Despite a high use of antibiotics and a significant burden of infectious disease, ongoing monitoring and reporting of antimicrobial resistant pathogens in rural and regional Australia is insufficient. Many geographically isolated regions of Australia have limited infrastructure, resources and fall outside of surveillance reach, limiting health services’ ability to provide an early warning signal and appropriate response. To monitor trends in the development of antimicrobial resistance (AMR), identify high-risk populations and to evaluate effectiveness of control and prevention in rural and regional Australia, a subnational surveillance system termed HOTspots was developed. To promote the best use of public health resources through the development of effective and efficient surveillance systems, we evaluated HOTspots and its prototype surveillance platform for data quality, acceptability, representativeness, and timeliness. We used the Centers for Disease Prevention and Control (CDC) guidelines for evaluating public health surveillance systems and assessed the four attributes using a descriptive analysis of quantitative data and a thematic analysis of qualitative data. We report that the HOTspots surveillance system and its prototype platform effectively captures and represents AMR data across Northern Australia. The descriptive analysis of HOTspots data demonstrated some variation in data completeness but that data validity and representativeness were high. Thematic analysis of interview transcripts found that the system was acceptable, with almost all study participants identifying timeliness, online accessibility, and community representativeness as drivers for adoption of the system, and that the system provided timely data. The evaluation also identified areas for improvement and made recommendations to the HOTspots surveillance system and its associated prototype platform.

Background: Outcomes after acute rheumatic fever (ARF) diagnosis are variable, ranging from recovery to development of severe rheumatic heart disease (RHD). There is no diagnostic test. Evaluation using the Australian clinical diagnostic criteria can result in a diagnosis of ‘definite’, ‘probable’ or ‘possible’ ARF. The ‘possible’ category was introduced in 2013 in Australia’s Northern Territory (NT). Our aim was to compare longitudinal outcomes after a diagnosis of definite, probable or possible ARF. Methods: We extracted data from the NT RHD register for Indigenous Australians with an initial diagnosis of ARF during the 5.5-year study period (01/01/2013 – 30/06/2019). Descriptive statistics were used to describe the demographic and clinical characteristics at initial ARF diagnosis. Kaplan-Meier curves were used to assess the probability of survival free of disease progression and the cumulative incidence risk at each year since initial diagnosis was calculated. Cox proportional hazards regression was used to determine whether time to disease progression differed according to ARF diagnosis and whether progression was associated with specific predictors at diagnosis. A multinomial logistic regression model was performed to assess whether ARF diagnosis was associated with RHD outcome and to assess associations between ARF diagnosis and clinical manifestations. A generalised linear mixed model (GLMM) was developed to assess any differences in the long-term antibiotic adherence between ARF diagnosis categories and to examine longitudinal trends in adherence. Results: There were 913 initial ARF cases, 732 with normal baseline echocardiography. Of these, 92 (13%) experienced disease progression: definite ARF 61/348 (18%); probable ARF 20/181 (11%); possible ARF 11/203 (5%). The proportion of ARF diagnoses that were uncertain (i.e. possible or probable) increased over time, from 22/78 (28%) in 2013 to 98/193 (51%) in 2018. Cumulative incidence risk of any disease progression at 5.5 years was 33.6 (23.6–46.2) for definite ARF, 13.5 (8.8–20.6) for probable and 11.4% (95% CI 6.0–21.3) for possible ARF. The probability of disease-free survival was lowest for definite ARF and highest for possible ARF (p=0.004). Cox proportional hazards regression indicated that disease progression was 2.19 times more likely in those with definite ARF than those with possible ARF (p=0.026). Progression to RHD was reported in 37/348 (11%) definite ARF, 10/181 (6%) probable ARF, and 5/203 (2%) possible ARF. The multinomial logistic regression model demonstrated a significantly higher risk of progression from no RHD to RHD if the initial diagnosis was definite compared to possible ARF (p<0.001 for both mild and moderate-severe RHD outcomes). The GLMM estimated that patients with definite ARF had a significantly higher adherence to antibiotic prophylaxis compared with probable ARF (p=0.024). Conclusion: These data indicate that the ARF diagnostic categories are being applied appropriately, are capturing more uncertain cases over time, provide a useful way to stratify risk and guide prognosis, and can help inform practice. Possible ARF is not entirely benign; some cases progress to RHD.