Know your HIV epidemic (KYE) report: review of the HIV epidemic in South Africa.

In order to update and consolidate South Africa’s evidence base for HIV-prevention interventions, it was decided by the Government of South Africa to commission a synthesis of the available data on the epidemiology of prevalent and incident HIV infections, and the wider epidemic context of these infections. This know your epidemic (KYE) approach has been successfully implemented in a number of sub-Saharan African countries.2 The process involves a desk review and secondary analysis of existing biological, behavioural and socio-demographic data in order to determine the epidemiology of new HIV infections. KYE reports present key findings and policy and programme recommendations which are grounded in local evidence and aim to support decision-making and improve HIV-prevention results. In 2010, South Africa also conducted a know your response (KYR) review, which critically assessed HIV-prevention policies, programmes and resource allocations. The overall results of this HIV epidemic review and the KYR review will be published in a separate, national KYE/KYR synthesis report

Patterns of homelessness and housing instability and the relationship with mental health disorders among young people transitioning from out-of-home care: Retrospective cohort study using linked administrative data

Objectives: The study examined the relationship between mental health, homelessness and housing instability among young people aged 15–18 years old who transitioned from out-of-home in 2013 to 2014 in the state of Victoria, Australia with follow-up to 2018. We determined the various mental health disorders and other predictors that were associated with different levels of homelessness risk, including identifying the impact of dual diagnosis of mental health and substance use disorder on homelessness. Methodology: Using retrospective de-identified linked administrative data from various government departments we identified various dimensions of homelessness which were mapped from the European Topology of Homelessness (ETHOS) framework and associated mental health variables which were determined from the WHO ICD-10 codes. We used ordered logistic regression and Poisson regression analysis to estimate the impact of homelessness and housing instability respectively. Results: A total homelessness prevalence of 60% was determined in the care-leaving population. After adjustment, high risk of homelessness was associated with dual diagnosis of mental health and substance use disorder, intentional self-harm, anxiety, psychotic disorders, assault and maltreatment, history of involvement with the justice system, substance use prior to leaving care, residential and home-based OHC placement and a history of staying in public housing. Conclusions: There is clearly a need for policy makers and service providers to work together to find effective housing pathways and integrated health services for this heterogeneous group of vulnerable young people with complex health and social needs. Future research should determine longitudinally the bidirectional relationship between mental health disorders and homelessness

Research using population-based administration data integrated with longitudinal data in child protection settings: A systematic review

Introduction: Over the past decade there has been a marked growth in the use of linked population administrative data for child protection research. This is the first systematic review of studies to report on research design and statistical methods used where population-based administrative data is integrated with longitudinal data in child protection settings. Methods: The systematic review was conducted according to Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement. The electronic databases Medline (Ovid), PsycINFO, Embase, ERIC, and CINAHL were systematically searched in November 2019 to identify all the relevant studies. The protocol for this review was registered and published with Open Science Framework (Registration DOI: 10.17605/OSF.IO/96PX8) Results: The review identified 30 studies reporting on child maltreatment, mental health, drug and alcohol abuse and education. The quality of almost all studies was strong, however the studies rated poorly on the reporting of data linkage methods. The statistical analysis methods described failed to take into account mediating factors which may have an indirect effect on the outcomes of interest and there was lack of utilisation of multi-level analysis. Conclusion: We recommend reporting of data linkage processes through following recommended and standardised data linkage processes, which can be achieved through greater co-ordination among data providers and researchers

RESEARCH USING POPULATION-BASED ADMINISTRATION DATA LINKED TO LONGITUDINAL SURVEYS IN OUT-OF-HOME CARE OR CHILD PROTECTION SETTINGS: A SYSTEMATIC REVIEW

Synthesis and reporting of studies using administrative datasets linked to longitudinal survey

A mixed methods evaluation of the breastfeeding memory aide CHINS

Breastfeeding rates remain persistently low in the United Kingdom (UK) despite wide‐scale rollout of UNICEF Baby Friendly Initiative training and accreditation. More must be done to ensure breastfeeding practitioners can provide effective support. The memory aide CHINS (Close, Head free, In‐line, Nose to Nipple and Sustainable) could help practitioners remember, recall, and apply breastfeeding theory in practice and this paper presents a UK evaluation of its impact. A concurrent, convergent mixed methods approach was adopted using Normalisation Process Theory (NPT) as an overarching framework. An online survey targeted breastfeeding practitioners and academics from the UK (n = 115). A sub‐set (n = 16) of respondents took part in qualitative focus groups. Survey data was subjected to descriptive and inferential statistical analysis, and the focus group data was analysed, using NPT. CHINS is widely used in breastfeeding education and practice largely because of its simplicity and ease of integration in everyday practice, as well as its sustained inclusion in UNICEF Baby Friendly Initiative training. CHINS has introduced a standardised approach to the principles of positioning for effective breastfeeding. Doing so has helped address inconsistencies and poor practice in this area, and CHINS plays a role in assisting practitioners in building confidence in their breastfeeding practice. More needs to be done to ensure the breastfeeding workforce develop and maintain the requisite skills to promote and support breastfeeding, including the role of memory aides such as CHINS in achieving this

Correction: Research using population-based administration data integrated with longitudinal data in child protection settings: A systematic review.

[This corrects the article DOI: 10.1371/journal.pone.0249088.]

Developing and using matrix methods for analysis of large longitudinal qualitative datasets in out-of-home-care research

publishedVersio

Developing and using matrix methods for analysis of large longitudinal qualitative datasets in out-of-home-care research

Managing and analysing large qualitative datasets pose a particular challenge for researchers seeking a consistent and rigorous approach to qualitative data analysis. This paper describes and demonstrates the development and adoption of a matrix tool to guide the qualitative data analysis of large-N interview data. The paper articulates the theoretical and conceptual underpinnings of the matrix analysis tool and how it was developed and applied in a longitudinal mixed methods out-of-home-care research study. Specific illustrations and examples of data integration and data analysis are provided to demonstrate the benefits and potentials of constructing matrix tools to guide research teams when working with large qualitative data sets alone or in combination with quantitative data sets

Developing and using matrix methods for analysis of large longitudinal qualitative datasets in out-of-home-care research

Managing and analysing large qualitative datasets pose a particular challenge for researchers seeking a consistent and rigorous approach to qualitative data analysis. This paper describes and demonstrates the development and adoption of a matrix tool to guide the qualitative data analysis of large-N interview data. The paper articulates the theoretical and conceptual underpinnings of the matrix analysis tool and how it was developed and applied in a longitudinal mixed methods out-of-home-care research study. Specific illustrations and examples of data integration and data analysis are provided to demonstrate the benefits and potentials of constructing matrix tools to guide research teams when working with large qualitative data sets alone or in combination with quantitative data sets