Population Pyramid for N ~ 3M
In the health insurance industry, a unique mathematical relationship exists between parents and children within a working class community. A review of the population age-gender layout for a program that covers a fairly representative cross section of working class America will have the above relationship of male:female across the standard working class age range of 18 to 64. (The 65-67 yo group of workers and workers who remained employed until the very end are excluded here.)
This compares with the standard population pyramid of all citizens of the United States so documented in the census records. The largest differences in populations between the two also represent the greatest period of inequality for the employed and insured versus the unemployed, uninsured.
The greatest ratio of F:M occurs somewhere between the ages of 29 and 33. This is when the greatest number of spouses and potential mothers (or stay-at-home fathers) are enrolled in such programs.
This greater number of women versus men occurs during the peak reproductive years, for which one or two parents or parents-to-be are working. It is during the age range of 18 to 44 that the peak childbearing years are seen in the a standard population researched for fecundity. In the above population pyramid, the equivalent to that period of fertility, pregnancy and child-related health care is the 30 year period of 18 to 47. This is also the period of time when there will be a number of relationships that will be seen between parents and their children.
The fact that F>M during this age band also influences the types and costs of health care services that are provided. There is this one series of relationships that I call the mother:child series, and even though the home parent may in fact be male some of the time for couples, it is usually the case that the mother is tending to a child’s daily activities, for the most part, including well care visits and related preventive events. For this reason, this analysis of a population I refer to as a study of the mother:child relationship, even though a given, rather small percentage of this population is going to involve men, fathers or male spouses.
It is important to note on the above pyramid that almost immediately once the age of 18 is reached, the women receiving health insurance coverage for a given 1-year age range is greater than men. This is due to the sociologically defined gender preference we have for men versus women when it comes to finding work once you reach 18 years of age. We tend to see more women because they are married to men who are working, and because the likelihood of working if you a male versus a female during this period in life. We see this rule F>M continue up the axis, until the age of about 50 to 54 is reached, then the ratio of F:M begins to level out and approach 1:1. Once 57-60 yo is reached, we tend to see more women than men surviving for the remaining decades of life.
The following depicts how the mother:child relationship recurs over time and space.
On this map, some areal comparisons are made for the mother:child age groups. As indicated on the above map, specific multistate regions of this country have greater numbers of mother-child patients than other parts of the country.
the following ICDs or disease groups relate to the above spatial distribution of mothers and their children in the US. We can use this regional analysis to make better sense of the small area analyses about to be demonstrated on how to monitor and develop intervention programs for specific mother-child population groups.
In the following, it appears as though there is no regionalism regarding adult-child relationships. However, the ICDs, and V/E-codes used to depict this are quite varied. Breaking Adult-child relationships down into specific subcategories is helpful in analyzing the complex relationships that exist in this country.
Adult-child relationships are in socioeconomically defined, school-defined, ethnic and racially-defined, locally defined, and psychologically defined. Categorizing kids in order to try to predict the next school tragedy is a difficult task, and may not be possible using this technique. However, other social problems are more treatable using this grid mapping approach, such as child and spouse abuse, homelessness, and even parental drinking and drug use or childhood street drug abuse. Malnutrition has several specific indicators worthy of monitoring, and with an automated routine using this NPHG it is possible to advance the results of such efforts well beyond the simple results obtained through monitoring immunizations, well visits, CHP activities and the like as part of a standardized quality assurance and HEDIS program.
Currently, the technology needed to improve upon the clinical outcomes of our health care is present, the infrastructure and knowledge base to carry it out are lacking, at both the basic employee and management levels. The current health care system could be a decade or more better in its technology advancements, and the big businesses behind monitoring our nation’s health could also avoid being so far behind.
The progress of health insurance companies today remains more like something from a late 20th century ideology with a late 20th century skill set. This is made even more apparent by recent software “advances” in this field, that not only repeat the mapping techniques already possible when Windows 97 was invented, but also offer absolutely no innovations in this field when its comes to statistical modeling. Granted, the software today is better, but the technological and knowledge based advancements developed with the newer tools are missing. There are no new equations and no new thought processes generated by these tools, they work with larger data sets but result in no advancements in the field.
The current highly popular software package in the current marketplace is the best example of this. The problems it poses to small businesses and medium sized investors is that once the mapping routine is figured out, no advancements will be able to be made by the users of this technology. This is because it offers no new ways, means, formulas or the like for solving big questions. NPHG avoids all of these problems, and does not have the burden on newer, more complicated skillsets to be learned, which a few years from now could even become obsolete.
The good thing about all of this as a GIS technician or analyst is that by having this mindset when you enter this work environment, you enable advancements to be made with yourself and your work setting. If GIS technicians like the company ignore the apparent slowness their profession has been taking and will continue to take unless changes are made, their skills will advance the industry in generation, but not enable it to reach its potential in terms of improving overall population health.
To be continued . . .
Work in progress