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Current State of Infectious Diseases in Southern Africa. Diana Dickinson. Overview. HIV epidemic) already dealt with, just a few personal TB ) insights Pneumococcus in detail Other regional problems Malaria Hepatitis B Herpes Simplex Cervical cancer associated with HPV

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Overview l.jpg
Overview

  • HIV epidemic) already dealt with, just a few personal

  • TB ) insights

  • Pneumococcus in detail

  • Other regional problems

    • Malaria

    • Hepatitis B

    • Herpes Simplex

    • Cervical cancer associated with HPV

    • KS associated with HHSV8

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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A global view of HIV infection of disease

38.6 million people [33.4‒46.0 million] living with HIV, 2005

HIV prevalence (%) in adults in Africa, 2005

2.4

2.5


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  • People living with HIV……….38.6 million of disease

    • Children 2.3

  • New HIV infections in 2005… 4.1 million

    • Children .54

  • Deaths due to AIDS in 2005.. 2.8 million

    • Children .38

    • NB 1/3 of all HIV deaths are in Southern Africa

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Estimated number of people living with HIV and adult HIV prevalence

Global HIV epidemic, 1990‒2005*

HIV epidemic in sub-Saharan Africa, 1985‒2005*

Number of people

living with HIV (millions)

% HIV prevalence,

adult (15‒49)

Number of people

living with HIV (millions)

% HIV prevalence,

adult (15‒49)

50

5.0

30

15.0

12.5

25

40

4.0

20

10.0

30

3.0

7.5

15

20

2.0

5.0

10

10

1.0

2.5

5

0

0.0

0

0.0

1990

1995

2000

2005

1985

1990

1995

2000

2005

*Even though the HIV prevalence rates have stabilized in sub-Saharan Africa, the actual number of people infected continues to grow because of population growth. Applying the same prevalence rate to a growing population will result in increasing numbers of people living with HIV.

Number of people living with HIV

% HIV prevalence, adult (15-49)

This bar indicates the range around the estimate

2.2


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Impact of AIDS on life expectancy in five African countries, 1970–2010

70

65

60

Botswana

55

South Africa

Life

expectancy

at birth

(years)

50

45

Swaziland

40

35

Zambia

30

Zimbabwe

25

20

1970–1975

1980–1985

1990–1995

2000–2005

1975–1980

1985–1990

1995–2000

2005–2010

Source: United Nations Population Division (2004). World Population Prospects: The 2004 Revision, database.

4.1


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People in sub-Saharan Africa on antiretroviral treatment 1970–2010

as percentage of those in need, 2002–2005

2005

2002

2003

2004

Source: WHO/UNAIDS (2005). Progress on global access to HIV antiretroviral therapy: An update on “3 by 5.”

7.2


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Age-specific prevalence of HIV in pregnant women, Botswana Sentinel Survey 2005 2003 22.8 38.6 49.7 45.9 41.5 34.4

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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  • So what influenced Botswana to be the trend setters??? Sentinel Survey 2005

  • Obviously the foresight and wisdom of Botswana’s leaders, but aided by…

    Brian Gazzard, Lisbon IAS 1999

    -projection of reduction of costs when HIV is treated

    The Durban AIDS Conference with Jeffrey Sach’s projection on how NO developing country could afford NOT to treat HIV

    Projected population graph with AIDS unchecked

    Lifetime risk of acquiring HIV of a 15 year old boy

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Projected population structure with and Sentinel Survey 2005

without the AIDS epidemic, Botswana, 2020

80

Projected population

structure in 2020

75

70

Males

Females

Deficits due to AIDS

65

60

55

50

Age in years

45

40

35

30

25

20

15

10

5

0

140

120

100

80

60

40

20

0

0

20

40

60

80

100

120

140

Population (thousands)

Source: US Census Bureau, World Population Profile 2000


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Lifetime risk of AIDS death for 15-year-old boys, Sentinel Survey 2005

assuming unchanged or halved risk of becoming

infected with HIV, selected countries

100%

90%

Botswana

80%

Zimbabwe

70%

Botswana

South Africa

Risk of dying of AIDS

Zambia

60%

Zimbabwe

50%

Kenya

South Africa

risk halved over next 15 years

Zambia

Côte d’Ivoire

40%

current level of risk maintained

Cambodia

Kenya

30%

Côte d’Ivoire

Burkina

Faso

20%

Cambodia

Burkina Faso

10%

0%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Current adult HIV prevalence rate

Source: Zaba B, 2000 (unpublished data)


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TB ( Sentinel Survey 2005 CROI 2006)

  • 2003

    9,000,000 new cases

    4,000,000 smear positive

    2,000,000 deaths

    Global TB incidence growing at 1% per year

    Risk of TB 5-15% per year HIV + (50x HIV-)

Anthony Harries Malawi, Ministry of Health


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Reported TB Case Rate Botswana, 1975–2004 Sentinel Survey 2005 and HIV Prevalence Antenatal Women, 1992-2005

HIV

TB

09/2006 TB Unit Ministry of Health Botswana


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Malawi illustrates this-- note increasing smear negative cases 30% treatment success and 60% mortality


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30-40% of all HIV deaths in Africa are due to TB usually diagnosed postmortem

Lucas 1993 Cote d’Ivoire

  • 40% of HIV wasted patients who died had TB

    Lewis 2005 Malawi

  • 10% of HIV patients with severe anemia had disseminated TB diagnosed by bone marrow C/S

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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  • Malawi- 1999 diagnosed postmortem

    • 2979 Health workers died- 50% TB

      - 40% AIDS

    • 105 TB control officers died

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Outcomes of TB in Malawi diagnosed postmortem

  • HIV +ve only 20% still alive 2 years after diagnosis (No treatment for HIV then)

  • HIV neg 50% only still alive at 7 yrs

  • 11-12% of TB notifications recurrences/relapse- strong HIV association

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Outcomes of Isoniazid Prophylaxis (IPT) on Incidence of TB diagnosed postmortem

  • IPT Reduces TB risk 40% (Wilkinson, BMJ 1998)

  • IPT Reduces risk of recurrence 50-80% (Churchyard AIDS 2003, Fitzgerald Lancet 2000)

    HAART reduces TB risk but NOT back to normal

    If patient has NO HAART

    9.7 risk of TB per 100 pt yrs

    If patient on HAART

    2.4 TB cases /100 pt yrs- Badri Lancet 2001

    continues reducing to 1% by 5 yrs Lawn AIDS 2005

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Deaths due to TB diagnosed postmortem

  • 60% of TB deaths in 1st 2 months

  • Early HAART after 2 weeks reduces deaths

  • However Increased IRIS with possible deaths with early HAART in first 3m

  • A balance has to be struck

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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What about other respiratory diseases? diagnosed postmortem

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Pneumococcal invasive illness has escalated in our region…

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Changing Patterns of Pneumococcal Infection in Southern Africa

  • Generally Increasing prevalence of invasive pneumococcal illness in developing countries. In RSA it seems to have replaced Haemophilus Influenza in LRTIs

    • Now 74% vs 12.9% Hib- reverse ratio

  • Increased prevalence of Paediatric (invasive) serotypes in HIV+ patients

  • Increased mortality-65% with meningitis Malawi -20% with pneumonia

  • Increased symptoms and signs with HIV+ patients

    • Pleurisy, haemoptysis, diarrhoea, meningitis,

  • Degree of risk CD4 driven

    • average CD4 in patients who died was 110 vs 170 in survivors

Keith Klugman CROI 2006


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Pneumococcal pneumonia is a disease of the very young and very old giving a U shaped curve in Western countries


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Percentage of distribution of deaths by age in southern Africa,

1985–1990 and 2000–2005

40

35

30

25

Percentage

of total deaths

20

15

10

5

0

0–4

5–19

20–29

30–39

40–49

50–59

60+

Age-groups

:

1985-1990

2000-2005

Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (2005). World Population Prospects: The 2004 Revision. Highlights. New York: United Nations.

4.2


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Note, modellers! Africa,

  • Risks now have changed-

    • HIV+ (Lost immunity to paediatric strains)

    • Young women

    • Small child in home

    • Health worker

    • Abuse of drugs,

    • smoking or alcohol

  • Antibiotic resistance and severity of illness increase with HIV

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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  • Morbidity reduced with HAART Africa- the Role of Mathematical Modelling

    • Spain, rate of invasive pneumococcal disease dropped from 24.1/1000 in 1985 to 2/1000 (We have yet to see those results in Southern Africa)

    • However still increased risk X 30 to 35x

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Pneumococcal vaccine Africa- the Role of Mathematical Modelling

  • Normal paediatric pneumococcal vaccine reduces prevalence of paediatric serotypes and greatly reduces risk

  • However other less virulent strains replace them

  • Note- NOT the 23 valent vaccine- seemed to increase morbidity in Rakai- ? Due to severe immunocompromisation?

Mahdi et al CID 2005, 40,1511-18


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Burden of disease in adults reduced by vaccination of children (USA)

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Malaria children (USA)

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Malaria children (USA)

  • Clinical Manifestations vary depending if occurs in stable or unstable transmission areas

    • Unstable

      • acute febrile disease, cerebral malaria and death;

      • still birth and abortion in pregnant women

    • Stable

      • Children chronic recurrent infections with anemia and growth retardation

      • Adults acquired immunity, asymptomatic,

      • Pregnant women, increased foetal growth retardation and increased infant mortality

  • Severity in adults and children invariably aggravated by HIV, especially in unstable areas; with increased risk of Intensive care and death (Cohen CID 2005, Grimwald Ped Inf Disease 2003)

  • Infants in stable areas get more frequent and severe anaemia (van Eijke,AJTMH,2002)

LaurenceSlutsker Kenya Med Res Station, Kisumu CROI 2006


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Cotrimoxazole Prophylaxis Africa- the Role of Mathematical Modelling

  • Ugandan cohort Lancet 2004 70% reduction of morbidity rate of severe malaria

  • Mali 97% efficacy to prevent infection in HIV neg children

  • Abidjan (Anglaret Lancet 1999) 5-6% reduction of morbidity

  • W Kenya- decreases in level of parasitaemia

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Effect of HIV on malaria Africa- the Role of Mathematical Modelling

  • 3 million excess cases

  • 5% increase of malaria deaths(65,000)

  • Increases parasitaemia with increasing immunosuppression, reduced clearance ability

  • Under 5 yrs of age, 1.7 fold increase in clinical disease

  • Max impact in unstable transmission areas

    • Botswana, Namibia, Zimbabwe. South Africa

    • Incidence increased 28% (14-40.7%)

    • Deaths increased 114% (37-188%)

    • Emergent Infectious Diseases 2005

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Effect of Malaria on HIV Africa- the Role of Mathematical Modelling

  • Reversible increase viral load (2 fold in pregnancy)

  • Malawi- increased neonatal mortality (AIDS 1999)

  • Possible reduction in CD4

  • No evidence of mother to child transmission increase

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Hepatitis B Africa- the Role of Mathematical Modelling

  • Worldwide huge burden

    • 2 billion people infected

    • 400 million chronic infection

    • 500,000 to 1 million deaths annually

      • Chronic hepatitis

      • Cirrhosis

      • Hepatocellular carcinoma

Jean Nachega


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Subsaharan Africa Africa- the Role of Mathematical Modelling

  • Horizontal transmission (Infected older siblings)

  • Acquired mainly between 6 months and 5 yrs

  • Some sexual transmission

    • Most exposed to HBV as children before HIV exposure

  • Some perinatal transmission (+ or- HIV)

  • Coinfection with HIV may result in

    • Reactivation of infection in silent chronic carriers

    • New HBV infection as protective immunity lost with HIV

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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  • HOWEVER Africa- the Role of Mathematical Modelling

    • Botswana our own stats show 40% incidence of exposure but <1% hepB sAG positive

      • Increased risk of Haart related hepatotoxicity

      • Increased liver related mortality

      • IDCC no longer screens for this as numbers are so small there is no impact on disease management

    • South Africa 2 studies concur

      • 41-43.3% evidence of previous or current infection Liver International 2005;25:201-213

        AIDS Read 2004;14(3):122-137

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Kaposi Sarcoma Africa- the Role of Mathematical Modelling

  • HHSV8 associated

    • Men more common in West

    • Similar prevalence of HHSv8 in M and F in sub saharan Africa

  • Incidence risen in Zimbabwe from

    • 2.3/100,000 in males and 0.3/100,000 in females pre HIV

    • Now 48/100,000 and 18/100,000 in 2001

  • Incidence risen in Uganda by 20 or 30 times in the last 2 decades, 81% HIV+

  • Incidence increased in South Africa by 2 (??)

  • Women seem to have more aggressive and symptomatic disease ?due to increased cytokines. Maybe biological difference?

    • Meditz U Zimbabwe

Robert Newton Univ of York UK


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Cervical Cancer Africa- the Role of Mathematical Modelling

  • Associated with oncogenic Human Papilloma Virus

  • Increases in Africa across all age groups

    • Uganda, increases predate HIV epidemic

  • An international Collaboration on HIV and Ca Cervix showed 1.88 increased incidence and no change with HAART

  • HIV-infected women more likely than HIV-negative women to be coinfected with HPV 1

    • (58% vs 24%; P < .01)

  • HIV infected women more likely to have multiple strains of HPV (clearance of HPV affected)

  • HIV-infected women more likely to have high-risk HPV infection 1

    • (23% vs 14%; P < .01)

      1 Duerr A, Paramsothy P, Jamieson DJ, et al. Effect of HIV infection on atypical squamous cells of undetermined significance. Clin Infect Dis. 2006;42:855-861.

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Genital Herpes Africa- the Role of Mathematical Modelling

  • Herpes Simplex 2 responsible for recurrent outbreaks of genital herpes

  • Increases HIV shedding in HIV+ patients

  • Increases infectiousness of HIV+ and the

    likelihood of infection in HIV- patient exposed to HIV (upregulates mucosal immune activity)

  • HIV increases severity of lesions and duration

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Other infectious diseases with differences Africa- the Role of Mathematical Modelling

  • Toxoplasmosis

    • COMMON opportunistic Infection in the west

    • <1% among our HIV patients

  • Cytomegalovirus

    • Causes devastating disease in very immune compromised people, may result in blindness

    • 50-65% previous exposure in the west

    • 99.5% Botswana

  • Cryptococcus

    • Very common in our setting

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Diarrhoea in HIV+ patients Africa- the Role of Mathematical Modelling

  • Cryptosporidium

  • Microsporidium

  • Isospora Belli

  • Salmonella, recurrent- not easily cleared

    As well as all the usual causes of diarrhoea

    Botswana has recently had a country wide epidemic of Cryptosporidium and enteropathogenic E Coli

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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Where does all this lead to? Africa- the Role of Mathematical ModellingWhere do modellers come in??

  • We need to be able to INFLUENCE POLICY- you can help us there

  • We need to be able to

    • predict the changing faces of the different diseases

    • Evaluate different prevention strategies

    • Evaluate different treatment interventions

    • Prioritise

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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We need you for Africa- the Role of Mathematical Modelling

  • Programme Planning

    • Costs of prevention and testing

    • Costs of treatment, both of HIV but other diseases

    • Costs of laboratory tests, diagnostic and monitoring

    • Human resource management, number of health workers required in different situations

    • Education of Health Care Workers, costs and personnel needed

    • Social programmes necessary

      • Orphan care, education

      • Feeding programmes

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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And for the fun things? Africa- the Role of Mathematical Modelling

  • Modelling even paints fitness landscapes of individual HIV viruses and enables prediction of resistance mutation patterns

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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  • Thank You for listening struggling at an individual level to make an impact

  • Thankyou also to Florence Doualla Bell

    • Who enabled you not to sit through 90 minutes today!!

  • Sala Sintle

09/2006 Facing the Challenges of Infectious Diseases in Africa- the Role of Mathematical Modelling


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