Evidence map›Paper›PMID 33160325›Full record

ArticleBMC public health2020

Comparison of self-reports and biomedical measurements on hypertension and diabetes among older adults in China.

Donghong Xie, Jiwen Wang

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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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

2 authors.

Donghong XieSchool of Sociology and Population Studies, Renmin University of China, Beijing, China. 2017000678@ruc.edu.cn.ORCID http://orcid.org/0000-0002-0205-6345
Jiwen WangSchool of Sociology and Population Studies, Renmin University of China, Beijing, China.

Funding

U.S.-China Fulbright Program no
6 · The paper itself

Abstract

backgroundResearchers interested in the effects of health on various life outcomes often use self-reported health and disease as an indicator of true, underlying health status. However, the validity of reporting is questionable as it relies on the awareness, recall bias and social desirability. Accordingly, biomedical test is generally regarded as a more precise indication of the disease.

methodsUsing data from the third wave of China Health and Retirement Longitudinal Study (CHARLS), we selected individuals aged 40-85 years old who participated in both health interview survey and biomedical test. Sensitivity, specificity, false negative reporting and false positive reporting were used as measurements of (dis) agreement or (in) validity, and binary and multinomial logistic regression were used to estimate under-report or over-report of hypertension and diabetes.

resultsSelf-reported hypertension and diabetes showed low sensitivity (73.24 and 49.21%, respectively) but high specificity (93.61 and 98.05%, respectively). False positive reporting of hypertension and diabetes were 3.97 and 1.67%, while false negative reports were extremely high at 10.14 and 7.38%. Educational attainment, hukou, age and gender affected both group-specific error and overall error with some differences in their magnitude and directions.

conclusionSelf-reported conditions underestimate the disease burden of hypertension and diabetes in China. Adding objective measurements into social survey could improve data accuracy and allow better understanding of socioeconomic inequalities in health. Furthermore, there is an urgent need to provide basic health education and physical examination to citizens, and promote the use of healthcare to lower the incidence and unawareness of disease in China.

Indexed as

Diabetes MellitusHypertensionAdultAgedAged, 80 and overChinaHumansLongitudinal StudiesMiddle AgedSelf ReportBiomedical testDiscrepancySelf-reportingSociodemographic characteristics

Identifiers

PMID33160325
PMCPMC7648423

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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.