Pragmatic tuberculosis prevention policies for primary care in low- and middle-income countries

Saunders, Matthew J.; Tovar, Marco A.; Datta, Sumona; Evans, Benjamin E.W.; Wingfield, Tom; Evans, Carlton A

doi:10.1183/13993003.00315-2018

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…Several randomised controlled trials assessing the impact of more complex mobile phone interventions on TB cure rates and preventive treatment adherence rates are ongoing and will contribute to this evidence . Such interventions may have more success in improving preventive treatment adherence rates as, in contrast to TB treatment, this is typically self‐administered and is an area in need of significant improvement .…”

Section: Discussionmentioning

confidence: 99%

Mobile phone interventions for tuberculosis should ensure access to mobile phones to enhance equity – a prospective, observational cohort study in Peruvian shantytowns

Saunders

Wingfield

Tovar

et al. 2018

Tropical Med Int Health

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ObjectivesMobile phone interventions have been advocated for tuberculosis care, but little is known about access of target populations to mobile phones. We studied mobile phone access among patients with tuberculosis, focusing on vulnerable patients and patients who later had adverse treatment outcomes.MethodsIn a prospective cohort study in Callao, Peru, we recruited and interviewed 2584 patients with tuberculosis between 2007 and 2013 and followed them until 2016 for adverse treatment outcomes using national treatment registers. Subsequently, we recruited a further 622 patients between 2016 and 2017. Data were analysed using logistic regression and by calculating relative risks (RR).ResultsBetween 2007 and 2013, the proportion of the general population of Peru without mobile phone access averaged 7.8% but for patients with tuberculosis was 18% (P < 0.001). Patients without access were more likely to hold a lower socioeconomic position, suffer from food insecurity and be older than 50 years (all P < 0.01). Compared to patients with mobile phone access, patients without access at recruitment were more likely to subsequently have incomplete treatment (20% vs. 13%, RR = 1.5; P = 0.001) or an adverse treatment outcome (29% vs. 23% RR = 1.3; P = 0.006). Between 2016 and 2017, the proportion of patients without access dropped to 8.9% overall, but remained the same (18%) as in 2012 among the poorest third.ConclusionAccess to mobile phones among patients with tuberculosis is insufficient, and rarest in patients who are poorer and later have adverse treatment outcomes. Thus, mobile phone interventions to improve tuberculosis care may be least accessed by the priority populations for whom they are intended. Such interventions should ensure access to mobile phones to enhance equity.

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Section: Discussionmentioning

confidence: 99%

Mobile phone interventions for tuberculosis should ensure access to mobile phones to enhance equity – a prospective, observational cohort study in Peruvian shantytowns

Saunders

Wingfield

Tovar

et al. 2018

Tropical Med Int Health

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“…individual contacts at high risk of tuberculosis, it identified households in which contacts are at high risk of tuberculosis that could then be prioritised for comprehensive, household-level interventions. 15 Furthermore, because tuberculosis is a disease that clusters in households, a household-level risk score might be a more effective method of prioritising resources to maximise epidemiological impact. In this much larger study, we used data on index patient, household, and contact characteristics to derive and externally validate such a score.…”

Section: Implications Of All the Available Evidencementioning

confidence: 99%

“…This individual score, which includes clinical and sociodemographic risk factors, predicts tuberculosis independently of baseline latent tuberculosis infection status and is being evaluated within the ongoing Community Randomised Evaluation of a Socioeconomic Intervention to Prevent Tuberculosis trial to facilitate targeted tuberculosis screening and preventive treatment 12, 13, 14. However, our experiences during that trial suggest that a score might be of greater pragmatic value to public health programmes if, rather than identifying individual contacts at high risk of tuberculosis, it identified households in which contacts are at high risk of tuberculosis that could then be prioritised for comprehensive, household-level interventions 15 . Furthermore, because tuberculosis is a disease that clusters in households, a household-level risk score might be a more effective method of prioritising resources to maximise epidemiological impact.…”

Section: Introductionmentioning

confidence: 99%

A household-level score to predict the risk of tuberculosis among contacts of patients with tuberculosis: a derivation and external validation prospective cohort study

Saunders

Wingfield

Datta

et al. 2020

The Lancet Infectious Diseases

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Background Far too often, one meets patients who went for years or even decades from doctor to doctor, without getting a valid diagnosis. This brings pain to millions of patients and their families, not to speak of the enormous costs. Often patients do not know well enough which factors (or combinations thereof) trigger their problems. Results If conventional methods fail, we propose the use of statistics and algebra to give doctors much more precise inputs from patients. We propose statistical regression for independent triggering factors for medical problems, and "balanced incomplete block designs" for non-independent factors. These methods might change a useless statement like "I feel very tired after meals" to a much more valuable "After a meal with many carbohydrates, but few vegetables, a moderate physical activity will usually force me into a wheelchair". In order to show that these methods do work, we briefly describe a real case in which these methods helped to solve a 60 year old problem in a patient, and give some more examples where these methods might be very useful. Discussion In this paper, we present a way of getting medical diagnoses when the methods in medicine are insufficient, too time consuming, or very expensive. By asking the patient to conduct tests (often at home) according to a very well-prepared schedule, statistics can use regression analysis to identify the factors (or combinations thereof) which trigger or worsen the patient's problems. This (very inexpensive) analysis can often give the patient's doctor(s) a very good and precise input for their diagnosis. Conclusions While regression is used in clinical medicine, it seems to be widely unknown among diagnosing doctors. In finding the reason(s) of rare diseases, doctors face very tough problems. So they deserve to know all tools which could offer some help. This can save the health systems much money, and the patients also a lot of pain.

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“…TB is a communicable disease and a major cause of ill health globally that affects the lungs (pulmonary) but can also affect other sites (extrapulmonary) [19,20]. Despite being a curable and preventable disease, TB is the leading cause of death from bacterial infection worldwide and undoubtedly representing a global public health priority [21][22][23][24]. TB is also the leading cause of morbidity and mortality among people living with HIV/AIDS [24], accounting for approximately 40% of deaths globally [20].…”

Section: Plos Onementioning

confidence: 99%

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

Otiende¹,

Achia

Mwambi

2020

PLoS ONE

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The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

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Pragmatic tuberculosis prevention policies for primary care in low- and middle-income countries

Cited by 6 publications

References 8 publications

Mobile phone interventions for tuberculosis should ensure access to mobile phones to enhance equity – a prospective, observational cohort study in Peruvian shantytowns

Mobile phone interventions for tuberculosis should ensure access to mobile phones to enhance equity – a prospective, observational cohort study in Peruvian shantytowns

A household-level score to predict the risk of tuberculosis among contacts of patients with tuberculosis: a derivation and external validation prospective cohort study

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

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