Module 3 (determinants of determinants) Flashcards
Socioeconomic position
The social and economic factors that influence what positions individuals hold within the structure of society; based on occupation and the purchasing power different occupational groups have
Determinants of SEP
Must be objective, measurable and meaningful
Measures of SEP
Used to quantify the level of inequality within or between societies; may highlight changes to population structures; needed to help understand the relationship between health and other social variables; associated with health and life changes
Why health inequalities should be reduced
They are unfair; they are avoidable; they affect everybody; reducing them can be cost effective
Measuring SEP for individuals
Education (increases opportunities for occupations and income opportunities); income; occupation; housing; assets and wealth
Measuring SEP for populations
Can see the different gradients in health/social outcomes based on the different levels of these measures; area measures (deprivation and access) and population measures (income inequality, literacy rates and GDP per capita)
Inequality
Measurable differences in health experiences or outcomes occurring between different population groups (the social gradient)
Inequity
An inequality that stems from injustice (unjust distribution of the social determinants of health and resources/services/opportunities in a way that doesn’t reflect health needs) and involves power relations
Habitus
The lifestyle, values, dispositions and expectations of a particular social group
Social capital
The norms and values that underpin society, and the social networks that provide an inclusive environment and sense of unity
SEP on D&W model
Individual lifestyle factors (your education, occupation and income; decisions you make; social and community influences; living and working conditions; general socioeconomic, cultural and environmental conditions; global determinants
Individual lifestyle factors
Education –> knowledge (able to pick up health measures); income –> material goods (ability to purchase health and essential materials); occupation –> status and power
Social and community influences
Your parents influence on education, occupation and income; commonly used to measure SEP for children and adolescents
Living and working conditions
Use area-based measures of SEP (NZDep most common); other measures include social fragmentation and accessibility indices (who has the opportunity to use particular services; health promoting and demoting)
Measuring area-level deprivation
Another way of measuring people’s relative position in society, but reports this based on where they live, not on them; focus on material deprivation; should be applied to conditions and quality of life that are of a lower standard that is ordinary in a particular society
Deprivation
State of observable and demonstrable disadvantage relative to the local community or the wider society or nation to which an individual, family or group belongs
Ways of describing changes in population
Population structure and population composition
Population structure
The age and sex distribution (affected by changes in fertility rates, mortality rates and migration); affects the rates at which fertility/mortality/migration occur in the population
Population composition
All attributes of the population other than the age and sex distribution (changes in fertility/mortality rates and migration)
Numerical ageing
Absolute increase in the population that is elderly; reflects previous demographic patterns and improvements in life expectancy
Structural ageing
Increase in the proportion of the population that is elderly; driven by decreases in fertility rates; began occurring in the 1800s
Natural decline
When deaths>births; combination of numerical/absolute and structural ageing; more elderly = more deaths
Absolute decline
When there is insufficient migration to replace the decreased births and increased deaths; not likely to happen in NZ for another 70 years; happening in some European/Asian countries
Demographic transition
4 stages; a pattern of changes in birth and death rates which causes a change in total population; all countries have gone through this or are going through; NZ and other developed countries are in stage 4
Measures of deprivation
NZDep and IMD
NZDep variables (9)
Communication; income; income; employment; qualifications; owned home; support; living space; transport
Communication NZDep
People under 65 with no access to the internet at home
Income NZDep
People between 18-64 receiving a means-tested benefit; people in equalised households with income below a threshold
Employment NZDep
People aged 18-64 who are unemployed
Qualification NZDep
People aged 18-64 with no qualification
Owned home NZDep
People not living in their own home
Support NZDep
People under age 65 living in a single parent family
Living space NZDep
People in equalised households below a bedroom occupancy threshold
Transport NZDep
People with no access to a car
General socioeconomic, cultural and environmental conditions
Macro scale; group populations with similar SEP levels together; cross-sectional or longitudinal analysis
Global determinants
Comparison of our country to global; income inequality, national income (GDP), literacy rates and free trade agreements
Stage 1 of demographic transition
High birth and death rate; low total population; pre-transition (pre-industrial)
Stage 2 of demographic transition
High birth rate and moderate death rate; low total population; declining mortality, birth rates remain high
Stage 3 of demographic transition
Moderate birth rate and low death rate; moderate total population; fertility rates begin to decline
Stage 4 of demographic transition
Low birth and death rates; high total population; low fertility and mortality
Crude birth rate
CBR; number of births/total population (including males)
General fertility rate
GFR; number of births/number of women of reproductive age (15-45)
Age-Specific fertility rate
ASFR; number of births to women in 5 year age bands
Total fertility rate
Sum of ASFR x 5; shows the likelihood that we remain above the replacement level
Data considerations
Ethics and privacy/confidentiality; purpose of data collection vs use in analysis; population vs population samples; representative sample of NZ population; objective vs subjective measures of health
Dependency ratio
Child: 0-14yrs / working age
Elderly: >64yrs / working age
Total: (child + elderly) / working age
Limitation with dependency ratio
Assumption that the working age group represents all people who are working; some in working age group may not be working, some elderly may still be working
Types of ageing
Numerical and structural ageing
Population impacts of ageing
Natural and absolute decline of the population
Important aspect of data
Quality of data is an important aspect of epidemiology; if we don’t have good data, it will be unreliable information
Implications for the workforce in the future
More elderly needing to be taken care of when they retire; less youth population to do so and fill in jobs; raising retirement will mean more tax is paid and more pension contributions but fewer jobs available to younger workers
New equity definition
Equity recognises that different people with different levels of advantage require different approaches and resources to get equitable health outcomes
Inequities affect everybody
e.g. our warm housing intervention resulted in a 50% reduction in reported days off school
Inequities are avoidable
e.g. our analysis indicates that the four DHBs in our study should prioritise Māori and Pacific patients for cancer and cardiovascular disease related surgery following NZ’s move to Level 1
Inequities are unfair
Our survey reported that on average, female employees earned 35% less than male employees. However, the difference in earnings were as high as 50% among senior leadership roles
Reducing inequities can be cost effective
Our results indicate that our intervention means that a female would have half the current number of tests AND a statistically significant reduction in cervical cancer cases
Denominators
Census population; HSU population; IDI population; choice of denominator influences who is incorporated in the analysis and who we end up targeting/allocating resources to
Census population
Everyone who answered the census and is a usual resident
HSU population
All health service users in the last 12 months prior to census night
IDI population
People who have used health, education, tax and other services prior to census night
Lorenz curve
Cumulative frequency graph of the proportion of wealth shared by different proportions of a population; the more concave the line, the greater the income inequality in that population; includes a line of absolute inequality and equality
Line of absolute inequality
Y-axis of Lorenz curve graph which shows the perfectly unequal income distribution
Line of absolute equality
45degree line on the Lorenz curve graph which shows the perfectly equal income distribution
Gini coefficient
The ratio of the area between the line of perfect equality and the observed Lorenz curve to the area between the line of perfect equality and the line of perfect inequality
Gini = (A) / (A+B)
0 = very equal society
1= very unequal society
The implications of income inequities
An unequal society (between higher and lower paid groups); less social cohesion and therefore less trust between groups; increased stress ( impacts mental and physical health); reduced economic productivity; poorer health outcomes
Commuter environment on transport mode and route choice
Air pollution, safety, travel time, cycleways; reasons why people choose to travel a particular way
Travel mode and route choice on health and well-being
Implications for separation of the road; breathing rate; travel time; positives with exercise
Air pollution exposure
Larger dosage for people closer to the centreline of the road (higher for runners, cyclists and walkers); can be reduced with cycle and walk ways further away from the road, and with noise barriers
Influences on more people cycling
Low traffic volume with low vehicle speed on residential streets; seperate cycleways on high-traffic roads; mixed land use (residences, commercial entities and civic facilities all co-located) so that destinations are walkable/cycleable from home
Individually minimising adverse health impacts while travelling
Route choice; side of the road; build infrastructure to encourage people to make good decisions
Urban planning features which are health promoting
Cycleways, bus lanes, developments to encourage active mode and public transport uptake
NZ IMD variables (7)
Employment; income; health; education; housing; crime; access
Employment IMD
Degree to which the working age people are excluded from employment
Income IMD
Extent of income deprivation (state funded financial assistance)
Health IMD
Areas with high levels of ill health or mortality
Education IMD
Youth disengagement; proportion of working age people lacking formal qualifications
Housing IMD
Proportion of people in: overcrowded housing and rented accomodation
Crime IMD
Victims per 1000, over 30 days
Access IMD
People with no access to a car
Pros of NZDep
Weights the domains; widespread and well known to analysts and policy makers
Cons of NZDep
Not everyone completes the census; can’t explore drivers of deprivation seperately
Pros of IMD
IMD uses information from the IDI, which includes more people than the census; can explore the drivers of deprivation (domains); better small area information; forms more specific solutions; weights domains
Cons of the IMD
IDI is a transactional dataset (deficit); new method, not used much yet
Deficit dataset
In order to be ‘counted’ you have had to have had an interaction with a government agency (WINZ, Corrections/Police, or the health system); it counts people that have had outcomes that are ‘bad’, what people don’t have (experienced economic hardship, been sick and needed hospitalisation, etc).
The ecological fallacy
The error that arises when information about groups of people is used to make inferences about individuals; cannot make assumptions about an individual only from where they live
Elements of a healthy environment
Clean air and water; appropriate housing; access to wholesome food; safe community spaces; access to transport; opportunities to incorporate exercise as part of daily life
The built environment
All buildings, spaces and products created or modified by people; structures (homes, schools, workplaces) and urban design (parks, business areas and roads)
Urban design improving active travel and physical activity
Street connectivity; traffic calming and other street design features; mix of residential, commercial and business uses; public open spaces and physical activity spaces
7 V’s of big data
Volume; velocity; variety; veracity; variability; value; visualisation
Volume (big data)
A larger computer capacity is required for processing and analysing
Velocity (big data)
Data is created/analysed instantly
Variety (big data)
A huge range of sources of data
Veracity (big data)
A lot of accuracy, creditability of truth, enabling objective decisions to be reached
Variability (big data)
High reproducible internal consistency of results
Value (big data)
The cost of storage/analysis/analysts pays off
Visualisation (big data)
Use of novel techniques to communicate patterns to the public
5 A’s of access
Access is viewed as a set of more specific areas (dimensions of access) of fit between the patient and the health care system; availability, accessibility, accomodation, affordability and acceptability
Availability
Existence of services barriers; the relationship of the volume and type of existing services (and resources) to the clients’ volume and type of needs
Accomodation
Organisational barriers; the relationship between the manner in which supply resources are organised and the expectation of clients
Acceptability
Psychosocial barrier; the relationship between client’s and provider’s attitudes to what constitutes appropriate care
Accessibility
Geographic barriers; the relationship between the location of supply and the location of clients, taking account of client transportation resources and travel time, distance and cost
Affordability
Financial barriers; the cost of provider services in relation to the client’s ability and willingness to pay fro these services
Māori disparities in health exemplified
Exemplified in health outcomes, exposure to the determinants of health, health system responsiveness and representation in health workforce
Māori health disparities in health examples
Unequal access to SDH; cardiovascular disease; cancer; injury; diabetes; mental health including self-harm; infectious diseases; disability; participation in the health workforce
Lessons from the Titanic relating to Māori health
Structural contribution (power, resources and opportunities of NZ society are organised by ethnicity and class deprivation) - more lifeboats and less barriers; societal contribution (values and assumptions widely held in NZ society about the deservedness of different groups of people) - level playing field
Determinants of ethnic inequalities in health
Differential access to health determinants or exposures leading to differences in disease incidence; differential access to health care; differences in quality of care received
5 domains
Demographic; economic; neighbourhood; environmental; social and cultural cultural
Demographic domain; risk and protective factors
Gender and sex, age, ethnicity; social norms, discrimination, gene-environment interactions in sensitive developmental windows
Demographic domain relevant SDGs
Gender equality
Economic domain; risk and protective factors
Debt, assets, employment, food security, housing; social causation (stress, helplessness, antisocial coping behaviours) and social drift (disability and stigma)
Economic domain relevant SDGs
No poverty; zero hunger; decent work and economic growth; industry, innovation and infrastructure; reduced inequalities
Neighbourhood domain; risk and protective factors
Structural characteristics of neighbourhood; urban migration, exposure to violence
Neighbourhood domain relevant SDGs
Clean water and sanitation; affordable and clean energy; sustainable cities and communities; responsible consumption and production
Environmental events domain; risk and protective factors
Natural hazards, industrial hazards; trauma, severe stress and insecurity
Environmental events domain relevant SDGs
Climate action, peace, justice and strong institutions
Social and cultural domain; risk and protective factors
Education and social cohesion; cognitive reserve and social skills and support
Social and cultural domain relevant SDGs
Quality education
Sustainable developmental goals not met by wellbeing domains
Gender equality and reduced inequalities
What is big data
Large or complex data sets; large amounts of information at a population, regional or local level or span different geographical areas; combining data from multiple sources to explore health outcomes
3 pros of the IDI
Identify characteristics of groups with positive/negative outcomes; identify risk/protective factors; de-identified
3 cons of the IDI
Only as good as the data in it; inherent selection biases from the choice of sources of data; can’t identify specific individuals at risk or for a specific intervention
