Note: this page and neighboring pages are from older teaching materials used for a lab on GIS and the corresponding lecture/discussion on ‘GIS, population health surveillance, epidemiology and public health’.
The problem with population pyramids is that we rely upon then as gross measurements of groups of people, not as finely detailed data sources providing us with insights into age-gender linked human behavior and age specific details regarding preventive health behavior. The resolution of data provided by pyramids is limited in any application outside their traditional uses. They are typically used to compare two populations to each other and can sometimes leave us uncertain about our conclusions when it comes to the exact age when childbirths begin to fall or exactly when heart attacks appear to have reached their peak. Traditional population pyramids also do not tell us the differences between 17 and 18 year olds usually, to distinct age groups for asthma patients since the 17 year old qualifies for children’s care coverage and the 18 year old for adult care coverage. We have equal problems dealing with other age ranges as well without the chance to review a detailed population pyramid. The differences in care for people attended to for the year just before retirement versus the year after cannot be compared. With the multiyear age band we also cannot compare specific health differences for when we reach 46 or 47, the peak adult age for smoking in women mostly, versus when we reach 57, the age when many chronic diseases have reached their peak in prevalence and begin to result in increased mortality with each year that passes.
The main problem with traditional population statistics is that we are usually presented with population age-gender data in specific age ranges or study groups, or in 5 and 10 year age bands. These pyramids are provided most often as part of some demographic work, but are often applied to epidemiological work. This same applicability does not exist for their use in preventive care management and risk analysis work.
For every one of my studies during the past 10 to 15 years, I have relied upon one year age band age-gender pyramids. This provides me with the exact data (years of age) I need to know in order to better understand the age of risk, the age of no risk, the age of highest incidence, the age of highest and lowest mortality and morbidity for specific disease types. Knowing how age relates to disease at such a level may seem somewhat picky and detail oriented, but it is the only way to know how to proceed in the development of prevention programs, whom to call and at what age regarding the need for a screening or when to consider initiating a more aggressive intervention program due to failures in health maintenance.
In terms of applying age data to epidemiological statistics, age groups limit the value of the outcomes of studies at times. When evaluating teenage health for example we sometimes want to exclude the 18 years of age and over, or include the 18 and 19 year olds as its own unique set. With studies of childhood care and health, some activities are best analyzed for every month of age. With infant, child, adolescent and teen age care, since visits for care tend to reach an all time low during the childhood years of the ages of about 7 to 9, it helps to establish a break in age groups at about 8, in order for age grouping to be linked with many of the childhood care population health measures. These processes cannot be done for 2 and 5 year band case counts.
Even adult care demographic data has the limits of specific age definitions. Those screenings for which QA is typically assessed and for which coverage exists by an insurance company, eligibility is sometimes defined be age criteria. Colorectal screenings for example are measured at 52 years of age or more for HEDIS, not 50+. Asthma studies use an age somewhere between 53 and 57 as the cut off age for quality improvement testing, depending upon the year, due to too many COPD patients accidentally reviewed. The young adult Chlamydia studies merge teenagers with young adults, and require a second young adult age range of equal form, not at all related to the standard census related age ranges. Retirees are reviewed for glaucoma at 68+, not 65+, normally with 84 as the cut off point, due to some Medicare related policies on coverage eligibility.
For this reason, any system responsible for monitoring population health should be collecting data in one year age increments, not two, five, or ten year increments. This results in data that is almost always directly applicable to studies regardless of age bands defined.
The major advantage to one year analysis in population health programs is knowing the peak age of prevalence for whatever is being evaluated. The peak age is when the highest number of patients are seen by a program and can be used to infer the following:
- That age when individuals may begin to be reducing in number due to mortality, or due to an ICD that correlates with some morbidity.
- That age when, generally speaking, costs for care and related services will begin to increase dramatically
- That age when preventive care is less important that palliative care and when case management processes have to begin.
For diagnoses or ICDs where two peaks exist, the younger of the two peaks designates the amount of initial activity taking place for that condition. With asthma for example, the peak age for around 12 to 15 years of age designates a time when many asthma related visits occur leading to a peak in the number of claims that are filed, to diagnose or rule asthma as a chronic disease syndrome in patients with allergies and/or chronic respiratory disease symptoms. With smoking, the teenage peak exists for obvious reasons, but the second peak developed in women around the age of 45 years of age suggests need for some unique prevention program to be developed targeting just this population. Compared with women, the peak age for smoking in adults occurs a few years earlier, again suggesting the need to developing a program that targets males differently that females.
A number of medical conditions exist which demonstrate these age specific gender inequalities at different levels. In some cases, males tend to begin visiting physicians for certain diseases or problems earlier than women. Men get their GERD and fibromyalgia diagnoses earlier. Women demonstrate earlier peaks in care metrics related to prevention, such as tending to request cancer screening procedures and heart disease related visits earlier than men, with peaks showing a one to five year difference.
These difference are why one year age pyramids have to become part of the standard population health assessment process. Due to a particular formula I use and tested for efficacy when evaluating patient health, this one year range can now be applied and not cause as much statistical problems as it did in the past. This new metric not only shows where gender differences exist, it attaches a statistical significance value to this measurement, adding one more very reliable way of evaluating ratios used for comparing two sets of results.
Abraham Maslow’s Model of Stages
A Maslowian-Eriksonian Interpretation of the Pyramid
We can look at a population pyramid and use it to determine the status of certain parts of the population based upon Abraham Maslow’s interpretation of the stages of getting older. It helps to apply this method because it provides insights into what to expect for certain intervention practices. Interventions fail at times sometimes due to age-related interferences. Age can produce interference due to level of maturity of the intervention.
This relationship between age and maturation go hand in hand with Maslow’s and Erikson’s interpretations of the stages of life, although in a fairly loose way. We can look at a pyramid, and then review Maslow’s stages of life, and finally apply Erikson’s philosophy on behaviors to the expectations we might have for certain interventions. Belongness and Love, followed by Esteem and Cognitiveness is the transition from older child to responsible adult. A continuous growth of this Cognitiveness turns to need for Balance and Artistry, stages in understanding and exploration that help lead one to the stage of self-awareness, and recognizing the human potential within.
By applying Maslow’s and Erikson stages to an intervention population, we are provided with insight into better methods of targeting a particular set of people when dealing with certain population health issues, and defining our methods of approach to people of certain ages.
For example, referring to the population pyramid, we can define the following age ranges on that pyramid, each in turn then related to certain sections of the Erikson model.
- Childhood = 0-17
- Early Adult = 18-24 or 29
- Adult 1 = 25-34, or 30-39
- Adult 2 = 35-44 or 40-54 Possible self-actualization
- Adult 2b = 45 or 55-64 Self-actualization
- Adult 3/Geriatrics = 65+ Self-actualization, Transcendence
- Geriatrics = 75+ Transcendence
These models can be used to design a very unique approach to a care management program for specific age groups.
The learning stages in life, and periods of development of self-worth and purpose, make it difficult sometimes to intervene with an individual’s goals. The internal, external foci kick in and some people are just not manageable in terms of health behaviors due to their focus on self and internal wants and needs. These can make interventions difficult in the youngest age groups.
The older a person is, the more insights into a purpose for their life are developed, thereby facilitating their personal growth and cognition, helping them to approach to the self-actualization and transcendental stages. By developing interventions and designing plans that integrate patient activities into the overall program, we assist in the self-actualizing process. Once meaning and purpose can be assigned to a health prevention request or requirement, the individuals participating in it find purpose in these actions. This is the first requirement for someone reaching the transcendental state, a stage in life where the best understanding develops, and the most accomplishments can be made.
Now, there is a lot more meaning and philosophy attached to the transcendental state, and in some other pages I discuss this a little. There are many people who reach such a state only temporarily, but long enough to understand themselves better and as an result can actualize some of those skills, abilities, purposes in life they realized they have. This is a very likely stage for elders to reach in any living setting, assuming the cultural stimuli exist in their environment that enable this experience to happen. Examples of this experience include a sociologist, anthropologist or family historian documenting the memories of that family elder. For those elders with this ability to contribute, we can see cultural richness develop, and cultural richness is what is needed for more culturally correct population public health programs to be developed, with that sub-cultural or cultural component to it, that part of the program that goes below the size of a neighborhood in my different size-based models of population types (community-neighborhood or culture-subculture).
In the public health world, this helps define whether there is failure or success in certain intervention programs. It perhaps deserves its own paradigm, like the health behavior model is a paradigm.