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Aim: The aim of this study was to examine the association between neighborhood characteristics and type 2 diabetes (T2D) comorbidity in serious mental illness (SMI). We investigated associations of neighborhood-level crime, accessibility to health care services, availability of green spaces, neighborhood obesity, and fast food availability with SMI-T2D comorbidity. Method: A series of multilevel logistic regression models accounting for neighborhood-level clustering were used to examine the associations between 5 neighborhood variables and SMI-T2D comorbidity, sequentially adjusting for individual-level variables and neighborhood-level socioeconomic disadvantage. Results: Individuals with SMI residing in areas with higher crime rates per 1000 population had 2.5 times increased odds of reporting T2D comorbidity compared to the individuals with SMI residing in lower crime rate areas after controlling for individual and areal level factors (95% CI 0.91-6.74). There was no evidence of association between SMI-T2D comorbidity and other neighborhood variables investigated. Conclusion: Public health strategies to reduce SMI-T2D comorbidity might benefit by targeting on individuals with SMI living in high-crime neighborhoods. Future research incorporating longitudinal designs and/or mediation analysis are warranted to fully elucidate the mechanisms of association between neighborhoods and SMI-T2D comorbidity.

Background: Access to primary care is important for the identification, control and management of cardiometabolic risk factors (CMRFs). This study investigated whether differences in geographic access to primary care explained area-level variation in CMRFs. Methods: Multilevel logistic regression models were used to derive the association between area-level access to primary care and seven discrete CMRFs after adjusting for individual and area-level co-variates. Two-step floating catchment area method was used to calculate the geographic access to primary care for the small areas within the study region. Results: Geographic access to primary care was inversely associated with low high density lipoprotein (OR 0.94, CI 0.91–0.96) and obesity (OR 0.91, CI 0.88–0.93), after adjusting for age, sex and area-level disadvantage. The intra-cluster correlation coefficient (ICCs) of all the fully adjusted models ranged between 0.4–1.8%, indicating low general contextual effects of the areas on CMRF distribution. The area-level variation in CMRFs explained by primary care access was ≤10.5%. Conclusion: The findings of the study support proportionate universal interventions for the prevention and control of CMRFs, rather than any area specific interventions based on their primary care access, as the contextual influence of areas on all the analysed CMRFs were found to be minimal. The findings also call for future research that includes other aspects of primary care access, such as road-network access, financial affordability and individual-level acceptance of the services in order to gain an overall picture of the area-level contributing role of primary care on CMRFs in the study region.

Objective: Endoscopic upper urinary tract instrumentation is a common urological procedure; often associated with prior ureteric stenting and postoperative urosepsis. This study aimed to explore the clinical utility of preoperative urine and intraoperative urine and ureteric stent cultures in the management of urosepsis post upper urinary tract instrumentation. Methods: Prospective study involving pre-stented patients whose upper urinary tract was instrumented at a single centre between 2017 and 2018. Five cultures were collected per patient. Patients were tracked for development of postoperative urosepsis. Results: The study included 227 patients, with a 5.7% postoperative urosepsis rate. Risk of urosepsis was significantly associated with female gender, steroid use and having a colonized preoperative urine culture, intraoperative bladder urine or stent-end culture, or kidney urine culture. Patients with a colonized intraoperative bladder urine were 11 times more likely to develop urosepsis and were colonized with the same organism isolated from urosepsis cultures for 50% of cases. Conclusions: To our knowledge this is the largest cohort study which analyses five different urinary tract cultures and the relationship with postoperative urosepsis. A positive intraoperative bladder urine culture is an independent predictor of postoperative urosepsis development and causative organism, which could guide antibiotic management for these patients.

Introduction Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches. Method A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into 'higher risk' and 'lower risk' values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran's I test and Local Indicators of Spatial Autocorrelation (LISA). Results A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of 'higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran's I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area. Conclusion

General practice electronic health record (EHR) data have significant potential for clinical research. This study demonstrates the feasibility of utilising longitudinal EHR data analysis to address clinically relevant outcomes and uses the relationship between lipid medication prescription and all-cause mortality in the elderly as an exemplar for the validity of this methodology. EHR data were analysed to describe the association of lipid medication use, non-use or cessation with all-cause mortality in patients aged ≥75 years. Survival analysis with Cox regression was used to calculate hazard ratios, which were adjusted for confounders. There was no significant difference in all-cause mortality among patients according to their use, non-use, or cessation of lipid medications. The outcomes of this study correlate well with the results of other research works. This single-practice study demonstrates the feasibility and potential of analysing EHR data to address important clinical issues such as the relationship between all-cause mortality and lipid medication prescription in the elderly.

Improving the walkability of built environments to promote healthy lifestyles and reduce high body mass is increasingly considered in regional development plans. Walkability indexes have the potential to inform, benchmark and monitor these plans if they are associated with variation in body mass outcomes at spatial scales used for health and urban planning. We assessed relationships between area-level walkability and prevalence and geographic variation in overweight and obesity using an Australian population-based cohort comprising 92,157 Sydney respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. Individual-level data on overweight and obesity were aggregated to 2006 Australian postal areas and analysed as a function of area-level Sydney Walkability Index quartiles using conditional auto regression spatial models adjusted for demographic, social, economic, health and socioeconomic factors. Both overweight and obesity were highly clustered with higher-than-expected prevalence concentrated in the urban sprawl region of western Sydney, and lower-than-expected prevalence in central and eastern Sydney. In fully adjusted spatial models, prevalence of overweight and obesity was 6% and 11% lower in medium-high versus low, and 10% and 15% lower in high versus low walkability postcodes, respectively. Postal area walkability explained approximately 20% and 9% of the excess spatial variation in overweight and obesity that remained after accounting for other individual- and area-level factors. These findings provide support for the potential of area-level walkability indexes to inform, benchmark and monitor regional plans aimed at targeted approaches to reducing population-levels of high body mass through environmental interventions. Future research should consider potential confounding due to neighbourhood self-selection on area-level walkability relations.

Introduction: A growing number of publications report variation in the distribution of cardiometabolic risk factors (CMRFs) at different geographic scales. A review of these variations may help inform policy and health service organisation. Aim: To review studies reporting variation in the geographic distribution of CMRFs and its association with various proxy measures of area-level socioeconomic disadvantage (ASED) among the adult ( ≥ 18 years) population across the world. Methods: A systematic search for published articles was conducted in four databases (MEDLINE (Ovid), PubMed, Scopus and Web of Science) considering the interdisciplinary nature of the review question. Population-based cross-sectional and cohort studies on geographic variations of one or more biological proxies of CMRFs with/without an analysed contextual association with ASED were included. Two independent reviewers screened the studies and PRISMA guidelines were followed in the study selection and reporting. Result: A total of 265 studies were retrieved and screened, resulting in 24 eligible studies. The review revealed reports of variation in the distribution of CMRFs, at varying geographic scales, in multiple countries. In addition, consistent associations between ASED and higher prevalence of CMRFs were demonstrated. The reports were mainly from industrialised nations and small area geographic units were frequently used. Conclusion: Geographic variation in cardiometabolic risk exists across multiple spatial scales and is positively associated with ASED. This association is independent of individual-level factors and provides an imperative for area-based approaches to informing policy and health service organisation. The study protocol is registered in International prospective register of systematic reviews (Register No: CRD42018115294) PROSPERO 2018.

Objectives. The aim of the present study was to determine the association between area-level socioeconomic disadvantage and glycaemic-related risk in health service users in the Illawarra-Shoalhaven region of New South Wales, Australia. Methods. HbA1c values recorded between 2010 and 2012 for non-pregnant individuals aged 18 years were extracted from the Southern.IML Research (SIMLR) database. Individuals were assigned quintiles of the Socioeconomic Indices for Australia (SEIFA) Index of Relative Socioeconomic Disadvantage (IRSD) according to their Statistical Area 1 of residence. Glycaemic risk categories were defined as HbA1c 5.0-5.99% (lowest risk), 6.0-7.49% (intermediate risk) and 7.5% (highest risk). Logistic regression models were fit with glycaemic risk category as the outcome variable and IRSD as the study variable, adjusting for age and sex. Results. Data from 29 064 individuals were analysed. Higher disadvantage was associated with belonging to a higher glycaemic risk category in the fully adjusted model (most disadvantaged vs least disadvantaged quintile; odds ratio 1.74, 95% confidence interval 1.58, 1.93; P < 0.001). Conclusion. In this geocoded clinical dataset, area-level socioeconomic disadvantage was a significant correlate of increased glycaemic-related risk. Geocoded clinical data can inform more targeted use of health service resources, with the potential for improved health care equity and cost-effectiveness.

Objectives The primary aim of this study was to describe the geography of serious mental illness (SMI)-type 2 diabetes comorbidity (T2D) in the Illawarra-Shoalhaven region of NSW, Australia. The Secondary objective was to determine the geographic concordance if any, between the comorbidity and the single diagnosis of SMI and diabetes. Methods Spatial analytical techniques were applied to clinical data to explore the above objectives. The geographic variation in comorbidity was determined by Moran's I at the global level and the local clusters of significance were determined by Local Moran's I and spatial scan statistic. Choropleth hotspot maps and spatial scan statistics were generated to assess the geographic convergence of SMI, diabetes and their comorbidity. Additionally, we used bivariate LISA (Local Indicators of Spatial Association) and multivariate spatial scan to identify coincident areas with higher rates of both SMI and T2D. Results The study identified significant geographic variation in the distribution of SMI-T2D comorbidity in Illawarra Shoalhaven. Consistently higher burden of comorbidity was observed in some urban suburbs surrounding the major metropolitan city. Comparison of comorbidity hotspots with the hotspots of single diagnosis SMI and T2D further revealed a geographic concordance of high-risk areas again in the urban areas outside the major metropolitan city. Conclusion The identified comorbidity hotspots in our study may serve as a basis for future prioritisation and targeted interventions. Further investigation is required to determine whether contextual environmental factors, such as neighbourhood socioeconomic disadvantage, may be explanatory. Implications for public health Ours is the first study to explore the geographic variations in the distribution of SMI and T2D comorbidity. Findings highlight the importance of considering the role of neighbourhood environments in influencing the T2D risk in people with SMI.

This study examined the association between neighbourhood socioeconomic disadvantage and serious mental illness (SMI)-type 2 diabetes (T2D) comorbidity in an Australian population using routinely collected clinical data. We hypothesised that neighbourhood socioeconomic disadvantage is positively associated with T2D comorbidity in SMI. The analysis considered 3816 individuals with an SMI living in the Illawarra and Shoalhaven regions of NSW, Australia, between 2010 and 2017. Multilevel logistic regression models accounting for suburb (neighbourhood) level clustering were used to assess the association between neighbourhood disadvantage and SMI -T2D comorbidity. Models were adjusted for age, sex, and country of birth. Compared with the most advantaged neighbourhoods, residents in the most disadvantaged neighbourhoods had 3.2 times greater odds of having SMI-T2D comorbidity even after controlling for confounding factors (OR 3.20, 95% CI 1.42-7.20). The analysis also revealed significant geographic variation in the distribution of SMI -T2D comorbidity in our sample (Median Odds Ratio = 1.35) Neighbourhood socioeconomic disadvantage accounted for approximately 17.3% of this geographic variation. These findings indicate a potentially important role for geographically targeted initiatives designed to enhance prevention and management of SMI-T2D comorbidity in disadvantaged communities.

Aim of the Study: This review aims to systematically synthesize the body of literature examining the association between neighborhood socioeconomic disadvantage and serious mental illness (SMI)-type 2 diabetes (T2D) co-occurrence. Methods: We conducted an electronic search of four databases: PubMed, Scopus, Medline, and Web of Science. Studies were considered eligible if they were published in English, peer reviewed, quantitative, and focused on the association between neighborhood disadvantage and SMI-T2D comorbidity. Study conduct and reporting complied with PRISMA guidelines, and the protocol is made available at PROSPERO (CRD42017083483). Results: The one eligible study identified reported a higher burden of T2D in persons with SMI but provided only a tentative support for the association between neighborhood disadvantage and SMI-T2D co-occurrence. Conclusion: Research into neighborhood effects on SMI-T2D comorbidity is still in its infancy and the available evidence inconclusive. This points to an urgent need for attention to the knowledge gap in this important area of public health. Further research is needed to understand the health resource implications of the association between neighborhood deprivation and SMI-T2D comorbidity and the casual pathways linking them.

Walkability describes the capacity of the built environment to promote walking, and has been proposed as a potential focus for community-level mental health planning. We evaluated this possibility by examining the contribution of area-level walkability to variation in psychosocial distress in a population cohort at spatial scales comparable to those used for regional planning in Sydney, Australia. Data on psychosocial distress were analysed for 91,142 respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. We fit conditional auto regression models at the postal area level to obtain smoothed "disease maps" for psychosocial distress, and assess its association with area-level walkability after adjusting for individual- and area-level factors. Prevalence of psychosocial distress was 7.8%; similar for low (7.9%), low-medium (7.9%), medium-high (8.0%), and high (7.4%) walkability areas; and decreased with reducing postal area socioeconomic disadvantage: 12.2% (most), 9.3%, 7.5%, 5.9%, and 4.7% (least). Unadjusted disease maps indicated strong geographic clustering of psychosocial distress with 99.0% of excess prevalence due to unobserved and spatially structured factors, which was reduced to 55.3% in fully adjusted maps. Spatial and unstructured variance decreased by 97.3% and 39.8% after adjusting for individual-level factors, and another 2.3% and 4.2% with the inclusions of area-level factors. Excess prevalence of psychosocial distress in postal areas was attenuated in adjusted models but remained spatially structured. Postal area prevalence of high psychosocial distress is geographically clustered in Sydney, but is unrelated to postal area walkability. Area-level socioeconomic disadvantage makes a small contribution to this spatial structure; however, community-level mental health planning will likely deliver greatest benefits by focusing on individual-level contributors to disease burden and inequality associated with psychosocial dist

Purpose: An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach. Method: An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice. Results: A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005-2014) with a majority (n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half (n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice. Conclusion: Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

Background and objectives The optimal role of general practice in population weight management remains unclear. The aim of this mixed-methods study was to test the feasibility of routinely weighing all adult patients attending their general practice as an intervention to aid weight management in clinical practice. Methods Consenting patients in six general practices were weighed at each presentation over a 12-month period. Data were analysed using linear mixed growth models. Participants' interviews at the completion of the study were thematically analysed. Results The overall weight loss in patients who completed the study (n = 217) was 0.51 kg (P = 0.26; not significant); in patients who were obese (n = 106) there was a greater weight loss of 1.79 kg (P = 0.04). Patients were receptive to the intervention; however, there was disruption to clinical workflow. Discussion Routinely weighing adult patients in general practice is feasible, requires resources and may be associated with weight loss in patients who are obese. Further research is required to inform support for implementation within practices.

Background: Individual-level studies support a positive relation between walkable built environments and participation in moderate-intensity walking. However, the utility of this evidence for population-level planning is less clear as it is derived at much finer spatial scales than those used for regional programming. The aims of this study were to: evaluate if individual-level relations between walkability and walking to improve health manifest at population-level spatial scales; assess the specificity of area-level walkability for walking relative to other moderate and vigorous physical activity (MVPA); describe geographic variation in walking and other MVPA; and quantify the contribution of walkability to this variation. Methods: Data on sufficient walking, sufficient MVPA, and high MVPA to improve health were analyzed for 95,837 Sydney respondents to the baseline survey of the 45 and Up Study between January 2006 and April 2010. We used conditional autoregressive models to create smoothed MVPA "disease maps" and assess relations between sufficient MVPA to improve health and area-level walkability adjusted for individual-level demographic, socioeconomic, and health factors, and area-level relative socioeconomic disadvantage. Results: Within-cohort prevalence of meeting recommendations for sufficient walking, sufficient MVPA, and high MVPA were 31.7 (95% CI 31.4-32.0), 69.4 (95% CI 69.1-69.7), and 56.1 (95% CI 95.8-56.4) percent. Prevalence of sufficient walking was increased by 1.20 (95% CrI 1.12-1.29) and 1.07 (95% CrI 1.01-1.13) for high and medium-high versus low walkability postal areas, and for sufficient MVPA by 1.05 (95% CrI 1.01-1.08) for high versus low walkability postal areas. Walkability was not related to high MVPA. Postal area walkability explained 65.8 and 47.4 percent of residual geographic variation in sufficient walking and sufficient MVPA not attributable to individual-level factors. Conclusions: Walkability is associated with area-level preval

of a Mini Oral Presentation at the 15th Asian Pacific Congress of Nephrology (APCN) and 52nd ANZSN ASM, 17-21 September 2016, Perth Convention and Exhibition Centre, Western Australia.

Background Planning and transport agencies play a vital role in influencing the design of townscapes, travel modes and travel behaviors, which in turn impact on the walkability of neighbourhoods and residents' physical activity opportunities. Optimising neighbourhood walkability is desirable in built environments, however, the population health benefits of walkability may be offset by increased exposure to traffic related air pollution. This paper describes the spatial distribution of neighbourhood walkability and weighted road density, a marker for traffic related air pollution, in Sydney, Australia. As exposure to air pollution is related to socio-economic status in some cities, this paper also examines the spatial distribution of weighted road density and walkability by socio-economic status (SES). Methods We calculated walkability, weighted road density (as a measure of traffic related air pollution) and SES, using predefined and validated measures, for 5858 Sydney neighbourhoods, representing 3.6 million population. We overlaid tertiles of walkability and weighted road density to define "sweet-spots" (high walkability-low weighted road density), and "sour- spots" (low walkability-high weighted road density) neighbourhoods. We also examined the distribution of walkability and weighted road density by SES quintiles. Results Walkability and weighted road density showed a clear east-west gradient across the region. Our study found that only 4 % of Sydney's population lived in sweet-spot" neighbourhoods with high walkability and low weighted road density (desirable), and these tended to be located closer to the city centre. A greater proportion of neighbourhoods had health limiting attributes of high weighted road density or low walkability (about 20 % each), and over 5 % of the population lived in "sour-spot" neighbourhoods with low walkability and high weighted road density (least desirable). These neighbourhoods were more distant from the city centre and scattere

of a poster presentation at the 2015 PHC Research Conference, Adelaide, 29-31 July, 2015.

Patients were weighed each time they presented to the practice over a 12 month period. 198 patients aged between 20 to 70 were recruited from six general practices.

Background Overweight and obesity lead to higher probability of individuals accessing primary care but adiposity estimates are rarely available at regional levels to inform health service planning. This paper analyses a large, community-derived clinical database of objectively measured body mass index (BMI) to explore relationships with area-level socioeconomic disadvantage for informing regional level planning activities. Materials and Methods The study included 91776 adults who had BMI objectively measured between 1 July 2009 and 30 June 2011 by a single pathology provider. Demographic data and BMI were extracted and matched to 2006 national census socioeconomic data using geocoding. Adjusted odds-ratios for overweight and obesity were calculated using sex-stratified logistic regression models with socioeconomic disadvantage of census collection district of residence as the independent variable. Results The prevalence of overweight or obesity was 79.2% (males) and 65.8% (females); increased with age to 74 years; and was higher in rural (74%) versus urban areas (71.4%) (p<0.001). Increasing socioeconomic disadvantage was associated with increasing prevalence of overweight (p<0.0001), obesity (p<0.0001) and overweight or obesity (p<0.0001) in women and obesity (p<0.0001) in men. Socioeconomic disadvantage was unrelated to overweight (p = 0.2024) and overweight or obesity (p = 0.4896) in males. Conclusion It is feasible to link routinely-collected clinical data, representative of a discrete population, with geographic distribution of disadvantage, and to obtain meaningful area-level information useful for targeting interventions to improve population health. Our results demonstrate novel area-level socioeconomic gradients in overweight and obesity relevant to regional health service planning.

of a paper that was presented at 2014 Primary Health Care Research Conference, Canberra, Australia, 23-25 July.

Poster that was presented at 2014 Primary Health Care Research Conference, Canberra, Australia, 23-25 July.

Background: Walkability describes the capacity of the built environment to support walking for various purposes. This paper describes the construction and validation of two objective walkability indexes for Sydney, Australia. Methods: Walkability indexes using residential density, intersection density, land use mix, with and without retail floor area ratio were calculated for 5,858 Sydney Census Collection Districts in a geographical information system. Associations between variables were evaluated using Spearman’s rho (ρ). Internal consistency and factor structure of indexes were estimated with Cronbach’s alpha and principal components analysis; convergent and predictive validity were measured using weighted kappa (κw) and by comparison with reported walking to work at the 2006 Australian Census using logistic regression. Spatial variation in walkability was assessed using choropleth maps and Moran’s I. Results: A three-attribute abridged Sydney Walkability Index comprising residential density, intersection density and land use mix was constructed for all Sydney as retail floor area was only available for 5.3% of Census Collection Districts. A four-attribute full index including retail floor area ratio was calculated for 263 Census Collection Districts in the Sydney Central Business District. Abridged and full walkability index scores for these 263 areas were strongly correlated (ρ=0.93) and there was good agreement between walkability quartiles (κw=0.73). Internal consistency ranged from 0.60 to 0.71, and all index variables loaded highly on a single factor. The percentage of employed persons who walked to work increased with increasing walkability: 3.0% in low income-low walkability areas versus 7.9% in low income-high walkability areas; and 2.1% in high income-low walkability areas versus 11% in high income-high walkability areas. The adjusted odds of walking to work were 1.05 (0.96–1.15), 1.58 (1.45–1.71) and 3.02 (2.76–3.30) times higher in medium, high and ve

Background: The resurgence of infectious syphilis in men who have sex with men (MSM) has been documented worldwide; however, HIV coinfection and syphilis reinfections in MSM in inner Sydney have not been published. Methods: For all laboratory syphilis notifications assessed as a newly notified case or reinfection, a questionnaire was sent to the requesting physician seeking demographic data and disease classification. Sex of partner and HIV status were collected for all infectious syphilis notifications in men received from 1 April 2006 to March 2011. Results: From April 2001 to March 2011, 3664 new notifications were received, 2278 (62%) were classified as infectious syphilis. Infectious syphilis notifications increased 12-fold from 25 to 303 in the first and last year respectively, and almost all notifications were in men (2220, 97.5%). During April 2006 to March 2011, 1562 infectious syphilis notifications in males were received and 765 (49%) of these men were HIV-positive and 1351 (86%) reported a male sex partner. Reinfections increased over time from 17 (9%) to 56 (19%) in the last year of the study and were significantly more likely to be in HIV-positive individuals (χ2 = 140.92, degrees of freedom= 1, P = <0.001). Conclusion: Inner Sydney is experiencing an epidemic of infectious syphilis in MSM and about half of these cases are in HIV-positive patients. Reinfections are increasing and occur predominantly in HIV-positive men. Accurate surveillance information is needed to inform effective prevention programs, and community and clinician education needs to continue until a sustained reduction is achieved.

In February, 2008, the South Eastern Sydney Illawarra Public Health Unit investigated an outbreak of cryptosporidiosis within the south east region of Sydney, Australia. Thirty-one cases with laboratory-confirmed cryptosporidiosis and 97 age- and geographically matched controls selected by random digit dialling were recruited into a case-control study and interviewed for infection risk factors. Cryptosporidiosis was associated with swimming at Facility A (matched odds ratio = 19.4, 95% confidence interval: 3.7-100.8) and exposure to household contacts with diarrhoea (matched odds ratio = 7.7, 95% confidence interval: 1.9-31.4) in multivariable conditional logistic regression models. A protective effect for any animal contact was also found (matched odds ratio = 0.2, 95% confidence interval: 0.1-0.7). Cryptosporidium hominis subtype IbA10G2 was identified in 8 of 11 diagnostic stool samples available for cases. This investigation reaffirms the importance of public swimming pools as potential sources of Cryptosporidium infection and ensuring their compliance with water-quality guidelines. The protective effect of animal contact may be suggestive of past exposure leading to immunity.