Population-based vs individual need practice

General Definitions

Population-based practice:

"While public health tends to focus on the public at large, population health pays greater attention 
 to more narrowly-defined communities" 
"Population-based practice reflects the priorities of the community...."
"It always begins with identifying everyone who is in the population-of-interest or the population-at-risk."

The NZ Ministry of Health (MoH) Health Service User population (HSU) is based on population criteria such as 
PHO enrolment and ethnicity - see HSU -and is used to define a population denominator.

Individual-based practice:

The needs of the person / patient are explored and managed independent of population based criteria such as 
race / ethnicity, gender, sexual orientation, religion etc.

Reasons we disagree with population based health in General Practice:
 
Population based policies have (until recently) been preferred by public health and political decision makers because it appears
to aim often limited resources to where the maximum need is but it not only misses those with a need not in the defined population
but it wastes money and resources on those in the defined population without the need. Counter intuitively this often results in
worse health statistics as we have seen occuring in the last 20 years after the introduction of the Primary Health Care Strategy 
specifically set up in 2001 to be population based. My concerns about this approach are multiple and include:

Patient resistance to being labelled. Most of my patients want to be "Kiwi" or "New Zealander" without reference to
their race, ethnicity, religion and many strongly object to any ethnicity or religious "required field" on computer 
forms to the extent that some are prepared to lie (like claiming to be a druid or Jedi or Atlantian!!);
We also see population based policies failing to understand the statistical concepts of RELATIVE vs ASOLUTE. 
Just because a certain minority group has a higher relative risk, there is often a higher absolute risk in the rest
of the population, so it is cheaper and more equitable to aim prevention and treatment at the problem instead of just the 
group with a higher relative risk. This approach still needs to address barriers to prevention and treatment a higher risk 
group has.  See also Base Rate Falacy below;




Figure 1: An example of a high Relative Risk (45% compared to 11%) driving costly and ineffective health policy.


Restricting a prevention or treatment to one or more groups is discriminatory and stereotyping (as well as cost inefficient);
If someone isn't in a particular ethnic, gender or deprivation group they may miss out on screening, prevention and 
even early treatment creating further inequity;




Figure 2: An example of Inequity caused by population based funding.


Defining people by ethnicity has other concerns:

Many people have more than one ethnicity but are forced to be labelled by one (if that provides some benefit);
 
    
There is confusion by both patients and policy makers between Race (biologic makeup) and Ethnicity (cultural identity);
 
    
Sometimes race affects a disease risk while many other conditions relate more to lifestyle factors;
    
Many determinants of health relate to income, housing, diet and education which may be poorer in certain ethnic groups 
but are not caused by physical race.
 
Preconception, ignorance and bias will lead to discrimination, not just for "traditionally discriminated against" groups. 
For instance who realises that the group with the highest suicide rate and lowest life expectancy (6 years lower world wide) 
are older (including white) men?! 
Base Rate Fallacy or base rate bias, is a type of fallacy in which people tend to ignore the base rate (e.g. general prevalence).
[1]. It been used recently to incorrectly claim ineffectiveness of COVID-19 vaccination (see fig 3) but can also be an example
of the relative risk fallacy mentioned above.



Figure 3: An example of the Base Rate Fallacy.



If the NZ Ministry of Health (MoH) and Health NZ (Te Whatu Ora) use Health Service User Populations based on PHO enrolment, 
the over 400,000 New Zealanders not enrolled in a PHO get ignored despite being a high risk group. Actual poorer health
experience in this group is at least partially the result of this discrimination.
 

Cardiovascular risk assessment - Population based vs actual individual risk A good example of where population based screening to determine risk (and thus management) commonly used in NZ is failing many people is cardiovascular risk screening using a Framingham Risk Score or the "PREDICT" Score used in PHO based General Practice to determine who should receive treatments like statins. It uses age, ethnicity, a diabetic test, smoking history, Blood Pressure and cholesterol profile to predict 5 year risk. However, we now have far more accurate individual arterial imaging (Calcium score ± CT angiography) available (but not publicly funded). This is a non-invasive way to identify and grade subclinical (asymptomatic) atherosclerosis (Coronary Artery Disease) that is actually present in the coronary arteries as opposed to just estimating the population based potential risk. Extensive scientific literature supports the much higher predictive value of the Calcium Score (predicting all coronary events and all-cause mortality) and recomends basing treatment and prevention on the identified disease shown, rather than on the population and risk factor prediction. [2] [3] [4] [5] This is clearly better than waiting for a coronrary syndrome or event to motivate "Secondary" prevention, which is much tighter than primary prevention based on risk factors. Figure 4: CT Calcium Scoring

References:

1. "Logical Fallacy: The Base Rate Fallacy". Retrieved 17-Sept 2024. Fallacyfiles.org/baserate.html
2. Aroney C. "Should coronary calcium scoring be used as the central tool for cardiac risk assessment?" Heart Lung Circ 2019: 28(2); 207-12 Abstract and link to article
3. Alexander Chua, Ron Blankstein and Brian Ko "Coronary artery calcium in primary prevention" AJGP (Australian Journal of General Practice) Vol 49, Issue 8, Aug 2020
   Link to article; doi: 10.31128/AJGP-03-20-5277
4. Mitchell JD, Paisley R, Moon P, Novak E, Villines TC. Coronary artery calcium and long-term risk of death, myocardial infarction, and stroke: The Walter Reed cohort study. JACC Cardiovasc Imaging 2018;11(12):1799–806. doi: 10.1016/j.jcmg.2017.09.003. PubMed
5. Budoff MJ, Shaw LJ, Liu ST, et al. Long-term prognosis associated with coronary calcification: Observations from a registry of 25,253 patients. J Am Coll Cardiol 2007;49(18):1860–70. doi: 10.1016/j.jacc.2006.10.079. PubMed