Examples of Application

Informal Introduction

If you made it to this page, you deserve to read it!

The information on this page is about this method I am developing for managing and monitoring population health at the large scale ‘Big Data” Managed Care program level.  Ideally, these are the type of programs that should be produced when requested, weekly, monthly, whenever.

Most of the examples provided here focus on patient health and diagnosis.  There are also programs that I developed which are focused on homeland security monitoring processes, urban population health patterns recognition, outbreaks detection, and cost analyses.

Like the population health mapping I developed, referred to as NPHG, I developed this protocol during an employment period, but the company saw no value in this method of analysis.  The truth is, most companies only talk about being innovative.  Few actually practice or search for and use truly innovative processes.

In 2014, I came across another expert in this field.  He was Director of a program in Washington DC devoted to exploring the different avenues of GIS utilization in heath and medicine.  When I told him I developed a way to substitute the bulk of GIS activities for a national program for which no mapping is done, he was interested, but unwilling to take a risk.

It takes less time to generate traditional maps when you map only the results, and spend little time on the processing of maps.  That is what the NPHG program accomplishes.  Background base map data is optional with NPHG.  These more detailed, topical maps are generated with the initial results using NPHG are worthy of focus and further reporting.

By putting all the efforts of mapping aside, you can instead spend much of that time engaging in hundreds to thousands queries, using an algorithm that, if you want, report only on results with statistically significant findings.

By being specific about what you want to see, you can carry out several thousand evaluations each morning, receive a condensed version of the findings (with very small thumb-sized maps), and have a second section of the report to be produced which has the most interesting results, those most in need of follow-up.

This process can be applied to evaluate infectious diseases of certain types, cancers of certain types, the toughest behavioral health conditions to provide additional care for, exceptionally high cost diseases such as hemophilia.

I am currently these SQLs and SAS and GIS projects on a large population with some of the most complex multicultural and culturally-rich settings in the United States.  These projects are posted here for my access.  Feel free to make use of them for your own managed care programs or special research topics.  I am posting these, because they are the topics most in need of detailed thesis and dissertation level work in terms of managed care program management and administration.  I also require access to my algorithms every now and then when working in new systems.

An Update

Yesterday (12/23/16), I reviewed the processes I am engaged in for the development of a system wide method for evaluating any and all patients for any kind of metric that can be used as a standardize measure across the various EMR systems.

I defined (once again) the following pretty standard measures that can be taken for a managed care EMR system. (This probably overlaps with other pages I have posted.) [The year I essentially completed this task during the 7/15-12/16 period is in the brackets.]

  1. Demographics, patient population [name; DOB; personal ID; insurance program ID, insurance name, type, company etc.; race, ethnicity (unfortunately often race and ethnicity are entered as one column)]  [2015]
  2. Demographics, regional [census based, census block or block-group preferred; last census, most recent year for estimated counts if available] [2015 – ]
  3. Standard Age-Gender [population pyramid level data assemblage for 1, 2, 5 and 10 year increments, for producing population pyramids of demographic features.] [2015]
  4. Standard Race focused interpretation of patient data, including Age-Gender pyramids produced by correlation with Age-Gender dataset. [2016]
  5. Standard Ethnicity-focused interpretaton of patient data, including Age-Gender pyramids.  [Note: Race and Ethnicity often don’t correlate well and produce aberrant groups whene used together.] [2015]
  6. Religion identification and reclassification routines (I have my classification system posted elsewhere in several places, but focus on major religious groups, followed by philosophically defined subgroup types of the minor groups.)  The following method most often used: Catholic, Christian (Trinity, Methodist, Episcopal, Baptist), Christian Derived (Seventh Day, Mormon, some Universalists), Judaic (Torah), Islamic (Koran, Unani, Rostefarian), Cultural (mostly the several Oriental and Indian), Natural Philosophy (Universalists, Quaker, Shaker, or similars), Modern (Practical, Agnostic, Atheistic), Post-Modern (Christian Science, Unity, Trascendentalists, Pagans), Other (Interdenominational), No Data or Unknown.  The first five are classical groups, the remaining may be grouped as Other and Unknown. [2015 – 2016]
  7. Insurer–focuses on Commercial (COM; all the commonly competitors), Medicare (MCR), Medicaid (MCD), combined MCR-MCD, CHP (optional), Metropolitan (MET: large areas may provide a cross population plan where just place of residence defined eligibility), Government (GOV; Fed., State, County, Town insurance plan), Military (MIL), Veteran’s (optional or included with Military), Homeless (HOM), Self-insured (SELF), Union or Contracted Worker Coverage (UNI), Drivers License-accident-related Coverage (DL); Inherited or Annuity-linked coverage (INH), NIOSH-WTC/OSHA/Worker’s Comp coverage (WC), Special Programs (Coal miners, RR or shipping industry etc, SPE). [2016]
  8. Cost (Virtual and True)–Virtual cost is the value of a procedure, event, visit, lab, etc. done on a patient.  Cost lists or “theoretical prices” can be exceptionally lengthy, although not as long as Procedures in general.  They may also be constructed as group costs and later as more detailed costs, by applying averages for true costs across the system, of true costs for subgroups of procedures and other items priced.  True cost is what is charged to payers (patient or insurer).  Virtual cost is an artificial value or estimate assigned to a procedure, as a guess, guestimate, or otherwise calculated value that applies to true life care situations.  Institutional Cost is a cost which is assigned to a process, step or action that uses a systems approach to the analysis; it defines a cost based upon true costs and stresses placed upon the infrastructure for a healthcare system; for example, patients are normally not changed for specific events during a visit, like the price of the educational material provided, or the documentation in writing taken by a nurse using a special reporting sheet, yet each of these count as part of a typical office visit process; in theory the price of the office visit may be divided by the numbers of these non-charged events that occur at a typical visit.  There are more than a million of these “procedures” coded in a detailed system.  The standard is to not change patients for these individual procedures because they are considered part of the service. [2016 – ]
  9. Cost Burden–temporal reviews of people demonstrate changing forms of coverage over time.  At some periods in life, an individual may have just one health care coverage, such as COM provided by the employer, followed by MIL and then SELF and then HOM, then COM, and then GOV, MET, UNI, and finally MCRMCD.  Independent non-duplicated scores or indices are attached to each type of coverage, which can be logarithmically related to cost/risk (using base 10 or less) when compares with a cost amount developed as another Price Sheet (virtual or true). [2016 – ]
  10. Visits – A visit is when the patient shows up for some reason.  A visit may have a direct disease or health state linked to that visit, such as assess for possible pregnancy, where physician decides to also engage in annual visit events, such as check vitals and give seasonal shots, and then reassess if any immunizations due, or renewals of unrelated drugs required, or referral to other specialist required, or annual exams rescheduled.  Each of these procedures is linked to that one event, and will usually related to conditions and diagnoses (ICDs) linked to that visit.  Thus a visit for one condition may have several ICDs attached to it, and numerous procedure identifiers.  [2016]
  11. Visit Types – Visits may also be differentiated by type of visit, such as routine visit, emergent/urgent care visit, outpatient walk-in visit, inpatient hospitalization visit, referral visit, office (admin) visit, pharmacy visit, etc. [2015]
  12. Activities – activities are events that happen at each visit, such as check in, personal med hx sheet, initial interview for why patient is visiting, patient’s two mental health opening questions, main medical concern, history, etc. etc.  These activities are often entered as discrete activity events that occur with a patient care visit.  For inpatients, there may be standard floor nurse pulse taking events for example, followed by a special services provider visit, who also registers the vital signed separately.  Activities are events that occur at discrete moments of time in patient health, and may be used to evaluate care process and sequences, relative to time passage.  [2016 – ]
  13. Provider activities/note — in nature, these are similar to the initiate activities just described; however, differentiating care related decisions made and actions taken may be the purpose of this documentation group and process.  In systems where these relationship are seen, the clinician’s decisions are made based upon a different set of findings and events, relative to the initial activities evaluations.  There are also overlaps, such as physician providing the patient with an educational packet due to a question that was raised. [2016 – ]
  14. Observations — a physician documents various observations, findings and conclusions as part of the healthcare process.  Observations are document for such things as vital signs, responses to orally administered survey questions (mental health evaluation), findings on a new form the patient is asked to fill out (genetic screening questions, domestic violence, chronic disease questionnaire).  These data may be evaluated for reasons related to completion, form quality and follow up to care. [2015 – ]
  15. Procedures – definition varies across systems, but these are the processes a patient goes through for specific reasons, like diagnoses, labs, xrays, assessment, scanning, recording, etc.  The renewal of a prescription may be covered as a procedure, although Rx data is typically reported as its own unique dataset and series of events.  These may be differentiated by visit types. [2015 – ]
  16. Outcomes/Results – procedure related outcomes.  Whereas procedures bears the name of the process, this data set is where the results are kept.  For example, a 20 measure blood test has Blood test under Procedures, using one or more identifiers with each identifier describing slightly different types; this entry of the blood test (where, when, etc.) has its outcomes entered differently in the Outcomes/Results dataset, so a 20 metric procedure has 20 rows of data.  As another example, the Xray can have multiple opinions documented (MD, assistant, technician, NP, residents, Manager), with one opinion per row, and for well planned systems, the final accepted diagnosis identified and so marked. [2015 – ] 
  17. Visit Activity Groups – Various activities can be clustered into particular forms of knowledge acquisition or physical and mental needs that apply to a each and every activity.  Some knowledge acquisition may be most relevant to long term quality of life issues, or behaviors, or  allied health histories.  An example of an activity group is nutrition, in which all the basics of nutrition are made reviewable using this additional identifier.  This dataset also applies to special services provided by individuals whose service focus on this topic, such as nutritional counseling.  There may be separate specialty groups linked to this identifier, such as unique clinical service groups of data, referred to by this column, that when visits provided even more highly detailed non-structured data about a patient’s medical state and history.  This grouper provides additional services for queries because it simplifies the filtering and mining processes. [2016 – ]
  18. Ratios — Three ratios are defined for this group as essential metrics: Visits:Patients Ratio (VPR), Procedures:Patients ratio (PPR), Procedures:Visits Ratio (PVR).  [2016]
  19. Risk Indices or Scores.  Population health frequencies, incidence, prevalence. Demographic counts for each. Elixhauser, Charlson, and Federal Chronic Disease Indices.  [2015-2016]
  20. ICD Groups.  ICDs groups should be managed as ICD9 and ICD10.  If the latter is the only method use, some grouping is required for reliable evaluation processes to be developed.  For ICD9 the n=about 14,500; for ICD10 the n=about 65,000.  Historically, using ICD9, I was able to develop the following sets of groupings. These can be correlated to the ICD10. [2015 – 2016]
    • n=135
    • n=303
    • n=475
    • n=750 about
    • n=1000 (integer ICD)
    • n=1275 (ICD, E and V codes)
  21. Special ICD groups.  Specific ICD code groups were defined for the projects.  These groups include ICDs or ICD ranges/content, and groups labels for the following common special studies topics.  These groups can be roughly defined using the population pyramid modeling processes developed.  In another presentation, the tendencies for diseases to demonstrate distinct pyramid forms in relation to age and gender may be used to better test and cluster these disease patterns.  Diabetes for example shows a progressive increase in counts in the population curve for both genders; schizophrenia demonstrates an asymmetry for Male versus Female with earlier adult age of onset; atrial fibulation demonstrates onset beginning at about 45 years of age and increases in number and frequency as the population ages, whereas diabetes cases reduce due to directly and indirectly-linked causes for mortality.   For each of these Special groups, the ICDs, V codes and some E codes may be included in the algorithm developed. [2016 – ]
    • Infectious Diseases (lower ICD)
    • STDs and HIV
    • HIV, related complications and comorbidities
    • Fractures
    • Cancers, by metastases patterns and organ system
    • Old Age ICDs
    • Prenatal-Postpartum ICDs
    • Foreign Born in-migrating diseases (incl. vectored, zoonotic)
    • Country or Region specific in-migrating behavioral, physical and socicultural ICDs (culturally-bound vs. culturally-linked)
    • Genomic diseases, all, or by system
    • Neurological Genomic or Developmental
    • The Mental Health Early Onset case ICDs
    • Early Onset High Fatality Diseases and ICDs
    • Broad Range Genetic, Genomic and Congenital/ Development Diseases
    • Chronic or Long term QOL Diseases
    • Old Age Onset diseases
  22. Spatial Modeling Lat-Long Datasets, point data.  i) Patients: developed for Zip Code (3- and 5-digit), Census Block or Block group (tracts are not used), and based upon need, per patient address (top security).  Used to develop spatial modeling analyses and presentations.  ii) Facilities;  iii) regions, towns, boroughs, etc.; iv) allied health or other healthcare/managed care associates locations;  v) complementary alternative health care provider data (if possible)
  23. Spatial Modeling Areal Analysis datasets, areal definitions and data points.  Regional boundaries and areal centroids (borough, county, township, town, village).  Functional, healthcare service related areas (Theissen polygons); equal area polygons.
  24. Spatial Modeling, Transformative Areal Models and Grids (political and economic grid mapping).  Traditional square grids and hexagonal grids(?).  Several modeling protocols for each.
  25. Spatial Modeling Axillary data.  Additional data gathered and developed for transforming spatial model into useful modeling or predictive tool, spatial analysis tool, interventions driven programming tool.

End Note

This page is a continuous “work in progress”.   I produced 34 categories of ICD/patient/provider/quality of service studies before starting this more detailed approach to the subject.  Once I find that master list, it will be posted in the first section.

Note: These research methods are also being converted to ICD10 methods.  A number of other coding methods (HCPCS, HICLs, etc.) are included as well.


RESEARCH PLAN (12/24/16)

I.  Project 1.   Domestic Violence Surveillance and Review

Goal: Develop a Domestic Violence and Abuse Data Capturing and Analysis Process for EMRs.

Steps –

  1. Develop a process for evaluating childhood health, neglect, and abuse related factors and events within the study population setting.
  2. Develop a process for evaluating adult neglect and abuse factors and events within the study population setting
  3. [Optional]  Develop a process for evaluating old-age adult and young to medium age adult-related neglect and abuse factors and events within the study population setting.

II.  Project 2.  Zika Surveillance

Goal: Develop a surveillance tool for Zika Virus cases and its followup impact.

Steps –

  1. Review zoonotic diseases for region
  2. Review West Nile extensively
  3. Review Ecological patterns for Zika vs. West Nile and relate to current setting
  4. Determine SES and popualtion density features related to Zika patterns based upon human travel/migration activities and local ecological and environmental patterns.
  5. Design prediction modeling algorithm

III. Project 3.  Genomics Studies

Goal: Evaluate EMR for data related to the various forms of disease patterns modeling accomplished, linked to mendelian and genomic related or defined disease patterns.

Steps –

  1. Define ICDs for Genomic and Mendelian Diseases, assign groupings
  2. Evaluate distributions, using standard AGRER and Spatial processes
  3. Identify any significant demographic and spatial patterns that might exist

IV. Project 4.  [Untitled]

Goal: to develop a long term model for changes in health and QOL, in relation to history and type of exposure to a major disaster.

Steps –

  1. Evaluate disaster survival population
  2. Evaluate research processes already engaged in for this research group
  3. Evaluate the publications and produce a list of the ICDs most often linked to the disaster
  4. Evaluate the ICDs most linked to the disaster type they were exposed to and reason for exposure
  5. Evaluate ICDs at all levels relative to these patients
  6. Identify new ICD groups with clustering or aggregates formed based upon exposure/disaster experience history
  7. Prepare control group of patients
  8. Prepare comparable risk groups based on address history and proximity to site at time of disaster
  9. Compare new risk groups to original 2500 patients evaluated, or subgroups of that evaluation.

V. Project 5.  Applications of EMR to Designing Quality of Care/Quality Improvement Education Programs that target Clinicians

Goal – to develop a way to mine, evaluate and model EMR to produce better short and long term care outcomes.

Steps –

  1. Produce data products that may be used to study and monitor Quality of Care activities and performance
  2. Define “Worst Risk/High Cost” indicators needed to evaluate ICD therapeutic paths (theoretical indicators derived cost, total numbers of rows after Patient-Visit-Procedure (PVP) review, and PVP ratios relative to Visit Type)
  3. Define ICDs for best documented, well-established “risks” with high cost history (well established ICDs for pregnancy with breach or positional complication, post-surgical complications, etc.)
  4. Define in more detail the other potential “Risk” and “High Cost” indicators needed to evaluate typical therapeutic paths, for examples:
    • IP followed by another IP within 30 or 45 days for IP-related ICD
    • IP followed by another IP or EP within 30 or 45 days for unrelated ICD
    • EP for MH followed by IP within 60 days
    • IP for MH not followed by CP within 30-45 days
    • ICD10 code defined IP or AC complication or “failure”
    • ICD10 code defined Proc related complication or failure
    • Rx refill compliance rates predictable failures
    • MH/Suicide related ICD with inadequate FU
  5. Design, Develop, Use and Improve [DUI] algorithms for assessing indicators of risk
  6. Develop tree models of specific therapeutic pathways, define lowest risk pathways
  7. Define intervention related potentials related to these tree models (apply costs; evaluate impacts of: new path, reduced time between steps, added procedure, etc.)
  8. Evaluate likelihood of outcomes using standard machine modeling methods (multiple regression)
  9. Evaluate pathway costs using standard machine modeling methods (tree models)
  10. Produce Cost-Pathway analysis, integrate with ICD risk and demographic data
  11. Design a method for promoting these findings or outcomes in the form of an intervention process (policy, classes, institutional emailing, etc.)
  12. Design, develop and implement [DDI] a Plan of Action
  13. Evaluate DUI and DDI
  14. Evaluate changes in outcomes produced by the pre- and post-educational or POA experience







Cultural-Familial Linked Healthcare Practices and Diagnoses

Research Question: How are the familial, genetic and developmental diseases distributed in terms of zip code tract and specific cultural regions defined for [the] City?

A number of ICDs and ICD groups have been identified as value surveillance tools used to assess the distribution of healthcare needs in [the] City.

The study of genetically based disease patterns has several levels of complexity and resource utilization.  Traditional studies have focused on familiar genetic disease patterns, with the focus on chronic diseases such as diabetes, obesity, hyperlipidemia, birth defects, congenital abnormalities, and most recently, epilepsy, endocrine diseases, and susceptibility to certain forms of cancer such as breast and cervical cancer.  This study divides diagnoses pertaining to possible genetically-based disease patterns into several groups: i) well-documented congenital disease patterns, often detected using standard chromosomal screening processes, ii)  congenital diseases and unanticipated birth relate diagnoses, some without a prior history (i.e. cystic fibrosis, tissue degenerative diseases, retinitis pigmentosa), iii) familial chronic disease patterns (the common familial heart disease patterns, along with HTN, HL, DM, and Obesity), iv) a number of very recent, genetic-screening findings, and v) specific behavioral health or cognitive function diagnoses with possible relationship to family history (ADD, epilepsy).  This study focuses on ICD related diagnoses that are related to the second category–congenital diseases and unanticipated birth relate diagnoses, some without a prior history.  Genetic screening practices are already in place for diagnoses defined by the first and fourth categories.  The third category (familial chronic diseases) has adequate services underway, many of which are well into their testing phase.  The fifth category, behavioral health, remains the least explored of these topics clinically and experimentally for the moment, and is not reviewed for this study.

To carry out this analysis, the following ICDs need to be thoroughly reviewed and very specialized lists developed for the population health analysis.


Congenital, Developmental Risk Assessment (The ICD 740.* Series Study)

The 740 to 759.99 series are all related to developmental disorders, usually part of the care program well after labor and delivery.  (BETWEEN 740 and 759.99)

The following ranges define the subgroups for this review, from this series.  These groups will be reviewed separately as well as merged into one dataset.  The standard state, county, borough, district, census block, block group, network, facility and zipcode methods of spatial analyses will be used.

  • 740-742.99-Nervous system (6 values),
  • 743-744.99-Eyes, ears, face, neck (27),
  • 745-747.99-Circulatory system (40),
  • 748-748.99-Respiratory (2),
  • 749-751.99-Digestive (23),
  • 752-752.99-Genital Organs (14),
  • 753-753.99-Urinary (10),
  • 754-756.99-Musculoskeletal (35),
  • 757-757.99-Integument (8),
  • 758-758.99-Chromosomal anomalies (18), and
  • 759-759.99-Other severe conditions (15).

Other childhood risk indicators possibly related to this study include:

  • Consciousness alteration (780, 780.0-coma, 780.01, 780.02, 780.03, 780.09)
  • Convulsions (780.3, 780.31, 780.32, 780.39)
  • Severe diaphoresis (780.8)
  • Fussy infant (780.91), Crying infant (780.92) and Excessive Crying (780.95)
  • Jaundice (782.4)
  • Cyanosis (782.5)
  • Petechiae (782.7)
  • Aphasia (784.3)
  • Aphonia (784.4, 784.41)
  • Alexia (784.61)
  • Apnea and other rhythmic breathing disorders or S/S (786.03 – 786.09)
  • Stridor (786.1)
  • Hemoptysis (786.3)
  • Chest Pain (786.5, 786.51, 786.52)
  • Rales (786.7)
  • NVD (787.0, 787.01, 787.02, 787.03)
  • Abdominal, Pelvic abnormality (789, 789.0, 789.1, 789.2, 789.3, 789.4, 789.5, 789.6)
  • Morbidity-Mortality S&S (798, sudden death, SIDS, 798.0,.1.,.2.,.8.,.9)
  • Asphyxia (799.0)
  • Respiratory Arrest (799.1)
  • Debility (799.3)
  • Cachexia (799.4)









Childhood Diseases and healthcare needed related to genetic disease and potential disease history features

Research Questions: Are there particular genetic diseases that exist in clusters in the ***** City area?  Are there adequate facilities and programs in place to meet their needs? 

The goal is to compare these results regionally, culturally, and in terms of identified religious groups.

The following 170 conditions or condition classes from ICD9 are identified for this study.

Endocrine  (271.0, 271.1, 271.3, 272.1, 272.2, 272.3, 272.4, 272.5, 272.6, 272.7, 273.8, 275.0, 275.1, 275.2, 275.3, 275.4, 275.41, 275.42, 275.49)

  1. Pompe’s Glyconeogenesis Disease (271.0)
  2. Galactosemia (271.1)
  3. Hereditary fructose intolerance (271.2)
  4. Congenital Lactose deficiency (271.3)
  5. Familial hypercholesterolemia (272.1)
  6. Frederickson hyperlipoproteinemia (272.2)
  7. Burger-Grutz Sydrome (272.3)
  8. Alpha-lipoproteinemia (272.4)
  9. Bassen-Kornzweig Syndrome (272.5)
  10. Lipodystrophy (272.6)
  11. Gaucher’s/Niemann-Pick Disease (272.7)
  12. Familial Trasferremia (273.8)
  13. Hereditary hemochromatosis (275.0)
  14. Wilson’s (Cu metab) Disease (275.1)
  15. Magnesemia (275.2)
  16. Hypophosphatasia (275.3)
  17. Calcemia, hyperparathyroidism (275.4, 275.41, 275.42, 275.49)

Metabolic Disorders (277, 277.0, 277.2, 277.3, 277.4, 277.5, 277.6, 277.7, 277.81, 277.85, 277.86, 277.87)

  1. Cystic fibrosis (277.0)
  2. Porphyria (277.1)
  3. Lesch-Nyhan Syndrome (277.2)
  4. Familial Mediterranean Fever (277.3)
  5. Crigler-Najjar/Gilbert’s Syndrome, hyperbilirubinemia (277.4)
  6. Mucopolysaccharidosis, Hurler’s, Hunter’s, Sanfillipo’s syndrome (277.5)
  7. Alpha-1-Antitrypsin deficiency, hereditary angioedema (277.6)
  8. Dysmetabolic syndrome (277.7)
  9. Primary carnitine deficinency (277.81)
  10. Fatty Acid oxidation metabolism (277.85)
  11. Zellweger Syndrome, peroxisomal metab. (277.86)
  12. Kearns-Sayre Syndrome, mitochondrial metab. (277.87)

Obesity (278.0) [focus on morbid, esp. cases with genetic predispositions in the form of lipid metabolism disturbances]


Immune Disorders (279, 279.11, 279.12, 279.13)

  1. DiGeorge Syndrome (279.11)
  2. Wiskott-Aldrich Syndrome (279.12)
  3. Nezelof’s Syndrome (279.13)


Digestive Tract – Oral (520, 520.0, 520.1, 520.2, 520.3, 520.4, 520.5 520.6, 520.7, 524, 524.0, 524.03, 524.1, 524.2, 524.3, 524.4, 524.5, 524.6, 524.7, 524.8, 528.1, 528.0

  1. Anodontia and other tooth disorders (520, 520.0, 520.1, 520.2, . . . 520.7)
  2. Dentofacial anomalies (524, 524.0, 524.03, 524.1, 524.2, 524.3 . . . 524.8)
  3. Cancrum oris, noma (528.1)
  4. Stomatitis (528.0)


Optic (362.7, 362.74, 362.76, 369.0, 371.5, 371.50, 371.51, 371.52, 371.53, 371.54, 371.55, 371.56, 371.57, 371.58, 377.17, 378, 378.0, 378.3, 378.30, 378.31, 378.32, 378.33, 378.34, 378.35, 378.5, 759.89, 743.45, 743.3, 377.43, 743.57, 743.58, 270.2, 389.*)

  1. Hereditary retinal dystrophy (362.7)
  2. Retinitis pigmentosa (362.74)
  3. Leber’s Congenital Amaurosis (362.76)
  4. Blindness, bilateral, profound (369.0)
  5. Hereditary Corneal Dystrophy (371.5, 371.50, 371.51, 371.52 . . . 371.58)
  6. Leber’s Hereditary optic neuropathy (377.17)
  7. Congenital Strabismus (378)
  8. Esotropia (378.0)
  9. Exotropia (378.1)
  10. Heterotropias (378.3, 378.30, 378.31, 378.32 . . . 378.35)
  11. Paralytic strabismus (378.5)
  12. Noonan Syndrome (759.89)
  13. Aniridia (743.45)
  14. Congenital Cataract (743.3)
  15. Optic Nerve hypoplasia (377.43, 743.57, 743.58)
  16. Albinism (270.2)
  17. Deafness (389.*)


Neurological (330.00, 330.1, 331.3, 331.81, 331.8, 332.0, 333.0, 331.1, 333.2, 333.4, 333.8, 333.81, 334.0, 334.8, 334.1, 335, 335.2, 335.20, 335.21, 335.22, 335.23, 335.24, 335.29, 340, 341.1, 342, 342.0, 342.1, 343, 343.0, 343.1, 343.2, 348.1, 348.0, 356.0, 356.2, 356.3, 356.1, 358.0, 358.01, 358.02, 359, 359.0, 359.1)

  1. Leukodystrophy, Krabbe Disease, Peliaeus-Merzbacher Disease (330.0)
  2. Batten/Tay Sachs Disease (330.1)
  3. Communicating hydrocephalus (331.3)
  4. Reye’s Syndrome (331.81)
  5. Cerebral degeneration (331.8)
  6. Primary Parkinsonism (332.0)
  7. Olivopontocerebellar atrophy/Shy-Drager syndrome (333.0)
  8. Familial Tremor (331.1)
  9. Lafora’s/Unverricht Myoclonal Disease (333.2)
  10. Huntington’s Chorea (333.4)
  11. Torsion Dystonia (333.8)
  12. Blepharospasm (333.81)
  13. Friedreich’s Spinocerebellar Ataxia (334.0)
  14. Ataxia-telangiectasia (Louis-Bar Syndrome) (334.8)
  15. Hereditary spastic paraplegia (334.1)
  16. Anterior Horn Cell Diseases (335)
  17. Motoneuron disease (335.2, 335.20, 335.21, 335.22, 335.23, 335.24, 335.29)
  18. MS (340)
  19. Schilder’s Disease, Diffuse myelinoclastic sclerosis (341.1)
  20. Hemiplegia (342, 342.0, 342.1)
  21. Cerebral Palsy (343, 343.0. 343.1, 343.2)
  22. Epilepsy (345.*)
  23. Anoxic Brain Damage (348.1)
  24. Cerebral Cysts (348.0)
  25. Hereditary Peripheral Neuropathy (356.0)
  26. Hereditary Sensory Neuropathy (356.2)
  27. Refsum’s Disease (356.3)
  28. Peroneal muscular atrophy (356.1)
  29. Myasthenia Gravis 358.0,. .01, .02)
  30. Benign Congenital Myopathy (359, 359.0)
  31. Muscular Dystrophy (359.1)


Genetic Blood Diseases (280.0, 282, 282.0, 282.2, 282.5, 282.6, 282.4, 282.43, 282.45, 282.44, 282.1, 282.8, 282.7, 282.9, 284.0, 284.01, 287.1, 288.2, 289.81, 289.7, 446.6, 759.89, 285.0, 286, 286.0, 286.1, 286.2, 282.3, 282.4, 282.6, 282.9, 773, 757.39)

  1. Pernicious anemia (280.0)
  2. Hereditary hemolytic anemias (282)
  3. Hereditary spherocytosis (282.0)
  4. G6PD (282.2)
  5. Sickle Cell trait (282.5)
  6. Sickle Cell anemia (282.6)
  7. Thalassemia (282.4)
  8. Delta-Thalassemia (282.45)
  9. Alpha-Thalassemia (282.43)
  10. Beta-Thalassemia (282.44)
  11. Hereditary elliptocytosis, SE Asian ovalocytosis (282.1)
  12. Hereditary somatocytosis (282.8)
  13. Hereditary hemoglobinopathy, Hereditary Persistant Fetal Hgb (282.7)
  14. Fanconi’s Anemia (284.0)
  15. Bernard-Soulier Syndrome (287.1)
  16. Diamond-Blackfan Anemia (284.01)
  17. May-Hegglin Anomaly (288.2)
  18. Antithrombin III deficiency (289.81)
  19. Protein C deficiency (289.81)
  20. Congenital Methemoglobinemia (289.7)
  21. Upshaw-Schulman Syndrome (446.6)
  22. Allport Syndrome (759.89)
  23. Hermansky-Pudlak Syndrome
  24. Grey Platelet Syndrome
  25. Quebec Platelet Disorder
  26. Sideroblastic Anermia (285.0)
  27. Hemophilia (286)
  28. Hemophila A (286.0)
  29. Hemophilia B (286.1)
  30. Hemophilia C (286.2)
  31. Von Willebrand’s Disease (286.4)
  32. Hypoprothrombinemia/Factor XIII deficiency/Congenital afibrinogenemia (286.3)
  33. Defibrination Syndrome (DIC) (286.6)
  34. Coagulation Defects (286.9)
  35. Newborn hemolytic disease (773)

Dyskeratosis congenita (757.39)

These ICDs will be reviewed as boroughs, network, facility and zip code datasets.



High Risk Prenatal Postpartum Care

Research Question: What are the highest risk regions for prenatal-postnatal care, based upon age-diagnostic/therapeutic features?

Populations of the following age categories are evaluated: Female <8, 8 to 12, 13 to 15, 16 to 17, 18-20, 21 to 24 (perhaps 25-29).  Evaluations focus on ICDs related to pregnancy, chlamydia, STDs.  V codes and E codes may also be evaluated for certain risk indicators.  Parent-abused Child codes may also be added for another dimension of this analysis.  HIV immunization rates may be included in this review as well.

Evaluate population health features, at the zip code tract, and if possible census block or block group level.   Include race and religious group focus.

This study duplicates portions of two standard MCD studies, focused on prenatal-postpartum care analysis, and chlamydia care for young adults.  V-codes and E-codes are added in order to assess parent-child relationship.  Detailed population pyramids will provide important data pertaining to how to evaluate the young parent:child ratio each study group has.  Certain religious or ethnic groups may demonstrate a greater number of children per primary adult or parental care provider.  Certain regions are expected to demonstrated higher ratios as well.  Standard census related zipcode-household income data may be incorporated into this study.



Childhood Wellness

Research questions: What is the rate of compliance for children undergoing the appropriate number of wellness visits between the ages of 0 and 24 months?  2 years and 5 years?  What is the rate of lead screening that these patients undergo?  Which well visits are missed the most?  What are the primarily preventive health procedures associated with each visit?   

A preliminary study of well visits in relation to immunization requriements for children suggested that the reason a child who undergoes a healthcare visit (emergent, urgent, therapeutic, or actual well visit with the appropriate goal(s) in mind) has certain visits that are required in order to complete the immunization process.  This study focuses on well visit behaviors, to determine which well visits are most adhered to versus those most likely to be missed.  Since each well visit has a particular set of expectations involving the assessment and lab related practices engaged in by the care givers, this study is meant to determine which well visits are most often missed, and those most likely attended, based upon months of age and time since the last visit.  The following time frame for 0-2 yo children is reviewed: 0-1 week, 0-1 mo, 1.5-3 mo, practitioner, 6 mo, 9 mo.,  12-15 mo, 18 mo, 2 yr., 3 yr, 4 yr, and 5 yr.  These evaluations will later be used to identify study population for assessing particular development disorders based upon ICDs, to determine if any relation exists between well visit practices or lack thereof, types of developmental and behavioral disabilities developed, in association with the standard demographic features (place, SES, MCD or not).

Evaluating this data by zip code tract, relate the outcomes to closest facilities and/or PCP care related opportunities.



Childhood Congenital and Development Disease Screening-First Diagnosis Age-specific Rates

Research question: How effective and timely are the initial screening-first visit-first test practices for children suspected to have a familiar or genetic disease?

Age specific rates are the rates at which a process is completed in health care by a certain age-related deadline or recommendation.  In the case of preschool children for example, learning related disability may need to be assessed by the age of 5, or the first month that child attends a regular school.  During the first days, weeks and months of a child’s lifespan, certain signs and symptoms should in theory lead to specific rule out procedures.  This study evaluates the earliest age in which the rule out process is fully carried out.  These measures are also reviewed in relation to expected healthcare visit rates and patterns. Delayed health care processes are evaluated using temporal and log regression models.   ICDs to be evaluated specifically for this study have yet to be determined.



Elementary, Middle School and High School Health Risk Rates

Research questions: What are the rates of risky behaviors for school age children?  Which of these are the best indicators for regional school children’s health?

A population health risk score can be developed using the following ICD data, and related initial and follow-up health care processes. V codes and E codes included.  Examples:  Elementary:  test results hx, STDs, smoking hx, drug hx/note, abuse hx, poor nutrition/malnutrition dx, chronic disease history already documented, 312-316 level learning disability dx, ASD dx,, wt/obesity  Middle School: test results hx, anorexia nervosa, STDs, smoking hx, drug hx/note, abuse hx, sexual abuse hx, counseling hx, violence syndromes, chronic disease history already documented, certain beh/psych dx,  ASD dx,, wt/obesity.    High School: anorexia nervosa, STDs, smoking hx, drug hx/note, abuse hx, sexual abuse hx, counseling hx, violence syndromes, chronic disease history already documented, certain beh/psych dx, ASD dx, wt/obesity. Algorithm to be developed.  Predictive modeling application.




Child Abuse Indicators

Research Questions: What are the primary indicators that to be used for evaluating regions and programs for child abuse related problems?  V-codes?  E-codes?

Nutritional deficiencies (260-269)  [BETWEEN 260 and 269.99]

  • Kwashiorkor (260)
  • Nutritional marasmus (261)
  • Severe protein-calorie malnutrition (262)
  • Other protein-calorie malnutrition (263)
  • Vitamin A deficiency (264, 264.0 – 264.9)
  • Thiamine deficiency (265), beriberi (265.0), Wernickes Encephalopathy (265.1), Pellagra (265.2)
  • Vitamin B deficiency (266, 266.0, 266.2)
  • Vitamin C deficiency (267)
  • Vitamin D deficiency (268, 268.0, 268.1, 268.2, 268.9)
  • Vitamin K deficiency (269.0)
  • Other vitamin/mineral deficiency (269.2, 269.3, 269)

Other ICD groups (need to work on this)

  • Injuries
  • Fractures
  • Dislocations
  • Infections
  • Behaviors (Parent)
  • Abuse
  • Abandonment





Chronic Obstructive Sleep Apnea

Research question: how common are the Sleep Apnea disturbances within the [city] area?

780.5, 780.50, 780.51. 780.52, 780.53, 780.54, 780.55, 780.56, 780.57, 780.58, 780.59

TBD/Work in Process.



Aging, Human Behavior and Fractures

Research question:  what is the relationship between age or age range/group, and particular types of fractures and dislocations?

An evaluation of all fracture and dislocation codes, as individual body part codes or groups of codes.  Open and closed fractures may be compared at the appendages level (Null hypothesis assumed for this group.)  Several fractures may be considered specific to particular public and behavioral health findings, linked to child abuse and elder abuse.  These distinct fracture/dislocation groups will be evaluated and compared.  Nutritional ICD predisposition to frequency as a comorbidity for specific fracture groups will also be evaluated.  Unique osteopathies are expected to impact these results for certain age groups and perhaps income levels for patients.  MCD, MCR and non-MCR/MCD program patient populations will be compared.



Cultural Richness and Diversity

Research Questions: What are the social, cultural and/or ethnic groups settings contained within the five boroughs of New York City?  In particular, are there specific zip code tracts and census block areas that may be evaluated in order to develop the tools needed to profile patient health at the community level?

More than likely, a number of ethnic or cultural communities exist within [the] City that serve as valuable research settings, for projects similar to those made famous by the Alameda studies carried out in Los Angeles, California over the past several decades.  For example, the most famous community-cultural settings in the some of the larger urban settings include communities rich in a cultural heritage, and language and mercantile behaviors closely linked to specific groups, such as the local “Little India”, Jamaica, Chinatown, Spanish Harlem, Black or African American Harlem, Hassidic Judaism, Russian-based Judaism (——), Middle Eastern Muslim communities.  This project explores the following ethnicity or culture related research questions: What are the demographic and business related indicators of these community settings? The business indicators? The related healthcare provisions that are unique in nature?  Another serious of questions to be explore are those which focus on health care provider locations and activities.  This includes a review of foreign language background, the availability of medical documents in the most appropriate foreign language, the types and amounts of healthcare provides that service these neighborhoods, their cultural specificity and appropriateness.  The presence of any unique services or public health related requirements and demands posed by these unique cultural settings.  The purpose of this study is to document the unique demographic and sociocultural features of the populations serviced by these ****** facilities, and to ultimately use this information to provide more highly targeted, culturally-linked, culturally-defined healthcare services.


Culturally-defined Special Service Needs and Requirements

Research question: Why are there differences in age-gender population distributions for male versus female patients, diagnosed as either a sickle cell disease patient and or a sickle cell carrier?

The diagnoses of sickle cell and being a sickle cell carrier can have a tremendous impact on how a patient makes certain decisions about lifestyle and life goals.  The presentation and diagnosis of either of these two conditions is different for women and men.  For the disease itself, sickle cell has similar impacts upon men and women, with women possibly experiencing better chances for survival through their mid-life years (40 to 60s) than men.  For the carriers, preliminary results suggest that the risk of survival through the midlife years is considerably less for men than for women.  More importantly, female carriers of sickle have an ability to survive through their reproductive years, a disease behavior not noted for male carriers. The reasons for this asymmetry are uncertain.  It is possible that late diagnosis of the condition may be responsible for women peaking in their reproductive years for diagnosis of this condition (genetic screening related to childbearing potential preventive acre work).  This explanation does not apply to older male carriers, who seem to reduced greatly in numbers by the time they reach 40 years of age.  These unique population–related features of sickle cell disease and carriers are in need of further exploration.  Either a focus group or case study methodology is recommended in order to determine if there are statistically significant differences between female versus male case or potential carrier case management practices.



Research questions: Is there a difference between two groups of patients either diagnosed as having sickle cell or diagnosed as a sickle cell carrier, when these two groups are defined as having either an African Americas heritage or a Hispanic heritage, according to EMR documentation?

A major concern about electronic health and electronic medical records data is the validity or truthfulness of these data.  A review of the EMR/EHR has revealed two primary demographic groups identified with documents revealing either a sickle cell disease or sickle cell carrier related diagnoses—African American, and Hispanic.  Very little research has been performed on the sociocultural impacts of being diagnoses with a disease that has significant quality of life and longevity related implications.   The diagnosis of having a sickle disease or being a carrier impacts the decisions patients make regarding family planning, engagement in further healthcare related activities, and the value that may be place by the patient upon healthcare providers, agencies, insurance programs, and long term lifestyle related living practices and activities.

To study this outcome a focus group approach is recommended in order to document the different interpretations these two cultural settings experience when faced with such a diagnosis.  The number of patients who checked the “Hispanic” box for ethnicity are very few in number for our patient population.  These suspected cases may be a result of patient entry error, patient choice of race related errors, data entry by technicians, or a “more than one race” related error.  For patients whose lifestyles are truly Hispanic in nature this offers a unique opportunity to closely evaluate how the interracial passage of a genetic disease like sickle cell impacts the patient. Hispanic patients who live a traditional Hispanic lifestyle without much influence related to African-American beliefs may experience this diagnosis differently from combined maternal-paternal African-American couples.  This study reviews and interprets the culturally linked patterns of behavior, related to the culturally-bound reactions and behaviors that may exist for a sickle cell disease or carrier patient diagnosis.



Research question: What are the relationships between religion and race with regard to the documention of spouse abuse cases, in which the abuse victim is the adult women or wife? 

A preliminary review of ICDs for the adult abuse code revealed disproportionate findings for the number of abuse cases documented through EMR/EHR databases.  Documentation of the ethnic background and religious affiliation of cases identified within the databases revealed distinct differences between the subpopulations that could be identified.  Not only was the absence or presence of these codes for groups different, the numbers of events or visits accounted for at the per patient level appeared to be significantly different as well.  This study is a further exploration of the use of this class of ICD data for analyzing population health features and producing very specific programs when needed to deal with such events as they appear in the EMR/EHR data.  A well-targeted case study approach is recommended for this project, requiring gender sensitive researchers and assistants be engaged in this case review process.  Female nurses, social workers and counselors are recommended for this project, which may be used to design a better targeted spouse abuse prevention program.




Culturally-bound Health

Research questions:  What are the culturally-bound health conditions that may appear in clinics?  Are these adequately documented? (unanswerable)  How can we identify them as the happen and therefore improve their documentation and treatment processes?

This is an ICD 10 study, with comparitive studies performed as well for prior ICD9 diagnosis for the same patient groups.  Database used: 10-20 years, all EMR.  A thorough study of cultural-bound health is lacking in the medical literature pertaining to managed care programs.  This study applies the basic studies and methods of research utilized primarily by medical anthropologists and social services agencies to a culturally diverse managed care population.  The cultural richness of this combination, in combination with its sizeable populations and vast amount of community, cultural-related services provided for complimentary healthcare practices within this region, provides ****** with one of the most unique opportunities to fully explore this important managed care research topic.  The following are examples of ICD10’s for culturally-bound syndromes, noted to exist by other allopathic care programs and agencies.  The related culturally-bound syndromes for each of these ICD10 diagnoses are listed.  Possible ICD9 codes that may relate to some of these diagnoses are provided in parentheses.  Other related ICD10s are also defined for each major group reviewed.

  • F32.11 – Moderate Depressive Episode with somatic syndrome (ICD9 296.2 or 296.3?). Example: nerves (northern Europe), nerfiza (Egypt), nerva (Greece), nervios (Mexico, Central and South America).  Possibly valid for ataque de nervios (Puerto Rico, Latin America), colerina (Andes, Bolivia, Columbia, Ecuador, Peru), ich’aa (Southwestern US Idigena), berserkergang (Scandinavian, Saami), carfard (Polynesia), benzi mazarazura (so. Afr. Shona), ahad idze be (New Guinea).  Related also to anfechtung (Hutterites), brain fag (Nigeria), colerina, pension, bilis (Mexico, Central and So. Amer.), hseih-bing or xie-bing (China), hwa-byung (Korean peninsula), qissaatuq (Inuit), narahati-e a sa and maraz-e a-sab (Islamic Republic of Iran). Considered partially linked to cultural stress due to low self-esteem. Experienced by women more than men in some cultures.  These have also been related to F48.0 and F45.1.
  • F40 – Phobias. Examples: taiijin kyofusho, shinkeishitshu, and anthropophobia (Japan).  Afflicts younger people. Perfectionism, oversocializing, events related to inferiority complex issues are noted.  Akin to adolescent acceptance behaviors.
  • F40.2 – Social or Specific phobia(s) 1) Examples: pa-leng (China, fear of cold winds), agua frio, aire frio, and frio (Mexico, Central and South America).  American terminology example of the temperature based philosophy (weakness, weak body or weak soul) includes frigiphobia.  Cultural basis is fear, anxiety, obsessiveness, related to the underlying humoural implications.  [Possibly ICD9=300.2, 300.2*]   2) Hwa-byung (Korea) is similar, with the exception that it is termed a “Firey Illness.”  Anger and rage are manifested, with changes in somatics, autonomics, and mood.  Woemn more than men.  Related to cultural oppression.  Has some rage-like features.
  • F40.8 – Phobic Anxiety Disorders [see 300.23 social phobias]  Continuation of the F40 series. Examples: taiijin kyofusho, shinkeishitshu, and anthropophobia (Japan).  Other possible associated diagnoses: anfechtung (Hutterites), itiju (Nigeria).
  • F44.3 – Trance and Possession Disorders. Examples: possession disorder, saka, ufufuyane (So. Africa, Bantu- Zulu, Kenya). Akin to a Voodoo spell.  Some relations with “magic” and stupor, seizures, trances. Scientific profiles include drug use suspected.  Young unwed women are most susceptible.  Related as well to aluro (Nigeria), phi bob (Thai), and zar (Egypt, Ethopia, Sudan).
  • F44.7 – Mixed dissociative (conversion) disorders. Examples:  pibloktoq (Inuit), uqamairineq (Inuit), possession disorder. [ICD9=300.1*, 300.11]  Seizure like behaviors.  Multisensory hallucinatory events.  “Soul loss” experience is common event.  Soul wandering is one interpretation of this event, that tends to occur in older kids and young adults.  Very much psychosocial in nature in terms of defining purpose and meaning, and assigning “power” to a young potential leader’s “purpose” within his or her group.
  • F44.8 – Other specific dissociative, conversion disorder. Examples: latah (Malaysia), pibloktoq (Inuit), uqamairineq (Inuit), old hag (Newfoundland), phii bob (Thai), aluro (Nigeria).   [ICD9=300.1*, 300.11].  Inuit feel it represents “soul lost” or wandering.  Dissociative hysterical cases occur.  Seizure like manifestations may ensue, followed by drowsiness (sleep paralysis).
  • F45.1 – Undifferentiated somatiform disorder. Examples: susto or espanto (Mexico to So. Amer.), nerves (var.).  ICD9 closest is possibly 308.*  “Soul loss” generated by the supernatural.  Social stressors may be related.  Related to lanit (Philippines), latah (Indonesia, Malaysia), malgri (Australian aborigines), mogo laya (New Guinea), narahati (Islamic Republic of Iran), and saldera (Amazonia).
  • F45.34 – Somatiform autonomic dysfunction of the genitourinary system. Examples: koro (Hainan Island of China, India, Singapore, Thailand), suk-yeong (Cantonese), suo-yang (Mandarin), jinjinia bamar (Assam), rok-joo (Thai), rabt (East India).  Anxiety and panic are some of the basic S/S, sense of coldness, retraction of body parts (penis) as a result.  Linked as well to F48.8.  The ICD9 closest to this is 302.7*-psychosexual dysfunction, 302.72-impotence,frigidity, and 302.73, 302.74-anorgasmia.  Possibly 306.*  The koro (Hainan island of China, India, Singapore, Thailand) may be associated with this as well. [See f48.8]
  • F48.0 – Neurasthenia. Example: nerves.  Possible shenjing shuarino (China), when patient is afflicted with a sense of mental and physical exhaustion, memory loss, inability to concentrate, irritability, headaches, weakening of nervous activities or nervous system (“neurasthenia”).
  • F48.8 – Other specific neurotic disorders. Examples: dhat (India), koro (Orient), latah (Malaysia), jiryan (Assam), shen k’uei, or shen-kui (Oriental), amurakh (Siberia), iekunii, ikota, okan, myriachit, menkeiti, (var. Orient), Lapp panic (Lapps/Sammi), bachtschhi (Thai), imu (Ainu indigena or Japan), mali-mali (Philippines), silok, susto (Mexico), yuan (Myanmar, formerly Burma), jumping (French Canada).  One part of this philosophy relies heavily upon the spirit of life concept.  Therefore, one route taken for this disorder and diagnosis is the belief in chi, which is found in ayurvedic and Oriental health philosophies, and possibly unaniism.  The four humours, five elements theory plays into this, and the loss of chi via semen reduction is important to the cause.  Shen is the spirit of life (Chinese and Japanese), expressed as “the sign of life” in a patient’s eyes.  This has been associated as well with F45.34.  Due to the primary sexual and “energy” nature of this condition, Western medicine identifies it with sexual neuroses.  Echopraxia, echolalia are also predominant behavioral symptoms.
  • F48.88 – Other specific dissociative (conversion) disorders. Examples: susto (Mexico). See F44.7 (pibloktoq, Inuit).
  • F68.8 – Other personality disorders of adult personality or behavior. Examples: amok (Malaysia), windigo (NE Native Amer.), hseih-ping (China), sar (Egypt and Sudan).  Possible ICD9: 301.5? 301.50?.  Windigo is an obsession, which can include cannabailistic ideology, and belief in being taken by nature’s “monsters”.  Once related to starvation history, now more behavioral and obsessive in nature.  Possibly akin to ostracizing and trying to prove one’s self to peers.
  • G47.4 – Narcolepsy and catalepsy (incl. sleep paralysis). Example: uqamairineq (Inuit).  (ICD9=306.0).  This may be too off-base for culturally bound behaviors and diagnoses.  A diagnosis of false epilepsy is more fitting.


  • Epilepsy – “Spirit Captures you and you fall down” (Laos, Native American)
  • Schizophrenia – Amish
  • Sudden Unexplained Nocturnal Death Syndrome (Laotians)

Preliminary studies demonstrated possible somatic-autonomic links for culturally-bound syndromes seeming to impact the heart and circulation, for example Obscure cardiomyopathy of Africa (452.2), Asian pseudo-RBBB or Brigada Syndrome, Takayasu’s Disease (Japanese) (446.7), Japanese Moyamoya (vessel disorder), Takotsubo (429.83).

The following behavioral psychology ICDs were considered possibly linked to culturally-bound syndrome behaviors and diagnoses:

  • 290.* Dementias
  • 300.* Anxiety states
  • 300.02 Generalized Anxiety Disorder
  • 300.1 Dissociative Disorders
  • 300.11 Conversion Disorder
  • 300.13 Fugue
  • 300.14 Identity
  • 300.16 Factitious Disorder
  • 300.21-.22 Agoraphobia
  • 300.23 Social Phobia (301.7 Antisocial)
  • 306.2 Acute Physiological Malfunctions of the Heart due to Mental Factors (306.8 bruxism, 306.6 endocrine)
  • 307 Sleep Disorders (307.45 Circadian, 307.46 Arousal)
  • 307.46 Sudden Affective Disorder (SAD)
  • 308 Acute Reaction to Stress
  • 309.4 Emotion and Conduct Adjustment Disorders
  • 309.21 Separation Anxiety
  • 309.29 Culture Shock
  • 309.81 PTSD
  • 309.* Adjustment Disorders
  • 309.1 Prolonged Depression
  • 310.1 Personality Change due to . . .


 Update 12/24/16

One more evaluation of the cultural-ICDs is needed, then this may be run using the standard Patient-Visit-Procedure tool.



Culturally-Linked Health

Research question: Are there Unani health care communities in the five boroughs region?  How do we identify them?  Do their patient population and related health issues and health prevention practices result in a unique healthcare burden for the traditional allopathic care programs and health insurance available to these communities?

Unaniism is a unique practice engaged in by a special class of practitioners found in parts of Indian, and the Iran-Iraq portions of the Middle East.  Unaniism is a very unique form of medicine in that it is of ancient Hippocratic decent, with certain Hippocratic medical practices definitive of modern western medical practice (allopathic) philosophies, and yet is practiced in a more traditional form in a number of very non-Western, non-allopathic health care settings.  The primary burden a non-western practice such as Unaniism might result in is the application of its traditional practices an beliefs a priori to the allopathic care practices and procedures covered by traditional health insurance programs in the U.S.  This community-focused exploration of non-traditional allopathic health care focuses on culture, culturally-distinct health care service programs, over the counter healthcare services and products, and the density of these healthcare clinical and non-clinical resources in the local community.   Two distinct cultural groups are identified as strongly linked to Unani healthcare practices—Muslim or combined African-American-Muslim communities and Indian or combined Indian-Muslim communities.

Note:  Assume that Culturally-based studies always require special IRB processes.  Studies related to the above at the ICD/EMR level are expected to produce some follow up queries.  At the clinical level, special case studies and focus group approaches may be needed.  Due to the diversity of cultures and types of manifestations of these diagnoses, small groups may not be available, suggesting in turn that a case study approach may need to be taken.  The research processes, and steps taken to develop this knowledge base, need to be documented further.  More examples of this type of research work that have already been engaged have to be uncovered and reviewed.







Research question: Could a problem resembling the recent Legionnaire’s disease happen again?

A review of the infectious diseases and their spatial behaviors, based upon events, cases, and various public health related environmental-ecological reviews of infectious disease data.

Update 12/24/16

A similar project is needed for surveillance of Zika virus in-migration and infection history.  Prototypes for organismal-vector-zoonotic disease patterns is being developed.  The ICDs (both 9 and 10) are being defined f0r this surveillance activity.)



Politically-based in-migration and Public Health

What are the unique stresses that the influx of Middles Eastern, NE African cultures might induce upon the ****** system?

A similar evaluation was performed extensively on the influx of Vietnamese, Laotians, Hmong, and Cambodians into the US during the 1970s, due to the Vietnam War.  The processes social services engaged in to prepare for this influx of new patients were documented.  The unique stresses this placed regarding public health, personal physical health, personal social health and personal behavioral health were documented at the time, and then re-reviewed and summarized in a separate study completed around 2001/2.  The protocols learned from that study will be applied to this particular ethnic group.




Research question: What is the distribution of antibiotic resistant organisms within the five boroughs settings, and their related healthcare facilities and patient populations, focused on a review of emergent care patient results versus patients engaged at least one hospital stay during the year? 

This study was first proposed by the Emergency Department and the ***** County Hospital, and focused on a review of E. coli test results, for comparison between in-patient (IP) and emergency patient (EP) populations.  IP and EP patients may be differentiated by a column in which this data is provded, and the length of stay [LOS] the patient experienced for the emergency care encounter.  A LOS of less than 2 days was used to define EP eligibility for this study, to avoid patients who were hospitalized for any significant lengthy of time due to their visit.  The original study focused on one healthcare facility, one bacterium test, for an undetermined number of prescription antibiotic medications.  The result for each evaluation made included information on the medication tested for antibiotic resistance, and the result of that study entered as either ‘resistant’ ‘not resistant’ or ‘undetermined’.  This study will be re-performed adding zip code data to the analytic process, and applying the entire algorithm and data interpretation process to all of the networks, regions and facilities involved with ****** programs.  The four ACO facilities, specialized care facilities, and long term care facilities are excluded from this work.  This process may be used to identify clusters of antibiotic resistant behaviors, geographically, institutionally, demographically, and perhaps socioeconomically.




STD rates

What are the current rates of STD diagnosis, in narrow band age groups, for the city, its networks, acute care facilities, pediatric versus family medicine practices, individual zipcode tracts? 

This is a follow up to the recent news indicating there is a resurgence in STDs in high school populations and young adults.  The goal is to evaluate population health features, including age band/sip specificity, for syphilis, gonorrhea, HIV/AIDs, Chlamydia.

Note: The processes developed for this work could be applied to any subsequent ICD focused surveillance projects engaged in.

A separate study is in the works, reviewing the local history of HIV/AIDs and paralleling the initial studies produced by Daniel Fox, SUNY-StonyBrook, Exec., Chief Editor Milbank Quarterly.




Fetal-Perinatal Health

Research question: Are there particular indicators of the quality of perinatal events experienced in ******?

The following groups of ICD indicators are under consideration for this study.

ICDs 760 to 779 cover perinatal period health conditions (evaluate using BETWEEN ‘770’ and ‘779.99’).  The 760 series reviews developmental conditions related to the development period, but not the development process, such as fetal exposure to alcohol or drugs, factors experienced by the fetus but related mostly to maternal related problems, large baby.  Gestational events are evaluated by the 764-766 series; size, growth rate and malnutrition are partially evaluated with these values.  767 refers to birthing process trauma, indicators of PCP skills and maternal participation activities.  Post-birth respiratory problems are indicated by the 768 to 770 series, with respiratory distress syndrome and meconium aspiration its most important features.  The 771 series refer to infectious diseases.  772 refers to hemorrhagic conditions, blood endocrine dyscrasias, isohemolytic disorders, jaundice related conditions, and hematological disorders.  Internal organs and integument problems result in 777 and 778 series diagnoses.  Other generally more severe problems are noted in the 779 series.

The 630 to 679 series refer to pregnancy, childbirth and puerperium related diagnoses.  632 is ectopic pregnancy.  636 is illegal abortion, 638-failed abortion.  Risk of early termination (640-641) may not related to human behavior induced risks.  Hypertension leads to the 642 series of complications.  643 refers to pregnancy related vomiting.  The series 644 to 647 are high risk indicators (BETWEEN 644 and 647.99, but see full list for details and decimal additions): 644 refers to premature delivery, 645 systems problems perhaps chronic disease related, 647 and 648 refer to infectious disease rates and congenital development related problems.  649.0 is tobacco use during pregnancy; 649.1 is for obesity as a complication.

The 650-655 series may not relate to risk assessment processes, because they are “natural” and unpredictable complications that develop, for the most part.  656 refers to mother-fetus (Rh) complications.  ICDs ranging from polyhydramnios (657) to abnormal fetal heart conditions (659.7) are useful for risk assessments (BETWEEN 657 and 659).  660-669 are labor related complications.  670 to 676, and 678 to 679 are also complications that may not be preventable.

Note:  Any Role for Neonatal thrombocytopenia (287.4, *)?


December 24, 2016 Updates


Immunization and Immunizable Disease patterns

Research Question: Are there pockets in the urban setting where certain groups of people demonstrate lower immunization completion rates? Do these settings also demonstrate a growing incidence of immunizable diseases?

The ICDs for immunizable diseases are employed to test the historical distribution of people with a history of those diseases.  Since the location of the individual at the time of the disease may not be known, a focus on the youngest kids is required, and a review of the V codes related to failure to engage in this preventive care process (the V64 series).  Other V codes worth reviewing as well are those related to immunizable disease history, like development problems related to these diseases, congenital onset of disease by infected parent, or later age onset of diagnosis due to lack of vaccine history and/or history of re-emergence of that disease by the patient.