An Education Driven Model for Non-Communicable Diseases Care

2016 ◽  
pp. 391-418 ◽  
Author(s):  
Fábio Pittoli ◽  
Henrique Damasceno Vianna ◽  
Jorge Luis Victória Barbosa
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Bayesian Networks ◽  
Chronic Diseases ◽  
Communicable Disease ◽  
Communicable Diseases ◽  
Treatment Recommendations ◽  
Non Communicable Diseases ◽  
Cause And Effect ◽  
Non Communicable Disease

Patients with chronic diseases should be made aware of their planned treatments as well as being kept informed of the progress of those treatments. The Chronic Prediction model was designed not only to educate patients and assist them with some chronic non-communicable disease, but to control the risk factors that affect their diseases. The model utilizes Bayesian networks to map three things: to identify the cause and effect relationships among existing risk factors; to provide treatment recommendations about these risk factors and; to aid caregivers in the treatment of the patients.

scholarly journals Prevalence of non-communicable diseases and their risk factors in Papua New Guinea: A systematic review

2020 ◽  
Vol 8 ◽  
pp. 205031212097384
Author(s):  
Patricia Rarau ◽  
Shuaijun Guo ◽  
Shaira Nicole Baptista ◽  
Justin Pulford ◽  
Barbara McPake ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Papua New Guinea ◽  
Disease Risk ◽  
Communicable Disease ◽  
New Guinea ◽  
Communicable Diseases ◽  
Non Communicable Diseases ◽  
Non Communicable Disease

Introduction: The mortality associated with non-communicable diseases has increased significantly in most countries in the World Health Organization Western Pacific Region over the last 20 years, as have the underlying risk factors. This study aimed to collate evidence on the prevalence of four major non-communicable diseases and their risk factors in Papua New Guinea in order to inform appropriate policy for their prevention and management. Methods: We performed a systematic review of Papua New Guinea-based population prevalence studies of cardiovascular diseases, type 2 diabetes mellitus, chronic respiratory diseases, and cancers, as well as non-communicable disease risk factors published before 2016. Five online databases were searched and screened against eligibility criteria according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: A total of 57 articles were included in this review, most of which (n = 48) were published prior to 2000. Eleven articles reported on diabetes, six reported on chronic lung disease/asthma, two reported on cardiovascular diseases, and two reported cancer as the primary outcome, while the remaining 36 papers reported non-communicable disease risk factors. Conclusion: This review demonstrated variations in the prevalence of non-communicable diseases (0%–19%) and their risk factors (0%–80.6%) attributed to the lifestyle and genetic diversity of the Papua New Guinea population. There is a strong suggestion that the prevalence of non-communicable diseases (particularly type 2 diabetes mellitus) and key non-communicable disease risk factors (hypertension, overweight, and obesity) has increased, but there is a lack of recent data. As such, there is an urgent need for new and up-to-date data in all areas of Papua New Guinea.

scholarly journals Tomato Role on Seven Deadliest Non-Communicable Disease

2021 ◽  
Vol 2 (8) ◽  
pp. 1295-1308
Author(s):  
Lathifah Dzakiyyah Zulfa ◽  
Dessyani Salim ◽  
Abigail Tirza Melia Silalahi ◽  
Sharon Levita Hutapea ◽  
Margaretha Maria Odilia Natasha
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Communicable Disease ◽  
Communicable Diseases ◽  
Chronic Obstructive ◽  
Non Communicable Diseases ◽  
Non Communicable Disease ◽  
Inflammatory Effect

Non-communicable diseases such as ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, Alzheimer, diabetes melitus, and kidney disease are leading causes of death in the world. There are many risk factors which can contribute to non-communicable diseases such as dietary. Vegetable consumption such as tomato may lower risk factors to non-communicable diseases because of its active ingredient, lycopene, retinol, alpha tomatine, and tomatidine. In this study, authors aim to explain the mechanism of tomato’s active compound in lowering risk factors of non-communicable disease based on biomarker found on each disease collected from recent epidemiological, in silico, in vitro, and in vivo researches. Lycopene and retinol have proven in reducing ischemic heart disease and stroke because of its anti-atherogenic properties and anti-inflammatory effect. Anti-oxidative and anti-inflammatory effect of lycopene also proven in lowering risk factors of chronic obstructive pulmonary diseases by modulate reverse cholesterol transport, so cholesterol homeostasis is created. In lung cancer, lycopene and other bioactive compound such as α-tomatine and tomatidine also have an anti-proliferative effect by interacting with Epidermal growth factor receptor (EGFR). Its ability to reduce the final product of lipid peroxidation level makes lycopene lower Alzheimer risk factor. There is much more function of tomato’s active coumpound although pure tomato has contradictive effect on some disease.

scholarly journals Macrolevel Association of COVID-19 with Non-Communicable Disease Risk Factors in India

2020 ◽  
Author(s):  
Kiran Gaur ◽  
RS Khedar ◽  
Kishore Mangal ◽  
Arvind K Sharma ◽  
Rajinder K Dhamija ◽  
...  
Keyword(s):  
Risk Factors ◽  
Disease Risk ◽  
Communicable Disease ◽  
State Level ◽  
Communicable Diseases ◽  
List Type ◽  
Indian States ◽  
Non Communicable Diseases ◽  
Obesity And Diabetes ◽  
Non Communicable Disease

ABSTRACTObjectiveGreater COVID-19 related mortality has been reported among persons with various non-communicable diseases (NCDs). We performed an ecological study to determine the association of state-level cases and deaths with NCD risk factors and healthcare and social indices.MethodsWe obtained cumulative national and state-level data on COVID-19 cases and deaths from publicly available database www.covid19india.org from February to end November 2020. To identify association with major NCD risk factors, NCDs, healthcare related and social variables we obtained data from public sources. Association was determined using univariate and multivariate statistics.ResultsMore than 9.5 million COVID-19 cases and 135,000 deaths have been reported in India at end November 2020. There is significant positive correlation (Pearson’s r) of state-level COVID-19 cases and deaths per million, respectively, with NCD risk factors- obesity (0.64, 0.52), hypertension (0.28, 0.16), diabetes (0.66, 0.46), literacy, NCD epidemiological transition index (0.58, 0.54) and ischemic heart disease mortality (0.22, 0.33). Correlation is also observed with indices of healthcare access and quality (0.71, 0.61), urbanization (0.75, 0.73) and human (0.61, 0.56) and sociodemographic (0.70, 0.69) development. Multivariate adjusted analyses shows strong correlation of COVID-19 burden and deaths with NCD risk factors (r2=0.51, 0.43), NCDs (r2=0.32, 0.16) and healthcare related factors (r2=0.52, 0.38).ConclusionsCOVID-19 disease burden and mortality in India is ecologically associated with greater state-level burden of NCDs and risk factors, especially obesity and diabetes.KEY MESSAGESThere is significant state-level variability in COVID-19 cases and deaths in India.In a macrolevel statistical analysis we find that Indian states with better human and sociodemographic indices, more literacy, longer age, greater burden of non-communicable diseases and risk factors have greater COVID-19 case burden and mortality.Non-communicable disease risk factors- obesity and diabetes are the most important determinants on multivariate analyses.

scholarly journals Healthcare Professionals’ Perceptions of Non-Communicable Diseases Risk Factors and Its Regional Distribution in Ethiopia

2017 ◽  
Vol 10 (1) ◽  
pp. 88 ◽  
Author(s):  
Melkamu Dugassa Kassa ◽  
Jeanne Grace
Keyword(s):  
Risk Factors ◽  
Physical Exercise ◽  
Healthcare Professionals ◽  
Disease Risk ◽  
Communicable Disease ◽  
Regional Distribution ◽  
Communicable Diseases ◽  
Non Communicable Diseases ◽  
Unhealthy Diet ◽  
Non Communicable Disease

INTRODUCTION: Non-communicable diseases (NCDs) are increasing as the main cause of death, disability, unproductivity and indisposition in Ethiopia.OBJECTIVES: The objectives of this study were to establish healthcare professionals’ perception on non-communicable disease risk factors and their regional distribution in Ethiopia.METHODS: A mixed method sequential explanatory design was conducted with a questionnaire survey obtaining quantitative replies from 312 healthcare professionals working in 13 referral hospitals in the first phase and qualitative data among 13 hospital managers in the second phase.RESULTS: Statistically significant prevalence of NCDs risk factors were reported with the lack of physical exercise (M=4.94, SD=.245, t (311) = 139.383; p < .0005), hypertension (M=4.89, SD=.312, t (311) = 107.021; p < .0005), and unhealthy diet (M=4.61, SD=.782, t (311) = 36.426; p < .0005) ranking as the top three leading NCDs risk factors. The prevalence and distribution of NCDs risk factors varied within Ethiopia, with a high perceived prevalence of lack of physical exercise, unhealthy diet, alcohol use, and blood glucose in Addis Ababa city followed by Amhara region. A high prevalence of tobacco use and hypertension was also observed in the regions of Benishangul Gumuz.CONCLUSION: The results revealed that the prevalence of NCDs risk factors are increasing in different regions of Ethiopia. Regionally specific non-communicable disease intervention strategies are required to revert the growing burden of the risk factors effectively.

scholarly journals Cross-sectional study on the prevalence of risk factors for non-communicable disease in a rural area of Kancheepuram, Tamil Nadu

2017 ◽  
Vol 4 (12) ◽  
pp. 4600
Author(s):  
Vijayakarthikeyan M. ◽  
Krishnakumar J. ◽  
Umadevi R.
Keyword(s):  
Risk Factors ◽  
Confidence Interval ◽  
Tobacco Use ◽  
Odds Ratio ◽  
Communicable Disease ◽  
Communicable Diseases ◽  
Metabolic Risk ◽  
Cross Sectional ◽  
Non Communicable Diseases ◽  
Non Communicable Disease

Background: Currently, non-communicable diseases (NCDs) are in the limelight replacing communicable diseases, which were the leading cause of death in most countries. Non communicable disease is the leading cause of death globally. Non communicable diseases (NCDs) kill 38 million people each year. This study aims to estimate the prevalence rate of NCD risk factors among adult population and to determine the association between behavioural and metabolic risk factors. Methods: This is a cross sectional descriptive study carried out in the rural field practice area attached to a Medical college in Kancheepuram district. The study group were 370 adults (20-60 years). The data was collected using a structured questionnaire with the help of WHO steps approach containing socio-demographic particulars, details regarding NCD risk factors and physical measurements. Data was analysed using SPSS 15 software. Prevalence of NCD risk factors was calculated using percentages and strength of association was tested between behavioural and metabolic risk factors. Results: Prevalence of risk factors for non-communicable disease are tobacco use (17.8%), alcohol use (17.3%), physical inactivity (50.2%), unhealthy diet (62%), overweight (38.1%), obesity (11.4%), hypertension (15.7%), diabetes (21.9%) respectively. There was strong statistical significant association between obesity (odds ratio-3.057, p=0.020 at 95% confidence interval (0.915-10.211)), hypertension (odds ratio-23.062, p=0.001 at 95% confidence interval (3.147-168.989)), diabetes (odds ratio-6.837, p=0.001 at 95% confidence interval (2.085-22.417)) and alcohol use and also between obesity (odds ratio-2.637, p=0.004 at 95% confidence interval (0.787-8.83)), hypertension (odds ratio-2.773, p=0.019 at 95% confidence interval (1.145-6.714)) and tobacco use. Conclusions: The prevalence of non-communicable diseases risk factors is high in this study. It is necessary to minimize the burden of growing non-communicable disease epidemic in the society, by curbing the rates of the risky behaviours at a very early stage by lifestyle modification.  

scholarly journals The Empowerment of Integrated Development Post of Non-Communicable Diseases in Efforts to Prevent and Control Non- Communicable Diseases

2016 ◽  
Vol 5 (3) ◽  
pp. 294
Author(s):  
Yandrizal Yandrizal ◽  
Rizanda Machmud ◽  
Melinda Noer ◽  
Hardisman Hardisman ◽  
Afrizal Afrizal ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Early Detection ◽  
Communicable Disease ◽  
Communicable Diseases ◽  
Combination Method ◽  
Integrated Development ◽  
Non Communicable Diseases ◽  
And Control ◽  
Non Communicable Disease

Non-Communicable disease has already been the main cause of death in many countries, as many as 57 million death in the world in 2008, 36 million (63 percent) is because of un-infectious disease, specifically heart illness, diabetes, cancer, and chronic respiratory diseases. Prevention and controlling efforts of un-infectious diseases developing in Indonesia is non-communicable disease integrated development post (Pospindu PTM). This research used combination method approach with exploratory design. Exploratory design with sequential procedure used combination consecutively, the first is qualitative and the second is quantitative method. Public Health Center formed Posbindu PTM has not disseminate yet to all stakeholders. Posbindu PTM members felt benefit by following this activity. Some of them did not know follow the activity because of unknown about it. There was  connection between coming behavior to Posbindu PTM to preventing behavior of non-communicable disease.Percentage for high blood pressure risk indicated 20-25 percent from all visitors. Formulation of its policy implementation started with stakeholder analysis; head of sub district, head of urban village, head of health department in regency/city, head of public health service, head of neighborhood Association, and the head of family welfare development.  Analysis of perception, power and authority found that every stakeholder had authority to manage the member directly or indirectly. It was not implemented because of the lack knowledge of stakeholders about the Posbindu PTM function.They would play a role after knowing the aim and advantage of the post by motivate the people to do early detection, prevention and control the non-communicable disease. The members were given wide knowledge about  early detection, preventing  and control the un-infectious disease, measuring and checking up their healthy continuously so that keep feeling the advantage of coming to the post.

scholarly journals Surveillance system evaluation of non-communicable diseases through integrated community-based intervention called ‘Posbindu PTM’ in Wonosobo district, Central Java province, Indonesia

2018 ◽  
pp. 15
Author(s):  
Rieski Prihastuti ◽  
Trisno Agung Wibowo ◽  
Misinem Misinem
Keyword(s):  
Surveillance System ◽  
Reporting System ◽  
Communicable Disease ◽  
Communicable Diseases ◽  
System Evaluation ◽  
Main Function ◽  
Community Based ◽  
Non Communicable Diseases ◽  
Surveillance System Evaluation ◽  
Non Communicable Disease

Purpose: Non-communicable diseases are leading cause of the global death, especially from cardiovascular disease, cancers, chronic obstructive pulmonary disease, and diabetes. Prevention and primary detection of non-communicable disease in Indonesia were done through integrated community-based intervention called ‘Posbindu PTM’. Implementation of ‘Posbindu PTM’ needed to be evaluated to determine each component in the non-communicable disease surveillance systems. Methods: This study was descriptive. Respondent were programmer in district health office and programmer in 24 primary health care in Wonosobo. Surveillance system evaluation that used was programs evaluation based on WHO (structure, main function, support function and quality of surveillance system). Results: The weakness of ‘Posbindu PTM’ in Wonosobo were lack of knowledge in the regulation, networking, collaborating, risk factor detecting and reporting; not availability of technical guidebook; low training participant; low monitoring and evaluation activity; also complex reporting system. There was 84% ‘Posbindu PTM’ that had not reported on time and 87,50 % programmer had not done the analysis, interpretation, and dissemination. This was related to the completeness of the report caused too many data that needed to be collected and affect the timeliness of the report. Conclusion: Strengthening ‘Posbindu PTM’ should be done in the reporting system aspect, especially in the timeliness and analysis of the report. Monthly reminder and refreshing in reporting system were done to improve the reporting system aspect.

Modeling Chronic Diseases in Relation to Risk Factors

2019 ◽  
Author(s):  
Pieter van Baal ◽  
Hendriek Boshuizen
Keyword(s):  
Risk Factors ◽  
Simulation Model ◽  
Chronic Diseases ◽  
Simulation Modeling ◽  
Disease Modeling ◽  
Healthcare Spending ◽  
Communicable Diseases ◽  
Future Research ◽  
Non Communicable Diseases ◽  
Multiple Chronic Diseases

In most countries, non-communicable diseases have taken over infectious diseases as the most important causes of death. Many non-communicable diseases that were previously lethal diseases have become chronic, and this has changed the healthcare landscape in terms of treatment and prevention options. Currently, a large part of healthcare spending is targeted at curing and caring for the elderly, who have multiple chronic diseases. In this context prevention plays an important role, as there are many risk factors amenable to prevention policies that are related to multiple chronic diseases. This article discusses the use of simulation modeling to better understand the relations between chronic diseases and their risk factors with the aim to inform health policy. Simulation modeling sheds light on important policy questions related to population aging and priority setting. The focus is on the modeling of multiple chronic diseases in the general population and how to consistently model the relations between chronic diseases and their risk factors by combining various data sources. Methodological issues in chronic disease modeling and how these relate to the availability of data are discussed. Here, a distinction is made between (a) issues related to the construction of the epidemiological simulation model and (b) issues related to linking outcomes of the epidemiological simulation model to economic relevant outcomes such as quality of life, healthcare spending and labor market participation. Based on this distinction, several simulation models are discussed that link risk factors to multiple chronic diseases in order to explore how these issues are handled in practice. Recommendations for future research are provided.

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