Biopsychosocial model is a useful worldview for primary care or family doctors. However, it is often considered as impractical or too complicated. The objective of this study is to review the implementation of the biopsychosocial model in clinical practice, and its contributions to clinical outcomes. Hermeneutic circle literature review was conducted to provide experiential learning in an attempt to understand biopscyhosocial model, first developed by George Engel. Literature search started with review articles in Medline and Scopus as search engines. Citations from previous articles, editorials, and research articles were identified and interpreted in the context of the knowledge derived from all identified relevant articles. The progress of biopsychosocial model has been slow, and primary care doctors do not implement biopsychosocial medicine in their practice, while biomedical thinking and approach are still the dominant model. Biopsychosocial research addressed chronic illnesses and functional disorders as conditions in need for biopsychosocial model implementation. As payment scheme, clinical guidelines and clinical performance indicators are biomedically oriented, there is no incentive for primary care doctors to adopt biopsychosocial model in their practice. Workload and lack of competence in primary care may hinder the implementation of biopsychosocial model. Biopsychosocial model helps primary care doctors to understand interactions among biological and psychosocial components of illnesses to improve the dyadic relationship between clinicians and their patients and multidisciplinary approaches in patient care. Biopsychosocial model potentially improves clinical outcomes for chronic diseases and functional illnesses seen in primary care.

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.

BackgroundChronic 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.MethodsOur 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.ResultsOn 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.ConclusionsOur 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.

IntroductionThe 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).AimsThe 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.MethodsThe 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.ResultsApproximately 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.ConclusionTo improve household health and reduce the inequality across different SES groups, this study emphasizes the inclusion of women's empowerment and community social capital into intervention programs addressing the dual burden of malnutrition.

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.

Leptospirosis is a potential threat to public health. An increasing number of people infected with Leptospira were reported in Bantul District, Yogyakarta special region with a case fatality rate (CFR) of 7.8%. Infected areas in the district have increased from 2 to 15 sub districts. Leptospirosis is caused by Leptospira bacteria and spread by direct contact with infected rodents and indirect contact through contaminated water or soil. Leptospira in rats, water and soil were detected using real-time quantitative polymerase chain reaction (qPCR). The sites of sampled materials were geocoded using Global Positioning System (GPS). Spatial analysis was used to predict the spread of Spira. This study aims to perform the mapping, clustering, and predicting the spread of Leptospira in Bantul Yogyakarta Indonesia. Data were collected from three sub-districts: Sedayu, Sewon and Bantul. The result showed that 38.04% from 368 samples were Spira positive. There were four significant clusters of infection spread source. Spira is predicted to spread in, and out from, Bantul District.

ObjectivesCoronary 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.MethodsThis 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.ResultsThe 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%.ConclusionsBased on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.

Family support and quality of life of diabetes mellitus patients in Panjatan II public health center, Kulon ProgoPurposeThis study aimed to determine the relationship between family support in terms of four dimensions (emotional, appraisal, instrumental, and information) to the quality of life of patients with type 2 diabetes at the health center II Panjatan Kulon Progo regency.MethodsThis research was a cross-sectional analytical study with sample size of 150 patients with diabetes mellitus type 2. Data analysis used Pearson correlation coefficient, independent t-test and simple linear regression tests.ResultsThere were correlations between the presence of family support and complications with the quality of life of diabetes mellitus patients. There were correlations of emotional, awarding, and instrumental dimensions of family support to the quality of life of diabetes mellitus patients.ConclusionIncreased support of emotional dimensions, reward dimensions and instrumental dimensions will improve the quality of life of patients with diabetes mellitus.