Treatment seeking behaviours, antibiotic use and relationships to multi-drug resistance: A study of urinary tract infection patients in Kenya, Tanzania and Uganda

Abstract

Antibacterial resistance (ABR) is a major public health threat. An important accelerating factor is treatment-seeking behaviour, including inappropriate antibiotic (AB) use. In many low- and middle-income countries (LMICs) this includes taking ABs with and without prescription sourced from various providers, including health facilities and community drug sellers. However, investigations of complex treatment-seeking, AB use and drug resistance in LMICs are scarce. The Holistic Approach to Unravel Antibacterial Resistance in East Africa (HATUA) Consortium collected questionnaire and microbiological data from adult outpatients with urinary tract infection (UTI)-like symptoms presenting at healthcare facilities in Kenya, Tanzania and Uganda. Using data from 6,388 patients, we analysed patterns of self-reported treatment seeking behaviours ('patient pathways') using process mining and single-channel sequence analysis. Among those with microbiologically confirmed UTI (n = 1,946), we used logistic regression to assess the relationship between treatment seeking behaviour, AB use, and the likelihood of having a multi-drug resistant (MDR) UTI. The most common treatment pathway for UTI-like symptoms in this sample involved attending health facilities, rather than other providers like drug sellers. Patients from sites in Tanzania and Uganda, where over 50% of patients had an MDR UTI, were more likely to report treatment failures, and have repeat visits to providers than those from Kenyan sites, where MDR UTI proportions were lower (33%). There was no strong or consistent relationship between individual AB use and likelihood of MDR UTI, after accounting for country context. The results highlight the hurdles East African patients face in accessing effective UTI care. These challenges are exacerbated by high rates of MDR UTI, suggesting a vicious cycle of failed treatment attempts and sustained selection for drug resistance. Whilst individual AB use may contribute to the risk of MDR UTI, our data show that factors related to context are stronger drivers of variations in ABR.

Fig 1. Selection of the analysis samples.

Fig 2. Treatment-seeking questions from the HATUA questionnaire and derived outcomes in this study.

Footnote: Adapted from another publication [11], published by the same authors. We retain the copyright and authorise this use.

Fig 3

A-D: Stages in data management, analysis and visualization for process mining. Attributions for the open-source images can be found inS4 Table.

Fig 4. Treatment seeking pathways visualized in a Sankey plot among patients in Kenya, Tanzania and Uganda.

The numbers by each node represent the absolute number and percentage of participants who took that step. The width of the arcs represents the frequency of the flow.

Fig 5

Ten most common treatment-seeking patterns visualized in Kenya (panel A), Tanzania (panel B) and Uganda (panel C). Number on the left represents the number of participants who experienced the trace. The percentage shows the proportion of participants who experienced the trace compared to the total patients from that country.

Fig 6. Percentage of patients taking ABs for their UTI-like symptoms at any point prior to being recruited, by country and type of provider.

Fig 7. Summary of distribution, sociodemographic and the behavioral characteristics of 10 treatment-seeking clusters.

Figure note: The far-left column shows the distribution of participants in each cluster (N/%). The blue cells are column percentages of cluster membership according to the country, gender and education distributions.