Hari Kusnanto

Bringing evidence to policy to achieve health-related MDGs for all : justification and design of the EPI-4 project in China, India, Indonesia, and Vietnam

Background : The Millennium Development Goals (MDGs) are monitored using national-level statistics, which have shown substantial improvements in many countries. These statistics may be misleading, however, and may divert resources from disadvantaged populations within the same countries that are showing progress. The purpose of this article is to set out the relevance and design of the “Evidence for Policy and Implementation project (EPI-4)”. EPI-4 aims to contribute to the reduction of inequities in the achievement of health-related MDGs in China, India, Indonesia and Vietnam through the promotion of research-informed policymaking. Methods : Using a framework provided by the Commission on the Social Determinants of Health (CSDH), we compare national-level MDG targets and results, as well as their social and structural determinants, in China, India, Indonesia and Vietnam. Results : To understand country-level MDG achievements it is useful to analyze their social and structural determinants. This analysis is not sufficient, however, to understand within-country inequities. Specialized analyses are required for this purpose, as is discussion and debate of the results with policymakers, which is the aim of the EPI-4 project. Conclusion : Reducing health inequities requires sophisticated analyses to identify disadvantaged populations within and between countries, and to determine evidence-based solutions that will make a difference. The EPI-4 project hopes to contribute to this goal.

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

Progress towards malaria elimination in Sabang Municipality, Aceh, Indonesia

BackgroundIndonesia has set 2030 as its deadline for elimination of malaria transmission in the archipelago, with regional deadlines established according to present levels of malaria endemicity and strength of health infrastructure. The Municipality of Sabang which historically had one of the highest levels of malaria in Aceh province aims to achieve elimination by the end of 2013.MethodFrom 2008 to 2010, baseline surveys of malaria interventions, mapping of all confirmed malaria cases, categorization of residual foci of malaria transmission and vector surveys were conducted in Sabang, Aceh, a pilot district for malaria elimination in Indonesia. To inform future elimination efforts, mass screening from the focal areas to measure prevalence of malaria with both microscopy and PCR was conducted. G6PD deficiency prevalence was also measured.ResultDespite its small size, a diverse mixture of potential malaria vectors were documented in Sabang, including Anopheles sundaicus, Anopheles minimus, Anopheles aconitus and Anopheles dirus. Over a two-year span, the number of sub-villages with ongoing malaria transmission reduced from 61 to 43. Coverage of malaria diagnosis and treatment, IRS, and LLINs was over 80%. Screening of 16,229 residents detected 19 positive people, for a point prevalence of 0.12%. Of the 19 positive cases, three symptomatic infections and five asymptomatic infections were detected with microscopy and 11 asymptomatic infections were detected with PCR. Of the 19 cases, seven were infected with Plasmodium falciparum, 11 were infected with Plasmodium vivax, and one subject was infected with both species. Analysis of the 937 blood samples for G6PD deficiency revealed two subjects (0.2%) with deficient G6PD.DiscussionThe interventions carried out by the government of Sabang have dramatically reduced the burden of malaria over the past seven years. The first phase, carried out between 2005 and 2007, included improved malaria diagnosis, introduction of ACT for treatment, and scale-up of coverage of IRS and LLINs. The second phase, from 2008 to 2010, initiated to eliminate the persistent residual transmission of malaria, consisted of development of a malaria database to ensure rapid case reporting and investigation, stratification of malaria foci to guide interventions, and active case detection to hunt symptomatic and asymptomatic malaria carriers.

The effect of gender and social capital on the dual burden of malnutrition: a multilevel study in Indonesia.

Introduction The paradoxical phenomenon of the coexistence of overweight and underweight individuals in the same household, referred to as the “dual burden of malnutrition”, is a growing nutrition dilemma in low- and middle-income countries (LMICs). Aims The objectives of this study were (i) to examine the extent of the dual burden of malnutrition across different provinces in Indonesia and (ii) to determine how gender, community social capital, place of residency and other socio-economic factors affect the prevalence of the dual burden of malnutrition. Methods The current study utilized data from the fourth wave of the Indonesian Family Life Survey (IFLS) conducted between November 2007 and April 2008. The dataset contains information from 12,048 households and 45,306 individuals of all ages. This study focused on households with individuals over two years old. To account for the multilevel nature of the data, a multilevel multiple logistic regression was conducted. Results Approximately one-fifth of all households in Indonesia exhibited the dual burden of malnutrition, which was more prevalent among male-headed households, households with a high Socio-economic status (SES), and households in urban areas. Minimal variation in the dual burden of malnutrition was explained by the community level differences (<4%). Living in households with a higher SES resulted in higher odds of the dual burden of malnutrition but not among female-headed households and communities with the highest social capital. Conclusion To improve household health and reduce the inequality across different SES groups, this study emphasizes the inclusion of womens empowerment and community social capital into intervention programs addressing the dual burden of malnutrition.

Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm

Objectives Coronary heart disease is the leading cause of death worldwide, and it is important to diagnose the level of the disease. Intelligence systems for diagnosis proved can be used to support diagnosis of the disease. Unfortunately, most of the data available between the level/type of coronary heart disease is unbalanced. As a result system performance is low. Methods This paper proposes an intelligence systems for the diagnosis of the level of coronary heart disease taking into account the problem of data imbalance. The first stage of this research was preprocessing, which included resampled non-stratified random sampling (R), the synthetic minority over-sampling technique (SMOTE), clean data out of range attribute (COR), and remove duplicate (RD). The second step was the sharing of data for training and testing using a k-fold cross-validation model and training multiclass classification by the K-star algorithm. The third step was performance evaluation. The proposed system was evaluated using the performance parameters of sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), area under the curve (AUC) and F-measure. Results The results showed that the proposed system provides an average performance with sensitivity of 80.1%, specificity of 95%, PPV of 80.1%, NPV of 95%, AUC of 87.5%, and F-measure of 80.1%. Performance of the system without consideration of data imbalance provide showed sensitivity of 53.1%, specificity of 88,3%, PPV of 53.1%, NPV of 88.3%, AUC of 70.7%, and F-measure of 53.1%. Conclusions Based on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.

Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease

Objectives The interpretation of clinical data for the diagnosis of coronary heart disease can be done using algorithms in data mining. Most clinical data interpretation systems for diagnosis developed using data mining algorithms with a black-box approach cannot recognize examination attribute relationships with the incidence of coronary heart disease. Methods This study proposes a system to interpretation clinical examination results for the diagnosis of coronary heart disease based the decision tree algorithm. This system comprises several stages. First, oversampling is carried out by a combination of the synthetic minority oversampling technique (SMOTE), feature selection, and the C4.5 classification algorithm. System testing is done using k-fold cross-validation. The performance parameters are sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV) and the area under the curve (AUC). Results The results showed that the performance of the system has a sensitivity of 74.7%, a specificity of 93.7%, a PPV of 74.2%, an NPV of 93.7%, and an AUC of 84.2%. Conclusions This study demonstrated that, by using C4.5 algorithms, data can be interpreted in the form of a decision tree, to aid the understanding of the clinician. In addition, the proposed system can provide better performance by category.

How is Indonesia coping with its epidemic of chronic noncommunicable diseases? A systematic review with meta-analysis

Background Chronic noncommunicable diseases (NCDs) have emerged as a huge global health problem in low- and middle-income countries. The magnitude of the rise of NCDs is particularly visible in Southeast Asia where limited resources have been used to address this rising epidemic, as in the case of Indonesia. Robust evidence to measure growing NCD-related burdens at national and local levels and to aid national discussion on social determinants of health and intra-country inequalities is needed. The aim of this review is (i) to illustrate the burden of risk factors, morbidity, disability, and mortality related to NCDs; (ii) to identify existing policy and community interventions, including disease prevention and management strategies; and (iii) to investigate how and why an inequitable distribution of this burden can be explained in terms of the social determinants of health. Methods Our review followed the PRISMA guidelines for identifying, screening, and checking the eligibility and quality of relevant literature. We systematically searched electronic databases and gray literature for English- and Indonesian-language studies published between Jan 1, 2000 and October 1, 2015. We synthesized included studies in the form of a narrative synthesis and where possible meta-analyzed their data. Results On the basis of deductive qualitative content analysis, 130 included citations were grouped into seven topic areas: risk factors; morbidity; disability; mortality; disease management; interventions and prevention; and social determinants of health. A quantitative synthesis meta-analyzed a subset of studies related to the risk factors smoking, obesity, and hypertension. Conclusions Our findings echo the urgent need to expand routine risk factor surveillance and outcome monitoring and to integrate these into one national health information system. There is a stringent necessity to reorient and enhance health system responses to offer effective, realistic, and affordable ways to prevent and control NCDs through cost-effective interventions and a more structured approach to the delivery of high-quality primary care and equitable prevention and treatment strategies. Research on social determinants of health and policy-relevant research need to be expanded and strengthened to the extent that a reduction of the total NCD burden and inequalities therein should be treated as related and mutually reinforcing priorities.

The world we want : focus on the most disadvantaged

The global commitment to the Millennium Development Goal (MDG) process has resulted in significant, positive changes in health-related MDGs on the global and country levels since 1990. However, while overall progress has been made, gaps in achievements between and within many countries have not decreased, with the poorest and most disadvantaged communities being the least likely to have benefitted. This is particularly the case in many emerging economies where the gap between the rich and poor, educated and uneducated, and minority and majority ethnic populations is actually increasing. For example, in India, where the Gross National Income in purchasing power parity in 2010 was

Millennium development goal four and child health inequities in indonesia: a systematic review of the literature.

3,468, use of antenatal care services increased by 12% from 1996 to 2008, but only 0.1% among the poor. In Indonesia, infant mortality rates are on the decline in all regions of the country except for the Eastern regions where they remain high. In Vietnam, inequity in home deliveries between poor, rural Kinh (majority) and minority mothers has increased in the last 5 years during a period of rapid economic growth. In urban China, domestic rural-to-urban migrants account for a significant proportion of notified cases of infectious diseases such as tuberculosis, which is mainly associated with the low-income, poor living conditions, limited access to health care and vulnerability to poor health of this population, and their exclusion from benefits for local residents such as health insurance. (Published: 10 April 2013) Citation: Glob Health Action 2013, 6 : 20919 - http://dx.doi.org/10.3402/gha.v6i0.20919

The utilisation of health centre management by tuberculosis team in the effort to increase case finding

Introduction Millennium Development Goal (MDG) 4 calls for reducing mortality of children under-five years by two-thirds by 2015. Indonesia is on track to officially meet the MDG 4 targets by 2015 but progress has been far from universal. It has been argued that national level statistics, on which MDG 4 relies, obscure persistent health inequities within the country. Particularly inequities in child health are a major global public health challenge both for achieving MDG 4 in 2015 and beyond. This review aims to map out the situation of MDG 4 with respect to disadvantaged populations in Indonesia applying the Social Determinants of Health (SDH) framework. The specific objectives are to answer: Who are the disadvantaged populations? Where do they live? And why and how is the inequitable distribution of health explained in terms of the SDH framework? Methods and Findings We retrieved studies through a systematic review of peer-reviewed and gray literature published in 1995-2014. The PRISMA-Equity 2012 statement was adapted to guide the methods of this review. The dependent variables were MDG 4-related indicators; the independent variable “disadvantaged populations” was defined by different categories of social differentiation using PROGRESS. Included texts were analyzed following the guidelines for deductive content analysis operationalized on the basis of the SDH framework. We identified 83 studies establishing evidence on more than 40 different determinants hindering an equitable distribution of child health in Indonesia. The most prominent determinants arise from the shortcomings within the rural health care system, the repercussions of food poverty coupled with low health literacy among parents, the impact of low household decision-making power of mothers, and the consequences of high persistent use of traditional birth attendants among ethnic minorities. Conclusion This review calls for enhanced understanding of the determinants and pathways that create, detain, and overcome inequities in child health in resource constraint settings like Indonesia and the promotion of actionable health policy recommendations and tailored investments.