Evidence map›Paper›PMID 30196149›Full record

ArticleSocial science & medicine (1982)2018

Measuring income for catastrophic cost estimates: Limitations and policy implications of current approaches.

Sedona Sweeney, Rachel Mukora, Sophie Candfield, Lorna Guinness, Alison D Grant, Anna Vassall

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Tuberculosis patients with higher levels of poverty face equal or greater costs of illness.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2019
    Trial
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  10. Use of a Sustainable Livelihood Framework-Based Measure to Estimate Socioeconomic Impact of Tuberculosis on Households.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
    Article
  11. Social Protection Interventions for TB-Affected Households: A Scoping Review.The American journal of tropical medicine and hygiene · 2023
    Article
  12. Article
  13. Article
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  15. Article
  16. The household economic burden of drug-susceptible TB diagnosis and treatment in The Gambia.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Observational
  17. The high costs facing TB-affected households in Mali.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Article
  18. The economic burden of TB faced by patients and affected families in Papua New Guinea.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Article
  19. Article
  20. 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

6 authors.

Sedona SweeneyTB Centre, London School of Hygiene & Tropical Medicine, London, UK. Electronic address: Sedona.Sweeney@lshtm.ac.uk.
Rachel MukoraThe Aurum Institute, Johannesburg, South Africa.
Sophie CandfieldTB Centre, London School of Hygiene & Tropical Medicine, London, UK.
Lorna GuinnessTB Centre, London School of Hygiene & Tropical Medicine, London, UK.
Alison D GrantTB Centre, London School of Hygiene & Tropical Medicine, London, UK; Africa Health Research Institute, School of Nursing and Public Health, University of KwaZulu-Natal, South Africa; School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
Anna VassallTB Centre, London School of Hygiene & Tropical Medicine, London, UK.

Funding

Medical Research Council G1100689Wellcome Trust
6 · The paper itself

Abstract

There is increasing global policy interest in estimating catastrophic costs incurred by households because of ill health, and growing need for information on disease-specific household cost data. There are several methodological approaches used to estimate income and no current consensus on the best method for estimating income in the context of a survey at the health facility. We compared six different approaches to estimate catastrophic cost among patients attending a health facility in South Africa. We used patient cost and income data collected June 2014-March 2015 from 66 participants enrolled in a clinical trial in South Africa (TB FastTrack) to explore the variation arising from different income estimation approaches and compared the number of households encountering catastrophic costs derived for each approach. The total proportion of households encountering catastrophic costs varied from 0% to 36%, depending on the estimation method. Self-reported mean annual income was significantly lower than permanent income estimated using an asset linking approach, or income estimated using the national average. A disproportionate number of participants adopting certain coping strategies, including selling assets and taking loans, were unable to provide self-reported income data. We conclude that the rapid methods for estimating income among patients attending a health facility are currently inconsistent. Further research on methods for measuring income, comparing the current recommended methods to 'gold standard' methods in different settings, should be done to identify the most appropriate measurement method.

Indexed as

Cost of IllnessHealth StatusAdultFemaleHealth ExpendituresHumansIncomeMaleMiddle AgedSocioeconomic FactorsSouth AfricaSurveys and QuestionnairesCatastrophic costCopingIncomeMethodsSouth AfricaTuberculosis

Identifiers

PMID30196149
PMCPMC6171470

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.