Evaluation and optimization of the syndromic management of female genital tract infections in Nairobi, Kenya.


Journal

BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551

Informations de publication

Date de publication:
22 Aug 2023
Historique:
received: 17 11 2022
accepted: 04 07 2023
medline: 24 8 2023
pubmed: 23 8 2023
entrez: 23 8 2023
Statut: epublish

Résumé

Genital tract infections pose a public health concern. In many low-middle-income countries, symptom-based algorithms guide treatment decisions. Advantages notwithstanding, this strategy has important limitations. We aimed to determine the infections causing lower genital tract symptoms in women, evaluated the Kenyan syndromic treatment algorithm for vaginal discharge, and proposed an improved algorithm. This cross-sectional study included symptomatic non-pregnant adult women presenting with lower genital tract symptoms at seven outpatient health facilities in Nairobi. Clinical, socio-demographic information and vaginal swabs microbiological tests were obtained. Multivariate logistic regression analyses were performed to find predictive factors for the genital infections and used to develop an alternative vaginal discharge treatment algorithm (using 60% of the dataset). The other 40% of data was used to assess the performance of each algorithm compared to laboratory diagnosis. Of 813 women, 66% had an infection (vulvovaginal candidiasis 40%, bacterial vaginosis 17%, Neisseria gonorrhoea 14%, multiple infections 23%); 56% of women reported ≥ 3 lower genital tract symptoms episodes in the preceding 12 months. Vulvovaginal itch predicted vulvovaginal candidiasis (odds ratio (OR) 2.20, 95% CI 1.40-3.46); foul-smelling vaginal discharge predicted bacterial vaginosis (OR 3.63, 95% CI 2.17-6.07), and sexually transmitted infection (Neisseria gonorrhoea, Trichomonas vaginalis, Chlamydia trachomatis, Mycoplasma genitalium) (OR 1.64, 95% CI 1.06-2.55). Additionally, lower abdominal pain (OR 1.73, 95% CI 1.07-2.79) predicted sexually transmitted infection. Inappropriate treatment was 117% and 75% by the current and alternative algorithms respectively. Treatment specificity for bacterial vaginosis/Trichomonas vaginalis was 27% and 82% by the current and alternative algorithms, respectively. Performance by other parameters was poor to moderate and comparable between the two algorithms. Single and multiple genital infections are common among women presenting with lower genital tract symptoms at outpatient clinics in Nairobi. The conventional vaginal discharge treatment algorithm performed poorly, while the alternative algorithm achieved only modest improvement. For optimal care of vaginal discharge syndrome, we recommend the inclusion of point-of-care diagnostics in the flowcharts.

Sections du résumé

BACKGROUND BACKGROUND
Genital tract infections pose a public health concern. In many low-middle-income countries, symptom-based algorithms guide treatment decisions. Advantages notwithstanding, this strategy has important limitations. We aimed to determine the infections causing lower genital tract symptoms in women, evaluated the Kenyan syndromic treatment algorithm for vaginal discharge, and proposed an improved algorithm.
METHODS METHODS
This cross-sectional study included symptomatic non-pregnant adult women presenting with lower genital tract symptoms at seven outpatient health facilities in Nairobi. Clinical, socio-demographic information and vaginal swabs microbiological tests were obtained. Multivariate logistic regression analyses were performed to find predictive factors for the genital infections and used to develop an alternative vaginal discharge treatment algorithm (using 60% of the dataset). The other 40% of data was used to assess the performance of each algorithm compared to laboratory diagnosis.
RESULTS RESULTS
Of 813 women, 66% had an infection (vulvovaginal candidiasis 40%, bacterial vaginosis 17%, Neisseria gonorrhoea 14%, multiple infections 23%); 56% of women reported ≥ 3 lower genital tract symptoms episodes in the preceding 12 months. Vulvovaginal itch predicted vulvovaginal candidiasis (odds ratio (OR) 2.20, 95% CI 1.40-3.46); foul-smelling vaginal discharge predicted bacterial vaginosis (OR 3.63, 95% CI 2.17-6.07), and sexually transmitted infection (Neisseria gonorrhoea, Trichomonas vaginalis, Chlamydia trachomatis, Mycoplasma genitalium) (OR 1.64, 95% CI 1.06-2.55). Additionally, lower abdominal pain (OR 1.73, 95% CI 1.07-2.79) predicted sexually transmitted infection. Inappropriate treatment was 117% and 75% by the current and alternative algorithms respectively. Treatment specificity for bacterial vaginosis/Trichomonas vaginalis was 27% and 82% by the current and alternative algorithms, respectively. Performance by other parameters was poor to moderate and comparable between the two algorithms.
CONCLUSION CONCLUSIONS
Single and multiple genital infections are common among women presenting with lower genital tract symptoms at outpatient clinics in Nairobi. The conventional vaginal discharge treatment algorithm performed poorly, while the alternative algorithm achieved only modest improvement. For optimal care of vaginal discharge syndrome, we recommend the inclusion of point-of-care diagnostics in the flowcharts.

Identifiants

pubmed: 37608250
doi: 10.1186/s12879-023-08442-2
pii: 10.1186/s12879-023-08442-2
pmc: PMC10463802
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

547

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Gloria S Omosa-Manyonyi (GS)

Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya. gomosa@uonbi.ac.ke.

Marloes de Kam (M)

Department of Internal Medicine, Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, NL, Netherlands.

Alma Tostmann (A)

Department of Medical Microbiology, Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, NL, Netherlands.

Mwasi A Masido (MA)

Nairobi City County, Health Services, Nairobi, Kenya.

Nyawira Nyagah (N)

Nairobi City County, Health Services, Nairobi, Kenya.

Moses M Obimbo (MM)

Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya.

Andre J A M van der Ven (AJAM)

Department of Internal Medicine, Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, NL, Netherlands.

Jaap Ten Oever (JT)

Department of Internal Medicine, Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, NL, Netherlands.

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Classifications MeSH