Page 356 - Special Topic Session (STS) - Volume 4

STS1080 Fionn M.

One summary interpretation is how factor 1 accounts for recorded trauma,
and factor 2 accounts for region of the respondent.
The second analysis was to characterize the socio-demographic data, and
then to see if the neurotic symptoms and common mental disorders data
could be explanatory and contextualized. In the third analysis: It was checked
whether neurotic symptoms and common mental disorders data should be
jointly analysed with the socio-demographic data.
To be noted here, is how Big Data inputs are required to calibrate and
validate, and Open Data sources are key.
In the Adult Psychiatric Morbidity in England, household survey, covered
was: Common mental disorders; Posttraumatic stress disorder; Suicidal
thoughts, attempts and self-harm; Psychosis; Antisocial and borderline
personality disorders; Attention deficit hyperactivity disorder; Eating disorder;
Alcohol misuse and dependency; Drug use and dependency; Problem
gambling; Psychiatric comorbidity,

4. Health and Medical Data Sources for Developing Countries
In the “Atlas of the African Health Statistics” (WHO, 2017, see
“Publications”), a 137 page document in 2017 from the African Health
Observatory, World Health Organization (WHO), Regional Office for Africa,
there are many comparative statistical evaluations. With data from World
Health Organization, and from UNICEF, and with lots of coverage of morbidity
and children, there is adolescent health coverage, and communicable diseases
like HIV, and coverage of malaria, tuberculosis, hepatitis, and many other
themes, including mental health, non-communicable diseases, accidents, etc.
There is also: health financing, health workforce, and in the chapter entitled
“Social determinants of health”, there are sections on “Water and sanitation”,
and “Access to electricity”.
From the African Development Bank Group (https://www.afdb.org/en), it
is very clear how economic development has to be based on, and linked to,
health and lifestyle, energy and environment.
Mathematics underpins, and is the basis for, all of Data Science and Big
Data analytics, see Murtagh (2017). Here too, multidisciplinarity is essential,
following the integration of data sources and of methodology. There will
remain many research issues for the multiple source data integration, where
there will be missing data and data with uncertainty, and the relevance of
qualita¬tive and quantitative data encoding. Data curation is a very important
current research challenge, see Murtagh and Devlin (2018), and also important
disrup-tive technological advances, especially Internet of Things, Smart Cities,
Smart Homes, these all provide important data sources, to be encoded,
integrated and with deployment of optimal methodologies. Such will be
playing a role in the developments and innovation in this project.

345 | I S I W S C 2 0 1 9