Other Statistical Behaviors

Other Numbers-derived Behaviors

Gender Differences

Sexually Transmitted Diseases – Cases in Point.  Your company decides it wants to produce a more effective program for reducing sexually transmitted disease (STD) rates.  The current prevention program in place mails letters to people when they reach 18 years of age and continues to monitor and periodically mail out reminder letters to people who are not married and are between 20 and 45 years of age.  For this age range this means that 1.5 million letters are sent per year at reduced costs for bulk mailing, but still costing about $250,000 per year for the mailing, and another $500,000 in printing and administrative costs, costing you approximately 50 cents per letter.

When you review the population health features  for this particular STD you find that the following age-gender distribution in prevalence exists for this particular STD.

 

Pyelonephritis Prevalence, with results of Stat Sig Moving Window analysis method 1, and Stat Sig Moving Window analysis method 2 for Upper Level Management presentation

The above tables illustrate that women experience this condition much more than men, approximately 10 times as often relative to men at the age of 25 years of age, the peak year for people with this diagnosis on record.  Age ranges that are statistically significant are illustate on the middle figure, and show that there is little to no statistically significant prevalence rates in men other than at the age of 14 to 16 years of age.  For women, there is a large age range where the rates stand out as being statistically significant, with the third graph emphasizing the scope to which the interventions to be developed need to be implemented.

The following are the results inferred by this method of modeling the age-gender relative prevalence rate statistics:

  • An intervention that targets teens has to be developed due to the bi-gender statistical significance illustrated for individuals 14 to 16 years of age.  Due to early onset of this problem in the female cases, you decide that this program perhaps should also have some activities in place as early as 12 years of age as well.
  • The highest risk group is the female population ranging in age from 17 to 36 years of age, with a possible risk group also found in the  48+ year olds for females, 59+ year olds for men.   These specific groups of people are targeted, with very different methods taken to interventions targeting men and women, and young to middle aged versus old.   Notice there was no need suggested by these statistics for targeting nearly all middle aged men and all women between the ages of 37 and 48.
  • The result of this review should be to redirect the costs of your intervention program, allowing it to more effectively target age-specific group types.    Chances are your method of engaging in these activities will adapt the traditional mailing method, but more selectively choose the types of members you mail to, cutting the cost for this mailing in half.  This money can then be redirected at targeting older people married or unmarried, either through bulk advertising means or by way of selecting the highest risk individual based on marital status.

Discussion

Numbers play a very important role in the types of plans that can be developed for condition or disease prevention, monitoring and maintenance.  During an early planning stage, the plans for implementing a prevention program for a fairly gender-specific condition like pyelonephritis by default may be likened to similar programs out there regarding the prevention of fairly common STDs such as syphilis, gonnorrhea, herpes, or AIDS.  However, each of these diseases has unique age-gender distributions implying that disease type or class alone is not always the best way to design programs, and pyelonephritis has features other than sexual activity to consider when designing an effective health education program with the goal of reducing pyelonephritis incidence and prevalence during the child-bearing years.  We wouldn’t for example utilize experiences from an adult or mid-child age inoculation program to design an intervention program targeting 0-2 year olds.  We might make use of the first two programs to provide important insights into program planning, but in the end it is the actual data and statistics that tell us which is the best way to design such activities. For this reason, the above age-gender pyramid tells us more than other STD programs about how to develop age-gender specific activities and written materials for preventing future disease onset. 

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Chronic versus Acute

Many if not most ICDs demonstrate differences in chronic versus acute case distributions in the pyramid.  Still, a number of ICDs demonstrate little to no difference between incidence-prevalence age-gender curves.  Acute versus Chronic Pancreatitis is one of the better examples of this statistical behavior.

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Acute Pancreatitis (577.0), versus Chronic Pancreatitis (577.1)

Other Acute versus Chronic comparisons which demonstrated little to no difference include: 

Chronic diseases with 3 or more levels demonstrate significantly more diversity in age-gender behaviors and usefullness in predicting long term outcomes.  Whereas there is a limited relationship between defining a disease state as acute or chronic, conditions preceding certain disease states do show age-gender differences that are useful in evaluating population health.  

The distribution of cases of several chronic versus very chronic liver diseases is one example of this.   

Four degrees of severity for Hypertension

The diagnosis of hypertension, either simple or with comorbidities, and renal failure and its predecessors are other more definitive examples of this ICD disease group pattern.

Acute Glomerulonephritis (580.*), Nephrotic Syndrome (581.*), Nephropathy (583.*), Acute Renal Failure (584.*), Chronic Renal Failure (585.*)

Errata

 

Types of errata:

    • ICD conditions too rare = numbers too low
    • Overreporting/Overcautious PCP practices = numbers too high for PCP’s patient age groups
    • Underreporting/Conservative Diagnostic Practices due to legal, ethnic or personal concerns = numbers too low

The way these graphs are calculated and produced, they structures have a tendency to demonstrate gender crossovers or intergender migration of the ICD being evaluated.  This results in graphs in which only one gender is affected by a condition during a particular periods of age .  When total numbers of cases are small, this might be explained as a result of small n and large variance.  But these undulations are seen as well for larger n’s as well, suggesting there is another cause for this phenomenon. 

The examples above in the top row are for very popular conditions, some nearly extinguished over time.  The alternating male-female pattern change suggests a possibility for gender specific genome-related changes for the organism responsible for the condition, i.e. preference for one gender over the other, cycling back and forth during the final years of existence.  

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“The Barbell Effect”

Diseases or condition with high prevalence in youngest and oldest age groups tend are fairly common, but tend to occur due to two circumstances:

  • younger age overreporting
  • younger age true prevalence behaviors
  • older age overrepresentation due to elimination of non-afflicted
  • older age reporting habits and standards
  • ICD classification and scoring methods

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“Barbells” are due to a variety of reasons.  The above four represent the following systems-induced pyramid forms.    The first is related to young age and old age effects upon health and ICD.  The youngest group is in the developmental years (maturity in the CNS).  The oldest age group consists of survival populations, in which due to age the likelihood and physiological causes for night blindness conmtinue to increase (much like ESRD or HTN/diabetes retinopathy).  The second ICD pertains to a genetic trait, that utilizes dietary practices for its treatment.  Again, old age communities show an artificially high amount of prevalence due to survival behaviors.  The third example is artificially induced barbell form due to systems generated analytic techniques.  A number of disease groups have multiple cause and effects leading to the diagnosis, such as genetically induced subgroups, aging-related subgroups, infectious disease induced cases, and late onset genetic disease physiological or environmental causes.  The fourth example is another systems-induced barbell, in which any remaining (outlier) ICD cases produced this result. [These “other unspecified” 306.* and 307.* psychological ICD series include hair plucking, lalling, lisping, nail-biting, thumb-sucking, etc.]

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Age Specificity Results

Age-Specificity.  Another very common anomaly of these graphs is systems-induced.  The systems effect related to the rules established for defining the ICD for a particular cases.  With ICDs, there are two very distinct sets of rules used to categorize a disease pattern by removing the possibility of age-randomized ICD assignment.  Some ICDs pertain only to children <18 years of age.   Others are ICDs which are somewhat humanized and subjective in that the way the diagnostician assigns the subcategory based on interpreting the medical record.  

The left chart, for example resembles a diagnosis of a learning disability.  The right chart resembles a family or school-related social behavior ICD pertaining to a condition assessed in children under 10 years of age.  The learning disabilities that tend to demonstrate nearly identical age-gender distributions are:

  • Specific Delays in Development 315.* Series
    • Reading Disorders 315.0
    • Dyscalculia 315.1
    • Speech of Language Disorder 315.3
    • Alexia 315.01
    • Developmental Disorders 315.02

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This subjective definition and assessment of outcomes is also true for a number of birth-defect related biological-behavioral, psychological and a few psychiatric disorders, for which mild, moderate and severe levels need to be assigned.  Severity of Down’s syndrome and a number of genetic diseases may fall into this category of outcomes definition.  To help further substantiate the ICD for many of these ailments or conditions, guidelines are typically established and use to prevent misdiagnosis and miscategorization of severity for most of the diseases.  In the case of Mental Retardation for example there are three levels of disability that are categorized, with specific sub-topics or features used to define the ICD down to the ###.n level.  There is no age-related categorization involved in this ICD defining process, so age distributions may show a tendency for the cases to be primarily evidence in younger age groups, but this is due to morbidity-mortality aspects of this kind of condition.  For this reason, early deaths are not unexpected and therefore early reductions in incidence-prevalence expected for amny of these individual.  Even more so important to note are the secondary conditions taht accompany these diagnoses.  Mental health, inherited cardiac anatomy and functionality states, inherited hormone and cognitive differences, all add to the complexity of these conditions and are responsible for their short-lived tendency for many ICDs.

[Insert: Child Abuse versus Adult Abuse ICDs]

The types of conditions demonstrating this fixed range outcome are not always predictable.  For some reason, child abuse for example has patients with claims identified for this diagnosis who are unexpectedly common in ages well past childhood.  This infers not only a past history of child abuse, resulting is later inclusion of this ICD in the records due to later events that took place in someone’s life, but also suggests the possibility of some selective filtering of how and when to use this child-related diagnosis for patients who are no longer in that age span.  It is very uncommon to see this type of behavior by diagnosticians present itself for other childhood related conditions, a history of which remains underreported.

Example of these ICDs with very limited age range distribution are:

  • Autism (Pervasive Developmental Disorders) Series – 299.0
    • Autism 299.0
    • Childhood Disintegrative Disorder 299.1
    • Asperger’s Syndrome and other Pervasive Developmental Disorders 299.8
    • Other Unspecified Pervasive Developmental Disorders 299.9
  • “Exploratory” forms of Drug Abuse
    • Cannabis 305.2
    • Hallucinogens 305.3
  • Adjustment Disorders
    • Separation Anxiety 309.21
    • Emancipation Disorder of late Childhood/Early Adulthood 309.22
    • Academic or Work Inhibition 309.23
  • Disturbance of Conduct Disorders
    • Impulse Control 312.3
    • Kleptomania 312.32
    • Pyromania 312.33 (Boys only)
    • Explosive Disorder 312.34, 312.35
    • Socialized Conduct Disorder 312.2
    • Undersocialized Conduct Disorder 312.0, 312.1
  • Diseases Specific to Childhood/Adolescence 313.* series
    • Overanxiety 313.0
    • Misery and Unhappiness  313.1
    • Sensitivity, Shyness, Social Withdrawal (Introversion) 313.2
    • Family Relationship Problems 313.3
    • Mixed Emotional Disturbance 313.4
    • Identity Disorder 313.82
    • Academic Underachievement 313.83
  • Hyperkinetic Syndromes 314.* Series
    • Attention Deficit Disorder 314.0
    • Other Hyperkineses 314.1-314.9
  • Specific Delays in Development 315.* Series
    • Reading Disorders 315.0
    • Dyscalculia 315.1
    • Speech of Language Disorder 315.3
    • Alexia 315.01
    • Developmental Disorders 315.02

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Gender Specificity

One of the more noticeable ICDs in the above list is that for pyromania.  This is a very rare event for females, and for males, its prevalence is strongest with two peaks in childhood.  Until now, it was possible that many of the ICDs registered in a medical record consisted of ‘rule-out’ as one of the major reasons the ICD is noted.  For example, whenever a patient interacts with a PCP or specialist due to the possibility of his/her manifesting this psychological syndrome, we expect to see the ICD at times noted in medical records where the family practitioner recommended the patient to a psychologist or psychiatrist with the goal of ruling out this mental health condition.  In such a case, it is possible that this ICD will be registered under a patient’s ID twice, once by the PCP and again by the counselor or pscyhiatrist.  With many childhood diseases, this weighting effect the ‘rule-out’ visits have on outcomes may in fact be the reason a large number of population pyramids demonstrate these bi-gender peaks of around 14-17 years old.

This bigender trait is not seen for pyromania, suggesting either there is an obvious monogender relationship between this disease and childhood mental health, or that this tendency for introduction of error due to rule-outs is actually fairly rare in the records.  

In the following series evaluated of childhood behavioral ICDs, two gender specific behaviors are noted.  The pyromania is monogeneric; gambling is male-dominant, with prevalence progressing well into the adult years.   Explosive disorder demonstrates a tendency to be more male-linked.  Note also that impulse control and kleptomania are bigender behaviors.   Undersocialization begins earlier or is diagnosed earlier in young girls versus young boys.

Age-Gender specific Psychologic and Psychiatric Behaviors

Both Gender and Culture are reviewed extensively on another page–focused on socioculturalism and ICDs. 

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Alcohol, Tobacco, and Cannabis Use and Addiction

Certain forms of abuse are very age-gender specific.  The above figure pertaining to the age-gender prevalence of gambling illustrates a feature seen in other human behavioral problems.  The gambling ICDs closely resembles the ICD for alcoholism and smoking.  This supports the past claims that AA programs designed to assist in alcoholism have their related similars related to gambling addicitions and drug addictions.  If we take a close look at the drug addiction IP pyramids, we see some very specific age-differences based on drug type, variety, street related history of use and popularity of slang names and brand names, and cost. 

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For another set of subjectively defined levels of disease, with very qualitative aspects sometimes overly-quantified as part of the diagnostics, we find the psychiatric conditions at times fit into this category of system-related ICD evaluations.

Bipolar behaviors for example, manifest themselves clinically as one of several levels of complexity.  The patient can be primarily manic, depressed, or a mixture of these two at two different levels of behavior and complexity.  People experiencing must be evaluated in details in order that the right ICD defining their condition be identified, even then, this ICD may only be valid for short period in time and may be completely different the next time an office visit takes place.  What is most problematic about this psychiatric diagnosis, as well as several other psychiatric diagnoses, is the fact that this particular psychiatric ICD, requires a highly subjective decision to be made by the diagnostician.  This routine often results in outcomes that appear very much like other conditions which are diagnosed as either acute or chronic.  At times there is no difference between these two ICDs.

Overreporting and Underreporting

Overreporting is when people overreact to the potential of experiencing a medical condition, thereby increasing their participation in care related clinical activities.  The pseudo-hypochondriacal approach to care is only occasionally displayed at the adult level, and seems to involve specific physiological and psychological, behavioral or even psychiatric conditions.  These events are more likely to take place involving children, and logically relate to the parental’s attitudes and responses to a child’s condition.  Most of the time, parents overreact to conditions as an act of safety and ‘best care’ related concerns.  This results in the peak often seen in the teenage years for many of the ICDs, in particular when the peak is not gender specific.

Underreporting is as much a parental response to a possible condition as it is a clinician’s response at times.  Underreporting at the parental level usually means that no visits occur and therefore not diagnosis is documented for a patient.  Underreporting at the clinician’s level takes place when certain signs are present, suggesting a particular health problem may be presented, but due to various personal ro professional reasons is not entered as such a possible case type defined by this ICD.  The types of ICDs impacted by this underreporting behavior are those where the final diagnosis cannot be easily accomplished.  For example, certain disease states, physical or mental, have flowcharts or protocols that can be followed to confirm a diagnosis.  Sometimes these processes are prety clear, as are their outcomes, like the 6 and 12 month repeated testing periods required to confirm a lyme disease or AIDs diagnosis.  But in the case of certain mental health disorders, the differences in type of mental health condition that exist may not be so obvious to a clinician, and in some cases may be quite variable over time due constantly changing patient behaviors.  For Bipolarism for example, an individual can fluctuate between one ICD and the next depending upon primary symptomatology at the time of the visit. For controversial diagnoses pertaining to specific forms of child abuse, such as shaken baby and battered child syndromes, these cases are underreported due to legal complications that ensue once such an ICD is entered into the medical records.  (Methods of correcting for this error can be developed as well using this evaluation method, for which see on another page.)

[Examples of ICDs]  

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