the listed health indicators from health centers, district health offices, and community hospitals (ANNEXES I to VI). Data management and analysis Collected data were organized and processed using Microsoft® Access and Microsoft® Excel. Maps were created with ArcView® 3.3 using the ESRI shapefile specification (*.shp). Spatial analysis was performed using GeoDa™ 0.95i. Results are presented through thematic maps and summary statistics. Spatial analyses of the various indicators were carried out. These include the 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. Through the evaluation of how each subdistrict is related to each other in terms of location and contiguity gives rise to numeric spatial weights essential in the calculation of the relevant statistics. Presence of global or overall clustering is given by the Moran’s I with p-values calculated based on conditional permutation as implemented in GeoDa. The level of significance was set at 0.05 under 999 permutations. Moreover, significant local clustering was assessed using local Moran statistics calculated through randomization (using 999 permutations). Subdistricts are then classified into 5 categories: not statistically significant, HIGH-HIGH, LOW-LOW, HIGH-LOW, and LOW-HIGH clusters. In the analysis, a subdistrict with high attribute value contiguous to one or more neighboring areas with similarly high values are classified as HIGH-HIGH clusters. Conversely, areas with low values in proximity to neighboring areas with similarly low values are 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 don’t 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 levels of specific indicators are not distributed homogeneously. DEMOGRAPHIC INDICES Indicators 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 districts range from 1.8 per 1,000 population in Nua Khlong, Krabi to 17 per 1,000 in Muang Ranong, Ranong. On average, the crude death rate in the affected district is at 4.63 + 2.95 per 1,000 (Table 3). Mortality rates of children under 5 years were highest in Hat Samran, Trang with 5 per 1,000. Followed by Muang Ranong, Ranong with 4.6 per 1,000 along with 11 other districts with rates <5 per 1,000. Other remaining districts recorded zero under 5 death rates (Fig 2). Plates 3 to 10 illustrate the subdistrict- level demographic indices in detail. Table 3. Summary Statistics for District-Level Demographic Indices Thailand, 2006 Demographic indices No. of districts Mean SD Min Max Crude death rate 23 4.63 2.95 1.80 17.00 Under 5 death rate 20 0.89 1.47 0 5.00 Figure 2.