Analisis Kesesuaian Klaim dengan Realitas Pembayaran Ppk Rujukan dalam Jaminan Kesehatan Masyarakat Miskin

The main goal of health development in lndonesia is providing quality health care and assuring community access to equal health services for all citizen (universal coverage). In assuring the access to health services for the poor. Ministry of Health has launched special health insurance program for the poor (Askeskin). PT Askes was assigned by MOH to manage this program. Therefore, it is very important to improve facilities and management capabilities of health insurance administering bodies and health care providers. These include, improvement in case management, hospital accounting system, medical record, etc. This will be very useful for verification process and reducing the fraud and abuse. This study was conducted in order to provide valuable input for the Improvement of financing mechanism and payment system of referral providers in health insurance program for the poor (Askeskin). The objectives of this study are to calculate hospital claim on case management of Askeskin members and its real payment by PT Askes and to calculate the differences between claim and reimbursement (real payment) based on hospital components. The study design is cross-sectional. A Stratified Random Sampling method was conducted to select the study sites based on Human Development Index (HOI) and Fiscal Capacity (refers to Ministry of Finance Data) of district and city. Districts and cities were then classified into high, middle, or low level. The 3 selected study sites were: Kampar District (HPI 34,1) in Riau Province which represent high HOI; North Bengkulu District (HPI: 30.4) in Bengkulu province which represent middle HOI, and Pontianak City (HPI: 27.7) in West Kalimantan Province which represent low HOI. The results show that tariff agreement of case management for Askeskin members was not in accordance with local real condition. Therefore, clear operational and technical Askeskin guidelines are needed to gain similar perception between PT Askes and health care providers· beside Improvement of socialization activities to the community. The amount of differences between claim and reimbursement varied among study sites. The difference was influenced by following factors: (1) existed guidelines that were not suitable with local specific real demand and (2) disparity among hospital facilities. Hospital with limited facility has difficulty to refer patient to another closed hospital which doesn't have contract with PT Askes. Furthermore, in some cases hospital with good facility cannot optimalize using their advanced equipment for treatment, because not stated in the contract with PT Askes. Contract review, tariff adjustment, and re-negotiation between hospitals and PT Askes should be encouraged to provide better services for Askeskin members

Sistem Informasi Geografis (Sig) dalam Bidang Kesehatan Masyarakat

Decentralization in health sector has enable to identify many health problems, population characteristics, and locally and more specific incidences. Health problems can be categorized based on geographic areas (national. regional,and local). The Geographic Information System (GIS) is one of tools for depicting the seventy of health problems and identifying health determinants specifically, as inputs for decision making process, also for epidemiological analysis and public health management. The basic framework of GIS is identification of Input, storage, data processing and output. The GIS main application consist of 1) the spatial description of a health event, 2) risk factors, high risk groups, and high risk areas identification, 3) health situation analysis, diseases pattern analysis, 4) surveillance and monitoring of public health, 5) the planning and programming of health activities; and (5) evaluation on health intervention. In Indonesia, the GIS hasbeen used in many work divisions of Ministry of Health, especially for diseases surveillance mapping. Beside the GIS is able to perform high quality products such as map, graph, and tables. The GIS map can be in the different form of various types and contents. The quality of GIS map depends on geographical precision, the representation of object's pattern and color; definition of the event which will be presented. Usually, the presentation format is developed by combining map, graph and table. Simplification on GIS data management, integration with public health data, and availability of GIS user friendly software will support policy making process according to geographical position. This article presents case in 1997-2001, on the spread of malaria cases in Ciamis District concentrated in some villages in southern coast, where mangrove forest and lagoon are usually found. It tends to spread from west to the east. Malaria cases spread mostly inareas at the height between 0-100 meters above sea level, and just a few cases were found in areas at more than 100meters above sea level. Finally, the capability of GIS to manage spatial data is enable to perform various scenarios as conducting strategic analysis. In order to minimaze bias and uncertain results, it recommends to conduct GIS analysis by multidiscipline members. It should be reminded that the final objective of GIS application in health sector is to improve the ability in plannmg, diagnosing, and intervention of health problems in many governmental administration level due tosupport the achievement of health development goals

Benefit Monitoring and Evaluation (Bme): a Case Study

Theoretically, the ultimate benefits of health development projects are reflected as increased incomes or tangible improvements in quality of life. They will only be forth coming if services provided by project have more direct effect for those who use the services. However the effects of health programs might be direct or indirect so that they are difficult to be measured comparing with other sectors. The study team conducted a study on Benefit Monitoring and Evaluation (BME) by using The Rural Health and Population Project (ADB Ill-Loan No.1299-lno) as objective of the study. The study was conducted in the year 2000, however, the results of this study is relevant to be published due to it is difficult to find the references, which showed the experiences of the BME study in the health sector. The prime objective of the Rural Health and Population Project was to assist the Government in raising the health status of the population and reducing total fertility rates through the improvement of quality, relevance, efficiency and effectiveness of community-based rural health and family planning (FP) delivery system. The Project adopted the following three strategic initiatives: (1) to change the role and orientation of the district hospital; (2) to improve community-based rural health, nutrition and FP service delivery and capabilities; and (3) to strengthen the organization and management at district level. To examine the extent, to which these reforms through the project implementation have intended benefits and effects, both individually and collectively, the evaluation team conducted a study to evaluate the progress on the field implementation of these reforms in the area of the project. The evaluation of benefits of projects will be conducted, whether or not the benchmarks of benefit monitoring was adequately documented when the project is prepared. The study team using a conceptual model called a Logical Framework (LF) a set of cause-and-effect relationship through which resources provided through the project are transformed so they contribute to achieving the objective of the intervention, and assumptions about external factors which affect these relationships. ALF enables one to describe a project in terms of three sequential relationships: inputs to outputs, outputs to effects and effects to impact. Assessment was used benchmark that information available in the project documents. Addition primary and secondary data needed was collected in the locations of the project. The study identified three group or stakeholders which have benefits of the projects; (1) the local authority; (2) The health provider; (3) the community or recipients. By using the benchmark which available in the regularly reporting and recording system the benefits of the project was assessed as; (1) no benefit; (2} minimal benefit; (3) and optimal benefit. Results of the study showed that (1) the local authority in general have optimal benefit, however several activities have minimal benefits; (2) the health providers have minimal benefit, some showed have no benefit, it is only improvement of medical services have an optimal benefit; (3) the community or recipients almost have optimal benefit

Risiko Penyakit Jantung pada Perokok Peserta Program Jamkesmas

Background: For years heart disease prevalence have increased. Results of Basic Health Research (known as riset Kesehatan Oasar-Riskesdas) 2007 showed that heart disease is the second major cause of death after stroke inmortality cases in Indonesia. As a matter of fact, other researches results indicate that smoking as a risk factor for heart disease and most of smoker are poor people. Indonesian government guarantees the poor to get free medical treatment through Jamkesmas program (social assistance program in health sector for the poor). In 2007 Cipto Mangunkusumo National Hospital (RSCM) noted that 60% of heart disease patients treated in this hospital is poor. The trends of cases and government budget to control heart disease are always increasing. Objective of this study was to know smoker prevalence, food consumption and activity for participants JAMKESMAS and also to know risk factor of heart disease for them Methods: Cross sectional was utilized as the study design The Riskesdas and Susenas 2007 data was used in this research. All research variables had been analyzed in univariat, bivariat (X2 test) and multivariat (logistic regression) by using a complex sample in SPSS v16. Results: The results shows that smokers Jamkesmas are most in aged 3-4 years do enough physical activities, and less eating fruits and vegetables, 1.8% of Jamkesmas smokers had diagnosed heart disease by health personnel. Based on statistical analysis, physical activity, fruit and vegetable consumption, smoking, gender and age is risk factor of heart disease

Tinjauan Implementasi Kartu Berobat yang Dijamin Pemerintah di Puskesmas Tahun 2005 (dalam Program Jaminan Kesehatan Masyarakat Miskin)

Implementation of the social health insurance based on the National Social Security Acf/SJSN (Law No. 4012004) was delegated to PT ASKES by the Ministry of Health, to manage health care program for the poor since January 2005. There were 36 million poor people covered by the program at the first semester. The study design was cross-sectional with collection of qualitative and quantitative data to describe the implementation program at Puskesmas. The total samples were 800 respondents with health cards chosen randomly from 4 districts/city in 4 provinces with different level of development (based on Indonesian Human Development Index). There were Karangasem District, Bali; Padang City, West Sumatra; Ende District, East Nusa Tenggara and Lebak District, Banten. Total samples analyzed were 796 household/respondents. About 69% respondents, used askeskin cards at Puskesmas, which increased utilization of the card after 2nd period of the program (after July 2005). About 15% respondents visited health centres without health cards, 15% did not visit the health centres because lack of transportation budget. About 94% of health cards were distributed appropriately to the poor: 74% classified as very poor. 13% classified as poor and 7% classified as nearly poor. About 80% respondents had general clinic services and 30% had MCH services and only 7% visited dentist. They were served 50% by doctor. 45% by midwives and 20% by the nurses. About 20% respondents with health cards still shared budget for medicines. About 85% respondents were satisfied with Puskesmas services. We recommend that safe guarding mechanism should be Implementing with all stakeholders, and socialization to health providers to the poor should be provided much more. Besides the standard implementation program should have several revisions

Identifikasi Wilayah dengan Permasalahan Kesehatan Anak Balita di Provinsi Jawa Timur

Background: Government Regulation no. 38 year 2007 about the distribution of governmental power between central, provincial and district/town was one reference for government. Despite of decentralization was expected to remain the relevance from the administration. Meanwhile, to view the performance or measured the success of development held by local districts, especially in the field of health has issued the Ministerial Regulation Rl No. 741/Menkes/Per/VII/2008 on Minimum Service Standards (SPM) in the Health Sector which is an effort to accelerate the achievement of the MDGs in 2015. Child health problems focused on the decline in mortality because even the trend was declining but the achievement is still rae from target, particularly the MDGs in 2015, either RPJPM or Minimum Service Standards (SPM). When in handling found of resource limitation, it is necessary to scale the priority to handle with the regional approach or program. The objective of the study is to know distribution areas and offers an alternative method of diagnosing the area of Children under 5 health problems so that it shows children under 5 priority areas. Methods: The data for this analysis from a survey called RISKESDAS 2007 with sample unit is children under 5. Variables used are; state of poor and malnutrition, the completed Immunization coverage, posyandu utilization, morbidity (diarrhea, pneumonia and TB), clean and healthy lifestyle. lack of clean water, lack of sanitation in the districts of East in Java. Results: This result is several thematic maps when it is overlay; find the two districts in the eastern part is relatively problematic area among the districts and other towns in East Java. Key words: Spatial analysis, children under 5 health, East Jav

Global injury morbidity and mortality from 1990 to 2017 : results from the Global Burden of Disease Study 2017

Correction:Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.Peer reviewe

The state of health in Indonesia's provinces, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Analysing trends and levels of the burden of disease at the national level can mask inequalities in health-related progress in lower administrative units such as provinces and districts. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to analyse health patterns in Indonesia at the provincial level between 1990 and 2019. Long-term analyses of disease burden provide insights on Indonesia's advance to universal health coverage and its ability to meet the United Nations Sustainable Development Goals by 2030. Methods We analysed GBD 2019 estimated cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), life expectancy at birth, healthy life expectancy, and risk factors for 286 causes of death, 369 causes of non-fatal health loss, and 87 risk factors by year, age, and sex for Indonesia and its 34 provinces from 1990 to 2019. To generate estimates for Indonesia at the national level, we used 138 location-years of data to estimate Indonesia-specific demographic indicators, 317 location-years of data for Indonesia-specific causes of death, 689 location-years of data for Indonesia-specific non-fatal outcomes, 250 location-years of data for Indonesia-specific risk factors, and 1641 location-years of data for Indonesia-specific covariates. For subnational estimates, we used the following source counts: 138 location-years of data to estimate Indonesia-specific demographic indicators; 5848 location-years of data for Indonesia-specific causes of death; 1534 location-years of data for Indonesia-specific non-fatal outcomes; 650 location-years of data for Indonesia-specific risk factors; and 16 016 location-years of data for Indonesia-specific covariates. We generated our GBD 2019 estimates for Indonesia by including 1 915 207 total source metadata rows, and we used 821 total citations. Findings Life expectancy for males across Indonesia increased from 62·5 years (95% uncertainty interval 61·3–63·7) to 69·4 years (67·2–71·6) between 1990 and 2019, a positive change of 6·9 years. For females during the same period, life expectancy increased from 65·7 years (64·5–66·8) to 73·5 years (71·6–75·6), an increase of 7·8 years. There were large disparities in health outcomes among provinces. In 2019, Bali had the highest life expectancy at birth for males (74·4 years, 70·90–77·9) and North Kalimantan had the highest life expectancy at birth for females (77·7 years, 74·7–81·2), whereas Papua had the lowest life expectancy at birth for males (64·5 years, 60·9–68·2) and North Maluku had the lowest life expectancy at birth for females (64·0 years, 60·7–67·3). The difference in life expectancy for males between the highest-ranked and lowest-ranked provinces was 9·9 years and the difference in life expectacy for females between the highest-ranked and lowest-ranked provinces was 13·7 years. Age-standardised death, YLL, and YLD rates also varied widely among the provinces in 2019. High systolic blood pressure, tobacco, dietary risks, high fasting plasma glucose, and high BMI were the five leading risks contributing to health loss measured as DALYs in 2019. Interpretation Our findings highlight that Indonesia faces a double burden of communicable and non-communicable diseases that varies across provinces. From 1990 to 2019, Indonesia witnessed a decline in the infectious disease burden, although communicable diseases such as tuberculosis, diarrhoeal diseases, and lower respiratory infections have remained a main source of DALYs in Indonesia. During that same period, however, all-ages death and disability rates from non-communicable diseases and exposure to their risk factors accounted for larger shares of health loss. The differences in health outcomes between the highest-performing and lowest-performing provinces have also widened since 1990. Our findings support a comprehensive process to revisit current health policies, examine the root causes of variation in the burden of disease among provinces, and strengthen programmes and policies aimed at reducing disparities across the country. Funding The Bill & Melinda Gates Foundation and the Government of Indonesia. Translation For the Bahasa Indonesia translation of the abstract see Supplementary Materials section

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere