Background: Sexually transmitted infections (STIs) and STI treatment remain a challenge in public health. There is little understanding of related factors influencing health seeking behaviour and delay of care among clinic attendees in Jamaica. Aim: To determine socio-demographic profile of clinic attendees with STI and identify factors associated with delay in seeking care for STI-related symptoms. Methods: A cross-sectional study was done. 201 adult patients presenting with STI symptoms from four health centres in Kingston and St Andrew were selected. A 24-item interviewer-assisted questionnaire was used to obtain data on socio-demographic characteristics, patients’ symptoms and duration, previous STIs, knowledge of complications and seriousness of STIs, and factors influencing decision to seek medical care. Results: Almost 75% delayed seeking care for STIs. Recurrent STIs was identified in 41% of patients. ‘Could not find time’ was the most commonly reported reason for delay in seeking care (36%). Females were 3.4 times more likely to delay seeking care for STI symptoms than males (OR 3.42, 95% CI: 1.73–6.73). Those with primary level education and below were 5 times more likely to delay seeking care for STI symptoms than those with at least secondary level education (OR 5.05, 95% CI: 1.09–23.46). Participants viewed staff as confidential (68%) and 65% thought health-care workers spent adequate time during consultations. Conclusion: Lower education level and the female gender are associated with delay in seeking care for STI-related symptoms. These factors should be considered when developing interventions to reduce delay in care for STI-related symptoms.

Background/Objectives: To estimate (a) survival after SARS-CoV-2 infection by COVID-19 vaccination status, and (b) COVID-19 vaccine effectiveness in a middle-income country. Methods: In this retrospective cohort study, secondary analysis of data from the national surveillance and vaccination databases was conducted. The primary outcome was COVID-19 death classified based on the WHO criteria. Data were analysed by vaccination status, age, sex, geographic region, and wave period. Kaplan–Meier curves were plotted; log-rank followed by multiple comparison tests were used to compare survival probabilities. Cox proportional-hazards models with time-varying covariates estimated hazard ratios (HR). Vaccine effectiveness was computed as (1-HR) × 100. Results: A total of 55,299 COVID-19 cases were captured by the national surveillance system between 1 April and 31 December 2021. Of these, 45,774 (1581 vaccinated, 44,193 unvaccinated) were included in the analysis. After a follow-up of 327 days, there were 22 deaths (case fatality rate (CFR) 1.5%) among 1581 COVID-19 vaccinated cases and 1821 deaths (CFR 4.1%) among 44,193 unvaccinated cases. There was one COVID-19 death per 10,000 person days in vaccinated cases compared with 2.7 COVID-19 deaths per 10,000 person days in unvaccinated cases. After adjustment for age, sex, and geographic region, the effectiveness against COVID-19 death across all vaccine types (ChAdOx1 nCoV-19, BNT162b2, Ad26.COV2.S, or BBIBP-CorV) was 68% (95% CI: 51–79). Effectiveness was 75% (95% CI: 59–84) for ChAdOx1 nCoV-19. Vaccine effectiveness across all vaccine types was higher in younger cases, (82% (95% CI: 52–93), 18–64 years vs. 63% (95% CI: 41–77), ≥65 years), females (84% (95% CI: 63–93), females vs. 53% (95% CI: 24–71), males) and those vaccinated in the past 3 months (71% (95% CI: 47–85), past 0–3 months vs. 56% (95% CI: 23–75), 3–6 months). Conclusions: COVID-19 vaccines were effective in preventing COVID-19 death in a population with low vaccination coverage. Limitations of the analysis include the use of surveillance data (under-reporting of cases, missing data), exclusion of partially vaccinated cases, and insufficient data on important confounders (circulating variants and comorbidities).

Globally, the COVID-19 pandemic caused significant healthcare, economic and social impacts. This study described characteristics of COVID-19 mortality and determined risk of COVID-19 death by wave period in Jamaica. A cross-sectional analysis was conducted using data on confirmed COVID-19 cases reported to the National Surveillance Unit from 5 August 2020 to 4 October 2022. Multivariate logistic regression was used to determine odds of death by wave period, adjusting for age, sex and health region. 148,262 cases and 3654 COVID-19 deaths were included in the analysis. Increased odds of COVID-19 death were associated with male sex, age, and health region. Odds of death increased among males (OR:1.4, 95%CI: 1.3–1.5), residents in the Western health region (OR:1.4, 95%CI: 1.3–1.5) and persons ≥ 40 years (OR:18.2, 95%CI: 15.8–20.9). In comparison to Wave 4, odds of death were OR:3.4 (95%CI: 3.0–3.8) for Wave 3 and OR:2.6 (95%CI: 2.2–3.0) for Wave 1. Higher risk of death among males and older persons were observed across wave periods. Odds of death was greatest during Wave 3. This reinforces the need for responsive and adaptive health systems, as well as social and clinical interventions among high-risk groups.