Impact of response

time on outcomes of

infectious disease

outbreaks in

developing countriesCapstone Dissertation, Master in Public Health

Stefano Malvolti, Johns Hopkins Bloomberg School of Public Health

The recent Ebola crisis stresses the

importance of timely response to outbreaks

Source: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6435a6.htm

This work tests some hypotheses on

outbreak response timing

Outcome of infectious disease outbreaks (measured as

number of cases) is influenced by timeliness of response

Different diseases triggers different response times thus making the impact

of different response times more or less relevant.

High reproduction numbers and long incubation period may make the

timeliness of detection and response more critical (a generalisation of the

prior hypothesis)

Stronger health systems and wealthier countries can afford a slight delay in

response since that answer to the health threat will be more effective and

thus likely to lead quickly to control of the outbreak under

Study sample built out of WHO Global

Alert Response database

GAR Database

2295 entries

Single Outbreaks

658

Relevant Single

Outbreaks

420

Outbreaks

Included

171

Combination of

multiple entries

In Endemic Areas

120

In Developed

Countries 67

Single Entry 67

Ongoing 16

Special Cases 1

Non infectious

diseases 5

Not Sufficient Info

229Single Case 20

ide

ntific

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ree

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Elig

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n

Basic descriptive and univariate statistics

applied to elicit valuable insights

Mean

Median

Correlation Coefficient between delay and cases

Chi-squared Test on transformed categorical distribution

Stratification (cut off points): by disease, reproduction number, Health

Systems strength (physicians/10k inhabitants), wealth (GNI / capita),

incubation period

Transformation: from continuous into categorical with two values based on cut off points (by disease, R0 = 3, HSS = 0.3 physicians/10k inhabitants,

GNI/capita =1800$, incubation = 7 days)

Type and quantity of data trigger

biases and limitation

Absence of many outbreaks into GAR database – selection biases

Secondary data sources - no categorise sources in term of quality and reliability (15% peer reviewed articles, 15% of data from US CDC or WHO) information biases

Exclusion based on Incomplete information related to several outbreaks -selection biases

Use of different sources for different data point for the same outbreak –analytical biases

Lack of transparency concerning the adherence of the various data sources to similar definitions for most of the parameters (e.g. index case or date of notification) - misclassification biases.

Index

Case

Notification

Date

Response

DateLast Case

Number of

Cases

Number of

Deaths

Data distribution and split among variables

reflect specific nature of the dataset

Average response time in developing

world is a major cause of concern

Average response time is 60.4 days

(2 months)

The fastest 25% responses took

approximately 3 weeks (22 days)

The slowest 25% responses took 3

months (89 days)

Up to a maximum of almost 9

months (259 days)

Response time has not improved over

time, another reason for concern

OVERVIEW

All Cholera Mening YF

# 171 37 34 30

LD - LC 51% 59% 50% 40%

LD - HC 11% 16% 12% 3%

All LD 63% 76% 62% 43%

HD - LC 30% 11% 26% 33%

HD - HC 8% 14% 12% 23%

DELAY

MEAN 60.4 38.1 63.0 75.9

STD DEV 49.8 46.2 33.9 44.2

MIN 3 3 15 9

1st Quartile 22 12 34.75 42.5

MEDIAN 49.0 16.0 57.0 69.5

3rd Quartile 89 48 91.5 110.25

MAX 259 193 142 165

2

5

Better performance of wealthier and strong

health systems countries confirmed

OVERVIEW

#

LD - LC

LD - HC

All LD

HD - LC

HD - HC

DELAY

MEAN

STD DEV

MIN

1st Quartile

MEDIAN

3rd Quartile

MAX

below 0.3 above 0.3 under 1800 above 1800

Weak HS Strong HS Low GNI High GNI

135 36 130 41

45% 75% 48% 61%

13% 3% 14% 2%

59% 78% 62% 63%

33% 17% 29% 32%

8% 6% 9% 5%

64.1 46.5 61.5 44.0

49.6 48.7 61.5 83.0

3 3 3 13.00

25 12 24 16

52.0 26.5 49.0 28.0

92.5 57.25 90 39

259 175 259 141

1

87

Substantial differences in response time

exist between different diseases

Cholera Meningitis Yellow Fever

Delay in response influences number of

cases but other variables play a role

Yellow Fever outbreak response

requires much longer time

Substantial difference in response time between diseases

Much longer delays in Yellow Fever response compared to the other

diseases.

Progressive reduction of focus as result of availability of vaccines for

diseases with higher mortality (e.g. Pneumococcal and Rotavirus vaccines)

may have played a role

More limited spread of disease and much smaller number of average cases

per outbreak may be perceived as less threatening by health authorities

and political decision makers.

Relevant gaps in availability and

quality of outbreak response data

Absence of a quality and systematic global cross-disease source of data

for outbreak

WHO’s Global Alert Response database where country-reported outbreaks

are meant to be recorded and updated includes only a limited number of

outbreaks and for the one included

key data are often missing,

final updates on the outcome of the outbreaks are almost never recorded,

output from other works (e.g. published papers or reports from other

implementing agencies) are not captured

overall quality of the data can be greatly improved (more updated or different

data can be found not infrequently in other validated sources).

More work to do!

Limited number of data point, quality limitations and

limited significance of the analysis hinder ability to draw

conclusions that by clearly identifying drivers of the problems provides compelling argument for change

extend and further validate the analysis than discuss

emerging insights

Absence of a solid complete and reliable source of

information greatly penalise future efforts aimed at

improving the understanding of the outbreaks and the best way of addressing them consider the creation of

a global outbreak database