title: HIV diagnoses in indigenous peoples: comparison of Australia, Canada and New Zealand

authors: Shea, Beverley

subjects: HIV

abstract: In industrial countries, a number of factors put indigenous peoples at increased risk of HIV infection. National surveillance data between 1999 and 2008 provided diagnoses for Aboriginal and Torres Strait Islanders (Australia), First Nations, Inuit and Métis (Canada excluding Ontario and Quebec) and Māori (New Zealand). Each country provided similar data for a non-indigenous comparison population. Direct standardisation used the 2001 Canadian Aboriginal male population for comparison of five-year diagnosis rates in 1999–2003 and 2004–2008. Using the general population as denominators, we report diagnosis ratios for presumed heterosexual transmission, men who have sex with men (MSM) and intravenous drug users (IDU). Age standardised HIV diagnosis rates in indigenous peoples in Canada in 2004–2008 (178.1 and 178.4/100 000 for men and women respectively) were higher than in Australia (48.5 and 12.9/100 000) and New Zealand (41.9 and 4.3/100 000). Higher HIV diagnosis rates related to heterosexual contact among Aboriginal peoples, especially women, in Canada confirm a widening epidemic beyond the conventional risk groups. This potential of a generalised epidemic requires urgent attention in Aboriginal communities; available evidence can inform policy and action by all stakeholders. Although less striking in Australia and New Zealand, these findings may be relevant to indigenous peoples in other countries.

text: HIV diagnoses in indigenous peoples: comparison of Australia, Canada and New Zealand In industrial countries, a number of factors put indigenous peoples at increased risk of HIV infection. National surveillance data between 1999 and 2008 provided diagnoses for Aboriginal and Torres Strait Islanders (Australia), First Nations, Inuit and Métis (Canada excluding Ontario and Quebec) and Māori (New Zealand). Each country provided similar data for a non-indigenous comparison population. Direct standardisation used the 2001 Canadian Aboriginal male population for comparison of five-year diagnosis rates in 1999–2003 and 2004–2008. Using the general population as denominators, we report diagnosis ratios for presumed heterosexual transmission, men who have sex with men (MSM) and intravenous drug users (IDU). Age standardised HIV diagnosis rates in indigenous peoples in Canada in 2004–2008 (178.1 and 178.4/100 000 for men and women respectively) were higher than in Australia (48.5 and 12.9/100 000) and New Zealand (41.9 and 4.3/100 000). Higher HIV diagnosis rates related to heterosexual contact among Aboriginal peoples, especially women, in Canada confirm a widening epidemic beyond the conventional risk groups. This potential of a generalised epidemic requires urgent attention in Aboriginal communities; available evidence can inform policy and action by all stakeholders. Although less striking in Australia and New Zealand, these findings may be relevant to indigenous peoples in other countries.

year: 2011

title: Street health: Practitioner service provision for Māori homeless people in Auckland

authors: Nikora, Linda Waimarie

abstract: Homelessness is more than a transient crisis. While access to housing might solve the needs of some, research has shown that the needs of homeless people are much more complex and more a symptom of systematic structural inequalities than an unanticipated emergency (Hopper, 1998). As in other countries, the homeless population in New Zealand is made up of a diverse population of men, women and intersex groups of various ages, sexualities and ethnic backgrounds. Māori are over-represented among this population due to ongoing processes of colonisation and socioeconomic exclusion (Groot et al., 2011). When compared with domiciled citizens, homeless people are more likely to experience a raft of illnesses and unmet health needs, violence, a sense of insecurity, exclusion and fear, and are more likely to commit sui- cide (Hodgetts et al., 2007). Homeless people often experience diverse ill- nesses including asthma, heart disease, diabetes, hepatitis, renal disease, dermatological conditions, malnutrition, oral disease, depression, schizo- phrenia, substance misuse and broken bones (Ellison-Loschmann & Pearce, 2006; Joly et al., 2011; Moore et al., 2007).

text: Street health: Practitioner service provision for Māori homeless people in Auckland Homelessness is more than a transient crisis. While access to housing might solve the needs of some, research has shown that the needs of homeless people are much more complex and more a symptom of systematic structural inequalities than an unanticipated emergency (Hopper, 1998). As in other countries, the homeless population in New Zealand is made up of a diverse population of men, women and intersex groups of various ages, sexualities and ethnic backgrounds. Māori are over-represented among this population due to ongoing processes of colonisation and socioeconomic exclusion (Groot et al., 2011). When compared with domiciled citizens, homeless people are more likely to experience a raft of illnesses and unmet health needs, violence, a sense of insecurity, exclusion and fear, and are more likely to commit sui- cide (Hodgetts et al., 2007). Homeless people often experience diverse ill- nesses including asthma, heart disease, diabetes, hepatitis, renal disease, dermatological conditions, malnutrition, oral disease, depression, schizo- phrenia, substance misuse and broken bones (Ellison-Loschmann & Pearce, 2006; Joly et al., 2011; Moore et al., 2007).

year: 2016

title: Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data

authors: Lynam, Adam David

subjects: machine learning

abstract: The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.
This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.
An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.

text: Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.
This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.
An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.

year: 2009