Bryn Mawr 2008

ITHW, Inc.ITHW, Inc.Innovative Technologies in Innovative Technologies in

Health and WellnessHealth and Wellness

David S. Lester, Ph.D. President, ITHW, Inc.

Executive VP, Gene Express, Inc.

October, 2008

A Systems Approach to Identifying Technology

Interventions Based on Patient-Centered Outcomes

REPORT CARDREPORT CARDHow well are we doing?How well are we doing?

“Well, at least until last night. In the past, as I have gotten more fit, my insulin needs would decrease fairly quickly. Well, 3 weeks into my new exercise regimen, and I'm already starting to see the difference. Especially on the day I work out with my trainer. Which was yesterday. I thought I had it all figured out. Hm. Not so much, it turns out. I ate before, tested afterward, ate a snack, went low anyway, covered the low, rebounded, went too high, made the mistake of correcting the high and wham, it's the middle of the night and I'm jolted from a dream, wicked low, pounding headache, sweaty all over, grasping for food. This was a hard core low. Really intense, really physical”

Blog sourceBlog sourceITHW, Inc.ITHW, Inc.

Innovative Technologies in Innovative Technologies in Health and WellnessHealth and Wellness

How well are we doing?How well are we doing?

“….. the episode turned out to be better than it sounds, but still is prompting an appointment with an endocrinologist to address my struggling control of my blood sugars of late. The worst part though was showing up at Flower Festival the next morning looking like I'd been beaten up in a bar fight.”

Blog sourceBlog source

• “By the way, few meters accurately measure very low readings. ……… I wish there was a consistent pattern to this, but there isn't .... one reason why living with DM is such a headache!”



Healthcare CommentHealthcare Comment

“ People have craved a different sort of experience. People are willing to pay for individualized care and individualized wellness.”

Eric Hedges, Duke Executive Health Program. (HealthlLeaders, August 2007, p.15)



The Key Is Identifying and Integrating Patient-Centric The Key Is Identifying and Integrating Patient-Centric Technology Strategies to Prove Value to Multiple StakeholdersTechnology Strategies to Prove Value to Multiple Stakeholders

Payers

Suppliers

Employers

Providers

Patients

Regulators

Caregivers

The Perception of Value Depends on The Perception of Value Depends on Stakeholder PerspectiveStakeholder Perspective

Pharma

ITHW, Inc.ITHW, Inc.Innovative Innovative

Technologies in Health Technologies in Health and Wellnessand Wellness

Regulators

Pharmaceutical Companies

Payers

Providers

Suppliers

Caregivers

Device Manufacturers

Employers

The Healthcare System of Today:The Healthcare System of Today:Which iPod do I trust?Which iPod do I trust?



The Patient of Today – The iPod:The Patient of Today – The iPod:What accessory do I choose?What accessory do I choose?

Eastern Medicines/Treatments

Generics

Pharmaceuticals

Supplements

Regulated Devices

Non-RegulatedDevices

NutraceuticalsPhysicalActivities



Developing Patient-Centric Technology Developing Patient-Centric Technology Strategies Strategies

Adding Value by Optimizing Key Points of Patient ImpactAdding Value by Optimizing Key Points of Patient Impact

Expanding Opportunities Expanding Opportunities Across the Cycle of Patient CareAcross the Cycle of Patient Care

DiagnosisDiagnosisDiagnosisDiagnosis InterventionIntervention AdherenceAdherence ControlControlControlControl

ImprovedImprovedOutcomesOutcomes

Redefining Redefining & Identifying& IdentifyingDiseases for Diseases for

Product Product DevelopmentDevelopment

Redefining Redefining Performance Performance & Execution & Execution of Clinical of Clinical

ValueValue

Product Product Superiority/Superiority/InferiorityInferiority

NovelNovelInformationInformation

Individualized Individualized MonitoringMonitoringPricingPricing



Depression*

Stroke*

Dementia*

Alzheimer’s disease*

Endocrine disorders

Foot ulceration

Respiratory distress

Hypertension*

Atherosclerosis*

Hyper-/Hypoglycemia*

Hyper-/Hypoinsulinemia*

Dyslipidemia/ Hypercholesterolemia*

Metabolic syndrome*

Neuropathy*

Compressed nerves*

Immune suppression

Focal neuropathy

Intermittent claudication

Bone disorders

Cancer

Gangrene

Nephropathy*

Obesity*

Our Proof-of-Principle (PoP) model focuses on integrating diabetes with Our Proof-of-Principle (PoP) model focuses on integrating diabetes with its associated complications and comorbidities (C&Cs) to better inform its associated complications and comorbidities (C&Cs) to better inform how Pfizer and its peers can improve patient healthhow Pfizer and its peers can improve patient health

• Diabetes has a profound impact beyond the disease itself

• Its comorbidities coincide with many Pfizer priorities

Retinopathy

Diabetic Macular Edema*

Vitreous hemorrhage

Glaucoma*

Cataracts

Coronary heart disease*

Myocardial Infarction

Angina

Sudden death

Heart failure*

Transient ischemic attacks

Erectile dysfunction*

Retrograde ejaculation

Decreased vaginal lubrication*

Neurogenic bladder

Urinary Tract Infections

Gastroparesis

Diabetes and its complications account for approximately 15% of total healthcare expenditures even though diabetes is present in only

approximately 6% of the population

Project Rationale & Goals . . .



Systems Dynamics Systems Dynamics Modeling of the Diabetic Modeling of the Diabetic

Patient OutcomesPatient Outcomes



We are utilizing a proven method that enables us to We are utilizing a proven method that enables us to integrate a wide range of dynamics and stakeholdersintegrate a wide range of dynamics and stakeholders

Introduction to System Dynamics . . .



MIT research shows that beyond three interacting MIT research shows that beyond three interacting feedback loops, intuition and conventional analysis break feedback loops, intuition and conventional analysis break downdown

??

??Debt & Equity

Earnings

PassengersFlown

Revenue(Unit Sales)

PhysicalCapacity

ServiceCapacity

ProductAttractiveness

ServiceQuality

Customers

Ability To Raise Capital Ability to

Attract & Hire

ShareholderValue

Employees

Cause-effect relationships close in on themselves to form feedback loops – interacting feedback loops generate performance over time




The complexity of diabetes and its C&Cs is The complexity of diabetes and its C&Cs is reflected in their extensive interacting feedback reflected in their extensive interacting feedback loopsloops

O b e s i t y

D i a b e t e s

D y s l i p i d e m i a

A t h e r o s c l e r o s i s

H y p e r t e n s i o n

S t r o k e

C H D

R e t i n o p a t h y

N e p h r o p a t h y

N e u r o p a t h y

D e p r e s s i o n

Atherosclerosis

Obesity

Stroke

CHD

Diabetes

DepressionDyslipidemia

Retinopathy

Neuropathy

Nephropathy

Hypertension


ITHW, Inc.ITHW, Inc.Innovative Technologies Innovative Technologies in Health and Wellnessin Health and Wellness

Our second step was to organize diabetes and its Our second step was to organize diabetes and its complications and comorbidities into ten groups for complications and comorbidities into ten groups for the PoP effortthe PoP effort

Fasting Plasma Glucose (FPG) levels at presentation

Type 2 Diabetes

DiabeticFPG 126-299 mg/dL

Non-diabeticFPG <100 mg/dL

Pre-diabeticFPG 100-125 mg/dL

Sever

e Sta

te

Moder

ate

State

Non-sta

te

Pre-s

tate

Complication or comorbiditySpecific classification index

ObesityCoronary Heart Disease

StrokeAtherosclerosis

DyslipidemiaHypertension

DepressionNephropathy

Neuropathy

Adult 45-64Adult 20-44

Adult 65+

Retinopathy

When we distinguish three age groups, the number of groups triples from 120 potential patient pools to 360 potential patient pools

Clinical landscape inventory . . .


Our second step was to cluster related technologies and Our second step was to cluster related technologies and determine a manageable number for the PoPdetermine a manageable number for the PoP

Identifi-cation of existing diabetes

tech-nologies

Grouped according to technologies’

function in diabetes care

– Technologies grouped according to mode of administration or mechanism of action whenever feasible

Examples: All non-invasive glucose monitoring devices, all emerging oral insulins

– The clustering enabled us to identify the most highly-leveraged and synergistic technology categories for this PoP effort

– We focused on diabetes technologies only for the PoP (C&C technologies were excluded)

– Sources of information included Pharmaprojects, company and patient group websites, industry reports and journal reviews

– Only emerging technologies with a predicated 2-7 year launch were included based upon clinical trial phase and Technology Readiness Levels (technologies ranked on a scale to assess maturity of evolving technologies)

Identification of emerging

diabetes technologies

Characterization according to clinical trial

phase, technology

readiness level, and launch date

Technology landscape inventory . . .



Example of a complication of diabetes: Example of a complication of diabetes: NephropathyNephropathy

Demographics/Epidemiology

Contributing factors

Diagnostic

Disease State Classification*Downstream

OutcomesSample Outcome

Approximately 25-50 % of Type II DM patients will develop kidney disease, although do not present with symptoms until 5-10 years post onset of disease. Patients from an Asian or Afro-Caribbean origin are twice as likely to develop diabetic kidney disease. Diabetic nephrology accounts for approximately 40% of all cases of new end stage renal disease (ESRD).

Hypertension, Atherosclerosis, Neuropathy.

Severity of condition depends upon comorbidities of patient.

Hyperglycemia and exposure to a high protein diet are important risks for development of proteinuria.

Albumin (urine sample, first passing of day), creatinine (blood sample)

Microalbumin-uria (marker of development of nephrology) -albumin levels over 30mg in 24h.

Macroalbumin-uria (marker for progression to Stage V: ESRD)- albumin levels above 300mg.

Serum creatinine levels outside normal range 0.8-1.3mg/dL indicates major kidney functional loss.

Assessment of findings provide clues to stage of renal disease:

Stage I: Time of diagnosis. Kidney size is increased. Glomerular Filtration Rate (GFR) is >90ml/min/1.73m2. Reversible by blood glucose control.

Stage II: 2-3 yrs post-diagnosis. Glomerular basement membrane thickens and decline in renal function initiated. Scar tissue formation occurs. GFR 60-89ml/min/1.73m2

Stage III: 7-15 yrs post-diagnosis. Microalbuminuria first appears. Glomerular damage has progressed and hypertension may be present. Patients are asymptomatic. GFR 30-59ml/min/1.73m2

Stage IV: Overt, or dipstick positive, diabetes. Almost all patients have hypertension. Suboptimal glucose control. GFR 15-29ml/min/1.73m2

Stage V: ESRD; GFR <15ml/min/1.73m2 Renal replacement required.

Coronary heart disease (due to macroalbu-minuria), Kidney failure

Ne

ph

rop

ath

y

* National Kidney Foundation/Kidney Disease Outcome Quality Initiative (NKF/KDOQI) classification system

Co

mp

lic

ati

on

/Co

mo

rbid

ity

Clinical landscape inventory . . .

Further details are provided in the supporting C&Cs AppendixITHW, Inc.ITHW, Inc.

Innovative Technologies Innovative Technologies in Health and Wellnessin Health and Wellness

P r e D M P o p u l a t i o n D M P o p u l a t i o n

P r e D M t o D M P r o g r e s s i o n

I m p a c t o f

C n C s o n

C o n t r o l o f

g l y c e m i c

l e v e l s

B a s e l i n e

T i m e f o r

P r o g r e s s i o n

I m p a c t o f

C n C s o n D M

I n t e r v e n t i o n

A d h e r e n c e

A d h e r e n c e

t o D M


B a s e l i n e

A d h e r e n c e

t o D M


C o n t r o l o f

G l y c e m i c

L e v e l s i n

t r e a t e d

p a t i e n t s

D M

D i a g n o s i s

C o v e r a g e o f

P o p u l a t i o n

D M i m p r o v e m e n t t o P r e D M


C o v e r a g e o f

P r e D M

p o p u l a t i o nB a s e l i n e

C o v e r a g e o f

D M

D i a g n o s i s

E f f e c t o f

G l y c C o n t r o l

o n D M


P r e D M d e a t h r a t e

A g g r e g a t e

s e v e r i t i e s o f

C n C

N o n D M t o P r e D M P r o g r e s s i o n

N o n D M P o p u l a t i o n

R a t e o f j o i n i n g a d u l t p o p u l a t i o n

P r e D M i m p r o v e m e n t t o N o n D M

N o n D M d e a t h r a t eD M d e a t h r a t e

B a s e l i n e


i n

P o p u l a t i o n

y

C o n t r o l o f

G l y c e m i c

L e v s i n

u n t r e a t e d

p a t i e n t s

G l y c e m i c

L e v e l C o n t r o l

i n T o t a l

P o p u l a t i o n

E f f e c t o f D M

o n Q o L

Uniform ‘care cycles’ are built onto the backbone Uniform ‘care cycles’ are built onto the backbone to reflect the relationships among patient care to reflect the relationships among patient care variablesvariables

We have included structure related to:

• Undiagnosed disease

• Access to care (intervention)

• Adherence to care

• Efficacy of care (control)

Generic care cycle:

Model structure . . .

Progression

Improvement

Backbone:


Technologies in Technologies in Health and WellnessHealth and Wellness



I m p a c t o f

C n C s o n

C o n t r o l o f

g l y c e m i c

l e v e l s

B a s e l i n e

T i m e f o r


I m p a c t o f

C n C s o n D M


A d h e r e n c e

A d h e r e n c e

t o D M


B a s e l i n e

A d h e r e n c e

t o D M


C o n t r o l o f

G l y c e m i c

L e v e l s i n

t r e a t e d

p a t i e n t s

D M

D i a g n o s i s

C o v e r a g e o f

P o p u l a t i o n



C o v e r a g e o f

P r e D M


C o v e r a g e o f

D M

D i a g n o s i s

E f f e c t o f


o n D M



A g g r e g a t e


C n C






B a s e l i n e


i n

P o p u l a t i o n

y

C o n t r o l o f

G l y c e m i c

L e v s i n

u n t r e a t e d

p a t i e n t s

G l y c e m i c


i n T o t a l

P o p u l a t i o n

E f f e c t o f D M

o n Q o L

Generic care cycle

The standardization of the impact of health care system variables in the The standardization of the impact of health care system variables in the model enabled consistent use of technologies related to the care cyclemodel enabled consistent use of technologies related to the care cycle

Non-intervention

2 Non-diagnosis

1Non-adherence

3Non-control of condition

4

*The language of these parameters is established such that a reduction in the parameter is always beneficial

to the population:

“up is bad, down is good”


1. Non-diagnosis: the fraction of people with a condition who have not yet been diagnosed

2. Non-intervention: the fraction of diagnosed patients who do not start and/or continue their care plan due to a variety of reasons related to access to care including reimbursement issues, lack of insurance, ‘physical’ access to care, providers not adequately following practice guidelines, etc.

3. Non-adherence: the fraction of diagnosed patients who do not adhere to their care plan, thus falling short of achieving its full intended benefit

4. Non-control: the fraction of diagnosed patients who receive and adhere to their care plan without achieving the efficacy benefit according to the technologies ‘label’




I m p a c t o f

C n C s o n

C o n t r o l o f

g l y c e m i c

l e v e l s

B a s e l i n e

T i m e f o r


I m p a c t o f

C n C s o n D M


A d h e r e n c e

A d h e r e n c e

t o D M


B a s e l i n e

A d h e r e n c e

t o D M


C o n t r o l o f

G l y c e m i c

L e v e l s i n

t r e a t e d

p a t i e n t s

D M

D i a g n o s i s

C o v e r a g e o f

P o p u l a t i o n



C o v e r a g e o f

P r e D M


C o v e r a g e o f

D M

D i a g n o s i s

E f f e c t o f


o n D M



A g g r e g a t e


C n C






B a s e l i n e


i n

P o p u l a t i o n

y

C o n t r o l o f

G l y c e m i c

L e v s i n

u n t r e a t e d

p a t i e n t s

G l y c e m i c


i n T o t a l

P o p u l a t i o n

E f f e c t o f D M

o n Q o L

Generic care cycle

We also included the structure for three We also included the structure for three performance measures (“simulation outcomes”)performance measures (“simulation outcomes”)

1. Non-diagnosis: the fraction of people with a condition who have not yet been diagnosed

2. Non-intervention: the fraction of diagnosed patients who do not start and/or continue their care plan due to a variety of reasons related to access to care including reimbursement issues, lack of insurance, ‘physical’ access to care, providers not adequately following practice guidelines, etc.

3. Non-adherence: the fraction of diagnosed patients who do not adhere to their care plan, thus falling short of achieving its full intended benefit

4. Non-control: the fraction of diagnosed patients who receive and adhere to their care plan without achieving the efficacy benefit according to the technologies ‘label’

5. Progression/improvement: the movement from one disease severity to another

6. Mortality: death due to diabetes or its C&Cs

7. Non-Quality of Life (Non-QoL): eg, bad days, as measured by validated instruments for measuring quality of life (out of scope for Phase 1b)

Non-intervention

2 Non-diagnosis

1Non-adherence

3Non-control of condition

4

Progression /Improvement

5Mortality

6

NQoL7


*The language of these parameters is established such that a reduction in the parameter is always beneficial

to the population:

“up is bad, down is good”ITHW, Inc.ITHW, Inc.

Innovative Innovative Technologies in Technologies in

Health and Health and WellnessWellness

Addition of technology impact points for all the diabetes Addition of technology impact points for all the diabetes populations and one C&C (obesity) expands the populations and one C&C (obesity) expands the complexitycomplexity

NQOL, e.g. “bad days”

Non-intervention

Non-diagnosis

Non-adherence

Noncontrolof condition

Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality

OBESITY

DIABETES


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality


Non-intervention

Non-diagnosis

Non-adherence


Progression

Mortality




The complexity is further enriched when the other The complexity is further enriched when the other C&Cs are addedC&Cs are added

CVD - STROKE

RETINOPATHY

NEPHROPATHY

DEPRESSION

NEUROPATHY

CVD - CHD

ATHEROSCLEROSIS

DYSLIPIDEMIA

HYPERTENSION

CVD - STROKE

RETINOPATHY

NEPHROPATHY

DEPRESSION

NEUROPATHY

CVD - CHD

ATHEROSCLEROSIS

DYSLIPIDEMIA

HYPERTENSION

CVD - STROKE

RETINOPATHY

NEPHROPATHY

DEPRESSION

NEUROPATHY

CVD - CHD

ATHEROSCLEROSIS

DYSLIPIDEMIA

HYPERTENSION




We quantified a total of 50 Diabetes technologies in the proof-We quantified a total of 50 Diabetes technologies in the proof-of-principle phaseof-principle phase

6 types of Diagnostic Tests

5 types of Glucose Monitoring Devices

3 types of Insulin Types

5 types of Insulin Delivery Methods

8 types of Oral Hyperglycemic Agents

1 related to Lifestyle Changes

2 related to Overall Management

Existing Diabetes Technology Categories

3 types of Continuous Glucose Monitoring Devices

4 types of Insulin Delivery Methods

10 types of Hyperglycemic Agents

1 related to Cellular Therapies

1 related to Telemedicine Technologies

1 related to Electronic Health Records

Emerging Diabetes Technology Categories

30 Existing Diabetes Technologies

20 Emerging Diabetes Technologies

* We have identified an additional 29 technology categories with market impact beyond 7years (out of the scope of Phase 1b)

Model quantification . . .

Further details are provided in the supporting Technologies AppendixITHW, Inc.ITHW, Inc.

Innovative Technologies Innovative Technologies in Health and Wellnessin Health and Wellness

100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000

LngActIns & NnInvDev

DisMgmtEx & NnInvDev

InhlBcl & NnInvDev

DisMgmtEx & LngActIns

InhlBcl & LngActIns

DisMgmtEx & InhlBcl

GIDiet & NnInvDev

GIDiet & LngActIns

GIDiet & DisMgmtEx

GIDiet & InhlBcl

IncrtnMmtcs & NnInvDev

Sulfonyl & NnInvDev

IncrtnMmtcs & LngActIns

DisMgmtEx & IncrtnMmtcs

IncrtnMmtcs & InhlBcl

DisMgmtEx & Sulfonyl

Sulfonyl & LngActIns

Sulfonyl & InhlBcl

GIDiet & IncrtnMmtcs

GIDiet & Sulfonyl

IncrtnMmtcs & Sulfonyl

Sum of Individual Deaths Averted

Combined Deaths Averted

The difference is the impact of synergistic effects.

The technologies have a broad range of effects on deaths and retinopathy The technologies have a broad range of effects on deaths and retinopathy cases averted, but there is little synergy among tested DM TLs. cases averted, but there is little synergy among tested DM TLs.

100,000 200,000 300,000 400,000 500,000 600,000

RpdActgInsOrlIns

TrnsdrmInsIntrnasIns

EmrgNnInvDevCellrImplntn

NnInvDevSurgImplntMntr

OthrInsEmrgSmInvDev

SbcutPmpElctrBldGluLgs

SmInvDevLngActIns

DisMgmtExIntActgIns

TyrPhsphtseInhbtrGPrtnCpldRcptrAgnFruBiphsphtseInhbtrChmknRcptrAntagn

InhlBclEmrEx

IonChanAntagnstTelemedEx

AmylnSynthDrvtvsGFAgnst

GIDietPptdHrmnThrpy

GLPDPheDrvtvs

BiguanMeglit

DPPIVAlphGlcsdsInhbtrs

PPARgAgnstThiazol

IncrtnMmtcsSulfonyl

AdherImpEx

Technologies which impact the largest fraction of the population

have a higher impact e.g. adherence programs

Technologies which improve control AND adherence (through fewer adverse

side effects) also have a large impact e.g. PPARgAgnst

Deaths Averted from Single Technologies

Generally, the TLs with the most impact affect wider populations, and effect

populations behaviorally as well as medically

Which technologies have the biggest impact?

Reference Point: 500k deaths averted represents about 1% of the 50 million total deaths in the base case from 2008-2025.

ILLUSTRATIVE RESULTS ONLY

Deaths Averted from Pairs of Technologies

Metric = Deaths averted compared to base case from 2008-2025

Synergy between technologies can occur when they act on the same populations but on different impact points, increasing the impact of each individual technology.

Likewise, technologies can “cannibalize” the effects of one another if they act in the same places on the same population.

Not surprisingly, there is little synergy among the technlogy paris tested, as the act in the same few areas. However, these results are

illustrative of how synergies or cannibalization can be analyzed.

Model results . . .


Technologies in Health Technologies in Health and Wellnessand Wellness

When the simulation combines medical and epidemiological When the simulation combines medical and epidemiological facts, interesting potential insights emergefacts, interesting potential insights emerge

For each impact point, the difference between the base case and an ideal situation (ie, no progress, no control problems, total adherence) was reduced by 50%. Here only the most influential half of impact points are shown.

Technologies acting on which impact points would have the most influence*?

Metric = Deaths averted compared to base case from 2008-2025

Not surprisingly, progression has the biggest impact on mortality.

Due to the fact that even Pre-Diabeties significantly increases the risk of death in Older Adults, and that many pre diabetics become diabetics, preventing the onset of Pre-Diabeties has an even bigger impact than preventing the onset of full blown diabetes

300,000 600,000 900,000 1,200,000

Progression, Pre Diabetic, Younger Adult

Non-Adherence, Pre Diabetic, Older Adult

Non-Adherence, Diabetic, Middle Aged Adult

Diagnosis Non-Coverage, Diabetic, Older Adult

Non-Control, Pre Diabetic, Middle Aged Adult

Non-Control, Pre Diabetic, Younger Adult

Non-Control, Pre Diabetic, Older Adult

Non-Control, Diabetic, Middle Aged Adult

Non-Adherence, Diabetic, Older Adult

Progression, Non Diabetic, Younger Adult

Non-Control, Diabetic, Older Adult

Progression, Pre Diabetic, Middle Aged Adult

Progression, Non Diabetic, Middle Aged Adult

Progression, Pre Diabetic, Older Adult

Progression, Non Diabetic, Older Adult

Keeping Non-diabetics from progressing to Pre-diabetics in general has more of an impact than keep Pre-diabetics from progressing to Diabetes because of the relatively larger Non-diabetic population, because Pre-diabetics exhibit some of the problems Diabetics Exhibit, and cutting down on Pre-diabetic development ultimately also decreases diabetes.

Following Progression, Control and Adherence have the biggest impacts.

While Progression of Non-diabetics to Diabetics has a larger impact than the progression of Pre-Diabetics to Diabetics, glucose control of diabetics has a larger impact than control of Pre-diabetics.

Reference Point: 1 million deaths averted represents about 2% of the 50M total US deaths in the base case from 2008-2025.

ILLUSTRATIVE RESULTS

ONLY

Model results . . .

* “Progression, Non Diabetic, Older Adult” represents the progression of older, non-diabetic adults to older, pre-diabetic adults


Technologies in Technologies in Health and Health and WellnessWellness

Adding Additional Adding Additional Patient SubgroupsPatient Subgroups



Preliminary categorization has identified several high-priority Preliminary categorization has identified several high-priority sub-groups for incorporation into the modelsub-groups for incorporation into the model

Smokers Age GenderTime with

diabetes

Alcohol consumers

EthnicityGenetic pre-disposition

For how many C&Cs are the sub-groups relevant?

8 6 5 3 7 8 6

Is the prevalence of diabetes & its associated C&Cs altered among the different sub-groups of the classification?

Yes Yes Yes Yes Yes Yes Yes

Do differences exist in the Relative Risks (RRs) among the different sub-groups of the classification?

Yes Yes Yes Yes Unclear Unclear No

Suggested priority 1 1 1 1 2 2 3

Is a difference in technology impact among the different sub-groups of the classification likely to be observed?

Yes Yes Yes Yes Unlikely No Unlikely

Revised priority with consideration of the technology impact consideration

1 1 1 1 2 3 3

e.g., Particular obesity treatments are not suitable for

children

e.g., Treatment efficacy might be influenced by sex

hormones

e.g., An anti-smoking treatment will not affect non-

smokers

* For additional information, see Appendix A: Supporting details for sub-group classification ** Priority ranking: 1 = highest

1

2

Considerations . . .


Technologies in Technologies in Health and WellnessHealth and Wellness

While expanding further patient sub-groups adds granularity to While expanding further patient sub-groups adds granularity to the model, the increased complexity must be managedthe model, the increased complexity must be managed

ObesityCoronary Heart Disease

Stroke

Atherosclerosis

Dyslipidemia

Hypertension

DepressionNephropathy

NeuropathyRetinopathy

Diabetic

Non-diabetic

Pre-diabetic

Sever

e Sta

te

Moder

ate

State

Non-sta

te

Pre-s

tate


Adult 65+

Obesity

Coronary Heart Disease

Stroke

Atherosclerosis

Dyslipidemia

Hypertension

Depression

Nephropathy

Neuropathy

Retinopathy

Diabetic

Non-diabetic

Pre-diabetic

Sever

e Sta

te

Moder

ate

State

Non-sta

te

Pre-s

tate


Adult 65+

Adding only 2 further sub-group classifications across all C&Cs, each with 4 sub-groups, significantly increases complexity

4x3x3x10 = 360 “slices” in the model

4x3x3x10x(4x4) = 5760 “slices” in the model

PoP (v1.0) Model

PoP (v1.1) Model

Considerations . . .



Addition of Adherence Addition of Adherence FactorsFactors



A number of factors have been identified that influence an A number of factors have been identified that influence an individual’s adherence to his/her treatment program*individual’s adherence to his/her treatment program*

* Adapted from Vlasnik 2005 and expanded by PA

Feat

ures

of t

he D

isea

se

Severity Sid

e E

ffec

ts

Fears of undesirable side effects

Long term benefits vs. acute side effects

Fears of long term safety of drugs

Threat of mortality

Chronicity

Morbidity

Perception of risk

Misunderstanding of prescribing instructions

Frequent changes to drug regimens

Concern about taking drugs, incl. fear of addiction/dependency

Medication taste

Problems swallowing tablets

Difficulty in opening drug containers

Difficulty in handling small/large tabletsInability to distinguish colors or identifying markings on medications

Ease of administration of medication

Interactions with drug rehabilitationTrea

tmen

t Reg

imen

Multiple physicians or health care providers prescribing medications

Limited faith in the medication or the provider

Physicians providing clear explanations, encouragement, reassurance, and follow-up

Pro

vide

r

Insurance and reimbursement variables

Flexible clinic hours

Acc

ess

to H

ealth

care

Location/ Physical environment/ Transportation

Dependent care issues

Language/ communication barriers

Patient

Household/ family dynamics

Limited social or family support

Broader problems requiring assistance in the home

Support

Patient

Physical difficulties limiting access to or use of medication packaging

Denial of the illness or its significance/ anger about the illness

Burden of taking regular medication

Limited education about the illness or the need for medication

Past noncompliance with regimens

Reduction, disappearance, or fluctuation of symptoms

Age

Income/homelessness

Cost of medication

Inability to read written instructions

Forgetfulness or confusion

Concurrent substance abuse

Cultural/religious/political beliefs

Apathy

Mental status/depression/stress

Agoraphobia Incarceration

Adherence

Burden of food/meal interactions

Drivers of adherence . . .

ITHW, Inc.ITHW, Inc.Innovative Technologies in Health Innovative Technologies in Health

and Wellnessand Wellness

For the purpose of this illustration, we’ve simplified the many potential sub-For the purpose of this illustration, we’ve simplified the many potential sub-groups and drivers into an illustrative modelgroups and drivers into an illustrative model

Adherence

Risk of non-adherencedue to lack of access to care

BaselineAdherence

Risk of non-adherence

Risk of non-adherencedue to impact of

Risk of

due to complexity

Risk of non-adherencedue to lack of readiness

Distance toaccess

Time toaccess

Cost ofcoverage

e.g., premiums

Cost oftreatments

e.g., co-pays

Actualside

Availableeducation

on sideeffects

Number ofconcurrenttreatments

Frequencyof doses

Availabilityof emotional

support

Frequency ofpersonal

reminders

Risk of non-adherencedue to inconvenience of care

due to cost of care

of treatment regimen

side effects on lifestyle

of patients for treatment

Time demandof treatment

The population is divided into eight categories based on three classifications:

Insurance Status (covered v. not covered)

Discipline to follow treatment (high discipline v. low discipline)

Level of family and personal obligation (high responsibility v. low responsibility)

ILLUSTRATIVERESULTS

Identifying cause and effect . . .

effects

non-adherence



Adherence

Risk of non-adherencedue to lack of access to care

BaselineAdherence

Risk of non-adherence

Risk of non-adherencedue to impact of

Risk of

due to complexity

Risk of non-adherencedue to lack of readiness

Distance toaccess

Time toaccess

Cost ofcoverage

e.g., premiums

Cost oftreatments

e.g., co-pays

Actualside

Availableeducation

on sideeffects

Number ofconcurrenttreatments

Frequencyof doses

Availabilityof emotional

support

Frequency ofpersonal

reminders

Risk of non-adherencedue to inconvenience of care

due to cost of care

of treatment regimen

side effects on lifestyle

of patients for treatment

Time demandof treatment

ILLUSTRATIVERESULTS

Identifying cause and effect . . .

effects

non-adherence

The two sample technologies appeal to the different sub-groups because of the The two sample technologies appeal to the different sub-groups because of the interventions’ relative appeal on different driversinterventions’ relative appeal on different drivers

The diet pill significantly lowers the time demand of treatment, helping all groups but especially helping those with low discipline

The diet pill significantly lowers the time demand of treatment, helping all groups but especially helping those with low discipline

The high cost of the E-coach program discourages use among all groups, but especially those without insurance coverage

The high cost of the E-coach program discourages use among all groups, but especially those without insurance coverage

The E-coach also increases reminders and personal support, especially aiding those with low discipline

The E-coach also increases reminders and personal support, especially aiding those with low discipline

The side effect of fatigue from the diet pill is unpleasant to all, but increases the risk of non-adherence much more for those with high family responsibility

The side effect of fatigue from the diet pill is unpleasant to all, but increases the risk of non-adherence much more for those with high family responsibility

ITHW, Inc.ITHW, Inc.Innovative Technologies in Health Innovative Technologies in Health

and Wellnessand Wellness

Disease & Treatment Provider

Health care

system

Patient

Patient barriers to adherence, e.g.:

Patient limitations in medication knowledge

Patient unwillingness to change behavior

Physicians barriers to adherence, e.g.:

Failure to explain benefits and side effects

No consideration of cost or lifestyle of patient

These drivers of adherence interact in complex ways, as seen from the These drivers of adherence interact in complex ways, as seen from the perspective of patients, providers, treatments and the healthcare systemperspective of patients, providers, treatments and the healthcare system

Health care systems barriers to adherence, e.g.: Limited access to health care Use of restricted

formulary/switching to different formulary

Lack of continuity of care

Disease and treatment barriers to adherence, e.g.: High medication costs Unpleasant or unwanted side

effects

Drivers of adherence . . .



Total Population very closely matches historical estimatesTotal Population very closely matches historical estimates

Total Population by Age Group

1995.0 1996.8 1998.6 2000.4 2002.2 2004.020,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

Younger Adults

Middle Aged Adults

Older Adults

SimulationHistorical Data

Step #1: Historical Calibration…



THANK YOUTHANK YOUPartners:

PA ConsultingJoe Alexander, Pfizer Human Health Technologies



Bryn Mawr 2008

Health & Medicine

Transcript of Bryn Mawr 2008