Evidence map›Paper›PMID 37383361›Full record

ArticleThe Lancet regional health. Southeast Asia2023

Hypertension treatment capacity in India by increased workforce, greater task-sharing, and extended prescription period: a modelling study.

Matti Marklund, Rajeev Cherukupalli, Priya Pathak, Dinesh Neupane, Ashish Krishna, Jason H Y Wu, Bruce Neal, Prabhdeep Kaur, Andrew E Moran, Lawrence J Appel and 1 more

Open access · diamondAbstract read
In one paragraph

Article in The Lancet regional health. Southeast Asia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

6 citing papers in PubMed, 15 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Article
  6. 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

11 authors at 5 institutions in 5 countries.

Matti MarklundThe Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Rajeev CherukupalliThe Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Priya PathakThe Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Dinesh NeupaneThe Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Ashish KrishnaResolve to Save Lives, New York, NY, USA.
Jason H Y WuThe George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Bruce NealThe George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Prabhdeep KaurNational Institute of Epidemiology, The Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Andrew E MoranResolve to Save Lives, New York, NY, USA.
Lawrence J AppelThe Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Kunihiro MatsushitaThe Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Johns Hopkins University · USUNSW Sydney · AUColumbia University Irving Medical Center · USIndian Council of Medical Research · INUppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The worldwide control rate for hypertension is dismal. An inadequate number of physicians to treat patients with hypertension is one key obstacle. Innovative health system approaches such as delegation of basic tasks to non-physician health workers (task-sharing) might alleviate this problem. Massive scale up of population-wide hypertension management is especially important for low- and middle-income countries such as India. Methods: Using constrained optimization models, we estimated the hypertension treatment capacity and salary costs of staff involved in hypertension care within the public health system of India and simulated the potential effects of (1) an increased workforce, (2) greater task-sharing among health workers, and (3) extended average prescription periods that reduce treatment visit frequency (e.g., quarterly instead of monthly). Findings: Currently, only an estimated 8% (95% uncertainty interval 7%-10%) of ∼245 million adults with hypertension can be treated by physician-led services in the Indian public health system (assuming the current number of health workers, no greater task-sharing, and monthly visits for prescriptions). Without task-sharing and with continued monthly visits for prescriptions, the least costly workforce expansion to treat 70% of adults with hypertension would require ∼1.6 (1.0-2.5) million additional staff (all non-physicians), with ∼INR 200 billion (≈USD 2.7 billion) in additional annual salary costs. Implementing task-sharing among health workers (without increasing the overall time on hypertension care) or allowing a 3-month prescription period was estimated to allow the current workforce to treat ∼25% of patients. Joint implementation of task-sharing and a longer prescription period could treat ∼70% of patients with hypertension in India. Interpretation: The combination of greater task-sharing and extended prescription periods could substantially increase the hypertension treatment capacity in India without any expansion of the current workforce in the public health system. By contrast, workforce expansion alone would require considerable, additional human and financial resources. Funding: Resolve to Save Lives, an initiative of Vital Strategies, was funded by grants from Bloomberg Philanthropies; the Bill and Melinda Gates Foundation; and Gates Philanthropy Partners (funded with support from the Chan Zuckerberg Foundation).

Indexed as

Community health workersConstrained optimizationHealthcare reformsHypertensionIndiaPrimary healthcarePublic healthcareSimulationTask-sharingTreatment coverage

Identifiers

PMID37383361
PMCPMC10306017
OpenAlexW4311356172

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.