Data from: Geospatial mobility, non-local partners, concurrent sexual partnerships, and gender influence longitudinal STI prevalence in rural eastern Africa

Published Mar 25, 2026 on Dryad. https://doi.org/10.5061/dryad.dv41ns26f

Data files

Mar 25, 2026 version files 4.39 MB

README.md

2.49 KB
sti-bivariate-table-figure-4.do

5.18 KB
sti-mediators-table.do

14.54 KB
sti-mobility-tables-1-2.do

2.23 KB
sti-mobility.codebook.xlsx

13.78 KB
sti-mobility.data.csv

1.02 MB
sti-mobility.dta

3.34 MB
sti-regression-table-3.do

237 B

Abstract

Mobility challenges HIV prevention efforts through associated risk behaviours and sexually transmitted infections (STIs). We characterized relationships between mobility and sexual risks on STI prevalence over time in East Africa. Geospatial mobility and sexual risk behaviours were collected in 12 communities using a sex- and HIV-stratified random sub-sample of 2,750 adults from a longitudinal cohort (2015-2019) of a HIV test-and-treat trial in Kenya and Uganda. Annual Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) testing was performed, and relationships of prevalent STIs with mobility, sexual concurrency, and higher HIV-risk sexual partners (defined as one-night stand, stranger, commercial sex worker/client, casual partner, or inherited partner/inheritor) were examined. The annual prevalence of CT or NG among 2,665 participants tested was 3.1% (95 % CI: 2.5-3.9) at baseline, 3.3% (95 % CI: 2.6-4.0) at year 1, 4.4 % (95 % CI: 3.0-5.2) at year 2, and 4.8 % (95 % CI: 4.0-5.7) at year 3. STI (CT, NG) prevalence was associated with migration in the past year, sexual partnership concurrency, being single, higher HIV-risk partners, age > 25, low household wealth, and the relationship between gender and work-related travel in past six months. The association between select STI prevalence and past six-months travel was mediated by higher HIV-risk sexual partners, partnership concurrency, out-of-town partner, and higher HIV-risk transactional sex partners. Geospatial mobility, sexual concurrency, and higher HIV-risk partnerships significantly influence longitudinal CT and/or NG prevalence in East Africa.

Dataset DOI: 10.5061/dryad.dv41ns26f

Description of the data and file structure

Geospatial mobility and sexual risk behaviours were collected in 12 communities using a sex- and HIV-stratified random sub-sample of 2,750 adults from a longitudinal cohort (2015-2019) of a HIV test-and-treat trial in Kenya and Uganda. Annual Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) testing was performed, and relationships of prevalent STIs (CT, NG) with geospatial mobility, sexual concurrency, and higher HIV-risk sexual partners (defined as one-night stand, stranger, commercial sex worker/client, casual partner, or inherited partner/inheritor) were examined.

Files and variables

sti-mobility.codebook.xlsx: The variables list and codebook for the sti-mobility.dta dataset in Excel format.

sti-mobility.dta: The geospatial mobility and STI (CT, NG) dataset in Stata dataset format (version 18).

sti-mobility.data.csv: A comma separated value (csv) version of the data used in the analysis.

sti-mobility-tables-1-2.do: The Stata do file to produce the data for Tables 1 & 2.

sti-regression-table-3.do: The Stata do file to produce the data for Table 3.

sti-bivariate-table-figure-4.do: The Stata do file to produce data and graph for Figure 4.

sti-mediators-table.do: The Stata do file to produce data for the mediators table 4.

Code/software

The statistical analysis software package Stata was used to analyze the data.

A csv version of the data is also provided and can be viewed in the open software application R.

Access information

Data was derived from the following sources:

From 2015-2019, survey data on mobility and sexual risk behaviours were collected every 6 months, with annual CT and NG urine testing using Cepheid GeneXpert (Xpert® CT/NG). Survey data with informed consent approval were collected using programmed tablets and took about 90 minutes to complete; topics included demographics, migration histories, mobility in past six months, and sexual behaviours.

Human subjects data

We received explicit consent from the participants to publish the de-identified data in the public domain. We de-identified the data by removing all personally identifiable information (PII) and replacing the field age by range categories.