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The burden of hypertension and unmet need for hypertension care among men aged 15-54 years: a population-based cross-sectional study in India.

Ajinkya KothavaleParul PuriSuryakant Yadav
Published in: Journal of biosocial science (2021)
Hypertension is one of the primary causes of morbidity and premature mortality among the working-age population in India. This study evaluated the burden of hypertension and unmet need for hypertension care among working-age men aged 15-54 years in India using data from the fourth round of the National Family Health Survey (NFHS-4, 2015-16). An individual was recognized as hypertensive if his blood pressure was over 140/90 mmHg or if he was consuming anti-hypertensive medication to lower his blood pressue. The study design was based on the Rule of Halves framework. Hypertensive cases were segmented into five analytical levels: (1) total, (2) screened, (3) diagnosed, (4) treated and (5) controlled cases. The prevalence of hypertension was 16% (n=16,254) among the men aged 15-54 years. Of the total hypertensive individuals, 63.2% (10,314) were screened, 21.5% (3428) were diagnosed, 12.6% (1862) were treated and only 6.1% (905) had controlled blood pressure. Of the screened individuals, 66.8% (6886) had never been diagnosed, 45.7% (1566) of those diagnosed had not receive treatment and 51.4% (957) of those treated still had uncontrolled blood pressure. The analyses revealed that 36.5% (5940) of hypertensive individuals were lost at the screening stage. The results demonstrate that there is a significant burden of hypertension and unmet need for hypertension care among men aged 15-54 in India. There is an urgent need to develop suitable strategies and programmes to manage this rising burden of hypertension among men, and reduce losses in the hypertension care continuum.
Keyphrases
  • blood pressure
  • hypertensive patients
  • heart rate
  • healthcare
  • quality improvement
  • palliative care
  • blood glucose
  • middle aged
  • type diabetes
  • mass spectrometry
  • metabolic syndrome
  • weight loss
  • big data