Sociodemographic and geographical inequalities in notifiable infectious diseases in Australia: a retrospective analysis of 21 years of national disease surveillance data

Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting

In northern Australia, a region with limited access to healthcare and a substantial population living remotely, antibiotic resistance adds to the complexity of treating infections. Focussing on Escherichia coli urinary tract infections (UTIs) and Staphylococcus aureus skin & soft tissue infections (SSTIs) captured by a northern Australian antibiotic resistance surveillance system, we used logistic regression to investigate predictors of a subsequent resistant isolate during the same infection episode. We also investigated predictors of recurrent infection. Our analysis included 98,651 E. coli isolates and 121,755 S. aureus isolates from 70,851 patients between January 2007 and June 2020. Following an initially susceptible E. coli UTI, subsequent recovery of a cefazolin (8%) or ampicillin (13%) -resistant isolate during the same infection episode was more common than a ceftriaxone-resistant isolate (2%). For an initially susceptible S. aureus SSTI, subsequent recovery of a methicillin-resistant isolate (8%) was more common than a trimethoprim-sulfamethoxazole-resistant isolate (2%). For UTIs and SSTIs, prior infection with a resistant pathogen was a strong predictor of both recurrent infection and resistance in future infection episodes. This multi-centre study demonstrates an association between antibiotic resistance and an increased likelihood of recurrent infection. Particularly in remote areas, a patient's past antibiograms should guide current treatment choices since recurrent infection will most likely be at least as resistant as previous infection episodes. Using population-level surveillance data in this way can also help clinicians decide if they should switch antibiotics for patients with ongoing symptoms, while waiting for diagnostic results.

Contribution of socio-economic factors in the spread of antimicrobial resistant infections in Australian primary healthcare clinics

OBJECTIVES

To effectively contain antimicrobial resistant (AMR) infections, we must better understand the social determinates of health that contribute to transmission and spread of infections.

METHODS

We used clinical data from patients attending primary healthcare clinics across three jurisdictions of Australia (2007-2019). Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) isolates and their corresponding antibiotic susceptibilities were included. Using multivariable logistic regression analysis, we assessed associations between AMR prevalence and indices of social disadvantage as reported by the Australian Bureau of Statistics (i.e. remoteness, socio-economic disadvantage and average person per household).

RESULTS

This study reports 12 years of longitudinal data from 43,448 isolates from a high-burden low resource setting in Australia. Access to health and social services (as measured by remoteness index) was a risk factor for increased prevalence of third-generation cephalosporin-resistant (3GC) E. coli (odds ratio 5.05; 95% confidence interval 3.19, 8.04) and methicillin-resistant S. aureus (MRSA) (odds ratio 5.72; 95% confidence interval 5.02, 6.54). We did not find a positive correlation of AMR and socio-economic disadvantage or average person per household indices.

CONCLUSIONS

Remoteness is a risk factor for increased prevalence of 3GC-resistant E.coli and MRSA. We demonstrate that traditional disease surveillance systems can be repurposed to capture the broader social drivers of AMR. Access to pathogen-specific and social data early and within the local regional context will fill a significant gap in disease prevention and the global spread of AMR.

HIV-1 genetic transmission networks among people living with HIV/AIDS in Sichuan, China: a genomic and spatial epidemiological analysis

BACKGROUND

Spatialized HIV genetic transmission networks can help understand dynamic changes of HIV-1 at the regional level. This study aimed to combine genomic, epidemiological, and spatial data to investigate the patterns of the HIV-1 epidemic at both individual and regional levels among people living with HIV (PLWH) with virological failure of antiretroviral therapy (ART).

METHODS

We evaluated the transmission patterns of 5,790 PLWH with identified pol sequences of the five main HIV-1 subtypes (B, CRF08_BC, CRF85_BC, CRF07_BC, and CRF01_AE) in Sichuan Province, China. A phylogenetic cluster was defined as a group of sequences with genetically similar HIV strains, with all phylogenetic clusters forming an HIV-1 genetic transmission network for each subtype. Logistic regression was used to identify the potential risk factors for phylogenetic clustering. Spatial analysis was applied to demonstrate the geographic patterns of phylogenetic clustering rates; intensity matrices and flow maps were made to demonstrate the intensity of transmission within and between cities.

FINDINGS

There were 2,159 (37.3%) of 5,790 PLWH, distributed in 452 phylogenetic clusters. Some individual clinical and behavioral factors were associated with phylogenetic clustering, including a viral load of >50,000 copies/ml (OR=1.16, 95%CI=1.02-1.33), infection of other sexually transmitted diseases (OR=1.38, 95%CI=1.12-1.69), and ≥5 non-marital sexual partners (OR=1.25, 95%CI=1.03-1.51), while >3 years of treatment since the initial ART was associated with less likelihood of phylogenetic clustering (OR=0.82, 95%CI=0.70-0.97). The phylogenetic clustering rates varied regionally and were highest in the central region of Sichuan, especially for subtype CRF08_BC. The significant spatial clusters of high and low phylogenetic clustering rates were detected in the east (Dazhou for B; Zigong and Luzhou for CFR08_BC) and west (Yaan and Ganzi for CRF07_BC), respectively. The proportion of intercity transmission varied across cities from 0.14 (Yibin) and 1.00 (Ganzi). Stronger intercity transmission than average existed between some cities, e.g., between Deyang and Neijiang. CRF07_BC was the most widespread subtype between cities, and CRF85_BC (a novel HIV-1 subtype) showed strong intercity transmission (e.g., between Yibin and Guangan).

INTERPRETATION

The phylogenetic clustering rates and intercity connections of HIV-1 have varied geographically, possibly due to varying human mobility, traffic convenience, and economic activities. Our findings enhanced the understanding of the dynamics of HIV-1 transmission from individual to city level, and demonstrated a novel cross-disciplinary (epidemiological, genetic, and spatial) approach by which we identified high-risk populations and areas. Our approach could be adapted to other regions for precision public health interventions.

FUNDING

The National Natural Science Foundation of China, Sichuan Science and Technology Program, Project of Sichuan Provincial Health Committee, Science and Technology Project of Sichuan Provincial Health Committee, Wuhan University 351 Talent Program, 2020, and the International Institute of Spatial Lifecourse Epidemiology (ISLE).

Antibiotic resistance in uropathogens across northern Australia 2007-20 and impact on treatment guidelines

BACKGROUND

Urinary tract infections are common and are increasingly resistant to antibiotic therapy. Northern Australia is a sparsely populated region with limited access to healthcare, a relatively high burden of disease, a substantial regional and remote population, and high rates of antibiotic resistance in skin pathogens.

OBJECTIVES

To explore trends in antibiotic resistance for common uropathogens Escherichia coli and Klebsiella pneumoniae in northern Australia, and how these relate to current treatment guidelines in the community and hospital settings.

METHODS

We used data from an antibiotic resistance surveillance system. We calculated the monthly and yearly percentage of isolates that were resistant in each antibiotic class, by bacterium. We analysed resistance proportions geographically and temporally, stratifying by healthcare setting. Using simple linear regression, we investigated longitudinal trends in monthly resistance proportions and correlation between community and hospital isolates.

RESULTS

Our analysis included 177 223 urinary isolates from four pathology providers between 2007 and 2020. Resistance to most studied antibiotics remained <20% (for E. coli and K. pneumoniae, respectively, in 2019: amoxicillin/clavulanate 16%, 5%; cefazolin 17%, 8%; nitrofurantoin 1%, 31%; trimethoprim 36%, 17%; gentamicin 7%, 2%; extended-spectrum cephalosporins 8%, 5%), but many are increasing by 1%-3% (absolute) per year. Patterns of resistance were similar between isolates from community and hospital patients.

CONCLUSIONS

Antibiotic resistance in uropathogens is increasing in northern Australia, but treatment guidelines generally remain appropriate for empirical therapy of patients with suspected infection (except trimethoprim in some settings). Our findings demonstrate the importance of local surveillance data (HOTspots) to inform clinical decision making and guidelines.

Identifying Environmental Determinants Relevant to Health and Wellbeing in Remote Australian Indigenous Communities: A Scoping Review of Grey Literature

The high prevalence of preventable infectious and chronic diseases in Australian Indigenous populations is a major public health concern. Existing research has rarely examined the role of built and socio-political environmental factors relating to remote Indigenous health and wellbeing. This research identified built and socio-political environmental indicators from publicly available grey literature documents locally-relevant to remote Indigenous communities in the Northern Territory (NT), Australia. Existing planning documents with evidence of community input were used to reduce the response burden on Indigenous communities. A scoping review of community-focused planning documents resulted in the identification of 1120 built and 2215 socio-political environmental indicators. Indicators were systematically classified using an Indigenous indicator classification system (IICS). Applying the IICS yielded indicators prominently featuring the "community infrastructure" domain within the built environment, and the "community capacity" domain within the socio-political environment. This research demonstrates the utility of utilizing existing planning documents and a culturally appropriate systematic classification system to consolidate environmental determinants that influence health and disease occurrence. The findings also support understanding of which features of community-level built and socio-political environments amenable to public health and social policy actions might be targeted to help reduce the prevalence of infectious and chronic diseases in Indigenous communities.

Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis, Germany, 2001 to 2017

BackgroundAlthough measles is endemic throughout the World Health Organization European Region, few studies have analysed socioeconomic inequalities and spatiotemporal variations in the disease's incidence.AimTo study the association between socioeconomic deprivation and measles incidence in Germany, while considering relevant demographic, spatial and temporal factors.MethodsWe conducted a longitudinal small-area analysis using nationally representative linked data in 401 districts (2001-2017). We used spatiotemporal Bayesian regression models to assess the potential effect of area deprivation on measles incidence, adjusted for demographic and geographical factors, as well as spatial and temporal effects. We estimated risk ratios (RR) for deprivation quintiles (Q1-Q5), and district-specific adjusted relative risks (ARR) to assess the area-level risk profile of measles in Germany.ResultsThe risk of measles incidence in areas with lowest deprivation quintile (Q1) was 1.58 times higher (95% credible interval (CrI): 1.32-2.00) than in those with highest deprivation (Q5). Areas with medium-low (Q2), medium (Q3) and medium-high deprivation (Q4) had higher adjusted risks of measles relative to areas with highest deprivation (Q5) (RR: 1.23, 95%CrI: 0.99-1.51; 1.05, 95%CrI: 0.87-1.26 and 1.23, 95%CrI: 1.05-1.43, respectively). We identified 54 districts at medium-high risk for measles (ARR > 2) in Germany, of which 22 were at high risk (ARR > 3).ConclusionSocioeconomic deprivation in Germany, one of Europe's most populated countries, is inversely associated with measles incidence. This association persists after demographic and spatiotemporal factors are considered. The social, spatial and temporal patterns of elevated risk require targeted public health action and policy to address the complexity underlying measles epidemiology.

Active surveillance of acute paediatric hospitalisations demonstrates the impact of vaccination programmes and informs vaccine policy in Canada and Australia

Sentinel surveillance of acute hospitalisations in response to infectious disease emergencies such as the 2009 influenza A(H1N1)pdm09 pandemic is well described, but recognition of its potential to supplement routine public health surveillance and provide scalability for emergency responses has been limited. We summarise the achievements of two national paediatric hospital surveillance networks relevant to vaccine programmes and emerging infectious diseases in Canada (Canadian Immunization Monitoring Program Active; IMPACT from 1991) and Australia (Paediatric Active Enhanced Disease Surveillance; PAEDS from 2007) and discuss opportunities and challenges in applying their model to other contexts. Both networks were established to enhance capacity to measure vaccine preventable disease burden, vaccine programme impact, and safety, with their scope occasionally being increased with emerging infectious diseases’ surveillance. Their active surveillance has increased data accuracy and utility for syndromic conditions (e.g. encephalitis), pathogen-specific diseases (e.g. pertussis, rotavirus, influenza), and adverse events following immunisation (e.g. febrile seizure), enabled correlation of biological specimens with clinical context and supported responses to emerging infections (e.g. pandemic influenza, parechovirus, COVID-19). The demonstrated long-term value of continuous, rather than incident-related, operation of these networks in strengthening routine surveillance, bridging research gaps, and providing scalable public health response, supports their applicability to other countries.

Distribution of midwives in Mongolia: A secondary data analysis

INTRODUCTION

Midwives are at the core of strengthening the health system, especially in the crucial period around pregnancy, childbirth, and the early weeks of life. In 2016, the national-level maternal mortality ratio in Mongolia was 48.6 deaths per 100,000 live births, but this was much higher (up to 212.9 deaths/100,000) in some rural provinces of the country. The wide variation in maternal mortality between urban and rural areas of Mongolia might be related to the distribution of midwives and equity of access to maternity care.

OBJECTIVES

In the present study, we aimed to determine the distribution of midwives in each province of Mongolia and to examine inequality in the distribution of midwives nationwide.

DESIGN

A secondary data analysis.

METHODS

Data from the Centre of Health Development and the National Statistical Office of Mongolia were obtained and analysed. First, we assessed the distribution of midwives at provincial and regional levels, along with the association between these distributions and the maternal mortality ratio in 2016. We then calculated the inequality of these distributions using the Gini coefficient and examined trends for the years 2010-2016. We compared results for urban, suburban, and rural provinces. Rural areas are sparsely populated and enormous in size, so it may be difficult access to basic healthcare services. It was considered important, therefore, to assess the number of midwives per 1000 km² as well as the commonly used indicator of per 10,000 population.

RESULTS

When the land area in each province was taken into consideration rather than only the population, wider variations between urban, suburban, and rural provinces became apparent. Provinces varied according to the number of midwives per 10,000 population by a factor of three (range 2.0-6.2/midwives); while provinces varied according to the number of midwives per 1000 km² by a factor of approximately 300 (range 0.2-61.2/midwives). The Gini coefficient for the number of midwives per 10,000 population in 2016, R = 0.201, revealed "relative" equality. This was slightly reduced from R = 0.305 in 2010 and indicated a shift toward equality. However, the Gini coefficient for the number of midwives per 1000 km² area indicated "severe" inequality of R = 0.524 in 2016. This was increased from R = 0.466 in 2010, indicating that no improvement has been seen over these years.

CONCLUSIONS

Our study suggests that two different measures of midwifery distribution should be used as indicators: number of midwives "per 10,000 population" and "per 1000 km² area". In rural areas such as parts of Mongolia, geographical features and population density are important features of the local context. To deliver healthcare services in a timely manner and within a reasonable distance for pregnant women who need care, the indicator of per 1000 km² area would be more suitable for rural and sparsely populated areas than the indicator of per 10,000 population, which is commonly used for urban and settled areas. Based on our findings, to reduce the wide gap in MMR between rural and urban areas, we recommend at least one midwife per 1000 km² area in rural regions of Mongolia.

Paediatric Salmonellosis-Differences between Tropical and Sub-Tropical Regions of Queensland, Australia

Salmonellosis is an important cause of morbidity in tropical regions.This study aims to describe the epidemiology of non-typhoidal Salmonellae (NTS) in children presenting to public hospitals in Queensland, Australia, over the past 20 years, with a focus on differences between tropical and sub-tropical zones in the region. This is a retrospective and descriptive cohort study of 8162 NTS positive samples collected in 0–17-year-olds from the Queensland public hospital pathology database (Auslab) over a 20-year period from 1997 to 2016. There were 2951 (36.2%) positive NTS samples collected in tropical zones and 5211 (63.8%) in the sub-tropical zones of Queensland, with a total of 8162 over the region. The tropical zone contributed a disproportionately higher number of positive NTS samples by population sub-analysis. Of the specimens collected, 7421 (90.92%) were faecal, 505 (6.2%) blood, 161 (1.97%) urine, 13 (0.16%) cerebrospinal fluid (CSF) and 62 of other origin. Other categories of specimen types isolated include swab, fluid, aspirate, lavage, bone, tissue, isolate and pus, and these were not included in sub-analysis. The most commonly identified serovars were Salmonella Typhimurium, Salmonella Virchow and Salmonella Saintpaul. This is the first and largest study that emphasises the high burden of invasive and non-invasive NTS infections resulting in hospital presentations in the paediatric population of tropical north Queensland, compared to the sub-tropics.

Hepatitis C virus notification rates in Australia are highest in socioeconomically disadvantaged areas

Background

Poor access to health services is a significant barrier to achieving the World Health Organization’s hepatitis C virus (HCV) elimination targets. We demonstrate how geospatial analysis can be performed with commonly available data to identify areas with the greatest unmet demand for HCV services.

Methods

We performed an Australia-wide cross-sectional analysis of 2015 HCV notification rates using local government areas (LGAs) as our unit of analysis. A zero-inflated negative binomial regression was used to determine associations between notification rates and socioeconomic/demographic factors, health service and geographic remoteness area (RA) classification variables. Additionally, component scores were extracted from a principal component analysis (PCA) of the healthcare service variables to provide rankings of relative service coverage and unmet demand across Australia.

Results

Among LGAs with non-zero notifications, higher rates were associated with areas that had increased socioeconomic disadvantage, more needle and syringe services (incidence rate ratio [IRR] 1.022; 95%CI 1.001, 1.044) and more alcohol and other drug services (IRR 1.019; 1.005, 1.034). The distribution of PCA component scores indicated that per-capita healthcare service coverage was lower in areas outside of major Australian cities. Areas outside of major cities also contained 94% of LGAs in the lowest two socioeconomic quintiles, as well as 35% of HCV notifications despite only representing 29% of the population.

Conclusions

As countries aim for HCV elimination, routinely collected data can be used to identify geographical areas for priority service delivery. In Australia, the unmet demand for HCV treatment services is greatest in socioeconomically disadvantaged and non-metropolitan areas.

Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study

BACKGROUND

China is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China.

METHODS

Global and local Moran's I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps.

RESULTS

Of the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high-low (unit with high incidence surrounded by units with high incidence, the same below) and high-high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low-low and low-high spatial cluster areas abound in provincial units in north and east China.

CONCLUSION

Despite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between their spatial distributions are present. Substantial evidence is available to support disease-specific, location-specific, and disease-combined interventions. Regarding provinces that show high-high/high-low patterns of multiple diseases, comprehensive interventions targeting different diseases should be established. As to the adjacent provincial units revealing similar patterns, coordinated actions need to be taken across borders.

Australia's National Notifiable Diseases Surveillance System 1991-2011: expanding, adapting and improving

We reviewed key attributes (flexibility, data quality and timeliness) of Australia's National Notifiable Diseases Surveillance System (NNDSS) over its first 21 years. Cases notified to NNDSS from 1991 to 2011 were examined by jurisdiction (six states and two territories) and sub-period to describe changes in the number of notifiable diseases, proportion of cases diagnosed using PCR tests, data quality (focusing on data completeness), and notification delays. The number of notifiable diseases increased from 37 to 65. The proportion of cases diagnosed by PCR increased from 1% (1991-1997) to 49% (2005-2011). Indigenous status was complete for only 44% notifications (jurisdictional range 19-87%). Vaccination status was complete for 62% (jurisdictional range 32-100%) and country of acquisition for 24% of relevant cases. Data completeness improved over the study period with the exception of onset date. Median time to notification was 8 days (interquartile range 4-17 days, jurisdictional range 5-15 days); this decreased from 11 days (1991-1997) to 5 days (2005-2011). NNDSS expanded during the study period. Data completeness and timeliness improved, likely related to mandatory laboratory reporting and electronic data transfer. A nationally integrated electronic surveillance system, including electronic laboratory reporting, would further improve infectious disease surveillance in Australia.