Evidence map›Paper›PMID 33354427›Full record

ArticlePeerJ2020

Determinants of behavioural and biological risk factors for cardiovascular diseases from state level STEPS survey (2017-19) in Madhya Pradesh.

Arun M Kokane, Rajnish Joshi, Ashwin Kotnis, Anirban Chatterjee, Kriti Yadav, G Revadi, Ankur Joshi, Abhijit P Pakhare

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Arun M KokaneCommunity and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
Rajnish JoshiGeneral Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
Ashwin KotnisBiochemistry, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
Anirban ChatterjeeCommunity and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
Kriti YadavCommunity and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
G RevadiCommunity and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
Ankur JoshiCommunity and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
Abhijit P PakhareCommunity and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, Madhya Pradesh, India.
All India Institute of Medical Sciences Bhopal · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNational and statewide assessment of cardiovascular risk factors needs to be conducted periodically in order to inform public health policy and prioritise allocation of funds, especially in LMICs. Although there have been studies from India which have explored the determinants of cardiovascular risk factors, they have mostly been from high epidemiological transition states. The present study assessed the determinants of cardiovascular (CVD) risk factors in a low epidemiological transition state (Madhya Pradesh) using the WHO STEPwise approach to surveillance (STEPS).

methodsA total of 5,680 persons aged 18-69 years were selected from the state of Madhya Pradesh through multi-stage cluster random sampling. Key CVD risk factors we sought to evaluate were from behavioural (tobacco, alcohol, physical activity, diet) and biological domains (overweight or obese, Hypertension, Diabetes, and Raised serum cholesterol). Key socio-demographic factors of interest were the caste and tribe groups, and rural vs urban location, in addition to known influencers of CVD risk such as age, gender and education level.

resultsThose belonging to the scheduled tribes were more at risk of consuming tobacco (OR 2.13 (95% CI [1.52-2.98]), and a diet with less than five servings of fruits and vegetables (OR 2.78 (95% CI [1.06-7.24]), but had had the least risk of physical inactivity (OR 0.31 (95% CI [0.02-0.54]). Residence in a rural area also reduced the odds of physical inactivity (OR 0.65 (95% CI [0.46-0.92])). Lack of formal education was a risk factor for both tobacco consumption and alcohol intake (OR 1.40 (95% CI [1.08-1.82]) for tobacco use; 1.68 (95% CI [1.14-2.49]) for alcohol intake). Those belonging to schedules tribes had much lower risk of being obese (OR 0.25; 95% CI [0.17-0.37]), but were at similar risk of all other clinical CVD risk factors as compared to other caste groups.

conclusionIn the current study we explored socio-demographic determinants of behavioural and biological CVD risks, and found that in Madhya Pradesh, belonging to a scheduled tribe or living in a rural location, protects against being physically inactive or being overweight or obese. Increasing age confers a greater CVD-risk in all domains. Being a male, and lack of formal education confers a greater risk for behavioural domains, but not for most clinical risk domains. Future efforts at curbing CVDs should be therefore two pronged -a population-based strategy targeting biological risk factors, and a more focussed approach directed at those displaying risky behaviour.

Indexed as

AlcoholBehavioural risk factorsDiabetesHypertensionObesityOverweightRaised blood glucoseRaised blood pressureRaised cholesterolTobacco

Identifiers

PMID33354427
PMCPMC7733649
OpenAlexW3111459414

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.