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8. The immunotoxicity, but not anti-tumor efficacy, of anti-CD40 and anti-CD137 immunotherapies is dependent on the gut microbiota

Author

Blake, Stephen J., James, Jane, Ryan, Feargal J., Caparros-Martin, Jose, Eden, Georgina L., Tee, Yee C., Salamon, John R., Benson, Saoirse C., Tumes, Damon J., Sribnaia, Anastasia, Stevens, Natalie E., Finnie, John W., Kobayashi, Hiroki, White, Deborah L., Wesselingh, Steve L., O’Gara, Fergal, Lynn, Miriam A., and Lynn, David J.

Published
2021
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11. Predictors of mortality shortly after entering a long-term care facility.

Author

Jorissen, Robert N, Wesselingh, Steve L, Whitehead, Craig, Maddison, John, Forward, John, Bourke, Alice, Harvey, Gillian, Crotty, Maria, Collaborators, STAAR-SA Study, and Inacio, Maria C

Subjects
*RISK assessment, *PREDICTION models, *AUSTRALIANS, *RESEARCH funding, *HEALTH status indicators, *LONG-term health care, *SEX distribution, *RETROSPECTIVE studies, *DESCRIPTIVE statistics, *LONGITUDINAL method, *NURSING care facilities, *CONFIDENCE intervals, *DRUGS, *PROPORTIONAL hazards models, *ACTIVITIES of daily living, MORTALITY risk factors
Abstract

Objective Moving into a long-term care facility (LTCF) requires substantial personal, societal and financial investment. Identifying those at high risk of short-term mortality after LTCF entry can help with care planning and risk factor management. This study aimed to: (i) examine individual-, facility-, medication-, system- and healthcare-related predictors for 90-day mortality at entry into an LTCF and (ii) create risk profiles for this outcome. Design Retrospective cohort study using data from the Registry of Senior Australians. Subjects Individuals aged ≥ 65 years old with first-time permanent entry into an LTCF in three Australian states between 01 January 2013 and 31 December 2016. Methods A prediction model for 90-day mortality was developed using Cox regression with the purposeful variable selection approach. Individual-, medication-, system- and healthcare-related factors known at entry into an LTCF were examined as predictors. Harrell's C-index assessed the predictive ability of our risk models. Results 116,192 individuals who entered 1,967 facilities, of which 9.4% (N  = 10,910) died within 90 days, were studied. We identified 51 predictors of mortality, five of which were effect modifiers. The strongest predictors included activities of daily living category (hazard ratio [HR] = 5.41, 95% confidence interval [CI] = 4.99–5.88 for high vs low), high level of complex health conditions (HR = 1.67, 95% CI = 1.58–1.77 for high vs low), several medication classes and male sex (HR = 1.59, 95% CI = 1.53–1.65). The model out-of-sample Harrell's C-index was 0.773. Conclusions Our mortality prediction model, which includes several strongly associated factors, can moderately well identify individuals at high risk of mortality upon LTCF entry. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Primary, allied health, geriatric, pain and palliative healthcare service utilisation by aged care residents, 2012–2017

Author

Inacio, Maria C., primary, Collier, Luke, additional, Air, Tracy, additional, Thapaliya, Kailash, additional, Crotty, Maria, additional, Williams, Helena, additional, Wesselingh, Steve L., additional, Kellie, Andrew, additional, Roder, David, additional, Lewis, Adrienne, additional, Harvey, Gillian, additional, Sluggett, Janet K., additional, Cations, Monica, additional, Gill, Tiffany K., additional, Khadka, Jyoti, additional, and Caughey, Gillian E., additional

Published
2023
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20. Severe cognitive impairment is linked to a reduced gut microbiome capacity to synthesise immunomodulators, neurotransmitters, and amino acids required for autophagy in residents of long-term aged care

Author

Shoubridge, Andrew P., primary, Carpenter, Lucy, additional, Flynn, Erin, additional, Papanicolas, Lito E., additional, Collins, Josephine, additional, Gordon, David, additional, Lynn, David J., additional, Whitehead, Craig, additional, Leong, Lex E.X., additional, Cations, Monica, additional, De Souza, David P., additional, Narayana, Vinod K., additional, Choo, Jocelyn M., additional, Wesselingh, Steve L., additional, Crotty, Maria, additional, Inacio, Maria C., additional, Ivey, Kerry, additional, Taylor, Steven L., additional, and Rogers, Geraint B., additional

Published
2023
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22. Targeted reduction of airborne viral transmission risk in long-term residential aged care

Author

Brass, Amanda, primary, Shoubridge, Andrew P, additional, Larby, Nicolas, additional, Elms, Levi, additional, Sims, Sarah K, additional, Flynn, Erin, additional, Miller, Caroline, additional, Crotty, Maria, additional, Papanicolas, Lito E, additional, Wesselingh, Steve L, additional, Morawska, Lidia, additional, Bell, Scott C, additional, Taylor, Steven L, additional, and Rogers, Geraint B, additional

Published
2022
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24. Provision of a comprehensive medicines review is associated with lower mortality risk for residents of aged care facilities: a retrospective cohort study

Author

Sluggett, Janet K, primary, Caughey, Gillian E, additional, Air, Tracy, additional, Moldovan, Max, additional, Lang, Catherine, additional, Martin, Grant, additional, Carter, Stephen R, additional, Jackson, Shane, additional, Stafford, Andrew C, additional, Wesselingh, Steve L, additional, and Inacio, Maria C, additional

Published
2022
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26. Additional file 1 of Medicines use before and after comprehensive medicines review among residents of long-term care facilities: a retrospective cohort study

Author

Sluggett, Janet K., Caughey, Gillian E., Air, Tracy, Moldovan, Max, Lang, Catherine, Martin, Grant, Carter, Stephen R., Jackson, Shane, Stafford, Andrew C., Wesselingh, Steve L., and Inacio, Maria C.

Abstract

Additional file 1: Supplementary Table 1. Codes used to identify medicine classes of interest, and corresponding prescription durations. Supplementary Table 2. Weekly number of defined daily doses of medicines per 1000 resident-days before and after the index date among individuals who did and did not receive an RMMR in the 0-3 months and 3-6 months after RACF entry. Supplementary Table 3. Weekly prevalence of medicines use during the study period among individuals with an index date in the 6-12 months after RACF entry. Supplementary Figure 1. Group assignment based on the date of an individual’s first RMMR relative to first entry into permanent residential aged care. Supplementary Figure 2. Time frame for assessing medicines use pre- and post-index date. Supplementary Figure 3. Time periods analyzed in the segmented regression models. Supplementary Figure 4. Weekly number of DDDs available for use per 1000 resident-days for individuals with an index date in the 0-3 months after RACF entry for medicines with possible changes in use post-RMMR compared to individuals without an RMMR. Supplementary Figure 5. Weekly number of DDDs available for use per 1000 resident-days for individuals with an index date in the 3-6 months after RACF entry for medicines with possible changes in use post-RMMR compared to individuals without an RMMR. Supplementary Figure 6. Weekly number of DDDs per 1000 resident-days in the four months before and after the index date for individuals with an index date within 6-12 months of RACF entry. Supplementary Figure 7. Weekly prevalence of medicines use in the four months before and after the index date for individuals with an index date in the 6-12 months after RACF entry.

Published
2022
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32. The composition of the gut microbiota following early-life antibiotic exposure affects host health and longevity in later life

Author

Lynn, Miriam A., primary, Eden, Georgina, additional, Ryan, Feargal J., additional, Bensalem, Julien, additional, Wang, Xuemin, additional, Blake, Stephen J., additional, Choo, Jocelyn M., additional, Chern, Yee Tee, additional, Sribnaia, Anastasia, additional, James, Jane, additional, Benson, Saoirse C., additional, Sandeman, Lauren, additional, Xie, Jianling, additional, Hassiotis, Sofia, additional, Sun, Emily W., additional, Martin, Alyce M., additional, Keller, Marianne D., additional, Keating, Damien J., additional, Sargeant, Timothy J., additional, Proud, Christopher G., additional, Wesselingh, Steve L., additional, Rogers, Geraint B., additional, and Lynn, David J., additional

Published
2021
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34. Predictors of short‐term hospitalization and emergency department presentations in aged care

Author

Inacio, Maria C., primary, Jorissen, Robert N., additional, Khadka, Jyoti, additional, Whitehead, Craig, additional, Maddison, John, additional, Bourke, Alice, additional, Pham, Clarabelle T., additional, Karnon, Jonathon, additional, Wesselingh, Steve L., additional, Lynch, Elizabeth, additional, Harvey, Gillian, additional, Caughey, Gillian E., additional, and Crotty, Maria, additional

Published
2021
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35. Additional file 5 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Abstract

Additional file 5: Table S4. Antibiotic use according to class of antibiotic during study period and annual hospital use 2017-2018.

Published
2021
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36. Additional file 10 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Abstract

Additional file 10: Table S9. Multivariate regression analysis of clinical variables and resistance genes.

Published
2021
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37. Additional file 2 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Abstract

Additional file 2: Table S1. Patient co-location categories that were assigned to determine spatial relation between patients.

Published
2021
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38. Additional file 1 of Conventional myelosuppressive chemotherapy for non-haematological malignancy disrupts the intestinal microbiome

Author

Papanicolas, Lito E., Sims, Sarah K., Taylor, Steven L., Miller, Sophie J., Karapetis, Christos S., Wesselingh, Steve L., Gordon, David L., and Rogers, Geraint B.

Abstract

Additional file 1: Supplementary Figure 1. Non-metric multi-dimensional scaling (nMDS) plot showing between group comparisons of faecal microbiome distribution and dispersal. nMDS plots depicted from Bray-Curtis resemblance of square root transformed, genus-level, relative abundance data. Shaded ovals represent 80% confidence interval. There is no difference between the pre-chemotherapy and post-chemotherapy (post-1) faecal microbiome distribution (PERMANOVA; p=0.99) or dispersion (PERMDISP; p=0.90). Supplementary Figure 2. Taxa bar plot showing the 11 phyla detected. Grouped by time point with Pre=pre-chemotherapy, Post1=7-12 days following chemotherapy, and Post2= at the end of a chemotherapy cycle (median 21 days post-chemotherapy). Showing predominance of Bacteroidetes and Firmicutes. Supplementary Figure 3. Taxa bar plot showing the 95 genera detected in ≥20% of samples. Grouped by time point with Pre = pre-chemotherapy, Post1 = 7–12 days following chemotherapy, and Post2 = at the end of a chemotherapy cycle (median 21 days post-chemotherapy).

Published
2021
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39. Additional file 6 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Abstract

Additional file 6: Table S5. Significant correlations between detected AMR genes and bacterial taxa (n=24 patients)

Published
2021
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40. Additional file 9 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Abstract

Additional file 9: Table S8. Univariate analysis of clinical variables and resistance genes.

Published
2021
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41. Additional file 3 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Subjects
mental disorders
Abstract

Additional file 3: Table S2. Primer sequences of antibiotic resistance and positive control (16S rRNA) genes for quantitative PCR.

Published
2021
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42. Additional file 4 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Subjects
genetic processes, information science, food and beverages, natural sciences
Abstract

Additional file 4: Table S3. Antimicrobial resistance genes identified through shotgun metagenomics.

Published
2021
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43. Additional file 8 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Abstract

Additional file 8: Table S7. Distribution of resistance genes according to patient location on the ward.

Published
2021
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44. Additional file 7 of Investigating potential transmission of antimicrobial resistance in an open-plan hospital ward: a cross-sectional metagenomic study of resistome dispersion in a lower middle-income setting

Author

Anushia Ashokan, Hanson, Josh, Ne Myo Aung, Kyi, Mar Mar, Taylor, Steven L., Choo, Jocelyn M., Flynn, Erin, Mobegi, Fredrick, Morgyn S. Warner, Wesselingh, Steve L., Boyd, Mark A., and Rogers, Geraint B.

Subjects
information science, natural sciences
Abstract

Additional file 7: Table S6. Proportion of genes detected in shotgun metagenomic sequencing and qPCR analysis.

Published
2021
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47. Hospitalisation for lower respiratory viral infections in older people in residential aged care facilities.

Author

Caughey, Gillian E., Jorissen, Robert N., Lang, Catherine, Wesselingh, Steve L., and Inacio, Maria C.

Subjects
RESPIRATORY diseases, HYPERTENSION, RESPIRATORY infections, PATIENT readmissions, TREATMENT effectiveness, SEX distribution, HOSPITAL mortality, RESIDENTIAL care, VIRUS diseases, HOSPITAL care, DESCRIPTIVE statistics, ELDER care, LONGITUDINAL method, COMORBIDITY, HEART failure, DISEASE risk factors, EVALUATION
Abstract

Objective: To quantify incidence, trends and outcomes associated with lower respiratory viral infection (LRVI) hospitalisations in Australian residential aged care facilities (RACFs). Methods: A population‐based cohort study of residents in RACFs aged ≥65 years from New South Wales (NSW), South Australia (SA) and Victoria (VIC) using data from the Registry of Senior Australians (2013‐2016) was conducted. Age‐ and sex‐standardised monthly and yearly LRVI hospitalisation incidences were calculated, and time trends and risk factors were assessed. Results: Of 268 657 residents included over the study period, 12% had ≥1 LRVI hospitalisation. Average annual incidence/1000 residents was 7.1 [6.9‐7.2] in 2013, increasing to 7.8 [7.7‐8.1] in 2016. Males, increasing co‐morbidity, presence of CHF, respiratory disease and hypertension had a higher incidence of LRVI hospitalisation. In‐hospital mortality was 14%. Within 30 days following discharge, 15% died and 8% were readmitted. Conclusions: Prior to COVID‐19, incidence of hospitalisation for LRVI in Australia's residential aged care population was increasing and was associated with significant morbidity and mortality. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Intestinal microbiology shapes population health impacts of diet and lifestyle risk exposures in Torres Strait Islander communities

Author

Mobegi, Fredrick M, primary, Leong, Lex EX, additional, Thompson, Fintan, additional, Taylor, Sean M, additional, Harriss, Linton R, additional, Choo, Jocelyn M, additional, Taylor, Steven L, additional, Wesselingh, Steve L, additional, McDermott, Robyn, additional, Ivey, Kerry L, additional, and Rogers, Geraint B, additional

Published
2020
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50. Author response: Intestinal microbiology shapes population health impacts of diet and lifestyle risk exposures in Torres Strait Islander communities

Author

Mobegi, Fredrick M, primary, Leong, Lex EX, additional, Thompson, Fintan, additional, Taylor, Sean M, additional, Harriss, Linton R, additional, Choo, Jocelyn M, additional, Taylor, Steven L, additional, Wesselingh, Steve L, additional, McDermott, Robyn, additional, Ivey, Kerry L, additional, and Rogers, Geraint B, additional

Published
2020
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