Abstrak Merokok merupakan salah satu faktor risiko terhadap penyakit yang membahayakan, seperti jantung, stroke, kanker, dan lain sebagainya. Perilaku masyarakat khususnya perokok aktif yang merokok di sembarangan tempat masih cukup memprihatinkan. Perokok membebankan risiko merokok bukan hanya pada diri sendiri tetapi juga kepada orang lain yang ada di sekitarnya. Analisis dilakukan dengan menggunakan data GATS (Global Adult Tobacco Survey) 2011, dimana desain penelitian adalah cross sectional. Pemilihan sampel menggunakan teknik sampling proportional probabilitas to size (PPS). Hasil dari analisis antara lain: masyarakat yang terpapar rokok di dalam rumah lebih banyak pada kelompok laki-laki dibandingkan perempuan, yang terbanyak pada kelompok umur 45-64 tahun dengan pendidikan tidak tamat SD, tempat tinggal di pedesaan, dan pekerjaan wiraswasta. Kebijakan keluarga yang mengizinkan merokok dalam rumah sebesar 46,9%, dan seseorang yang merokok dalam rumah setiap hari mencapai 62,5%. Masyarakat yang terpapar rokok di ruang kerja sebesar 51,4%, dan kantor yang mengizinkan merokok dalam ruang kerja sebesar 38,4% dan yang tidak ada kebijakan sebesar 19,8%. Terpapar rokok di kantor pemerintahan 66,4%, di universitas 55,3%, di sekolah atau fasilitas pendidikan lainnya 40,3%, di fasilitas keagamaan 17,9%, di fasilitas kesehatan 18,4%, di bar atau klub 91,8%, dan transportasi umum 70,8%. Hasil ini dapat menjadi data dasar untuk mengembangkan intervensi program pengendalian tembakau yang efektif, termasuk menyediakan layanan berhenti merokok, terutama di fasilitas kesehatan. Pemerintah pusat dan daerah perlu meningkatkan sosialisasi tentang bahaya merokok di tempat-tempat umum dan dampaknya terhadap masyarakat khususnya yang bukan perokok; yaitu dengan membuat peraturan yang jelas dan tegas tentang pelarangan merokok di tempat-tempat umum dan memberikan sangsi yang tegas terhadap yang melanggar peraturan tersebut. Upaya layanan berhenti merokok dapat dilaksanakan dengan meningkatkan kegiatan promosi oleh tenaga kesehatan, sosialisasi 'Quitline' Kementerian Kesehatan, skrining CO2, bantuan konseling dan mengembangkan metode terapi berhenti merokok bagi para perokok aktif di berbagai fasilitas kesehatan yang tersedia. Kata kunci: rokok, perokok pasif, pengendalian tembakau Abstract Smoking is one of the risk factors for severe diseases, such as heart disease, stroke, cancer, and so on. The behavior of active smokers who smoke arbitrarily at many public places is still quite alarming. Smokers impose the risk of smoking not only on themselves but also to others around them. The analysis was performed using GATS (Global Adult Tobacco Survey) 2011 data, where the research design was cross-sectional. The sample selection uses a proportional probability to size (PPS) sampling technique. The results of the analysis show people who are exposed to cigarettes in the house are mostly males than females with the characteristics were at age groups 45-64 years old, educational level was not completed elementary school, living in rural areas, and self-employee. Family policies that allow smoking in the home were 46.9%, and someone who smokes in the house every day reaches 62.5%. People who are exposed to cigarettes in the workspace were 51.4% and offices that allow smoking in the workspace were 38.4% and those without any 'free smoking area' policy were 19.8%. Exposure to cigarettes was 66.4% in government offices, 55.3% in universities, 40.3% in schools or other educational facilities, 17.9% in religious facilities, 18.4% in health facilities, 91.8% in bars or clubs, and 70.8% in public transportation. These results could be a reference or base evidence in developing an effective tobacco control program, including providing smoking cessation services. Central and local governments need to increase awareness about the risk of smoking in public places and their impact on public health, especially for non-smokers, by issuing a strict regulation on free smoking areas in public places and enforce punishment to people who violate these regulations. The efforts to stop smoking services can be implemented by increasing promotion activities by health workers, socialization of the Ministry of Health 'Quitline', CO2 screening, counseling assistance and developing methods of smoking cessation therapy for active smokers in existing health facilities. Keywords: cigarette exposure, passive smokers, tobacco control
Medicine is an important component that cannot be replaced in health service. Indonesia National Agency of Drug and Food Control conducted survey to assess knowledge, attitude, and practice (KAP) of communities on selecting safe and quality medicines. The aim of the study is to get description KAP of community in choosing a safe medicine. Data were collected in West Java, DKI Jakarta, and South East Sulawesi. Sampling calculation use probability proportional to size sampling and census block. There were 1271 households as samples that analysed. Data results were analysed using descriptive and index analysis. Knowledge relates to criteria of quality medicines, rules for antibiotics use, and medicines logo. Attitude relates to how to select over the counter medicines, reasons of taking traditional medicines, and opinion about giving half dose of adults medicines to children. Practice relates to source of medicines information, the way to buy prescribe medicines, and reading label information. The results showed that KAP of communities on selecting safe and quality medicines close to 50%. According to score of index analysis are 4.65 (1 to 10 scale), it is recommended that information, education, and communication has to be delivered to communities intensively and continuously by the government.
Introduction:Although Indonesia is not a signatory to the World Health Organization Framework Convention on Tobacco Control, 84% of local governments have adopted Smoke-Free Areas (SFAs) as a national policy. This study examines exposure to secondhand smoke (SHS) in adolescents who have never smoked after 8 years of SFA implementation.Methods:We used data from the 2019 Global Youth Tobacco Survey and a cross-sectional research design to find 6121 students from 148 schools in 30 provinces in Indonesia who met the research inclusion criteria. To identify risk factors regarding SHS exposure, multivariable logistic regression analysis was performed.Results:Although the local government had adopted an SFA policy, 61.1% of adolescents aged 11–17 years, especially middle and high school students, have relatively high exposure to SHS. School environments had the highest prevalence of SHS exposure (50.5%), followed by public places (49.9%) and at home (46.2%). A significant risk factor for exposure to SHS in the school environment is observed in teachers smoking inside school buildings (odds ratio [OR] =4.32, 95% confidence interval [CI]: 3.81–4.89); exposure to SHS at home and in public place (OR = 3.29, 95% CI: 2.93–3.70), and exposure to tobacco advertising, promotion, and sponsorship on offline and online media (OR = 2.07, 95% CI: 1.70–2.52).Conclusion:SFA policies must be evaluated and strengthened before they can be implemented to reduce smoking-related illnesses and economic losses. In addition, it is important to educate families and society about implementing SFA in school environments, public places, and at home.
BACKGROUND:Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. METHODS:Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0-100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target-1 billion more people benefiting from UHC by 2023-we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. FINDINGS:Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2-47·5) in 1990 to 60·3 (58·7-61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9-3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010-2019 relative to 1990-2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6-421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0-3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5-1040·3]) residing in south Asia. INTERPRETATION:The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people-the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close-or how far-all populations are in benefiting from UHC. FUNDING:Bill & Melinda Gates Foundation.
Publisher's version (útgefin grein) ; Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (>= 65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0-100 based on the 2.5th and 97.5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target-1 billion more people benefiting from UHC by 2023-we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45.8 (95% uncertainty interval 44.2-47.5) in 1990 to 60.3 (58.7-61.9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2.6% [1.9-3.3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010-2019 relative to 1990-2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0.79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388.9 million (358.6-421.3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3.1 billion (3.0-3.2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968.1 million [903.5-1040.3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people-the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close-or how far-all populations are in benefiting from UHC. ; Lucas Guimaraes Abreu acknowledges support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (Capes) -Finance Code 001, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG). Olatunji O Adetokunboh acknowledges South African Department of Science & Innovation, and National Research Foundation. Anurag Agrawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship IA/CPHS/14/1/501489. Rufus Olusola Akinyemi acknowledges Grant U01HG010273 from the National Institutes of Health (NIH) as part of the H3Africa Consortium. Rufus Olusola Akinyemi is further supported by the FLAIR fellowship funded by the UK Royal Society and the African Academy of Sciences. Syed Mohamed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. Marcel Ausloos, Claudiu Herteliu, and Adrian Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDSUEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Till Winfried Barnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Juan J Carrero was supported by the Swedish Research Council (2019-01059). Felix Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. Vera Marisa Costa acknowledges support from grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundacao para a Ciencia e a Tecnologia (FCT), IP, under the Norma TransitA3ria DL57/2016/CP1334/CT0006. Jan-Walter De Neve acknowledges support from the Alexander von Humboldt Foundation. Kebede Deribe acknowledges support by Wellcome Trust grant number 201900/Z/16/Z as part of his International Intermediate Fellowship. Claudiu Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program for Competitiveness, Project ID P_40_382. Praveen Hoogar acknowledges the Centre for Bio Cultural Studies (CBiCS), Manipal Academy of Higher Education(MAHE), Manipal and Centre for Holistic Development and Research (CHDR), Kalghatgi. Bing-Fang Hwang acknowledges support from China Medical University (CMU108-MF-95), Taichung, Taiwan. Mihajlo Jakovljevic acknowledges the Serbian part of this GBD contribution was co-funded through the Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Aruna M Kamath acknowledges funding from the National Institutes of Health T32 grant (T32GM086270). Srinivasa Vittal Katikireddi acknowledges funding from the Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15), Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15) and an NRS Senior Clinical Fellowship (SCAF/15/02). Yun Jin Kim acknowledges support from the Research Management Centre, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). Kewal Krishan acknowledges support from the DST PURSE grant and UGC Center of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Manasi Kumar acknowledges support from K43 TW010716 Fogarty International Center/NIMH. Ben Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. Ivan Landires is a member of the Sistema Nacional de InvestigaciA3n (SNI), which is supported by the Secretaria Nacional de Ciencia Tecnologia e Innovacion (SENACYT), Panama. Jeffrey V Lazarus acknowledges support by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III/ESF, European Union [CP18/00074]). Peter T N Memiah acknowledges CODESRIA; HISTP. Subas Neupane acknowledges partial support from the Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital. Shuhei Nomura acknowledges support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). Alberto Ortiz acknowledges support by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. George C Patton acknowledges support from a National Health & Medical Research Council Fellowship. Marina Pinheiro acknowledges support from FCT for funding through program DL 57/2016 -Norma transitA3ria. Alberto Raggi, David Sattin, and Silvia Schiavolin acknowledge support by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4 -Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). Daniel Cury Ribeiro acknowledges support from the Sir Charles Hercus Health Research Fellowship -Health Research Council of New Zealand (18/111). Perminder S Sachdev acknowledges funding from the NHMRC Australia. Abdallah M Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. Milena M Santric-Milicevic acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 175087). Rodrigo Sarmiento-Suarez acknowledges institutional support from University of Applied and Environmental Sciences in Bogota, Colombia, and Carlos III Institute of Health in Madrid, Spain. Maria Ines Schmidt acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. Sheikh Mohammed Shariful Islam acknowledges a fellowship from the National Heart Foundation of Australia and Deakin University. Aziz Sheikh acknowledges support from Health Data Research UK. Kenji Shibuya acknowledges Japan Ministry of Education, Culture, Sports, Science and Technology. Joan B Soriano acknowledges support by Centro de Investigacion en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Rafael Tabares-Seisdedos acknowledges partial support from grant PI17/00719 from ISCIII-FEDER. Santosh Kumar Tadakamadla acknowledges support from the National Health and Medical Research Council Early Career Fellowship, Australia. Marcello Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary, AB, Canada. ; "Peer Reviewed"
