Evaluasi Program Terpadu Pengendalian Malaria, Pelayanan Ibu Hamil dan Imunisasi di Kabupaten Hulu Sungai Selatan dan Kota Banjarbaru Provinsi Kalimantan Selatan

Background: To reduce child and maternal mortality, as well as mortality and morbidity of malaria, an integrated malaria control program along with antenatal care and immunization has been implemented through malaria screening and provision of LLIN to pregnant women and the provision of LLIN to children under five who received full immunization. Objective: The objective of this study is to evaluate integrated malaria control program in Hulu Sungai Selatan District and Banjarbaru City, South of Kalimantan Province by exploring input, process and output of the program. Method: The study uses evaluation formative approach using qualitative method with exploratory qualitative design. Data is collected through in-depth interviews, focus group discussion, checklist of observation and documents related to the integrated program. Data analysis was performed with the reduction and presentation of the data, visualization, conclusions, and verification that describe the input, process and output variabels relevant to integrated malaria control program. Result: The dominant challenges in the input are commodity, funds, as well as the organization of integrated programs. Implementation of the integrated program is not optimal in the form of policies, capacity building, QA, supervision, and recording and reporting. The integrated program did not achieve the intended output in terms of LLIN coverage for children under f ive as well as pregnant women ANC coverage (Trimester I and IV). Conclusion: The implementation of integrated malaria control program in general was relatively weak in terms of input, process and output. Adequate inputs and processes to strengthen the implementation of the integrated program are necessary, so it can be one of the exit strategies for malaria control in pregnant women and children under five. Latar Belakang: Dalam upaya menurunkan angka kematian ibu dan anak serta angka kesakitan dan kematian akibat malaria, telah dilaksanakan program terpadu pengendalian malaria, pelayanan ibu hamil dan imunisasi melalui skrining malaria dan pemberian kelambu berinsektisida pada ibu hamil serta pemberian kelambu pada balita yang mendapat imunisasi lengkap.Tujuan: Penelitian ini bertujuan untuk mengevaluasi program terpadu di Kabupaten Hulu Sungai Selatan dan Kota Banjarbaru Provinsi Kalimantan Selatan dengan mengeksplorasi input, proses dan output program. Metode: Penelitian ini merupakan penelitian evaluasi formatif, dengan metode kualitatif dan desain penelitian kualitatif eksploratif. Pengumpulan data dengan wawancara mendalam, diskusi kelompok terarah serta observasi dan checklist dokumentasi. Hasil: Tantangan yang paling besar dan dominan pada input adalah komoditi, dana, serta organisasi program terpadu. Belum optimalnya pelaksanaan proses program terpadu berupa kebijakan, capacity building, QA , supervisi, serta pencatatan dan pelaporan. Tidak tercapainya output program terpadu yaitu cakupan kelambu pada balita dan cakupan kunjungan ANC ibu hamil (K1 atau K4). Kesimpulan: Program terpadu pengendalian malaria, pelayanan kesehatan ibu hamil dan imunisasi belum optimal pada komponen input, proses dan output. Adekuatnya input dan proses dapat memperkuat pelaksanaan program terpadu, sehingga dapat menjadi salah satu exit strategi pengendalian malaria pada ibu hamil dan balita

Plasmodium vivax Malaria Endemicity in Indonesia in 2010

BACKGROUND: Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. METHODS: Plasmodium vivax Annual Parasite Incidence data (2006-2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985-2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1-99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR(1-99) endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface. RESULTS: We estimated 129.6 million people in Indonesia lived at risk of P. vivax transmission in 2010. Among these, 79.3% inhabited unstable transmission areas and 20.7% resided in stable transmission areas. In western Indonesia, the predicted P. vivax prevalence was uniformly low. Over 70% of the population at risk in this region lived on Java and Bali islands, where little malaria transmission occurs. High predicted prevalence areas were observed in the Lesser Sundas, Maluku and Papua. In general, prediction uncertainty was relatively low in the west and high in the east. CONCLUSION: Most Indonesians living with endemic P. vivax experience relatively low risk of infection. However, blood surveys for this parasite are likely relatively insensitive and certainly do not detect the dormant liver stage reservoir of infection. The prospects for P. vivax elimination would be improved with deeper understanding of glucose-6-phosphate dehydrogenase deficiency (G6PDd) distribution, anti-relapse therapy practices and manageability of P. vivax importation risk, especially in Java and Bali

Plasmodium falciparum Malaria Endemicity in Indonesia in 2010

BACKGROUND: Malaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010. METHODS: Plasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006-2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985-2009). After quality control, 2,516 were included into a national database of age-standardized 2-10 year old PfPR data (PfPR(2-10)) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR(2-10) endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface. RESULTS: We estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas. CONCLUSION: While further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies

\u3ci\u3ePlasmodium falciparum\u3c/i\u3e Malaria Endemicity in Indonesia in 2010

Background: Malaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010. Methods: Plasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006–2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985–2009). After quality control, 2,516 were included into a national database of age-standardized 2–10 year old PfPR data (PfPR2–10) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR2–10 endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface. Results: We estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas. Conclusion: While further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching preelimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies

\u3ci\u3ePlasmodium vivax\u3c/i\u3e Malaria Endemicity in Indonesia in 2010

Background: Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. Methods: Plasmodium vivax Annual Parasite Incidence data (2006–2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985–2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1–99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR1–99 endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface

The <i>Plasmodium vivax</i> malaria <i>Pv</i>PR_1–99 endemicity map.

<p>Model-based geostatistical point estimates of the annual mean <i>Pv</i>PR_1–99 for 2010 within the stable spatial limits of <i>P. vivax</i> malaria transmission, displayed as a continuum of light green to red from 0% to 7% (see map legend). Areas within the stable limits in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037325#pone-0037325-g001" target="_blank">Figure 1</a> that were predicted with high certainty (>0.9) to have <i>Pv</i>PR_1–99 less than 1% were classified as unstable areas (shaded medium grey areas). The rest of the land area was defined as unstable risk (medium grey areas, where <i>Pv</i>API<0.1 per 1,000 pa) or no risk (light grey, where <i>Pv</i>API = 0 per 1,000 pa).</p

Area and population at risk of <i>Plasmodium vivax</i> malaria in 2010 throughout the Indonesian archipelago.

<p>No risk, unstable and stable risk areas correspond to <i>Pv</i>API = 0 per 1,000 pa, 0<<i>Pv</i>API<0.1 per 1,000 pa and <i>Pv</i>API≥0.1 per 1,000 pa.</p

Evaluation of model performance.

<p>(A) Scatter plot of actual versus predicted point-values of <i>Pv</i>PR_1–99. (B) Sample semi-variogram of standardized model Pearson residuals estimated at discrete lag and a Monte Carlo envelope (dashed line) representing the range of values expected by chance in the absence of spatial autocorrelation. (C) Probability-probability plot comparing predicted credible intervals with the actual percentage of true values lying inside those intervals. In the top and bottom plots the 1∶1 line is also shown (dashed line) for reference.</p

The distribution of <i>Plasmodium vivax</i> prevalence surveys in Indonesia between 1985 and 2010.

<p>The 4,457 community surveys of <i>P. vivax</i> prevalence conducted between 01 January 1985 and 25 November 2011 are plotted. The survey data are shown in white (<i>Pv</i>PR = 0%), yellow (<i>Pv</i>PR>0%–5%) and red (<i>Pv</i>PR>5%). Areas were defined as stable (dark grey areas, where <i>Pv</i>API≥0.1 per 1,000 pa), unstable (medium grey areas, where <i>Pv</i>API<0.1 per 1,000 pa), or no risk (light grey, where <i>Pv</i>API = 0 per 1,000 pa).</p