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Neighborhood clustering of non-communicable diseases: results from a community-based study in Northern Tanzania

Overview of attention for article published in BMC Public Health, March 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

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15 tweeters

Citations

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10 Dimensions

Readers on

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98 Mendeley
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Title
Neighborhood clustering of non-communicable diseases: results from a community-based study in Northern Tanzania
Published in
BMC Public Health, March 2016
DOI 10.1186/s12889-016-2912-5
Pubmed ID
Authors

John W. Stanifer, Joseph R Egger, Elizabeth L. Turner, Nathan Thielman, Uptal D. Patel

Abstract

In order to begin to address the burden of non-communicable diseases (NCDs) in sub-Saharan Africa, high quality community-based epidemiological studies from the region are urgently needed. Cluster-designed sampling methods may be most efficient, but designing such studies requires assumptions about the clustering of the outcomes of interest. Currently, few studies from Sub-Saharan Africa have been published that describe the clustering of NCDs. Therefore, we report the neighborhood clustering of several NCDs from a community-based study in Northern Tanzania. We conducted a cluster-designed cross-sectional household survey between January and June 2014. We used a three-stage cluster probability sampling method to select thirty-seven sampling areas from twenty-nine neighborhood clusters, stratified by urban and rural. Households were then randomly selected from each of the sampling areas, and eligible participants were tested for chronic kidney disease (CKD), glucose impairment including diabetes, hypertension, and obesity as part of the CKD-AFRiKA study. We used linear mixed models to explore clustering across each of the samplings units, and we estimated absolute-agreement intra-cluster correlation (ICC) coefficients (ρ) for the neighborhood clusters. We enrolled 481 participants from 346 urban and rural households. Neighborhood cluster sizes ranged from 6 to 49 participants (median: 13.0; 25th-75th percentiles: 9-21). Clustering varied across neighborhoods and differed by urban or rural setting. Among NCDs, hypertension (ρ = 0.075) exhibited the strongest clustering within neighborhoods followed by CKD (ρ = 0.440), obesity (ρ = 0.040), and glucose impairment (ρ = 0.039). The neighborhood clustering was substantial enough to contribute to a design effect for NCD outcomes including hypertension, CKD, obesity, and glucose impairment, and it may also highlight NCD risk factors that vary by setting. These results may help inform the design of future community-based studies or randomized controlled trials examining NCDs in the region particularly those that use cluster-sampling methods.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 98 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 21%
Student > Doctoral Student 14 14%
Student > Ph. D. Student 12 12%
Student > Bachelor 10 10%
Researcher 9 9%
Other 13 13%
Unknown 19 19%
Readers by discipline Count As %
Medicine and Dentistry 34 35%
Nursing and Health Professions 18 18%
Social Sciences 7 7%
Agricultural and Biological Sciences 3 3%
Economics, Econometrics and Finance 3 3%
Other 10 10%
Unknown 23 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 01 May 2016.
All research outputs
#1,146,266
of 11,547,117 outputs
Outputs from BMC Public Health
#1,435
of 7,929 outputs
Outputs of similar age
#43,463
of 289,965 outputs
Outputs of similar age from BMC Public Health
#46
of 222 outputs
Altmetric has tracked 11,547,117 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,929 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has done well, scoring higher than 81% of its peers.
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 289,965 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 222 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.