A Work in Progress


Statement: This is based on a single review of an exceptionally large dataset, for a population receiving a unique form of health care.  It may or may not represent the entire US population, but most certainly represents an adequate research population sample due to its exceptional size.  I should acknowledge the assistance I received from Perot Systems (now bought by a competitor) specialists in developing this methodology back during my first years of development of this methodology.   In addition, some of the local coworkers’ knowledge about using these datasets and evaluating internal data integrity and SQL requirements are appreciated; this assisted me greatly in developing the original tools and formulas I needed to perform these large population analyses for the periods of 2004 and/or 2005. 

These analyses were also performed using statistical significance measurement tools develop in order to compare two sets of results.  This is used to demonstrate where statistical significance exists between two groups of any series of age-gender measurements.  An additional benefit of this tool is that its enables exceptionally different populations to be evaluated, using a statistically valid technique not yet released as personal IP.  This method has undergone more than 10 years of testing by this point, and is used to show when specific age ranges have male <> female in stat sig fashion, or ICD1 <> ICD2 for specific age ranges.  The importance of this tool is that it enables one for the first time to define the specific age ranges where these differences exist.  All prior methods of population analysis use much cruder methods to define age-gender specific intervention groups.  This also allows for population heterogeneity to exist in studies performed, heterogeneities  that otherwise introduce significant error in the outcomes for the more traditional methods used to evaluate population results. 


This is a review of the basic diabetes epidemiological population health data, based on population pyramid age-gender analyses.  The method used for this review displays pyramids depicting relative incidence-prevalence relationships.  These relationships are just odds ratios for each 1-year age increment, for each gender.   It provides a more detailed review of how the ICDs evaluation distribute by age for a single population.  The sources for these counts are based on activities patients engage in regarding their diabetes state.  Therefore, the prevalence demonsgtrate for a specific age range may be off slightly due to lack of participation by specific age groups.  These age groups for inactive patients or patients not seeking health care for their syndrome include those transitioning between two coverage plans, for which the common age range of missed opportunities are the 18-25 year old and 62 to 68 year old groups. 

The first represent “very healthy” people not yet diagnoses with whatever chronic diseases they might experience during their older years.  These people, for a variety of reasons, also fail to fully engage in health care activities such as visits, etc., and so are absent from many medical record libraries. 

The second age group[ represents a transitional group transitions from emplyee health coverage to medicare, retirement coverage.  Typically this period of inactivity also produces “divet” or notch in the population curves, typically not included in the graphics provided.  The noth occurs between 62 years of age and 68.  It is important to realize that these are typically very unhealthy people whose costs for health care can be very high.

A third group worth mentioning are the children, 0-17 years of age.  These counts tend to peak somewhere between 7 and 9 years of age.  This peak possibly represents over-visits generated for rule out purpsoes.  Therefore, these ICDs may be over-represented.  The ratios between gender within this age range also demonstrates some inequality, the reasons for which are uncertain.  Very young children who are boys tend to outnumber those who are girls, and those who are teenage girls can at times greatly outnumber boy teenagers.  The former example cannot easily be explained.  The latter situation has sociocultural explanations to help define why it happens, for example teenage care and visits due to sexual activity related needs, or parents with more sensitivity to health of the baby boy, or teenage girl, outweighing similar care and activities for the opposing gender. 

The Diabetic Population



Primary versus Secondary

Primary diabetes is that due to no other confounder of health directly responsible for the condition.  There are long term complications associated with metabolic syndrome and obesity that are linked to diabetes.  There is also gestational diabetes related to pregnancy.  Secondary diabetes is linked to other conditions and treatments and occurs due to that treatment process.  The following comparison is between primary and secondary diabetes cases without complications.


Uncontrollable or Not

In general, there is no difference in age-gender distributions regarding whether a diabetic case is effectively controlled or not.  This is based however on some reverse logic applied to these statistics.  The uncontrolled cases are approximately equal in age distribution as the cases lacking any diagnosis indicating lack of control.  Since the two conditions, control or lack thereof, are possibly interrelated in many if not most cases of diabetics, it is possible the two are not expected to be different due to equal likelihoods that these exchanges between ICDs can occur in multiple ways.  One doesn’t expect cases to always be fully controlled or  fully uncontrolled, but instead be interchangeable in terms of this diagnosis due to the likelihood that an individual can experiences periods of control or lack thereof. 


Juvenile (Type I) vs. Adult(Type II)

When the Adult versus Juvenile cases are co0nsidered separately, a distinct difference in control is seen for the uncontrolled cases versus the cases with no mention of lack of control.  Juvenile diabetes has an early age of onset and mode or maximum incidence-prevaloence (IP), versus adult onset which has the older age peak in IP rates.   There are different mechanisms underlying each of these two forms of diabetes, the Juvenile form demonstrate more difficulty perhaps with control and  treatment, thereby demonstrating the high IP rates at a very young age.  This is the period when experimenting with treatment and lifestyle take palce, voluntarily on behalf of the patient-doctor relationship, and to some extent on behalf of personal decisions made as the individual reaches the teen years of age.



Three Common Diagnoses


Systems-related Complications

The following ICD used to define complications demonstrates a fairly repetitive pattern, related mostly to age of diabetes itself, with no preference biologicall or in terms of gender physiological and behavioral differences.


Stages of Diagnoses and Severity

Renal histories are demonstrate as follows based on: a) mere presence of inferred renal involvement linked to the diabetes, evidence for stage 5 of chronic renal failure, b) a condition linked to a diabetes history but not necessarily dependent on this diagnosis history, and c) the end-stage renal disease state (ESRD) in which dialysis is required for continued living to take place according to quality-of-life requirements.  ESRD also does not necessarily have a direct relationship only to diabetes, but is very strongly linked to a long history of diabetes, especially complicated forms of diabetes mellitus



Systems Complications

The following are more complex systems related problems that develop due to diabetes.

Ophthalmic Complications

Like renal failure, ophthalmic complications and certain diseases are almost a certain outcome of long term care with lack of adequate blood glucose control.  The following three conditions occur almost equally across the age ranges of patients, even though their rates per 10,000 people may be considerably different.  Diabetes induced cataract and retinopathy are two of the most common conditions resulting from years of experience with this chronic diseases, and are indicators of disease management problems during the very early years of treatment following diabetes diagnosis.  Diabetes cataract has a slightly younger age of onset and incidence-prevalence rate development and growth. 

Ophthalmic complications is for a diabetes case with evidence for any of several ophthalmic diseases or disorders.  Diabetic retinopathy requires the diabetes diagnosis also exist fo the patient, and so is a subset of the overall diabetic population.  Diabetic cataract is also a subset of the overall diabetic population.



The following ophthalmic complications related to Diabetes are unique in that they have gender differences in their distribution, and very unusual age distribution differences.  

Diabetic glaucoma demonstrates male-female IP differences for the  old-age group (65+).  Male peaks are at the retirement age; female peaks are at the middle post-retirement age (@ 85 yo).  There is also a possible early diagnosis working class IP peak for women who are approximately 53-55 years of age.  This might suggest early prevention-related diagnostics, which confers with the later diagnosis of the same for men (men typically engage in preventive care one  decade or more later than women, especially for chronic cardiac diseases). 

There are minor differences in the distribution of diabetes related glaucoma diagnoses for children with diabetes.  The double peak for both genders may be due to simple visit and screening behaviors, or they may be a consequence of a higher prevalence of Juvenile Onset diagnoses related to the younger group and the late childhood related metabolic syndrome related diabetes diagnoses noted for teen age children.

The dumbell appearance of the Diabetes related blindness related to two or more age-related risk factors for diabetes.  Childhood onset of diabetes also impacts less biologically mature neurological tissue related to the eye and its embryonic endo-epi (not meso?) layer derived tissues.  The retina for example is more at risk during early childhood due to development and maturation related problems, whereas during the much older years, age related deterioration problems are more the cause for blindness.  The various forms of diabetic retinopathy, evaluated separately showed almost no deviation from the overall curve produced withj the sum of these 7 different diagnoses.  This means the sub-categories of the ICDs and human in nature and not really involved at all with overall prognostics regarding the patients long term outcomes due to the specific retinopathy type.



Synopsis and Conclusion



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