Evidence map›Paper›PMID 35809220›Full record

ArticleJournal of clinical hypertension (Greenwich, Conn.)2022

Validity of self-reported hypertension in India: Evidence from nationally representative survey of adult population over 45 years.

Mrigesh Bhatia, Priyanka Dixit, Manish Kumar, Laxmi Kant Dwivedi

Abstract read
In one paragraph

Article in Journal of clinical hypertension (Greenwich, Conn.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

4 authors.

Mrigesh BhatiaDept. of Health Policy, London School of Economics, London, UK.
Priyanka DixitSchool of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, India.
Manish KumarInternational Institute for Population Sciences, Mumbai, India.
Laxmi Kant DwivediInternational Institute for Population Sciences, Mumbai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Self-reported measures of health, in the context of developed countries, are well-researched and commonly regarded as reliable predictors of the underlying health of the population. However, the validity of these measures is under-researched and questionable in the context of low- and middle-income countries. The authors used Longitudinal Ageing Study in India (LASI) survey data from India to compare self-reported hypertension with biometrically-measured hypertension. The results are reported in terms of sensitivity, specificity, and kappa as a measure of agreement. Logistic regression was undertaken to examine the characteristics of those who were unaware of their hypertensive status. Our analysis showed a low sensitivity of 56% and a high specificity of 90.5%. Agreement between self-reported data and biometric measurement of hypertension was observed to be moderate (κ = 0.48). Large variations were observed among states and sub-groups. The odds of false negative reporting of hypertension were lower in the individuals with higher age, high education, and greater wealth status. The authors conclude that self-reported hypertension has important limitations and may be a source of systematic bias. It is recommended that planning and policy-making in India be based more on an objective assessment of hypertension.

Indexed as

HypertensionAdultHumansIndiaPrevalenceSelf ReportSocioeconomic Factorsbiometric measurementhypertensionIndiaself-reported measuresensitivityvalidity

Identifiers

PMID35809220
PMCPMC9659862

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.