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Attention Score in Context
| Title |
Tuberculosis treatment delays and associated factors within the Zimbabwe national tuberculosis programme
|
|---|---|
| Published in |
BMC Public Health, January 2015
|
| DOI | 10.1186/s12889-015-1437-7 |
| Pubmed ID | |
| Authors |
Kudakwashe C Takarinda, Anthony D Harries, Barnet Nyathi, Mkhokheli Ngwenya, Tsitsi Mutasa-Apollo, Charles Sandy |
| Abstract |
BackgroundDelayed presentation of pulmonary TB (PTB) patients for treatment from onset of symptoms remains a threat to controlling individual disease progression and TB transmission in the community. Currently, there is insufficient information about treatment delays in Zimbabwe, and we therefore determined the extent of patient and health systems delays and their associated factors in patients with microbiologically confirmed PTB.MethodsA structured questionnaire was administered at 47 randomly selected health facilities in Zimbabwe by trained health workers to all patients aged ¿18 years with microbiologically confirmed PTB who were started on TB treatment and entered in the health facility TB registers between 01 January and 31 March 2013. Multivariate logistic regression was used to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CIs) for associations between patient/health system characteristics and patient delay >30 days or health system delay >4 days.ResultsOf the 383 recruited patients, 211(55%) were male with an overall median age of 34 years (IQR, 28-43). There was a median of 28 days (IQR, 21-63) for patient delays and 2 days (IQR, 1-5) for health system delays with 184 (48%) and 118 (31%) TB patients experiencing health system delays >30 days and health system delays >4 days respectively. Starting TB treatment at rural primary healthcare vs district/mission facilities [aOR 2.70, 95% CI 1.27-5.75, p¿=¿0.01] and taking self-medication [aOR 2.33, 95% CI 1.23-4.43, p¿=¿0.01] were associated with encountering patient delays. Associated with health system delays were accessing treatment from lower level facilities [aOR 2.67, 95% CI 1.18-6.07, p¿=¿0.019], having a Gene Xpert TB diagnosis [aOR 0.21, 95% CI 0.07-0.66, p¿=¿0.008] and >4 health facility visits prior to TB diagnosis [(aOR) 3.34, 95% CI 1.11-10.03, p¿=¿0.045].ConclusionPatient delays were longer and more prevalent, suggesting the need for strategies aimed at promoting timely seeking of appropriate medical consultation among presumptive TB patient. Health system delays were uncommon, suggesting a fairly efficient response to microbiologically confirmed PTB cases. Identified risk factors should be explored further and specific strategies aimed at addressing these factors should be identified in order to lessen patient and health system delays. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 238 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | <1% |
| United States | 1 | <1% |
| Indonesia | 1 | <1% |
| Unknown | 234 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 54 | 23% |
| Researcher | 37 | 16% |
| Student > Postgraduate | 22 | 9% |
| Student > Bachelor | 21 | 9% |
| Student > Ph. D. Student | 14 | 6% |
| Other | 42 | 18% |
| Unknown | 48 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 79 | 33% |
| Nursing and Health Professions | 42 | 18% |
| Social Sciences | 15 | 6% |
| Environmental Science | 8 | 3% |
| Immunology and Microbiology | 8 | 3% |
| Other | 26 | 11% |
| Unknown | 60 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2015.
All research outputs
#15,318,515
of 22,783,848 outputs
Outputs from BMC Public Health
#11,327
of 14,852 outputs
Outputs of similar age
#210,261
of 353,575 outputs
Outputs of similar age from BMC Public Health
#171
of 234 outputs
Altmetric has tracked 22,783,848 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,852 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,575 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 234 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.