Evidence map›Paper›PMID 29699537›Full record

ArticleBMC public health2018

Performance of centralized versus decentralized tuberculosis treatment services in Southern Brazil, 2006-2015.

Mara Cristina Scheffer, Rodrigo Ivan Prim, Leticia Muraro Wildner, Taiane Freitas Medeiros, Rosemeri Maurici, Emil Kupek, Maria Luiza Bazzo

Abstract read
In one paragraph

Article in BMC public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

7 authors.

Mara Cristina SchefferDepartamento de Análises Clínicas for Programa de Pós-Graduação em Farmácia da, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Rodrigo Ivan PrimDepartamento de Análises Clínicas for Programa de Pós-Graduação em Farmácia da, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Leticia Muraro WildnerDepartamento de Análises Clínicas for Programa de Pós-Graduação em Farmácia da, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Taiane Freitas MedeirosDepartamento de Análises Clínicas for Programa de Pós-Graduação em Farmácia da, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Rosemeri MauriciDepartamento de Clínica Médica, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Emil KupekDepartamento de Saúde Pública, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Maria Luiza BazzoDepartamento de Análises Clínicas for Programa de Pós-Graduação em Farmácia da, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil. m.l.bazzo@ufsc.br.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis (TB) control programs face the challenges of decreasing incidence, mortality rates, and drug resistance while increasing treatment adherence. The Brazilian TB control program recommended the decentralization of patient care as a strategy for combating the disease. This study evaluated the performance of this policy in an area with high default rates, comparing epidemiological and operational indicators between two similar municipalities.

methodsThis study analyzed epidemiological and operational indicators on new cases of pulmonary tuberculosis reported in the Brazilian Notifiable Diseases Information System between 2006 and 2015. In addition, to characterize differences between the populations of the two studied municipalities, a prospective cohort study was conducted between 2014 and 2015, in which patients with new cases of culture-confirmed pulmonary tuberculosis were interviewed and monitored until the disease outcome. A descriptive analysis, the chi-square test, and a Poisson regression model were employed to compare TB treatment outcomes and health care indicators between the municipalities.

resultsTwo thousand three hundred nine cases were evaluated, of which 207 patients were interviewed. Over the 2006-2015 period, TB incidence per 100,000 population in the municipality with decentralized care was significantly higher (39%, 95% CI 27-49%) in comparison to that of the municipality with centralized care. TB treatment default rate (45%, 95% CI 12-90%) was also higher in the municipality with decentralized care. During the two-year follow-up, significant differences were found between patients in centralized care and those in decentralized care regarding treatment success (84.5 vs. 66.1%), treatment default (10.7 vs. 25.8%), illicit drug use (27.7 vs. 45.9%), and homelessness (3.6 vs. 12.9%). The operational indicators revealed that the proportion of control smear tests, medical imaging, and HIV tests were all significantly higher in the centralized care. However, a significantly higher proportion of patients started treatment in the early stages of the disease in the municipality with decentralized care.

conclusionsThese data showed a low success rate in TB treatment in both municipalities. Decentralization of TB care, alone, did not improve the main epidemiological and operational indicators related to disease control when compared to centralized care. Full implementation of strategies already recommended is needed to improve TB treatment success rates.

Indexed as

AdolescentAdultBrazilCitiesFemaleHumansIncidenceMaleMiddle AgedProspective StudiesTreatment OutcomeTuberculosisTuberculosis, PulmonaryUrban Health ServicesYoung AdultDecentralizedPublic healthTreatment outcomeTuberculosis

Identifiers

PMID29699537
PMCPMC5922025

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.