It is now the first week of January 2013 and there are two big changes now happening in the health care information system.

The first change is taking place due to decisions made about the most appropriate use of medical data. Until recently HIPAA has dominated the fate of personal health information. We have been prevented from using personal health information to learn more about population health in general due to the fears that someone else might learn about an individual’s medical history, enough to cost that individual a job or further health insurance coverage. Several avenues for making appropriate use of medical data have arisen in recent months, resulting in ways to retain anonymity with health data and allow for this personal health information [PHI] to be applied to research in order to produce better programs and more reliable research outcomes, and ultimately result in improved population health and health insurance program performance.

The second change taking place is due to the new national health plan guidelines and programs being established, focused on managed care. Managed care is a nearly ten years old idea in health care management, fifteen or even twenty years if you include the courses and textbooks for programs on health care administration being circulated. Only recently has managed care received the attention of people and politicians seen today.

This behavior itself makes a statement about the kinds of behavior politicians and people engage in when it comes to health care insurance. In general, they didn’t pay much attention to new ideas until they become a necessity. By then, it is time for everyone else paying heed to this new subject to get on the bandwagon and become a part of the important health care changes taking place. At least this is how politicians, people who benefit from such a change, and those with some axe to grind might think. But such events also show us how people live and behave like herds of followers, being part of the present, not creators or inventors.

So how do these two major changes in the health care and the health care information system, big data and managed care, come together?

They come together in that big data can be used to monitor and take the necessary steps to improve overall population health and reduce the costs for healthcare that are continuing to increase in this country. Big data provides us with the opportunity to monitor small areas of people covered by local insurance programs, compare their health and healthcare events and activities with people from a much larger area, and determine more exactly where changes need to be made and how to make them to improve the health care system. Big data offers us the basic information we need to have in order to understand how populations work when it comes to disease prevention and health promotion behaviors. It is the statistics we obtain from these various ill people who are diagnosed along with the conditions of well behaved individuals that we are able for the first time to better understand the characteristics of a healthy population. It is no longer just probabilities and guess work that are applied to population health measurements in order to predict a person’s future. The statistics of population health at the national or large regional level provide us with the background information needed to uncover regional differences in health patterns, make better predictions about what actions may need to be taken in the future in order to prepare for certain high costs, and hopefully how to prolong life and quality of life as people get older, as the entire U.S. population in general becomes older.

This sharing of clinical performance and health outcomes also allows events that occur in one region of the country to be related to populations elsewhere in the country. This sharing can provide us with important insights into public health problems involving just the local populations. For the first time, we have the kind of data required to identify certain cultural rich areas for example and compare them with other similar culturally rich parts of this country, using this to determine if a medical problem or health care related behavior typical to one region might also be occurring elsewhere for similar reasons. We can even better understand basic human behavior traits using this method, such as learning about why certain people are skipping doctor appointments, ignoring the need for cancer screening, or habitually filling you prescriptions late in order to save a few bucks here and there. These human behaviors can be monitored locally, regionally and nationally, and then used to define where new clinics need to be built, or where better transportation programs need to be developed for people to see their doctors, or with what kind of community setting can the best patient-doctor relationship be developed.

Before there was managed care there were numerous other programs out there providing health insurance coverage. Each had its own nuances about whom you could see to receive care, what kind of care you were going to get, the types and brands,of medications you were allowed to take, and how much this was going to cost everybody involved. Very little of this practice has changed, with the exception that by placing a patient in a managed care program, there is a guarantee that the patient will be better monitored and his or her medical care experiences evaluated and taken into account as part of an annual review that will be generated for comparison with other health care programs out there. Even this last process isn’t that unique to managed care. The other forms of medical care delivered are also monitored and such. The only difference is that a well defined program is established for managed care with close monitoring within of patient attitudes and behaviors enough to place pressure on the patient to engage in his or her own health care as a personal responsibility, with a sense of teamwork in terms of relating to the overall program and its health care givers. Until managed care was around, agencies, insurers, and program administrators allowed patients to be lazy about following up on necessary visits, and health care, implementation and continuation processes. Now, if patients misbehave, that misbehavior is recognized, identified, and becomes part of the statistical measures being taken judging the overall performance of doctors and a health care facility and insurance program.

The kinds of improvements these two changes in the health care information technology field can undergo are:

A. To make better use of the big data world that is out there for evaluating national populations health and using these evaluations for regional and small area reviews.

B. To apply the above to the small business and small population settings in order to improve overall population health and provide better patient care.

The way to accomplish the two above goals is through the applications of geographic information systems and spatial analysis routines to each of these parts of the healthcare information system. The applications of new statistical processing to big data, because it is big data with need for such innovations insufficient on its own. A count of health features for millions of people is more impressive, but not necessarily more useful than data on the health of a half million people. To know the health of one-hundred million people may be impressive, but unless you employ that knowledge appropriately all you are doing is counting more people. This is like the people who,can see as far as Jupiter claiming some sort of seniority or higher level of importance versus those who can only see Venus. If those who can see Venus are more productive with their information and have a greater impact upon their Society, then they are the more important of the two groups in the long run.

In health care this may very well be the case. Big businesses may not be as inventive, creative and innovative as they like to portray themselves to be. Smaller health programs perhaps have a better likelihood for better results in health care, demonstrating better control of the flock so to speak. This is because big business and its mismanagement of big data has stalled any potential for development of better programs within the much larger settings that now exist in health care. For this reason, there are two methods promoted here regarding making better, more useful analytical use of big data, carried out as part of the smaller, more locally focused health insurance providers out there.

The way many businesses are going right,now is to implement some sort of GIS to their major programs. This adds a need for still more information, more manpower, and more skills. The other way to go is to improve upon just the knowledge base and manpower, producing a spatial analysis technique that doesn’t require the costly and time consuming GIS tasks. Both of these methods are presented on my site. In addition, I provide details about how to better analyze large population health data and use it to improve upon very local or small area programs, and how to engage in small population, small area health analysis in spite of lacking and use it successfully to design interventions without adequate large area data for use as comparisons. Finally, the issues and concerns about engaging in cultural studies is reviewed, in particular the methods we might use to identify culturally-distinct base populations.


Bring all of this up to date, it is December 2015, and the first year of a full fledged Obamacare is now almost over.

I am required to register on this plan, because my contracting agency offers it, an my wife, whom I would normally received health insurance from, is about to change her job.

Ironically, I am now in charge of monitoring the quality of care that a population of about 1.75M people in New York City are receiving.  Two thirds of these patients are on either a medicaid or medicare program.  One third are on an employee health plan, and I note that an exceptionally large number of these individuals were contracted self-employed workers, like the construction workers, maintenance and temp employees you pass by every day and wouldn’t even know that they are received mediocre health coverage.

But this is not about health coverage and the quality of that managed care plan.  Instead, this page is to provide details about what can be done to improve surveillance in a traditional healthcare plan.

The majority of health insurance companies only monitor population health on a per need basis.  They analyze what they have to report on.  They do not review the health of their patients proactively, like one might hope they’d do.

Instead, they are only you check casher and managed care overseers, monitoring the care you receive, saying little to nothing to improve the quality of care given to you, and even less regarding what you need to receive for health care.

Because insurance programs as so terribly retroactively managed–they base their needs for change and improvement of the results of last years’ activities, as part of their required annual HEDIS or Meaningful Use review, they don’t do anything to determine what you need.  They encourage the doctors to query into this instead, but provide no guidance.  For what little insurance companies actually do for our health, one has to wonder why in the hell do we agree to have them manage our money, tell us what we can pay or must pay.

Insurance companies are very bad at self-monitoring their performance, and their reporting can sometimes be faulty.  The most faulty thing companies do is misreport.  They selectively report on advancements or improvements, and bypass reporting on failures.  They may be successful for the last 2 years of something, and then fail.  The government overseers will judge their performance to be a failure, but they will call it “two out three good, one bad.”  They won’t tell you they failed the 3-year test needed by government to say your program has improved.

But these are the nitty gritty problems that insurance companies have a managed care program overseers.  What is really bad is the fact that they have an opportunity to fully manage care, yet they choose not to.  Instead, what they do is monitor a few things, then search for reasons and methods for positively reporting on those studies.  And what they report on is the same old stuff.  It has been for the past 20 to 30 years.

The insurance companies focus on asthma, diabetes, heart attack, stroke, rheumatoid arthritis, cancer, smoking, maybe drinking, usually not drugs.  They’ll look at other very popular topics, such as AIDs or HIV, osteoporosis, glaucoma, ageing and health.  Now, these are all very important population health topics.  But by focusing on the older diseases now for decades, they have pretty much ignored the uncommon to rare health disorders.  They have trashed to quality of care received or receivable by patients with MS, epilepsy,  anorexia nervosa, bipolarism, etc., so many diseases that a good quarter of the US population is underattended to by insurance companies.

To demonstrate this, I produced the following descriptions of projects I do, which took only a few weeks to develop and engage in.  These hundreds to thousands of metrics can very easily be done.  So why don’t insurance companies do this?

They do not do this for several major reasons, all of which have to do with neoinstitutionalism.

Insurance companies like most other big businesses have a problem with inexperienced, undertrained, untrained management, directors, vice presidents and officers.  Show me a company that has an officer that can routine run a logistic regression on his salary in relation to projects accomplished on an annual basis, and I’ll accept the fact that you do have at least one smart manager.  But the truth is, management in health insurance is not well educated enough to tell the differences in quality of care provided, or know the difference between primary and secondary Parkinsonism.

The following programs are research projects management could be and should be doing, but cannot.





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 urban setting?

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

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 borough, 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 urban area?  Are there adequate facilities and programs in place to meet their needs?  [i.e. Riverhead, Long Island–Childhood Chondromalacia clusters]

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 this region?

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 multiple sections of the urban setting?  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 New York 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.  The most famous community-cultural settings in the city region include communities rich in a cultural heritage, and language and mercantile behaviors closely linked to specific groups, such as “Little India”, Jamaica, Chinatown, Spanish Harlem, Black or African American Harlem, Hassidic Judaism, Russian-based Judaism (the Island), 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 local 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 American 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 sizable populations and vast amount of community, cultural-related services provided for complimentary healthcare practices within this region, provides the region 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.

  • 11 [these codes changed to partially conceal] – 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.
  • 40 – 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.
  • 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.
  • 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).
  • 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).
  • 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.
  • 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).
  • 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).
  • 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]
  • 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”).
  • 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.
  • 88 – Other specific dissociative (conversion) disorders. Examples: susto (Mexico). See F44.7 (pibloktoq, Inuit).
  • 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.
  • 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:

  • * Dementias
  • * Anxiety states
  • 02 Generalized Anxiety Disorder
  • 1 Dissociative Disorders
  • 11 Conversion Disorder
  • 13 Fugue
  • 14 Identity
  • 16 Factitious Disorder
  • 21-.22 Agoraphobia
  • 23 Social Phobia (301.7 Antisocial)
  • 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)
  • 46 Sudden Affective Disorder (SAD)
  • 308 Acute Reaction to Stress
  • 4 Emotion and Conduct Adjustment Disorders
  • 21 Separation Anxiety
  • 29 Culture Shock
  • 81 PTSD
  • * Adjustment Disorders
  • 1 Prolonged Depression
  • 1 Personality Change due to . . .






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 in this setting?

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.



Politically-based in-migration and Public Health

What are the unique stresses that the influx of Middle Eastern, NE African cultures might induce upon the given health care 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 large area 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 Kings 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 company’s 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 Long Island and the city, its 5 boroughs/counties, 8 networks, 11 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 a city?

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, *)?


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.