1

Western Cape Burden of Disease

HIV and Tuberculosis

Beverly Draper

David Pienaar

Thomas Rehle

Warren Parker

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Overview

• The current HIV & TB situation in the Western Cape• What explains the current situation?

- Risks

- Interactions between HIV & TB• The predicted future situation • Proven interventions• Potential multi-sectoral strategies

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TB and HIVThe current situation in the Western Cape

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HIV distribution over timeEstimated Provincial versus National prevalence

N ationa l versus H IV Preva lence W estern Cape Trends

13.1

7.6

10.4

14.216.0

22.4

27.9

15.7

15.4

1.161.66

8.68.7

7.15.2

6.3

12.4

3.1

30.229.5

24.5

24.8

26.5

22.8

0.8 1.4

2.4

4.3

0

5

10

15

20

25

30

35

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

PR

EV

AL

EN

CE

W Cape NATIONAL

So urc e: HIV An te n ata l S ur vey s D e par tm en t of He alth W es tern C ap e

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HIV distribution over timeEstimated prevalence in selected sub-districts

Antenatal HIV prevalence (%), selected WC sub-districts

0

5

10

15

20

25

30

35

2001 2002 2003 2004 2005

Khayalitsha Gugulethu/Nyanga Knysna/Plett

Klein Karoo Klipfontein George

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0 - 4.9%

5 - 9.9%

10-14.9%

15-19%

20-24%

25-29%

30+

HIV PREVALENCE BY METROPOLE AREAS 2005

Vredendal

MalmesburyCeres/

TulbaghCentral Karoo

Klein Karoo

Mossel Bay /Hessequa George Knysna /Plett Bay

Bredasdorp/ SwellendamCaledon /Hermanus

Worcestor/Robertson

Stellenbosch

Paarl

Vredenberg

2005 HIV PREVALENCE NON-METROPOLE AREAS

Blaauberg

T East

Oostenberg

HeldebergSouth Peninsula

Cape Town Central

MPlainAthlone

Nyanga/Gugulethu

T West

Khayelitsha

Source: DoH, 2005 HIV Antenatal Survey

Compiled by Dr Najma Shaikh

Estimated HIV prevalence, Western Cape sub-districts - 2005

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Cape Town

~209 000

Cape Town

~209 000

Wellington/Paarl ~8 800Wellington/Paarl ~8 800

Mossel Bay/George/Knysna and Bitou ~17 000Mossel Bay/George/Knysna and Bitou ~17 000

Stellenbosch ~5 100Stellenbosch ~5 100

Worcester/De Doorns ~6 500Worcester/De Doorns ~6 500

Theewaterskloof/Grabouw ~5 400Theewaterskloof/Grabouw ~5 400

Estimated HIV cases1 for selected areas2 of the Western Cape - 2007

1. Dorrington R, Centre for Actuarial Research

2. The 6 areas selected represent ~90% of estimated total of ~283 000

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TB distribution – timeRegistered cases

Western Cape TB caseload 1997-2005

25000

30000

35000

40000

45000

50000

1997 1998 1999 2000 2001 2002 2003 2004 2005

TB program data, PGWC DOH

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District distribution of TB (PGWC ETR 2005 data)

District cases % cases

METRO SUBTOTAL 25 950 57.5

C W'LAND SUBTOTAL 6 942 15.4

EDEN SUBTOTAL 5 581 12.4

W COAST SUBTOTAL 3 587 7.9

O'BERG SUBTOTAL 2 412 5.3

C KAROO SUBTOTAL 658 1.5

TOTALS 45 130 100

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TB distribution (PGWC ETR 2005 data)

CategoryNumber of cases

seen peryear at the facility

Number of facilities in

the category

Total case load

of the category

Percent each category

contributes to provincial case load

Average no. cases

per clinic per year

1 >400 22 15 413 34.2 7012 200-400 44 12 373 27.5 2813 100-199 60 8 277 18.3 1384 50-99 75 5 656 12.6 755 <50 194 3 343 7.4 17

TOTALS 395 45 062 100 114

Categorisation of TB clinics

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Distribution of high burden TB clinics (TB ‘hotspots’) in the Western Cape – 2005

All 22 ‘high burden’ clinics are located in the indicated areas

(DOH ETR 2005 data)

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0 - 4.9%

5 - 9.9%

10-14.9%

15-19%

20-24%

25-29%

30+

HIV PREVALENCE BY METROPOLE AREAS 2005

Vredendal

MalmesburyCeres/

TulbaghCentral Karoo

Klein Karoo

Mossel Bay /Hessequa George Knysna /Plett Bay

Bredasdorp/ SwellendamCaledon /Hermanus

Worcestor/Robertson

Stellenbosch

Paarl

Vredenberg

2005 HIV PREVALENCE NON-METROPOLE AREAS

Blaauberg

T East

Oostenberg

HeldebergSouth Peninsula

Cape Town Central

MPlainAthlone

Nyanga/Gugulethu

T West

Khayelitsha

Source: DoH, 2005 HIV Antenatal Survey

Compiled by Dr Najma Shaikh

Overlap between HIV prevalence and TB hotspots

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•18/22 of the highest burden clinics are in the metro

•12/18 are in a 10km x 15km area that straddles 5 sub-districts

(ETR 2005 data)

Approximately 25% of the registered TB cases in the province

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Current service difficulties

• Significant stigma: HIV and TB• Most HIV cases and an unknown amount of TB

cases are undiagnosed• Delayed health seeking behaviour: late entry into

care, HIV and/or TB, with advanced disease. • TB diagnostic challenges -HIV reduces the accuracy of the standard TB test• Human resources challenges• TB adherence difficulties • ART adherence difficulties• High proportion of re-treatment TB cases• Emerging TB resistance - mono-drug, MDR and XDR

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What explains the current situation?

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• There are proven risk factors associated with high prevalence HIV areas:

The ‘deprivation cluster’ of:

1. Migration2. Overcrowding3. Poverty4. Malnutrition

Why do certain areas carry the burden of disease?

Produces (and reproduces) social vulnerability

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• By exacerbating the following known risk factors:

1. Not knowing one’s HIV status2. Stigma and discrimination 3. Age mixing4. Early sexual debut5. Transactional sex6. Partner turnover/concurrency7. Alcohol misuse

How does social vulnerability impact on individual HIV risk?

-disempowerment

-poor decision-making skills

-economic necessity

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• By making some people more likely to transmit HIV and others more vulnerable to HIV

1. Sex & age

2. Viral load

3. Sexually transmitted infections

4. Mother to child transmission

How do biological factors impact on individual HIV risk?

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What are the risks factors for being exposed to TB?

• The ‘deprivation cluster’ of: impoverishment, poor nutrition, migration, overcrowded dwellings, existing high TB prevalence and incidence, poor education, ignorance of TB transmission mechanisms and of TB symptoms

“85-90% of those people with normal immunity

who inhale TB do not develop disease”

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• “by far the most powerful risk identified is

concurrent HIV infection”

What are the risks factors for inhaled TB progressing to

tuberculosis disease?

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How HIV impacts on TB in the Western Cape

• More TB in the HIV+ population at all stages of HIV disease, but especially with advanced HIV disease

• TB program placed under increased pressure:

- number of cases - clinical time required to make a diagnosis• Greater potential for poor clinical outcomes • Greater potential for drug resistance• Greater risk of exposure to TB for the general

population• More deaths due to TB/HIV

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HIV and TB in clinical practice

TB HIV &TB HIV

60-70% of TB patients in Khayalitsha are HIV+

60-70% of ART patients either had concurrent TB or have had a previous episode of TB

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The predicted future situation

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How do we predict future caseload?

• HIV • A person can only get it once and then they’ve got it for

life. • A “relatively simple” modeling exercise, but it is being

complicated by the impact of interventions

• TB• A person can get it, be cured, and get it again. The risk

of this happening increases as HIV disease becomes more severe. Also affected by background HIV prevalence which changes over time

• Mathematically complex

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Estimated HIV scenario1 for selected areas of the Western Cape to 2015

1. Dorrington R, Centre for Actuarial Research

2008 2009 2010 2015

Cape Town 219 087 227 251 233 578 243 164

M. Bay/Grg/Knysna/Bitou 17 097 17 773 18 330 19 652

Wellington/Paarl 9 163 9 434 9 630 9 750

Wrcstr/DeDoorns 6 754 6 999 7 193 7 476

Thwtrsklf/Grbw 5 689 5 934 6 135 6 616

Stellenbosch 5 352 5 462 5 522 5 272

Provincial Totals ~297 000 ~309 000 ~318 000 ~335 000

Future burden - HIV

= approx. 30 new cases/day

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Future burden -TB

(Lawn S. et al, CID, 2006;42:1040-7)

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In summary, what does the future hold?

• We know where the high prevalence HIV areas are• We have a good idea of how many HIV cases to expect• We know TB is going to occur where HIV is prevalent • We cannot predict TB caseload as accurately• Evidence suggests TB is going to continue to increase even

after HIV prevalence stabilises• This TB is likely to be more difficult to diagnose

• Adherence, and consequently, resistance is likely to play an increasing role

• Further down the line, HIV resistance is likely to become a problem

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Proven interventions

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Proven interventions that reduce HIV transmission

• PMTCT. An excellent intervention• Condoms. They work. But we can’t get

people to use them consistently (or, at all)• STI treatment. Reduces risk of HIV

transmission• Circumcision. Reduces risk of acquisition in

the male only. There are concerns about logistics and perceived invulnerability

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Proven interventions that impact on behaviour

• Mass media campaigns. Increase the number of people who present for HIV testing

• VCT. People who test HIV+ are more likely to engage in safer sex. Not a uniform finding though, social context influences this.

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Proven interventions that impact on TB risk

• Isoniazid preventative therapy. Lowers risk of active TB in those with a positive TST. But, need to prove that TB is not present otherwise might contribute to drug resistance. Very difficult thing to prove.

• Anti-retroviral therapy. Lowers the risk incompletely. Still 5-10 times more chance of getting TB than an HIV uninfected person

• Radio campaigns. Improved health seeking behaviour of those with TB symptoms

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How do we approach this?

• There are a limited number of interventions that have hard proof of efficacy. Might have to rely on logic and plausibility

• There are clear health sector demands, these need to be addressed

• There are areas beyond the health sector where the potential exists for multi-sectoral interventions

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Health sector strategies

• Strengthen PMTCT• Increase VCT and ‘opt-out’ testing• Strengthen TB program capacity considerably - Active case finding - Diagnostic skill: doctors and CNPs - Diagnostic equipment- X-rays - Laboratory services – diagnosis, and resistance testing

- Monitoring and recording capacity - ?Investigate alternative adherence models

• Strengthen ART roll-out• Establish best models for TB/HIV integration

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Potential multi-sectoral strategies

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What should we be doing about the “deprivation cluster”?

We need to address the root causes

1. Migration – ‘push’ and ‘pull’ factors

2. Overcrowding – housing quality

3. Poverty – socio-economic conditions

4. Malnutrition – grants, food vouchers

The problem is, apart from the fourth, these are ‘structural’, and in some cases, national issues, with medium to long term timelines.

Although these issues must certainly be addressed, they will not rapidly improve infectious disease outcomes

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What should we be doing about the social/individual factors?

We need to impact on social attitudes and individual behaviour.

We desperately need to stop HIV transmission

• Heighten awareness of individual risk in high-prevalence areas • Reduce stigma and discrimination • Normalise HIV testing in relationships• Reduce risky sexual behaviour -consistent condom use -delay sexual debut -encourage monogamous relationships -discourage age mixing -avoid alcohol and drug misuse • Optimise health seeking behaviour• Very importantly, support and encourage adherence behaviour

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Potential strategies

1. Introduce epidemiologically-led behavioural interventions

2. Target hotspots first

3. Identify and manage at-risk groups earlier

4. Integrate prevention and treatment

5. Adapt the relevant services within the social cluster platform of public services

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Questions

• What is it going to take to impact on behaviour?• How to ‘normalise’ HIV testing?• How to cope logistically with more people

testing?• How to get other sectors involved?• Best ways to impart health information?• What information to impart?• Can health services cope?