Can data shape the future of mental health support? – The Guardian 20160907

From: Can data shape the future of mental health support? – The Guardian 20160907

Open data is being used to design resources for people with mental health conditions to help them find the right support

Head on digital screen.

If you’re experiencing a mental health issue, one of the people you probably least want to speak to about it is your employer. Disclosing depression or anxiety has long been seen as the last workplace taboo, for fear of repercussions. This is despite the existence of the Equality Act 2010, which protects employees with physical and mental disabilities from discrimination.

But just over a third of workers with a mental health condition discuss it with their employer, according to a survey of 1,388 employees carried out by Willis PMI Group, one of the UK’s largest providers of employee healthcare and risk management services. The research found that 30% of respondents were concerned that they wouldn’t receive adequate support, 28% believed their employer wouldn’t understand, and 23% feared that disclosing it would lead to management thinking less of them.

A culture of fear and silence can have a huge impact on productivity – the charity Mind estimates [pdf] that mental ill health costs the economy £70bn a year. The challenge is that seeking help involves taking ownership of the problem, says Mark Brown, development director of social enterprise Social Spider and founder of the now defunct mental health and wellbeing magazine One in Four. And finding support online can be a time-consuming and frustrating experience.

“Just serving up ever great slabs of information – the internet is awash with it – isn’t going to help anyone to know what to do,” says Brown. “We often confuse the provision of information with the solving of problems. Knowing information is different from knowing how to put that information into action.”

Brown believes that bringing together information with public and open data into a single digital space is one way that could innovate how advice is delivered.

Plexus is aiming to achieve just this. Built by the digital studio M/A, with funding from the Open Data Institute, the knowledge base is being used to design resources for people with mental health conditions, their families, and even employers, to find support available in local areas, seek advice on how best to cope with returning to work after a period off and understand employee rights and employer responsibilities.

Plexus has pooled data from a couple of dozen organisations including NHS Choices, Department for Work and Pensions, the Office for National Statistics and Citizens Advice. In some cases the information has been pulled from APIs; in other instances it has been scraped using web data platform import.io.

The first tool Plexus developed is a chatbot called Grace, which is currently in beta testing. It enables users to record thoughts and feelings anonymously, receive feedback in the form of a newsletter and log in to an online dashboard to see a more detailed analysis, including whether there are any patterns in mood emerging over a period of time. The tool also offers guidance from the various governmental and charity websites under easy-to-navigate sections, such as legal rights and preparing for work.

“Through machine learning, Grace will intuitively know when our users are mostly likely to want to speak with us, be able to see the positive and negative nature of the user’s reply, and adapt the questions to encourage more positive responses,” explains Martin Vowles, creative director and co-founder of M/A. “We’re hoping this approach will allow us to offer a unique tool to each user which helps them understand and develop their mental wellbeing.”

Brown says that the potential for machine learning to tailor information and services is exciting. “It’s very good at looking at big piles of data for patterns. When we know certain things to be correct from one dataset, it can begin to make guesses about lots of other things based on what the machine is being fed.”

The sensitive nature of data being submitted by users on a platform like Grace, though, means many people are likely to be uneasy about their data being made accessible. To get round this, Plexus allows users to decide how their data is shared, with data licences lasting between 13 and 26 weeks. Vowles hopes that “as users become more trusting of Grace and what it can do for them, they’ll become more trusting with [us] using their anonymised personal data.”

Plexus aims to release a series of open datasets, including qualitative, quantitative and information on resources accessed by Grace users, to enable NGOs and local authorities to understand the country’s mental health provision. It’s hoped that they’d then use the knowledge to devise new strategies and ensure targets are met and resources and services available in local areas are of an acceptable standard.

There are also plans to make certain data available to employers, but “this has to be on the employee’s terms”. Vowles imagines that involving employers in the process of receiving support could allow them to get a clearer picture of mental health in the workplace. They could then adapt to make employees feel more comfortable and ensure their business has adequate support in place.

The potential to use open data to shape how future mental health support is delivered is an area that has been underexplored. At the end of last year, the Royal Society of Arts launched an interactive platform with Mind that allows members of the public to find out how well local health providers are looking after people with mental health conditions. The full dataset is available to download and includes data extracted from Public Health England, as well as metrics such as percentage of people with a mental health condition in employment in local areas. Plexus, however, is the first tool to use open data with the aim of providing people with a holistic view of their mental wellbeing.

Brown supports the idea of using open datasets and combine them, but stresses that any tool or platform has to benefit users. The data and information must be digestible and it needs to help them understand and take away from it what they need.

“It’s often extremely easy to forget that people with mental health difficulties are people first and foremost – not objects or problems.”

The United Nations Secretary-General’s High-Level Panel on Access to Medicines Releases Final Report – 20160914

The United Nations Secretary-General’s High-Level Panel on Access to Medicines Releases Final Report

High-Level Panel on Access to Medicines

Letter from Panel Co-Chairs: Click Here

THE FINAL REPORT (PDF)

UNITED NATIONS SECRETARY-GENERAL’S HIGH-LEVEL PANEL ON ACCESS TO MEDICINES CALLS FOR NEW DEAL TO CLOSE THE HEALTH INNOVATION AND ACCESS GAP

Whether it’s the rising price of the EpiPen, or new outbreaks of diseases, like Ebola, Zika and yellow fever, the rising costs of health technologies and the lack of new tools to tackle health problems, like antimicrobial resistance, is a problem in rich and poor countries alike.

According to a High-Level Panel convened to advise the UN Secretary-General on improving access to medicines, the world must take bold new approaches to both health technology innovation and ensuring access so that all people can benefit from the medical advances that have dramatically improved the lives of millions around the world in the last century.

For decades, many international treaties and national constitutions have enshrined the fundamental right to health and the right to share in the benefits of scientific advancements.  Yet, while the world is witnessing the immense potential of science and technology to advance health care, gaps and failures in addressing disease burdens and emerging diseases in many countries and communities remain. The misalignment between the right to health on the one hand and intellectual property and trade on the other, fuel this tension.

The UN Secretary-General established the High-Level Panel to propose solutions for addressing the incoherencies between international human rights, trade, intellectual property rights and public health objectives. The report recommendations come at the end of a ten-month process for the Panel under the leadership of Ruth Dreifuss and the former President of the Swiss Confederation and Festus Mogae, the former President of the Republic of Botswana.

“Policy incoherencies arise when legitimate economic, social and political interests and priorities are misaligned or in conflict with the right to health,” said President Ruth Dreifuss. “On the one hand, governments seek the economic benefits of increased trade.  On the other, the imperative to respect patents on health technologies could, in certain instances, create obstacles to the public health objectives and the right to health.”

The Panel has formulated a set of concrete recommendations to help improve research and development of health technologies and people’s access to vital therapies that are currently priced out-of-reach of patients and governments alike. The Panel’s report points out that the cost of health technologies are putting a strain on both rich and poor countries.

“With no market incentives, there is an innovation gap in diseases that predominantly affect neglected populations,  rare diseases and a crisis particularly with antimicrobial resistance, which poses a threat to humanity,” said Malebona Precious Matsoso, Director General of the National Department of Health of South Africa. “Our report calls on governments to negotiate global agreements on the coordination, financing and development of health technologies to complement existing innovation models, including a binding R&D Convention that delinks the costs of R&D from end prices.”

The Panel suggested that initially governments should form a working group to begin negotiating a Code of Principles for Biomedical R&D, and report annually on their progress in negotiating and implementing the Code in preparation for negotiating the Convention.

The Panel examined the way in which the application of the flexibilities found in the WTO Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) has facilitated access to health technologies, and how WTO Members can tailor national intellectual property law, competition law, government procurement and drug regulatory laws and regulations to fulfil public health obligations.

The new report noted with grave concern reports of governments being subjected to undue political and economic pressure to forgo the use of TRIPS flexibilities. The Panel felt strongly that this pressure undermines the efforts of governments to meet their human rights and public health obligations and violates the integrity and legitimacy of the Doha Declaration.

“WTO Members must make full use of TRIPS flexibilities as reaffirmed by the Doha Declaration on TRIPS and Public Health.  This is essential to promote access to health technologies,” said Michael Kirby, member of the High-Level Panel and chair of the Expert Advisory Group. “In particular, governments and the private sector must refrain from explicit or implicit threats, tactics or strategies that undermine the right of WTO Members to use TRIPS flexibilities.  WTO Members must register complaints against undue political and economic pressure.  They need to take strong, effective measures against offending Members.”

Transparency was a recurring theme throughout the report of the High-Level Panel. The Panel repeatedly raised concerns regarding the negative impact of insufficient transparency on both health technology innovation and access. The Panel was also critical of the lack of transparency surrounding bilateral free trade and investment negotiations. The Panel views transparency as a core component of robust and effective accountability frameworks needed to hold all stakeholders responsible for the impact of their actions on innovation and access.

“A paradigm shift in transparency is needed to ensure that the costs of R&D, production, marketing, and distribution, as well as the end prices of health technologies are clear to consumers and governments,” said President Festus Mogae. “Governments should require manufacturers and distributors of health technologies to disclose these costs and the details of any public funding received in the development of health technologies, including tax credits, subsidies, and grants.”

The Panel also recommended the UN General Assembly convene a Special Session no later than 2018 on health technology innovation and access to agree on strategies and an accountability framework that will accelerate efforts towards promoting innovation and ensuring access in line with the 2030 Agenda for Sustainable Development.

Aligning Ontario’s Scheme for Identifying Census Divisions with Canada’s

Ontario’s Ministry of Finance regularly updates its population projections for the province; its most recent updates were published in the Spring 2016. These population projections are organized into 4 different datasets:

  • projections for the whole province
  • projections for each census division
  • projections for each Local Health Integration Network (LHIN)
  • projections for each Ministry of Children and Youth Services’ Service Delivery Division (SDD) region

Unfortunately (even inexplicably), Ontario uses a different scheme for identifying Census Divisions from Canada’s. We may use this map:

Ontario 2011 Census Divisions - Statistics Canada

and this map:

MofF - Chart 00

allow us to generate the following table of alignments:

Table 4. Aligning Ontario’s scheme for identifying Census Divisions with Canada’s.
CD_ID
(Ontario)
CD_ID
(Canada)
CD_ID
(Ontario)
CD_ID
(Canada)
1 20 26 13
2 18 27 47
3 24 28 1
4 21 29 41
5 19 30 34
6 29 31 37
7 22 32 42
8 28 33 40
9 46 34 36
10 25 35 38
11 44 36 39
12 26 37 32
13 14 38 31
14 15 39 57
15 43 40 56
16 16 41 51
17 30 42 48
18 23 43 49
19 6 44 53
20 10 45 52
21 12 46 54
22 9 47 60
23 7 48 59
24 11 49 58
25 2

We will need to make use of this Table of alignments when we come to map the Ministry of Finance’s population projections onto the boundaries of Ontario’s Local Health Integration Networks (LHINs).

Disclaimer: This post is my personal work and is not sponsored or endorsed by Youthdale Treatment Centres in any way. This work  is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Population Projections in Ontario: The Case for Returning to Life Cycle Groupings

Background

Every year the Ministry of Finance updates its population projections for Ontario and each of its 49 census divisions to reflect the most recent trends and historical data. The Spring 2016 update is based on new 2015 population estimates from Statistics Canada and reflects minor changes in trends in fertility, mortality and migration.

Map of Ontario Census Divisions
Figure 1. Map of Ontario Census Divisions. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.

The Ministry of Finance includes several Charts and Tables that reference various demographic groupings in its analysis of these population projections:

Demographic Groupings
Single Years (0, 1, …, 90+) Statistical Table 6
Five-Year Groupings (0-4, 5-9, …, 85-89, 90+) Statistical Tables 7-10
Life Cycle Groupings (0-14, 15-64, 65+) Charts 5-6, 10-12
Statistical Table 2

To assist in planning, delivering, and evaluating human services – particularly those for young people – we want to differentiate Youth (15-24 years old) from Adult (25-64) instead of using the Ministry of Finance’s original definition of “Adult”: 1

  • Children (0- 14 years old)
  • Youth (15 – 24)
  • Adults (25 – 64)
  • Seniors (65+)

For the interested reader, we have compiled a set of Statistical Tables that restate the population projections for Ontario in terms of our Life Cycle Groupings.

Now, let’s review the highlights of the Ministry of Finance’s analysis.

Provincial overview

The Ministry of Finance considers three scenarios of population growth in Ontario. The medium-growth or reference scenario – the most likely to occur if recent trends continue – projects population growth of 30.1 per cent, from 13.8 million in 2015 to more than 17.9 million in 2041 (Chart 1).

Ontario population, 1971 to 2014
Chart 1. Ontario population, 1971 to 2014. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.

The rate of population growth in Ontario in the reference scenario is projected to decline gradually from 1.2 per cent to 0.8 per cent annually (Chart 2).

Annual rate of population growth in Ontario, 1971 to 2041
Chart 2. Annual rate of population growth in Ontario, 1971 to 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.

Components of population growth

In any given year, the share of population growth due to natural increase versus net migration varies. While natural increase trends evolve slowly, net migration can be more volatile, mostly due to swings in inter-provincial migration and variations in international immigration.

Natural increase

The number of births and deaths in Ontario has been rising slowly and at a similar pace over the last decade. As a result, natural increase has been fairly stable at about 50,000 annually. The rate of population growth due to natural increase over the projection period is affected by two main factors:

  • The passage of the baby boom echo generation (children of baby boomers) through peak fertility years will result in an increased number of births through the late 2010s and early 2020s.
  • The transition of large cohorts of baby boomers into the Seniors group.

Overall, natural increase is projected to be fairly stable around 55,000 over the first decade of the projections, followed by a steady decline to less than 17,000 by 2041. The share of population growth accounted for by natural increase (versus net migration) is projected to decline from 32 per cent in 2016 to 11 per cent by 2041 (Chart 3).

Net migration

Net migration to Ontario has averaged about 77,000 per year in the past decade. Net migration is projected to be higher at the beginning of the projection period than it has been during the past few years, as net losses of population through inter-provincial migration have recently turned to gains and federal immigration targets have been raised significantly.

Ontario’s annual net migration gain is projected to increase from 114,000 in 2016 to 130,000 by 2041. The share of population growth accounted for by net migration (versus natural increase) is projected to rise from 68 per cent in 2016 to 89 per cent by 2041 (Chart 3).

Contribution of natural increase and net migration to population growth in Ontario, 1971 to 2041.
Chart 3. Contribution of natural increase and net migration to population growth, 1971 to 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.

Age structure

The Ministry of Finance displays the distribution of age among the people of Ontario in the familiar form of an age pyramid (Chart 4) and shows how age structure impacts the share of population (Chart 5) and the rate of population growth (Chart 6) accounted for by three Life Cycle Groupings (0-14, 15-64, 65+ years old).

Using our four Life Cycle Groupings ((0-14, 15-24, 25-64, 65+ years old), we have redrawn the projected share of population (Chart 5-PGA) and the projected rate of population growth (Chart 6-PGA):

Age pyramid in Ontario, 2015 and 2041.
Chart 4. Age pyramid in Ontario, 2015 and 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.
Proportion of population aged 0-14, 15-64, and 65+ in Ontario, 1971 to 2041
Chart 5. Proportion of population aged 0-14, 15-64, and 65+ in Ontario, 1971 to 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.
Proportion of population aged 0-14, 15-24, 25-64, and 65+ in Ontario, 2016 to 2041
Chart PGA 5. Proportion of population aged 0-14, 15-24, 25-64, and 65+ in Ontario, 2016 to 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.
Annual rate of growth of population age groups 0-14, 15-64, and 65+ in Ontario, 1971 to 2041
Chart 6. Annual rate of growth of population age groups 0-14, 15-64, and 65+ in Ontario, 1971 to 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.
Annual rate of growth of population age groups 0-14, 15-24, 25-64, and 65+ in Ontario, 2017 to 2041
Chart PGA 6. Annual rate of growth of population age groups 0-14, 15-24, 25-64, and 65+ in Ontario, 2017 to 2041. Source: Ministry of Finance, Ontario Population Projections Update, Spring 2016.

The Ministry of Finance includes the following analysis (pp. 8-10):

By 2041, there will be more people in every age group in Ontario compared to 2015, with a sharp increase in the number of seniors. Baby boomers will have swelled the ranks of seniors; children of the baby boom echo generation will be of school-age; and the baby boom echo cohorts, along with a new generation of immigrants, will have bolstered the population aged 15–64. ...

The number of children aged 0–14 is projected to increase gradually over the projection period, from 2.2 million in 2015 to almost 2.7 million by 2041. The share of children in the population is projected to decrease from 15.9 per cent in 2015 to 14.9 per cent by 2041. By the late 2030s, the number of children is projected to grow at a much slower pace than other age groups, reflecting the smaller number of women in their 20s and 30s. ...

Within the 15–64 age group, the number of youth aged 15–24 is initially projected to decline slightly, from a high of 1,827,000 in 2015 to a low of 1,725,000 by 2022. The youth population is then projected to resume growing, reaching almost 2.1 million by 2041. The youth share of total population is projected to decline from 13.2 per cent in 2015 to 11.1 per cent by 2033, followed by a small rise to 11.5 per cent by 2041. ...

In this last paragraph, the Ministry of Finance makes its one and only substantial reference to “youth” – yet it’s a pretty important point: While the number of Youth in Ontario is projected to decline over the next five years, their numbers will increase steadily thereafter – in fact, becoming the fastest growing demographic by the end of the projection period. The Ministry’s graphics (Charts 5-6), unfortunately, obscure what’s going on here – as is immediately apparent when we differentiate Youth (15-24 years old) from Adult (25-64) using the Life Cycle Groupings  (Charts PGA 5-6).

Next time: We “drill down” to the level of the region and census division – where the planning, delivery and evaluation of human services take place or where, at least, I’d argue that they should. Meanwhile, the interested reader may want to consult our Statistical Tables that restate the population projections for Ontario in terms of the Life Cycle Groupings.

  1. In fact, Statistics Canada used these four Life Cycle Groupings until 2007. In this sense, we’re arguing for the return to Life Cycle Groupings when it comes to understanding population projections – especially when one is concerned with young people. The Ministry of Finance refers to “youth” only twice – once in passing while noting that some census divisions of Northern Ontario experiencing net out-migration, mostly among youth (p. 12) and once in the context of a more substantial discussion of changes in the annual rate of population growth due to age structure (p. 10, and see below).

Health Regions in Ontario – Boundaries and Correspondence with Census Geography

This issue describes in detail the health region limits as of December 2015 and their correspondence with the 2011 and 2006 Census geography. Health regions are defined by the provinces and represent administrative areas or regions of interest to health authorities. This product contains correspondence files (linking health regions to census geographic codes) and digital boundary files. User documentation provides an overview of health regions, sources, methods, limitations and product description (file format and layout).

This issue contains the health region limits as of December 2015 and their correspondence with 2011 Census geography.

The boundaries, health region codes and health region names in Ontario have not changed.

Appendices and tables

Background

In recent years there has been an increasing demand for relevant health information at a ‘community’ level. As a result, health regions have become an important geographic unit by which health and health-related data are produced.

Health regions are legislated administrative areas defined by provincial ministries of health. These administrative areas represent geographic areas of responsibility for hospital boards or regional health authorities. Health regions, being provincial administrative areas, are subject to change.

The 2015 Health Regions: Boundaries and Correspondence with Census Geography reflects the boundaries as of December 2015 and provides the geographic linkage to 2011 and 2006 Censuses.

Description

The generic term “health region” applies to a variety of administrative areas across Canada that are defined by provincial ministries of health. To complete the Canadian coverage, each northern territory is represented as health region.

The following table describes the health regions, by province, with reference to the provincial legislation under which these areas have been defined.

Health region code structure

A four digit numeric code is used to uniquely identify health regions. The first two digits represent the province, and the second two digits represent the health region. These codes reflect the same codes used by the provincial ministries of health. For those provinces where a numeric code is not applicable, a two-digit code was assigned. Ontario uses a 4-digit code for public health units. This code was truncated to the last two digits for consistency in the national health region code structure. Since Ontario has two sets of health regions, which do not entirely relate hierarchically, their codes are unique within the province.

The names of the health regions also represent the official names used by the provinces.

See Appendix 1 Health regions in Canada, 2015 (names and codes).

Correspondence files

Production of health region level data requires geographic coding tools. Since census geography does not recognize provincial health region boundaries, a health region-to-census geography correspondence file provides the linkage between health regions and their component census geographic units. These correspondence files use the smallest relevant census geographic unit.

To accommodate various data sources producing health region level data, linkage has been created for both 2011 and 2006 Census geographies. The layout of these correspondence files includes the seven-digit Standard geographic classification (SGC) code. The SGC code uniquely represents census subdivisions (CSD).

Most health regions comprise entire CSDs (see Table 2). However, there are some cases where health regions do not conform to municipalities. The 2006 Census linkage was created at the dissemination area (DA) level and block level for British Columbia, Alberta, Saskatchewan, Manitoba, and Ontario (LHINs). Even these smaller geographic areas (DA/blocks) sometimes straddle health region boundaries. In those cases, the entire DA (or block) was assigned, in conjunction with the affected province, to just one health region and therefore represents a ‘best fit’ with census geography.

Other data sources use postal codes to geographically reference data records. These data are first converted to census geographic units using the Statistics Canada postal code conversion file, and then linked to health regions based on the correspondence file.

The dissemination area/block-to-health region (DA/block-to-HR) correspondence files provided in this publication are available in CSV format.

Record layout

The record layout of the files is shown in the following tables.

http://www.statcan.gc.ca/pub/82-402-x/2015002/t/tbl03-eng.htm
Variable name Comments
DBUID2011 Uniquely identifies a dissemination block (composed of the 2-digit province or territory unique identifier followed by the 2-digit census division code, the 4-digit dissemination area code and the 2-digit dissemination block code)
CSDUID2011 Uniquely identifies a census subdivision (composed of 2-digit province or territory unique identifier followed by the 2-digit census division code and 3-digit census subdivision code)
HRUID2015 Uniquely identifies a health region (composed of 2-digit province or territory unique identifier followed by the 2-digit health region code)
HRNAME_ENGLISH Health region name, English
HRNAME_FRENCH Health region name, French
DBPOP2011 2011 Census dissemination block population

Health regions and standard geography

For the most part, health regions can be described as groupings of counties (census divisions) or municipalities (census subdivisions). This description holds especially true in the Atlantic provinces, Quebec, and Ontario (with minor exceptions in northern Ontario). In the western provinces, health regions are less likely to follow census division or census subdivision boundaries.

The following table provides a count, by province, of census subdivisions that fall in more than one health region.

Table summary
This table displays the results of Census subdivisions linked to more than one health region. The information is grouped by Provinces with splits (appearing as row headers), 2006 Census subdivisions and 2011 Census subdivisions (appearing as column headers).
Provinces with splits 2006 Census subdivisions 2011 Census subdivisions
Nova Scotia – District Health Authorities 1 0
Ontario – Local Health Integration Networks 9 11
Ontario – Public Health Units 1 4
Manitoba 7 6
Saskatchewan 45 46
Alberta 9 6
British Columbia 6 20

Census subdivisions Health region codes Health region names Population % population split in census subdivisions
Ontario – Local Health Integration Network
3519028 3505 Central West 30,476 10.6
3508 Central 257,825 89.4
Subtotal 288,301 100
3520005 3505 Central West 130,193 5
3506 Mississauga Halton 109,344 4.2
3507 Toronto Central 1,149,993 44
3508 Central 631,372 24.1
3509 Central East 594,158 22.7
Subtotal 2,615,060 100
3521005 3505 Central West 39,123 5.5
3506 Mississauga Halton 674,320 94.5
Subtotal 713,443 100
3521024 3505 Central West 59,460 100
3508 Central 0 0
Subtotal 59,460 100
3528052 3502 South West 13,416 21.2
3504 Hamilton Niagara Haldimand Brant 49,759 78.8
Subtotal 63,175 100
3542004 3502 South West 11,487 93.5
3503 Waterloo Wellington 799 6.5
Subtotal 12,286 100
3542015 3502 South West 6,871 72.2
3512 North Simcoe Muskoka 2,649 27.8
Subtotal 9,520 100
3542045 3502 South West 3,866 59.9
3512 North Simcoe Muskoka 2,587 40.1
Subtotal 6,453 100
3543003 3508 Central 10,564 99.6
3512 North Simcoe Muskoka 39 0.4
Subtotal 10,603 100
3543021 3508 Central 1,063 5.7
3512 North Simcoe Muskoka 17,442 94.3
Subtotal 18,505 100
3560090 3513 North East 0 0
3514 North West 7,031 100
Subtotal 7,031 100

Boundary files

The health region boundaries provided in this product are based on 2011 Census geographic units. The smallest geographic unit available has been used as the building block to define health regions. In general, the legislated limits respect these units, but they do not respect DAs or blocks once the legislated boundaries are digitized. In all provinces except British Columbia, Alberta, Saskatchewan, Manitoba and Ontario (LHINs), the dissemination area was used to define health regions. However, in several instances, the actual physical legal limits split DAs. In the Prairie provinces and B.C. the dissemination block (DB) was used to improve the accuracy of these boundaries. Even with this, the physical legal boundaries do not always reflect the legislated limits recognized by the provinces thus creating many instances of split dissemination blocks.

The limits that did not respect STC geometry (the splits) were digitized by utilizing maps, spatial layers and/or descriptions supplied by and with the cooperation of the authority for each province.

Method used to create health region 2015 boundary files

All processes and procedures to update the digital boundary files were carried out using ESRI Inc.® ArcGIS TM 10.2.2, Safe Software Inc. FME ® Desktop 2015, Pitney Bowes Software Inc.® MapInfo 11.5.1, Microsoft ® Access 2007, and Microsoft ® Excel 2007.

Boundary file formats

All digital health region boundaries in this publication are available in two formats: An ESRI ® shapefile format and MapInfo® table format. We’ll be using the ESRI shapefile, which is supplied in a zip file. This file expands to provide four files of different extensions which are: (DBF, SHP, PRJ and SHX). Boundary files are provided as a national boundary file and are provided as individual provincial boundary files.

Projection information

The disseminated projection coordinate system of the health region boundary files is as follows:

  • Lambert Conformal Conic
  • Datum = NAD83
  • Units = meters
  • Spheroid = GRS 1980
  • Parameters:
    • 1st standard parallel: 49° 00′ 00″
    • 2nd standard parallel: 77° 00′ 00”
    • Central Meridian: -91° 52′ 00”
    • Latitude of Projection Origin: 63° 23′ 26.43”
    • False Easting: 6200000
    • False Northing: 3000000

“Health region” refers to administrative areas defined by the provincial ministries of health.

See Table 6 Health regions in Canada – by province and territory
See Map 14 Health Regions and Peer Groups in Canada, 2015

Health region boundary changes

See the following tables for history of changes since 2000:

Health region peer groups

In order to effectively compare health regions with similar socio–economic characteristics, health regions have been grouped into ‘peer groups’. Statistics Canada used a statistical method to achieve maximum statistical differentiation between health regions. Twenty–four variables were chosen to cover as many of the social and economic determinants of health as possible, using data collected at the health region level mostly from the Census of Canada. Concepts covered include:

  • basic demographics (for example, population change and demographic structure),
  • living conditions (for example, socio-economic characteristics, housing, and income inequality), and
  • working conditions (for example, labour market conditions).

Peer groups based on 2015 health region boundaries and 2011 Census of Population and 2011 National Household Survey data are available. There are currently nine peer groups identified by letters A through I.  There have been no changes made to peer group assignments since 2014.

See Table 8 Health regions 2015 by peer group
See Table 9 Summary table of peer groups and principal characteristics

A more detailed discussion on the rationale and methods involved in the development of peer groups is available in Health Region (2014) Peer Groups – Working paper.

Health region boundary files

Digital boundary files reflecting health region limits in effect as of December 2015.

Boundary files (documentation)

ArcInfo

ARCINFO COMPREHENSIVE DIGITAL BOUNDARY FILES

Correspondence files

Code-to-code correspondence between health regions and 2011 and 2006 Census geographic units.

Correspondence files (documentation)

2011

Health region–to–2011 Census dissemination blocks for Ontario available in CSV format via a zipped file.

2011 Comprehensive Correspondence files Download

All Canada Correspondence files Download

2006

Health region–to–2006 Census dissemination area (blocks for Ontario in CSV format).

2006 Comprehensive Correspondence files Download

All Canada Correspondence files Download

Reference maps

Health regions and peer groups

This series of reference maps show the boundaries, names and codes of health regions and peer groups in Canada, by province.

About the maps

2014 reference maps
2013 reference maps
2011 reference maps (from issue 2011001 of 82-583-X)
2007 reference maps (from issue 2010001 of 82-583-X)

Health Quality Ontario – Hospital Quality Improvement – Resources

IBM Healthcare Could Have Done Better Today

Today @IBMHealthcare tweeted this …

‏@IBMHealthcare Beyond the basics: Crafting an in-depth #healthcare #security strategy

… which linked to IBM’s Security Thought Leadership White Paper Healthcare Securing the healthcare enterprise: Taking action to strengthen cybersecurity in the healthcare industry (March 2015).

While I can’t comment on IBM’s business solutions “to strengthen cybersecurity in the healthcare industry,” I am surprised at the quality of information that IBM relies on to describe “the nature of today’s cyber attackers” to its potential customers.

For example, IBM presents a figure (reproduced below) and references a CNN Money report, Hospital network hacked, 4.5 million records stolen (August 18, 2014).

Leading source of data leaks in healthcare institutions
Figure 1. IBM’s leading source of data leaks in healthcare institutions

In fact, CNN is not the source for Figure 1. Another IBM publication, MSS Industry overview – Healthcare: Research and intelligence report (October 7, 2014) presents the same figure, and references “Chronology of Data Breaches Security Breaches 2005-Present, Privacy Rights Clearinghouse.” IBM seems to have generated Figure 1 by querying an API on the Privacy Rights Clearinghouse website.

I wonder why IBM does not use authoritative, readily available data on breaches of protected health information to make its business case and to educate the public.

For instance, a research letter (Liu, Musen & Chou, 2015) published recently in the Journal of the American Medical Association1 described breaches of protected health information that had been reported from 2010 through 2013 by entities covered by the Health Insurance Portability and Accountability Act in the United States . Under the Health Information Technology for Economic and Clinical Health Act (2009), breaches involving the acquisition, access, use, or disclosure of protected health information and thus posing a significant risk to affected individuals must be reported.

Recently, we extended the original dataset of Liu et. al. to include breaches of health information up to the present. Table 1 summarizes the number of incidents and victims of breaches of health information in the United States from January 2010 to August 2015, inclusive.

Counts and Victims of Health Information Breaches - US 2010-2015
Table 1. Number of incidents and victims of breaches of health information. † 2015 data are for January – August inclusive only.

Notice the tremendous spike in the number of victims in 2015 – a dramatic development that IBM took no note of today.

Figure 2 depicts the distribution of victims/breach of health information as a series of boxplots.

Distribution of number of victims/incident (log scale) of breach of health information U.S. 2010-2015
Figure 2. Distribution of victims/incident (log scale) of breach of health information. † 2015 data are for January – August inclusive only.

We see that in seventy-five percent of all incidents, the number of victims/breach over the year has fallen consistently below 104 (10,000). A small number of incidents have involved 100,000 – 1,000,000 victims/breach, and an even smaller number have involved 1,000,000 – 10,000,000 victims/breach. Incidents involving more than 10,000,000 victims/breach made their first appearance in 2015.

 

In light of these dramatic developments, it’s a shame that IBM is relying on outdated information when it comes to educating the public and identifying potential solutions “to strengthen cybersecurity in the healthcare industry.”

 

  1.  Liu V, Musen MA, Chou T. Data Breaches of Protected Health Information in the United States. JAMA. 2015;313(14):1471-1473. doi:10.1001/jama.2015.2252.

Breaches of Health Information (US 2010 – 2015)

A research letter (Liu, Musen & Chou, 2015) published recently in the Journal of the American Medical Association1 described breaches of protected health information that had been reported from 2010 through 2013 by entities covered by the Health Insurance Portability and Accountability Act in the United States . Under the Health Information Technology for Economic and Clinical Health Act (2009), breaches involving the acquisition, access, use, or disclosure of protected health information and thus posing a significant risk to affected individuals must be reported.

We extend the original dataset of Liu et. al. to include breaches of health information up to the present. 2

Table 1 summarizes the number of incidents and victims of breaches of health information in the United States from January 2010 to August 2015, inclusive.

Counts and Victims of Health Information Breaches - US 2010-2015
Table 1. Number of incidents and victims of breaches of health information. † 2015 data are for January – August inclusive only.

The most striking feature is the fluctuation in the number of victims over time generally – and the tremendous spike in the number of victims in 2015 particularly.

Figure 1 depicts the distribution of victims/breach of health information as a series of boxplots.

Distribution of number of victims/incident (log scale) of breach of health information U.S. 2010-2015
Figure 1. Distribution of victims/incident (log scale) of breach of health information. † 2015 data are for January – August inclusive only.

We see that in seventy-five percent of all incidents, the number of victims/breach over the year has fallen consistently below 104 (10,000). A small number of incidents have involved 100,000 – 1,000,000 victims/breach, and an even smaller number have involved 1,000,000 – 10,000,000 victims/breach. Incidents involving more than 10,000,000 victims/breach made their first appearance in 2015.

Table 2 presents the Medians and Inter-Quartile Ranges of the distributions of victims/breach.

Median and IQR of Victims of Health Information Breaches - US 2010-2015
Table 2. First Quartile (Q1), Median, Third Quartile (Q3), and Inter-Quartile Range (IQR) of the distribution of victims/incident of breach of health information. † 2015 data are for January – August inclusive only.

The median number of victims of breaches of health is tending to increase over time, with a related increase in the dispersion of the number of victims/breach about the median.

Our focus in a few subsequent posts will be understanding the dynamics and implications of those breaches that have compromised the health information of 100,000+ patients.

Name Date Victims
Affinity Health Plan, Inc. 2010-04-14 344,579
Millennium Medical Management Resources, Inc. 2010-04-29 180,111
AvMed, Inc. 2010-06-03 1,220,000
Siemens Medical Solutions, USA, Inc 2010-06-04 130,495
Governor’s Office of Information Technology 2010-07-09 105,470
Iron Mountain Data Products, Inc. (now known as 2010-07-19 800,000
BlueCross BlueShield of Tennessee, Inc. 2010-11-01 1,023,209
Triple-S Management, Corp.; Triple-S Salud, Inc.; 2010-11-04 475,000
Medical Card System/MCS-HMO/MCS Advantage/MCS Life 2010-11-09 115,000
Ankle + Foot Center of Tampa Bay, Inc. 2011-01-03 156,000
Seacoast Radiology, PA 2011-01-10 231,400
GRM Information Management Services 2011-02-11 1,700,000
EISENHOWER MEDICAL CENTER 2011-03-30 514,330
Oklaholma State Dept. of Health 2011-04-11 132,940
IBM 2011-04-14 1,900,000
NA 2011-05-27 400,000
The Nemours Foundation 2011-10-07 1,055,489
Science Applications International Corporation (SA 2011-11-04 4,900,000
Sutter Medical Foundation 2011-11-17 943,434
Utah Department of Technology Services 2012-04-11 780,000
Emory Healthcare 2012-04-18 315,000
South Carolina Department of Health and Human Services 2012-04-24 228,435
Memorial Healthcare System 2012-08-16 105,646
Alere Home Monitoring, Inc 2012-10-18 116,506
Crescent Health Inc. – a Walgreens Company 2013-02-22 109,000
Digital Archive Management 2013-05-07 189,489
RCR Technology Corporation 2013-07-01 187,533
Shred-it International Inc. 2013-07-11 277,014
Advocate Health and Hospitals Corporation, d/b/a Advocate Medical Group 2013-08-23 4,029,530
AHMC Healthcare Inc. and affiliated Hospitals 2013-10-25 729,000
Horizon Healthcare Services, Inc 2014-01-03 839,711
Triple-C, Inc. 2014-01-24 398,000
St. Joseph Health System 2014-02-05 405,000
Indian Health Service 2014-04-01 214,000
Sutherland Healthcare Solutions, Inc. 2014-05-22 342,197
Montana Department of Public Health and Human Services 2014-07-07 1,062,509
Community Health Systems Professional Services Corporation 2014-08-20 4,500,000
Xerox State Healthcare, LLC 2014-09-10 2,000,000
Touchstone Medical Imaging, LLC 2014-10-03 307,528
Walgreen Co. 2014-12-15 160,000
Georgia Department of Community Health 2015-03-02 557,779
Georgia Department of Community Health 2015-03-02 355,127
Virginia Department of Medical Assistance Services (VA-DMAS) 2015-03-12 697,586
Anthem, Inc. Affiliated Covered Entity 2015-03-13 78,800,000
Premera Blue Cross 2015-03-17 11,000,000
Advantage Consolidated LLC 2015-03-18 151,626
CareFirst BlueCross BlueShield 2015-05-20 1,100,000
Beacon Health System 2015-05-22 306,789
University of California, Los Angeles Health 2015-07-17 4,500,000
Medical Informatics Engineering 2015-07-23 3,900,000
Empi Inc and DJO, LLC 2015-08-20 160,000

 

  1.  Liu V, Musen MA, Chou T. Data Breaches of Protected Health Information in the United States. JAMA. 2015;313(14):1471-1473. doi:10.1001/jama.2015.2252.
  2. Our source of data is the Breach Portal: Notice to the Secretary of HHS Breach of Unsecured Protected Health Information, Office for Civil Rights, U.S. Department of Health and Human Services, accessed at https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf on September 1, 2015.