Understanding dental caries as a non-communicable disease

AbstractThe recent developments in the science underpinning our knowledge of both the initiation of dental caries and the subsequent behaviour of lesions over time gives us a solid base to understand caries differently. Advances in understanding the human and oral microbiome have come in parallel with the recognition of the importance of balancing protective and pathological risk factors. Caries prevention and management is now about controlling risk factors to maintain a balanced intraoral biofilm ecology that guards against a continuing low pH driven by the frequent consumption of sugars. Thus, caries control is no longer about attempts at eradicating any specific microorganism. Further, the present knowledge leads to the classification of dental caries as a non-communicable disease (NCD), which is vitally important from a policy perspective (both globally and at the country level). Caries shares similar risk factors with other chronic/systemic diseases, which provides opportunities for developing common prevention strategies and promoting health equity through action on the social determinants of health. So, preventing and controlling caries should be integrated across the so-called upstream, midstream and downstream levels and these activities can also help to control other NCDs.

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Baseline risk factor prevalence among adolescents aged 15–17 years old: findings from National Non-communicable Disease Monitoring Survey (NNMS) of India

BMJ Open ◽

10.1136/bmjopen-2020-044066 ◽

2021 ◽

Vol 11 (6) ◽

pp. e044066

Author(s):

Prashant Mathur ◽

Vaitheeswaran Kulothungan ◽

Sravya Leburu ◽

Anand Krishnan ◽

Himanshu Kumar Chaturvedi ◽

...

Keyword(s):

Physical Activity ◽

Risk Factors ◽

Rural Areas ◽

Communicable Disease ◽

National Level ◽

Developed Countries ◽

Overweight And Obesity ◽

Baseline Risk ◽

Attitudes And Practices ◽

Non Communicable Disease

ObjectiveTo generate national estimates of key non-communicable disease (NCD) risk factors for adolescents (15–17 years) identified in the National NCD Monitoring Framework and, study the knowledge, attitudes and practices towards NCD risk behaviours among school-going adolescents.Design and settingA community-based, national, cross-sectional survey conducted during 2017–2018. The survey was coordinated by the Indian Council of Medical Research—National Centre for Disease Informatics and Research with 10 reputed implementing research institutes/organisations across India in urban and rural areas.ParticipantsA multistage sampling design was adopted covering ages between 15 and 69 years—adolescents (15–17 years) and adults (18–69 years). The sample included 12 000 households drawn from 600 primary sampling units. All available adolescents (15–17 years) from the selected households were included in the survey.Main outcome measuresKey NCD risk factors for adolescents (15–17 years)—current tobacco and alcohol use, dietary behaviours, insufficient physical activity, overweight and obesity.ResultsOverall, 1402 households and 1531 adolescents completed the survey. Prevalence of current daily use of tobacco was 3.1% (95% CI: 2.0% to 4.7%), 25.2% (95% CI: 22.2% to 28.5%) adolescents showed insufficient levels of physical activity, 6.2% (95% CI: 4.9% to 7.9%) were overweight and 1.8% (95% CI: 1.0% to 2.9%) were obese. Two-thirds reported being imparted health education on NCD risk factors in their schools/colleges.ConclusionThe survey provides baseline data on NCD-related key risk factors among 15–17 years in India. These national-level data fill information gaps for this age group and help assess India’s progress towards NCD targets set for 2025 comprehensively. Though the prevalence of select risk factors is much lower than in many developed countries, this study offers national evidence for revisiting and framing appropriate policies, strategies for prevention and control of NCDs in younger age groups.

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Identifying individuals with multiple non-communicable disease risk factors in Kenya: a latent class analysis

Public Health ◽

10.1016/j.puhe.2021.07.031 ◽

2021 ◽

Vol 198 ◽

pp. 180-186

Author(s):

R.S. Mkuu ◽

T.D. Gilreath ◽

A.E. Barry ◽

F.M. Nafukho ◽

J. Rahman ◽

...

Keyword(s):

Risk Factors ◽

Latent Class Analysis ◽

Latent Class ◽

Disease Risk ◽

Communicable Disease ◽

Class Analysis ◽

Non Communicable Disease

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Non-communicable disease (NCD) risk factors and diabetes among adults living in slum areas of Dhaka, Bangladesh

PLoS ONE ◽

10.1371/journal.pone.0184967 ◽

2017 ◽

Vol 12 (10) ◽

pp. e0184967 ◽

Cited By ~ 14

Author(s):

Lal B. Rawal ◽

Tuhin Biswas ◽

Nusrat Nausheen Khandker ◽

Shekhar Ranjan Saha ◽

Mohammed Mahiul Bidat Chowdhury ◽

...

Keyword(s):

Risk Factors ◽

Communicable Disease ◽

Non Communicable Disease ◽

Slum Areas

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Multiple Imputation to Correct for Nonresponse Bias: Application in Non-communicable Disease Risk Factors Survey

Global Journal of Health Science ◽

10.5539/gjhs.v8n1p133 ◽

2015 ◽

Vol 8 (1) ◽

pp. 133 ◽

Cited By ~ 2

Author(s):

Hamid Heidarian Miri ◽

Jafar Hassanzadeh ◽

Abdolreza Rajaeefard ◽

Majid Mirmohammadkhani ◽

Kambiz Ahmadi Angali

Keyword(s):

Risk Factors ◽

Multiple Imputation ◽

Blood Sugar ◽

Disease Risk ◽

Communicable Disease ◽

Fasting Blood Glucose ◽

Nonresponse Bias ◽

Natural Logarithm ◽

Predictive Mean Matching ◽

Non Communicable Disease

BACKGROUND: This study was carried out to use multiple imputation (MI) in order to correct for the potential nonresponse bias in measurements related to variable fasting blood glucose (FBS) in non-communicable disease risk factors survey conducted in Iran in 2007. METHODS: Five multiple imputation methods as bootstrap expectation maximization, multivariate normal regression, univariate linear regression, MI by chained equation, and predictive mean matching were applied to impute variable fasting blood sugar. To make FBS consistent with normality assumption natural logarithm (Ln) and Box-Cox (BC) transformations were used prior to imputation. Measurements from which we intended to remove nonresponse bias included mean of FBS and percentage of those with high FBS. RESULTS: For mean of FBS results didn’t considerably change after applying MI methods. Regarding the prevalence of high blood sugar all methods on original scale tended to increase the estimates except for predictive mean matching that along with all methods on BC or Ln transformed data didn’t change the results. CONCLUSIONS: FBS-related measurements didn’t change after applying different MI methods. It seems that nonresponse bias was not an important challenge regarding these measurements. However use of MI methods resulted in more efficient estimations. Further studies are encouraged on accuracy of MI methods in these settings.

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Prevalence, clustering and sociodemographic distributions of non-communicable disease risk factors in Nepalese adolescents: secondary analysis of a nationwide school survey

BMJ Open ◽

10.1136/bmjopen-2018-028263 ◽

2019 ◽

Vol 9 (5) ◽

pp. e028263 ◽

Cited By ~ 4

Author(s):

Raja Ram Dhungana ◽

Bihungum Bista ◽

Achyut Raj Pandey ◽

Maximilian de Courten

Keyword(s):

Physical Activity ◽

Risk Factors ◽

Alcohol Consumption ◽

Vegetable Intake ◽

Disease Risk ◽

Communicable Disease ◽

Fruit And Vegetable Intake ◽

Fruit And Vegetable ◽

Insufficient Physical Activity ◽

Non Communicable Disease

ObjectivesTo assess the prevalence, clustering and sociodemographic distribution of non-communicable disease (NCD) risk factors in adolescents in Nepal.DesignData originated from Global School Based Student Health Survey, Nepal conducted in 2015–2016.SettingThe study sites were the secondary schools in Nepal; 74 schools were selected based on the probability proportional to school enrolment size throughout Nepal.Participants5795 school-going children aged 13–17 years were included in the study.Primary outcomesNCD risk factors: smoking, alcohol consumption, insufficient fruit and vegetable intake, insufficient physical activity and overweight/obesity were the primary outcomes. Sociodemographic distributions of the combined and individual NCD risk factors were determined by Poisson regression analysis.ResultsFindings revealed the prevalence of smoking (6.04%; CI 4.62 to 7.88), alcohol consumption (5.29%; CI 4.03 to 6.92), insufficient fruit and vegetable intake (95.33%; CI 93.89 to 96.45), insufficiently physical activity (84.77%; CI 81.04 to 87.88) and overweight/obesity (6.66%; CI 4.65 to 9.45). One or more risk factors were present in 99.6%, ≥2 were in 83% and ≥3 were in 11.2%. Risk factors were more likely to cluster in male, 17 years of age and grade 7. Prevalence of smoking (adjusted prevalence ratio (aPR)=2.38; CI 1.6 to 3.51) and alcohol consumption (aPR=1.81; CI 1.29 to 2.53) was significantly high in male, and in 16 and 17 years of age. Prevalence of insufficient physical activity and overweight/obesity was significantly lower in higher grades.ConclusionInsufficient fruit and vegetable intake and insufficient physical activity were highly prevalent in the populations studied. Risk factors were disproportionately distributed and clustered in particular gender, age and grade. The study population requires an age and gender specific preventive public health intervention.

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