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Sexually transmitted infections and migration in Uganda: a population-based study.
Sex Transm Infect
August 2024
Rakai Health Sciences Program, Kalisizo, Central Region, Uganda.
Objectives: Migration is associated with increased risk of HIV infection in Africa, but evidence about non-HIV sexually transmitted infection (STI) burden among African migrants is limited.
Methods: We used data from the Sexually Transmitted Infection Prevalence Study, a cross-sectional population-based study of chlamydia, gonorrhoea, trichomoniasis, syphilis and herpes simplex virus type 2 prevalence in southern Uganda, to compare STI prevalence between adults aged 18 and 49 years with and without a recent history of migration. Migration status was determined using household census data, with a recent migration history defined as having moved into one's community of current residence within the last ~18 months.
Risk factors for curable sexually transmitted infections among youth: findings from the STICH population survey in Zimbabwe.
Sex Transm Infect
November 2024
The Health Research Unit Zimbabwe, Biomedical Research and Training Institute, Harare, Zimbabwe.
Objectives: Youth are at high risk of sexually transmitted infections (STIs) in Africa. We aimed to determine the risk factors for curable STIs in youth in Zimbabwe.
Methods: A population-based survey was conducted among randomly selected 18-24 year-olds in 16 communities across two provinces in Zimbabwe to ascertain outcomes for a cluster randomised trial investigating the impact of community-based STI screening for youth on population prevalence of STIs.
Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.
BMC Infect Dis
January 2024
Chinese Preventive Medicine Association, Beijing, China.
Background: Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.
Methods: Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models.
STI pathogens in the oropharynx: update on screening and treatment.
Curr Opin Infect Dis
February 2024
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Purpose Of Review: The rise in antimicrobial resistance in several STI pathogens such as Neisseria gonorrhoeae has become a public health threat as only one first-line treatment remains. Reducing screening interval for gonorrhoea and chlamydia in high-prevalence populations has been proposed to address antimicrobial stewardship, but this remains controversial. This review aimed to revisit the epidemiology of infections at the oropharynx and review the current screening recommendations and treatment guidelines in different populations.
View Article and Find Full Text PDFInterruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China.
BMC Public Health
October 2023
Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital, Xinxiang Medical University, Xinxiang, 453000, Henan Province, People's Republic of China.
Background: Interrupted time series (ITS) analysis is a growing method for assessing intervention impacts on diseases. However, it remains unstudied how the COVID-19 outbreak impacts gonorrhea. This study aimed to evaluate the effect of COVID-19 on gonorrhea and predict gonorrhea epidemics using the ITS-autoregressive integrated moving average (ARIMA) model.
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