Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identifying the latent classes of modifiable risk behaviours among diabetic and hypertensive individuals in Northeastern India: a population-based cross-sectional study
Stockholm University, Faculty of Social Sciences, Department of Psychology, Stress Research Institute. Indian Statistical Institute, India.ORCID iD: 0000-0002-6016-8943
2022 (English)In: BMJ Open, E-ISSN 2044-6055, Vol. 12, no 2, article id e053757Article in journal (Refereed) Published
Abstract [en]

Objective To identify the latent classes of modifiable risk factors among the patients with diabetes and hypertension based on the observed indicator variables: smoking, alcohol, aerated drinks, overweight or obesity, diabetes and hypertension. We hypothesised that the study population diagnosed with diabetes or hypertension is homogeneous with respect to the modifiable risk factors.

Design A cross-sectional study using a stratified random sampling method and a nationally representative large-scale survey.

Setting and participants Data come from the fourth round of the Indian National Family Health Survey, 2015–2016. Respondents aged 15–49 years who were diagnosed with either diabetes or hypertension or both were included. The total sample is 22 249, out of which 3284 were men and 18 965 were women.

Primary and secondary outcome measures The observed variables used as latent indicators are the following: smoking, alcohol, aerated drinks, overweight or obesity, diabetes and hypertension. The concomitant variables include age, gender, education, marital status and household wealth index. Latent class model was used to simultaneously identify the latent class and to determine the association between the concomitant variables and the latent classes.

Results Three latent classes were identified and labelled as class 1: ‘diabetic with low-risk lifestyle’ (21%), class 2: ‘high-risk lifestyle’ (8%) and class 3: ‘hypertensive with low-risk lifestyle’ (71%). Class 1 is characterised by those with a high probability of having diabetes and low probability of smoking and drinking alcohol. Class 2 is characterised by a high probability of smoking and drinking alcohol and class 3 by a high probability of having high blood pressure and low probability of smoking and drinking alcohol.

Conclusions Co-occurrence of smoking and alcohol consumption was prevalent in men, while excess body weight and high blood pressure were prevalent in women. Policy and programmes in Northeastern India should focus on targeting multiple modifiable risk behaviours that co-occur within an individual.

Place, publisher, year, edition, pages
2022. Vol. 12, no 2, article id e053757
National Category
Public Health, Global Health and Social Medicine
Identifiers
URN: urn:nbn:se:su:diva-249970DOI: 10.1136/bmjopen-2021-053757ISI: 000762416400007PubMedID: 35210340Scopus ID: 2-s2.0-85125300561OAI: oai:DiVA.org:su-249970DiVA, id: diva2:2016540
Available from: 2025-11-26 Created: 2025-11-26 Last updated: 2025-11-26Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Chungkham, Holendro Singh

Search in DiVA

By author/editor
Chungkham, Holendro Singh
By organisation
Stress Research Institute
In the same journal
BMJ Open
Public Health, Global Health and Social Medicine

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 21 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf