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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Analysis of vaccination campaign effectiveness and population immunity to support and sustain polio elimination in Nigeria
|
|---|---|
| Published in |
BMC Medicine, March 2016
|
| DOI | 10.1186/s12916-016-0600-z |
| Pubmed ID | |
| Authors |
Alexander M. Upfill-Brown, Arend Voorman, Guillaume Chabot-Couture, Faisal Shuaib, Hil M. Lyons |
| Abstract |
The world is closer than ever to a polio-free Africa. In this end-stage, it is important to ensure high levels of population immunity to prevent polio outbreaks. Here, we introduce a new method of assessing vaccination campaign effectiveness and estimating immunity at the district-level. We demonstrate how this approach can be used to plan the vaccination campaigns prospectively to better manage population immunity in Northern Nigeria. Using Nigerian acute flaccid paralysis surveillance data from 2004-2014, we developed a Bayesian hierarchical model of campaign effectiveness and compared it to lot-quality assurance sampling data. We then used reconstructed sero-specific population immunity based on campaign history and compared district estimates of immunity to the occurrence of confirmed poliovirus cases. Estimated campaign effectiveness has improved across northern Nigeria since 2004, with Kano state experiencing an increase of 40 % (95 % CI, 26-54 %) in effectiveness from 2013 to 2014. Immunity to type 1 poliovirus has increased steadily. On the other hand, type 2 immunity was low and variable until the recent use of trivalent oral polio vaccine. We find that immunity estimates are related to the occurrence of both wild and vaccine-derived poliovirus cases and that campaign effectiveness correlates with direct measurements using lot-quality assurance sampling. Future campaign schedules highlight the trade-offs involved with using different vaccine types. The model in this study provides a novel method for assessing vaccination campaign performance and epidemiologically-relevant estimates of population immunity. Small-area estimates of campaign effectiveness can then be used to evaluate prospective campaign plans. This modeling approach could be applied to other countries as well as other vaccine preventable diseases. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 50% |
| United States | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 77 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 18% |
| Student > Master | 13 | 17% |
| Student > Ph. D. Student | 13 | 17% |
| Student > Bachelor | 6 | 8% |
| Lecturer > Senior Lecturer | 3 | 4% |
| Other | 6 | 8% |
| Unknown | 23 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 22% |
| Nursing and Health Professions | 5 | 6% |
| Biochemistry, Genetics and Molecular Biology | 4 | 5% |
| Social Sciences | 4 | 5% |
| Computer Science | 3 | 4% |
| Other | 17 | 22% |
| Unknown | 28 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 03 April 2016.
All research outputs
#13,373,196
of 23,577,654 outputs
Outputs from BMC Medicine
#2,830
of 3,569 outputs
Outputs of similar age
#140,159
of 302,222 outputs
Outputs of similar age from BMC Medicine
#39
of 47 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,569 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.5. This one is in the 19th percentile – i.e., 19% 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 302,222 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 47 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.