bogota workshop on health services pandemic preparedness dr. oscar j mújica; dpc/paho bogota;...

Bogota workshop on health services pandemic preparedness

Dr. Oscar J Mújica; DPC/PAHO

Bogota; COLOMBIAApril 19-21, 2006

Pandemic Impact Assessment

pandemic influenza pandemic influenza isis a real & serious a real & serious disease… disease…

Hien TT et al., New England J Med 2004;350:1179-1188

DAY 5 DAY 7 DAY 10


the modeling problemthe modeling problem

influenza pandemics in 20th Century: 1918; 1957; 1968

massive & notable impacts

experts predict another pandemic: can not reliable predict when

can not reliable predict how, who, where

plan for next pandemic: it is a global health priority

need numbers


PAHO/CDC pandemic impact assessment PAHO/CDC pandemic impact assessment workshopsworkshops

to estimate the burden of disease attributable to influenza pandemic: excess of deaths, hospitalizations, and outpatient visits

to evaluate the response capacity (i.e., the potential to cope with excess demand) from hospitals and outpatient health facilities (surge capacity)

to estimate the magnitude of the loss of workdays

to derive gross estimates of direct economic impact attributable to pandemic influenza


FluSoftware: FluAid, FluSurge & FluSoftware: FluAid, FluSurge & FluWorkLossFluWorkLoss


1957-58 influenza pandemic: geographic 1957-58 influenza pandemic: geographic spreadspread

02/57

04/57

06/57

05/57

06/57

07/57

08/57

06/57

07/57

C.W. Potter, Textbook of Influenza, 1998


resource availability for influenza pandemic:Physicians: 30%

Registered nurses: 40%hospital beds: 25%

availability of morgue slots (as a % of the total number of hospital beds): 5% 10% in Northern Americaduration of (first) pandemic wave: 8 weeksestimated rate of outpatient visits: 20 flu patients/caretaker/day

average hospital stay (bed occupancy due to flu): 1 weeks

minimum most likely maximum

high 0.126 0.220 7.650non-high 0.014 0.024 0.125

high 0.100 2.910 5.720non-high 0.025 0.037 0.090

high 2.760 4.195 5.630non-high 0.280 0.420 0.540

high 2.100 2.900 9.000non-high 0.200 0.500 2.900

high 0.830 2.990 5.140non-high 0.180 1.465 2.750

high 4.000 8.500 13.000non-high 1.500 2.250 3.000

high 289.0 346.0 403.0non-high 165.0 197.5 230.0

high 70.0 109.5 149.0non-high 40.0 62.5 85.0

high 79.0 104.5 130.0non-high 45.0 59.5 74.0

Risk Distribution (rates per 1,000 pop)

age group risk1968 pandemic scenario

Mortality

0 - 19

19 - 64

65+

Hospitalization

0 - 19

19 - 64

65+

Outpatient Visits

0 - 19

19 - 64

65+

FluSoft modeling data & assumptionsFluSoft modeling data & assumptions


default values: US national estimates; CDC's Advisory Committee on Immunization Practices, ACIP

lowest 15medium 25highest 35

clinical attack rate (%)

default LAC0 - 14 6.4 11.215-64 14.4 18.065+ 40.0 45.0

agegroupHigh-Risk Prevalence



other assumptions: employment & marriage rate

?01/19

03/1804/18

06/18

05/18

06/18

06/18

?

C.W. Potter, Textbook of Influenza, 1998

1918-19 influenza pandemic: geographic 1918-19 influenza pandemic: geographic spreadspread


1918 pandemic influenza case fatality rate

gender weighted average case fatality rates locales* 0 14 y.o. 15 64 y.o. 65+ y.o.

locales 1 1.3% 2.6% 4.4% locales 2 0.7% 1.3% 1.9% locales 3 0.9% 3.5% 6.4%

average 0.96% 2.46% 4.21%



Locales, all US: 1 (New London; Baltimore; Maryland); 2 (Macon; Spartanburg; San Antonio; Augusta; Des Moines; Little Rock; Louisville); 3 (San Francisco)

age group (years) distribution of cases

by age group (%)

0 14 años 47.6% 15 65 años 48.9%

65+ años 3.5%

Frost WH. Public Health Reports 1920;35:584-97



minimum most likely maximum

high 1.508 2.633 91.546non-high 0.168 0.287 1.496

high 0.886 25.780 50.675non-high 0.221 0.328 0.797

high 9.261 14.076 18.891non-high 0.940 1.409 1.812

Risk Distribution (rates per 1,000 pop)

age group risk1918 pandemic scenario

Hospitalization

0 - 19

19 - 64

65+

0-19 y.o. 11.9668119-64 y.o. 8.85917965+ y.o. 3.355454

1918 scaling factor

The scaling factor was obtained by comparing the calculated death rates, by age group, from estimates of death for the U.S. population

estimated pandemic impact in the worldestimated pandemic impact in the world

500 (23)1,253 (59)Ill, self care

Maximum (%)Minimum (%)

2,137 (100)2,136 (100)Totals

1,601 (75)875 (40)Very ill**

28.1 (1.3)6.4 (0.3)Seriously ill*

7.4 (0.3)2 (0.2)Deaths

Global totals (millions)Outcomes

500 (23)1,253 (59)Ill, self care

Maximum (%)Minimum (%)

2,137 (100)2,136 (100)Totals

1,601 (75)875 (40)Very ill**

28.1 (1.3)6.4 (0.3)Seriously ill*

7.4 (0.3)2 (0.2)Deaths

Global totals (millions)Outcomes

clinical attack rate = 35%; first pandemic wave (8 weeks)

* Ideally, be hospitalized ** Ideally, see a medical doctor Dr. M. Meltzer, CDC; personal communication


estimated pandemic impact in the USestimated pandemic impact in the US

1.9 million209,000Deaths

742,50064,875Mechanical ventilation

1.485 million128,750ICU care

9.9 million865,000Hospitalization

45 million45 millionOutpatient medical care

90 million90 millionIllness

Severe (1918-like)

Moderate 1958/68-likeCharacteristic

1.9 million209,000Deaths

742,50064,875Mechanical ventilation

1.485 million128,750ICU care

9.9 million865,000Hospitalization

45 million45 millionOutpatient medical care

90 million90 millionIllness

Severe (1918-like)

Moderate 1958/68-likeCharacteristic

Source: U.S. Dept Health and Human Services Pandemic Influenza Plan: Part 1. Page 18.Available at: http://www.dhhs.gov/pandemicflu/plan/pdf/part1.pdf


estimated potential impact in LACestimated potential impact in LAC

clinical attack rate = 25%; first pandemic wave (8 weeks)FluAid/FluSurge modeling – Pandemic Impact Subregional Workshops Nov/Dec 2005


1968 1918moderate severe

334,163 2,418,469[131,630 - 654,960] [627,367 - 5'401,035]

1,461,401 11'798,613[459,051 - 1'937,503] [3'189,747 - 16'418,254]

76,187,593 68'470,386[59'738,730 - 109'207,769] [58'114,124 - 92'227,761]

* annual average burden of mortality observed': 3'410,000 deaths

outpatient visits

probable health impact

pandemic scenario

deaths *

hospitalizations

estimated potential impact in LACestimated potential impact in LAC

pandemic scenario

health outcome range MexicoCentral

AmericaThe

CaribbeanAndean

AreaBrazil

Southern Cone

most likely 61,148 20,538 25,860 68,865 113,416 44,336minimum 22,721 7,454 11,048 25,367 44,075 20,965

maximum 122,131 43,207 49,136 138,721 220,044 81,721


maximum 362,724 130,059 144,357 412,889 648,852 238,622

most likely 14,552,192 5,503,786 5,479,122 16,717,177 25,202,794 8,732,522minimum 11,401,412 4,339,647 4,300,140 13,108,492 19,708,912 6,880,127

maximum 20,792,426 7,707,972 7,904,874 23,812,305 36,354,664 12,635,528


maximum 1,024,158 372,261 393,990 1,169,314 1,808,203 633,109

most likely 2,228,186 778,060 868,259 2,529,440 3,999,208 1,395,460minimum 595,520 213,389 238,688 677,652 1,067,437 397,061

maximum 3,122,762 1,152,739 1,190,685 3,573,889 5,472,072 1,906,107

most likely 13,094,642 4,911,408 4,990,981 15,060,771 22,594,485 7,818,098minimum 11,100,064 4,177,383 4,230,699 12,765,345 19,168,387 6,672,247

maximum 17,569,963 6,668,192 6,532,503 20,131,106 30,675,158 10,650,839

Disease Control & Prevention, DPC; PAHO/WHO, Washington DC 2005

deaths

hospitalizations

outpatient visits

deaths

hospitalizations

outpatient visits

1918

1968

Clinical Attack rate = 25%; first 8-week pandemic wave


Latin America & the Caribbean:Latin America & the Caribbean:distribution of hospital admission excessdistribution of hospital admission excess

(AT25%; scenario 1968; 1st pandemic wave)

0

10000

20000

30000

40000

50000

60000

70000

1 8 15 22 29 36 43 50

Days of outbreak

Dai

ly #

of a

dmis

sion

s


Latin America & the Caribbean:Latin America & the Caribbean:potential pandemic impact on health servicespotential pandemic impact on health services

(AT25%; scenario 1968; 1st pandemic wave)

1 2 3 4 5 6 7 8

weekly admissions 88,408 147,346 221,020 279,958 279,958 221,020 147,346 88,408

peak admissions/day 43,626 43,626

N° of influenza patients in hospital 40,138 66,897 100,345 127,104 129,608 108,611 81,515 52,813

% of hospital capacity needed 26% 43% 65% 82% 84% 70% 53% 34%

N° of influenza patients in ICU 13,261 28,123 43,187 57,046 61,737 60,057 47,722 32,952

% of ICU capacity needed 98% 209% 321% 423% 458% 446% 354% 245%

N° of influenza patients on ventilators 6,631 14,061 21,594 28,523 30,868 30,029 23,861 16,476

% usage of ventilator 344% 730% 1122% 1482% 1604% 1560% 1240% 856%

Hospital Admission

Hospital Capacity

ICU Capacity

Ventilator Capacity

weekPotential Impact on Hospital Surge Capacity


Latin America & the Caribbean:Latin America & the Caribbean:workdays loss attributable to influenza pandemicworkdays loss attributable to influenza pandemic

Work days lost; LAC scenario 196815% 25% 35%

most likely 309,023,424 515,039,041 721,054,657minimum 283,542,305 472,570,509 661,598,713maximum 344,182,797 573,637,995 803,093,194

Work days lost; LAC scenario 191815% 25% 35%

most likely 437,899,073 729,831,789 1,021,764,463minimum 339,780,057 566,299,961 792,820,047maximum 535,941,384 893,235,692 1,250,529,924


Latin America & the Caribbean:Latin America & the Caribbean:costs from workdays lost due to pandemic costs from workdays lost due to pandemic

influenzainfluenza

Costs of Work days lost ($ppp); LAC scenario 196815% 25% 35%

most likely 9,004,942,587 15,008,237,645 21,011,532,703minimum 8,262,422,778 13,770,704,630 19,278,986,482

maximum 10,029,486,711 16,715,811,186 23,402,135,660

Costs of Work days lost ($ppp); LAC scenario 191815% 25% 35%

most likely 12,760,378,982 21,267,298,343 29,774,216,459minimum 9,901,190,869 16,501,980,870 23,102,776,169

maximum 15,617,331,932 26,028,888,071 36,440,441,997


Latin America & the Caribbean:Latin America & the Caribbean:non-discounted value of human life lost due to non-discounted value of human life lost due to

influenza pandemic ($ppp)influenza pandemic ($ppp)

scenario 15% 25% 35%

1968-like 5,013,896,236 8,356,485,392 11,699,074,5471918-like 39,386,141,711 65,643,564,602 91,900,987,220

direct cost from excess hospitalization (ICU & non-ICU) and excess outpatient visits has not yet been summarized.


on estimations…on estimations…

they are PRELIMINARY, and EXPLORATORY

they are ILLUSTRATIVE

they are NOT PREDICTIONS of what inevitably may happen

they should serve as an AID IN PLANNING



Dr. Oscar J Mújica; DPC/PAHO

Bogota; COLOMBIAApril 19-21, 2006

a primer on transmission dynamics(or how pandemic influenza will move among us…)

what good are models…?what good are models…?

first, because life is full of choices, risks, uncertainty, and trade-offs

then, because we do need to make sound decisions in a setting of absence of absolute certainty (and we do need a rationale for them)

and then because sound decisions demand evidence, structure, consistency, and simplification:

models illustrate level of knowledge

models show how we think things are connected and happen

models add simplification = helps clarify

models can help identify what is “most important”

sensitivity analyses, confidence intervals

no single answer


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CASES

first case

outbreak detection & responseoutbreak detection & response


detection/report

laboratoryconfirmationresponse

opportunity for control

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opportunity for control

first case

detectionreport

laboratoryconfirmation response

timelytimely outbreak detection & response outbreak detection & response


pandemic spread potentialpandemic spread potential

0

1,000

2,000

3,000

4,000

5,000

case

s

1 2 3 4 5

time

What must happen at time 4 for this event to continue be seen as a public health problem?

Basically, an equal number of incident (new) cases must be generated in the population per unit time…

In other words, the ‘goal’ for each new case at time t is “to infect one” up to time t+1


to understand this, we need to build a to understand this, we need to build a model…model…

Why? –because we need evidence, structure, consistency, and simplification in order to make sound decisions…

)1(sin txyNxtd

xd

N = total populationX = number of susceptible persons (as a fraction of N)Y = number of infectivesµ = death rateΒ = transmission rateδβ = force of infectionω = angular frequency or oscillation in susceptibility

At any given moment in time, the fraction of susceptible persons in a population is dependent upon (a function of) the number of susceptible persons who die minus the number of susceptible persons who become infective. The latter fluctuates in a cyclical pattern.

Duncan et al. The dynamics of measles epidemics. Theoret Pop Bio 1997;52:155-163


pandemic spread potentialpandemic spread potential

k . β . D = 1

transmission efficiency

rate of infective contact

duration of infectivity

R0 =

R0 = basic reproductive rate of an epidemic


pandemic influenza: propagation pandemic influenza: propagation dynamicsdynamics

duration of infectivity (D, days)

2

3

65

4

7

0

10

20

30

40

50

60

70

80

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

transmission efficiency (B)

infe

ctiv

e c

onta

ct r

ate

(num

ber/

week)


so, let’s build a simple model…so, let’s build a simple model…

influenza pandemic propagation:

assume a basic reproductive rate (Ro) = 1.5

assume an average generation time (days) = 3.0

then repeat …

A B C

1

díanúmero de

casos nuevos

total acumulado

2 0 1 13 3 2 34 6 2 55 9 3 86 12 5 137 15 8 218 18 11 329 21 17 49

daynumber of new cases

cummulative total

multiply this number times 1.5 …

then place answer in cell bellow …

new cases are being

generated every 3 days…


pandemic spread according to our simple pandemic spread according to our simple model…model…

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0 3 6 9 12 15 18 21 24 27 30

days

case

s

incidence

cummulative incidence


epidemic propagation scenariosepidemic propagation scenarios

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1200

1 2 3 4 5 6 7 8 9 10 11 12

tiempo

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1 2 3 4 5 6 7 8 9 10 11 12

tiempo

0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 10 11 12

tiempo

R0 > 1

epidemic expansion

R0 = 1

epidemic equilibrium

R0 < 1

epidemic contraction

R0 = basic reproductive rate of an epidemic


epidemiological ‘goal’ for outbreak epidemiological ‘goal’ for outbreak containmentcontainment

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t

c

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

t

c

…it is imperative to quickly reduce R0 in situ (locally)


correlation of interventionscorrelation of interventionsfor pandemic containmentfor pandemic containment

personal protective equipment

isolation, quarantine &

social distance

transmission efficiency

rate of infective contact

duration of infectivity

antiviral prophylaxis and therapy

k . β . D = 1R0 =


pandemic influenza: potencial for pandemic influenza: potencial for containmentcontainment

duration of infectivity (D, days)

2

3

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7

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0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

transmission efficiency (B)

infe

ctiv

e c

onta

ct r

ate

(num

ber/

week)

Longini IM et al; Science 2005:309

AvT: antiviral therapy

Pv: pre-vaccination

AvCp: antiviral chemoprophylaxis

Q&I: quarantine & isolation

2

1

0

3

R0AvT1.6

Q&I + Pv + AvCp

2.4

Pv + AvCp2.10

10

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

t

c

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t

c


pandemic containment: evidence from pandemic containment: evidence from modelingmodeling

Longini IM et al; Science 2005:309


0

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

t

c


GTAP: Geographically Targeted Antiviral Profilaxis Longini IM et al; Science 2005:309


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t

c


Longini IM et al; Am J Epidemiol 2004:159


the most certain model…the most certain model…


time (weeks)

impact

disease burdenHS surge capacityeconomic & social

unprepared

prepared

as a way of conclusion…as a way of conclusion…

Plan, Plan, Plan…

Prepare, Prepare, Prepare…

Practice, Practice, Practice…

What to do…?, Take “home message”…?


bogota workshop on health services pandemic preparedness dr. oscar j mújica; dpc/paho bogota;...

Documents

potential pandemic impact

pandemic influenzaflusoftware

worldestimated pandemic

pandemic wavelatin america

estimated potential

laclatin america

dept health

health servicesat25