Appendix – Model Technical details and validation

This supplementary material provides technical details of the model design including parameter values, model assumptions and results of model validation. Figure 1 shows the basic model design. Briefly, the model is an individual-based simulation of the full population of Zimbabwe, representing births and death, HIV incidence and disease progression and the development of several NCDS. The model uses data and estimates that are available for the period 1950 to 2015, and is then used to forecast future trends in the period 2015 and 2035.

Figure 1. Schematic of the multi-disease model for Zimbabwe. The model simulates demography (blue), HIV-infection (orange), and non-communicable diseases (green). The model accounts for demographic and medical risk factors for HIV-infection and for development of non-communicable diseases (red arrows to individual conditions and group of conditions). The model starts in 1950 and runs until 2035, making projections from 2015 onwards. Demographic and non-communicable disease patterns are simulated from 1950 and the HIV-epidemic from 1975. From 2015, the model is used to forecast the timing and nature of non-communicable diseases in the general population and HIV-positive population.

1. Technical details

Demographic simulation (age, sex and mortality)

The model simulated the whole population of Zimbabwe from 1950 until 2035. Demographic simulation of the population of Zimbabwe are based on data from United Nation’s (UN) Department of Economic and Social Affairs.(1) These include sex-specific age composition in 1950, age-specific fertility rates between 1950 and 2015, age-and-sex-specific mortality rates between 1950 and 2015. Rates are presented by 5-year time periods, with the model assuming that rates within these periods are constant. Cause-specific mortality was fitted by hand in the model (including mortality for HIV and each of the NCDs included) by reducing overall mortality estimates in HIV-negative and patients without NCDs by matching to overall mortality estimates provided by UN estimates. Projections beyond 2015 assume that fertility and mortality rate remain constant at the 2015 levels.

HIV epidemic

The model simulates the HIV epidemic from 1975, including new HIV infection, CD4 count progression, ART initiation and HIV-related death. Annual age-and-sex-specific HIV incidence rates (including paediatric infection) were taken from the official UNAIDS estimates.(2) These incidence estimates are derived through fitting a model to available data, largely prevalence estimates in Demographic and Health Surveys. ART roll-out and scale-up, including the expanding ART eligibility criteria over time, are based on data on the number of people starting ART in Zimbabwe from UNAIDS estimates, including number of persons on ART by CD4 count.(2)

Estimates of CD4 count at seroconversation (Table 1), CD4 count progression rates (Table 2), and mortality by CD4 count (Table 3) were taken from a study by Mangal et al, AIDS (under review) of a joint analysis of all seroconverter data in Europe, Africa, South America and Asia (3). Projections of the number of new HIV-infections and people starting ART assume that incidence and ART trends remain constant the 2015 level.

Table 1. CD4 count at HIV-infection. Age-and-sex-specific probability of being in specific CD4 count category at seroconversion. Source: Mangal et al AIDS (3)

Age Category

CD4 count category

>500

500-350

350-250

Men

<30

0.58

0.25

0.17

30-40

0.53

0.26

0.20

40-55

0.48

0.26

0.23

>55

0.4

0.26

0.28

Women

<30

0.63

0.22

0.15

30-40

0.58

0.23

0.18

40-55

0.53

0.23

0.21

>55

0.44

0.23

0.26

Table 2. Rate of CD4 count progression. Source: Mangal et al AIDS (3)

CD4 count progression

From >500 to 500-350

From 500-350 to 350-250

From 350-250 to 250-200

From 250-200 to 200-100

From 200-100 to 100-50

From 100-50 to <50

Men

0.19835

0.32913

0.33628

0.50936

0.57271

0.86702

Women

0.18456

0.30458

0.31120

0.47137

0.52999

0.80236

Table 3. Age-and-sex-specific mortality rate by CD4 count. Source: Mangal et al AIDS (3)

Age Category

CD4 count category

>500

500-350

350-250

250-200

200-100

100-50

<50

Men

<25

0.00321

0.00912

0.00733

0.01281

0.02161

0.03741

0.84544

25-35

0.00403

0.01145

0.00921

0.01608

0.02713

0.04696

1.06123

35-45

0.00497

0.01412

0.01136

0.01984

0.03347

0.05794

1.30933

>45

0.00712

0.02022

0.01626

0.02841

0.04792

0.08296

1.87488

Women

<25

0.00334

0.00948

0.00762

0.01331

0.02245

0.03887

0.87847

25-35

0.00419

0.01189

0.00957

0.01671

0.02819

0.04879

1.10269

35-45

0.00517

0.01467

0.01180

0.02061

0.03478

0.06020

1.36048

>45

0.00740

0.02101

0.01690

0.02952

0.04980

0.08620

1.94813

NCDs

The model simulates the development of a number of NCDs including: asthma, chronic kidney disease (CKD), depression, diabetes, hypertension, strokes and cancers. The individual cancers, which were chosen based on the most prevalent non-AIDS defining cancers (as determined by 2012 GloboCan (4)) in Zimbabwe, were: colorectal, liver, oesophageal, breast, cervical and prostate cancer as well as ‘other’ cancer, which comprise all other cancers.

An extensive literature review was carried out to obtain age-specific incidence or prevalence to parameterise the development of NCDs. The literature review focused on published studies recording age-specific prevalence or incidence rates for Zimbabwe or neighbouring countries (with the implicit assumption that that neighbouring countries experience similar age-specific prevalence patterns as Zimbabwe). The review found a paper by Negin et al, using survey of a representative sample of people in South Africa report data on 2008 age-specific prevalence of asthma, depression, diabetes, hypertension and stroke.(5) Questions included whether individuals had every been diagnosed with a number of NCDs, with asthma and depression validated against a set of symptom questions and related diagnostic algorithm.(5) A paper from Tanzania by Stanifer et al was identified, presenting age-specific estimates of CKD prevalence for 2014 (6). CKD was defined as estimated glomerular filtration rate ≤60ml/min/1.73m2 using the Modification of Diet in Renal Disease equation and/or persistent albuminuria (6), Age-specific cancer incidence estimates were taken from the GloboCan dataset for Zimbabwe, which used a number of data sources and methods, including regional incidence data and mortality data to provide national cancer-specific incidence estimates.(4) Age-standardized cancer rates are presented in Table 4.

Table 4. Age-standardized cancer rates (ASR) per 100,00 (both sexes). Source: Globocan (4)

cc

Breast cancer

Cervical cancer

Colorectal cancer

Liver cancer

Oesophagus cancer

Prostate cancer

Other cancers

ASR

38.8

40.1

8.6

5.4

17.6

31.6

39.7

Where studies presented prevalence estimates, age-specific incidence rates were calculated, assuming age-specific prevalence estimates are constant over time as per Equation 1, fitting I(a) to solve for P(a), where I(a) stands for incidence at age a and p(a,b) for the probability of an individual infected at age a surviving to age b. We assumed that NCD prevalence/incidence only varied significantly by HIV status if found to do so in a comprehensive study by Schouten and colleague in HIV-positive patients and age-and-sex-matched uninfected controls (7). Therefore, where prevalence estimates were presented by HIV-status, prevalence data was pooled between HIV-negative and HIV-positive individuals to provide average estimates for both groups. Estimates were pooled by weighing for the proportion of patients in each group as generated by the model. For example, if 80% of people in Zimbabwe in the year of the reported prevalence were HIV-negative and 20% were HIV-positive, prevalence for HIV-negative counted 8/10 and HIV-positive 2/10 towards the pooled average. We used age-specific population estimates (number of people in the population) generated by the model to back-calculate estimates of the number of new cases diagnosed to match prevalence levels observed. These were used to calculate incidence estimates.

Equation 1.

The model further accounts for the propensity for one NCD to be associated with an increased risk to develop another– for example for hypertension to increase the risk of having a stroke through common causal pathways. Parameters defining these increased risks were based on the output of an extensive literature review including studies from all regions.(8) These links are illustrated by the red arrow in Figure 1. The increased risk for HIV-positive individuals to develop certain NCDs was incorporated into the model through parameters based on a large study The Netherlands that compares NCD risk amongst HIV-positive individuals and uninfected controls.(7) The mechanisms driving increased NCD risk amongst HIV-positive patients are complex and their relative contribution a topic of ongoing research.(9)

2. Model calibration

A number of model checks were carried out to ensure that the model was able to reconstruct demographic and epidemiological trends robustly. This was done by comparing model output to available data.

Demography

The model-based simulation of the demographic trends in Zimbabwe were compared to data from United Nations’ (UN) Department of Economic and Social Affairs for Zimbabwe (1). The model was able to accurately reconstruct the Zimbabwean demographic transitions observed by national surveillance between 1950 and 2015. Figure 2

shows the comparison between UN surveillance data and model output. The model was able to reconstruct the population growth between 1950 and 2015 (Figure 2), as well as accurately reconstruct age distribution in between 1950 and 2015 (Figure 3 shows comparison to 2015 data as a snapshot). Fertility and mortality patterns were accurately simulated in the model, recreating both the number of births and death between 1950 and 2015 (Figure 4A and Figure 4B) and births and deaths by age group (Figure 4C and Figure 4D). Any discrepancies between the data and the model can in part be explained by the stochastic nature of the model, with results presented below based on one model run.

We have further checked the model between 2015 and 2035 against all available projections generated from UN Department of Economic and Social Affairs for Zimbabwe (Figure 5) (1). We compared both the projected number of people in Zimbabwe and the age composition of the population between 2015 and 2035. We find that the model is able to recreate the future projected trends well (Figure 5).

Figure 2.Comparison of the total population (both sexes) measures for Zimbabwe from UN Department of Economic and Social Affairs for Zimbabwe to the model output between 1950-2015. Data source: UN Department of Economic and Social Affairs for Zimbabwe.(1)

Figure 3. Comparison of the age structure in July 2015 for Zimbabwe from UN Department of Economic and Social Affairs for Zimbabwe to the model output. Data source: UN Department of Economic and Social Affairs for Zimbabwe.(1)

Figure 4. Comparison of demographic measures for Zimbabwe from UN Department of Economic and Social Affairs for Zimbabwe to the model output between 1950 and 2015. Specifically; A. The number of births between 1950 and 2015, B. Number of births by age group of the mother between 2005 and 2015, C. Number of deaths between 1950 and 2015, and D. Number of deaths by age group in July 2015. Data source: UN Department of Economic and Social Affairs for Zimbabwe.(1)

A. B.

C. D.

Figure 5. Comparison of demographic projections for Zimbabwe from UN Department of Economic and Social Affairs for Zimbabwe to the model output between 2015 and 2035 for A. Total population and B. population by age groups. Data source: UN Department of Economic and Social Affairs for Zimbabwe (1).

A. B.

HIV epidemic

The model-based simulation of the HIV epidemic in Zimbabwe was compared to data from UNAIDS for Zimbabwe.(2) The output demonstrates that the model-based reconstruction of the HIV epidemic in Zimbabwe was able to reconstruct both the incidence and prevalence transition in the country between 1975 and 2015, as well as trends in treatment between 2004 and 2015.

Figure 5 A and B shows the number of new HIV-infections from the start of the epidemic in 1975 to 2015 amongst men and women respectively, illustrating that the model was able to capture the increase in the number of new infections from 1975, the peak in 1993 and gradual decrease from 1993 to 2015. When comparing the patterns of overall HIV-infections, the output also managed to reconstruct the increase in HIV prevalence amongst men and women (Figure 5 C and D respectively) from 1975 to the peak in 2000, followed by a gradual decrease, as well as the age-specific prevalence estimates as reported by DHS (2,10).

Figure 6 shows that the model was able to recreate the increasing number of persons on antiretroviral therapy (ART) between 2004 and 2015, including trends by sex (Figure 6 B and C) and in children Figure 6D). The model was further able to match trends in ART initiation by CD4 counts in men and women (Figure 6E and F). Differences between UNAIDS estimates (Figure 6 - data) and our computed prevalence estimates (Figure 6 - model) arise due to differing assumptions relating to natural history and disease progression.

Figure 6. Comparison of measures of HIV epidemic in Zimbabwe from UNAIDS estimates to mode output. A. and B. show the number of new HIV-infections between 1975 and 2015 in men and women respectively. C. and D. show the year total number of HIV-infections men and women respectively between 1975 and 2015. E. compares age-specific prevalence estimates from the model to DHS data. Data stands for UNAIDS estimates and DHS data (for E). Data source: UNAIDS epidemic estimates for Zimbabwe.(2,10).

A. B.

C. D.

E.

Figure 7. Comparison of number of people on ART from UNAIDS estimates for Zimbabwe and model output between 2004 and 2015 for A. both sexes, B. women, C. men, D. children. Comparison by CD4 count are shown in E and F for men and women respectively. The solid colours illustrate data and the dotted colours model output. Data source: UNAIDS epidemic estimates for Zimbabwe (2).

A. B.

C. D.

E. F.

NCD epidemic

We compared model-based age-specific prevalence in 2008 of asthma, depression, hypertension, diabetes and ever having had a stroke to 2008 age-specific prevalence data from Negin et al (Figure 7 A to E). We also compared age-specific prevalence estimates of CKD generated by the model to 2014 age-specific prevalence of CKD from Stanifer et al (Figure 7 F). The results show that the model was able to accurately reconstruct age-specific prevalence patterns.

Figure 8. Comparison of age-specific prevalence from data to mode output for Zimbabwe, including A. asthma, B. depression, C. diabetes, D. stroke, E. hypertension, and F. CKD. A to E were compared to 2008 age-specific prevalence data from Negin et al (2008) (5) and F to 2014 age-specific prevalence data from Stanifer et al (6).

A. B.

C. D.

E. F.

Cancer epidemic

We compared model-based overall cancer-specific prevalence in 2012 of colorectal, liver, oesophagus, and stomach cancer as well ‘other’ cancers (any other cancer except the aforementioned) to 2012 GloboCan estimates for Zimbabwe (Figure 8)(11). The results show that the model was able to accurately reconstruct cancer-specific prevalence patterns in 2012.

Figure 9. Comparison of cancer-specific prevalence from model to 2012 GloboCan in Zimbabwe. Data source: GloboCan estimates for Zimbabwe (11).

Mortality

We compared model-based cause-specific deaths in 2013 to the Institute for Health Metrics and Evaluation’s Global Burden of Disease 2013 estimates of causes of death for Zimbabwe (Figure 9) (12). The results show that the model was able to accurately reconstruct the percentages of death contributed by individual conditions.

Figure 10. Comparison of causes of death from model to 2013 Global Burden of Disease. Data source: Institute for Health Metrics and Evaluation’s Global Burden of Disease 2013 estimates of causes of death for Zimbabwe (12).

Sensitivity analyses

Changes in HIV incidence and ART coverage

The model was re-run with changes in both HIV incidence and ART coverage. Results comparing baseline (HIV incidence remains stable at 2015 level and ART coverage gradually increases to 90:90:90 targets by 2035) to a scenario where HIV incidence increases or decreases annually by 10% and where ART coverage remains stable at 2015 level are presented in Table 5. We have also tested scenarios where ART was never introduced in 2004 (in which case mean age would be 34 by 2035), HIV incidence remained at highest level (mean age would be 31 by 2035), and HIV had no impact on NCD incidence (36% would be diagnosed with at least one NCD by 2035).

Table 5. Sensitivity analyses comparing baseline scenario for HIV incidence and ART coverage to variations in ART coverage and HIV incidence.

Projection in 2035

Baseline*

ART coverage remains at 2015 level

10% annual increase in HIV incidence

10% annual reduction in HIV incidence

Mean age

45

45

45

45

Total HIV population

1.0 million

0.95 million

1.02

0.98

Proportion with ≥ 

1 NCD

59%

58%

59%

59%

Number of people with at least one NCD

0.84 million

0.77 million

0.84 million

0.84 million

*Baseline defined as HIV incidence that remains stable at 2015 levels up until 2035 and where ART coverage increases steadily to 90:90:90 targets.

Addition of Myocardial Infraction

Due to the lack of robust age-specific data on myocardial infraction (MI) in the region, MI was not included in the model. We did however carry out additional analyses, where MI was included in the model and incidence rates were assumed to be similar to the incidence of stroke. We assumed interactions between pre-existing NCDs and risk of MI (Figure 1) to be the same as stroke. The results showed that the proportion with at least one NCD by HIV status would not vary greatly, reflecting the low incidence of MIs assumed. The proportion of HIV-negative people suffering from at least one NCD would increase from 14.3% to 14.4% in 2015 and 21.2% to 21.3% by 2035. Amongst HIV-positive this would increase from 33.2% to 33.3% in 2015 and from 59.4 to 59.5% by 2035.

References

1. United Nations. United Nations - Department of Economic and social Affairs [Internet]. 2015 [cited 2016 Aug 1]. Available from: http://esa.un.org/unpd/wpp/Download/Standard/Population/

2. UNAID. UNAIDS - Epidemic Projection Package [Internet]. 2015 [cited 2016 May 5]. Available from: http://www.unaids.org/en/dataanalysis/datatools/spectrumepp

3. Mangal TD, UNAIDS Working Group on CD4 Progression and Mortality Amongst HIV Seroconverters including the CASCADE Collaboration in EuroCoord. Joint estimation of CD4+ cell progression and survival in untreated individuals with HIV-1 infection. AIDS. 2017 May 15;31(8):1073–82.

4. GloboCan 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012 [Internet]. [cited 2016 May 27]. Available from: http://globocan.iarc.fr/old/age-specific_table_r.asp?selection=227716&title=Zimbabwe&sex=0&type=0&stat=0&window=1&sort=0&submit=%C2%A0Execute

5. Negin J, Martiniuk A, Cumming RG, Naidoo N, Phaswana-Mafuya N, Madurai L, et al. Prevalence of HIV and chronic comorbidities among older adults. AIDS. 2012 Jul 31;26 Suppl 1:S55–63.

6. Stanifer JW, Maro V, Egger J, Karia F, Thielman N, Turner EL, et al. The Epidemiology of Chronic Kidney Disease in Northern Tanzania: A Population-Based Survey. PLoS ONE. 2015 Apr 17;10(4):e0124506.

7. Schouten J, Wit FW, Stolte IG, Kootstra N, van der Valk M, Geerlings SG, et al. Cross-sectional comparison of the prevalence of age-associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV Cohort Study. Clin Infect Dis. 2014 Sep 2;

8. Smit M, Brinkman K, Geerlings S, Smit C, Thyagarajan K, van Sighem A, et al. Future challenges for clinical care of an ageing population infected with HIV: a modelling study. The Lancet Infectious Diseases [Internet]. 2015 Jun [cited 2015 Jun 15]; Available from: http://linkinghub.elsevier.com/retrieve/pii/S1473309915000560

9. Althoff KN, Smit M, Reiss P, Justice AC. HIV and ageing: improving quantity and quality of life. Curr Opin HIV AIDS. 2016 Jul 8;

10. Goverment of Zimbabwe, The DHS Program, ICF International, Rockville, Maryland USA. Zimbabwe: Demographic and Health Survey 2015 [Internet]. [cited 2017 Jul 11]. Available from: http://dhsprogram.com/pubs/pdf/FR322/FR322.pdf

11. GloboCan. GloboCan - Estimated Incidence by age [Internet]. 2012 [cited 2016 May 27]. Available from: http://globocan.iarc.fr/old/age-specific_table_r.asp?selection=227716&title=Zimbabwe&sex=0&type=0&stat=0&window=1&sort=0&submit=%C2%A0Execute

12. IHME. Institute for Health Metrics and Evaluation - MDG Viz [Internet]. [cited 2016 Aug 20]. Available from: http://www.healthdata.org/institute-health-metrics-and-evaluation

2

25 20 15 10 5 0 5 10 15 20 25

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980+

Percentage

AgeGrou

p

Women- Model Women- Data

Men- Model Men- Data

2520151050510152025

0-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80+

Percentage

A

g

e

G

r

o

u

p

Women-ModelWomen-Data

Men-ModelMen-Data

0.0

500.0

1,000.0

1,500.0

2,000.0

2,500.0

3,000.0Nu

mbe

rofb

irths(1,000)

Calendartime

Data

Model

0.0

500.0

1,000.0

1,500.0

2,000.0

2,500.0

3,000.0

N

u

m

b

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r

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h

s

(

1

,

0

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0

)

Calendartime

Data

Model

0

100

200

300

400

500

600

700

800

900Nu

mbe

rofb

irth(1,000)

Agegroup

Data

Model

0

100

200

300

400

500

600

700

800

900

N

u

m

b

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o

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(

1

,

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Agegroup

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0200400600800

1,0001,2001,4001,6001,8002,000

Numbe

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eaths(1,000)

Calendartime

Data

Model

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

N

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Calendartime

Data

Model

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50

100

150

200

250

300

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mbe

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eaths(1,000)

AgeGroups

Data

Model

0

50

100

150

200

250

300

350

N

u

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b

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(

1

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AgeGroups

Data

Model

0

50,000

100,000

150,000

200,000

250,000

300,000

2015 2020 2025 2030 2035

Num

bero

fpeople(x1,000)

Year

Population projection

Data

Model

0

50,000

100,000

150,000

200,000

250,000

300,000

20152020202520302035

N

u

m

b

e

r

o

f

p

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e

(

x

1

,

0

0

0

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Year

Populationprojection

Data

Model

0

10,000

20,000

30,000

40,000

Numbe

rofp

eople

Year

AgeGroupsprojections

0-24 25-69 70

0

10,000

20,000

30,000

40,000

N

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Year

AgeGroupsprojections

0-2425-6970

020,00040,00060,00080,000

100,000120,000140,000160,000180,000200,000

Nrofn

ewHIVinfections

Year

ModelData

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

N

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o

f

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H

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Year

Model

Data

020,00040,00060,00080,000

100,000120,000140,000160,000180,000200,000

Nrofn

ewHIVinfections

Year

ModelData

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

N

r

o

f

n

e

w

H

I

V

i

n

f

e

c

t

i

o

n

s

Year

Model

Data

0

500,000

1,000,000

1,500,000

2,000,000Nrofm

enwith

HIV

Year

Data

Model

0

500,000

1,000,000

1,500,000

2,000,000

N

r

o

f

m

e

n

w

i

t

h

H

I

V

Year

Data

Model

0

500,000

1,000,000

1,500,000

2,000,000Nrofw

omen

with

HIV

Year

Data

Model

0

500,000

1,000,000

1,500,000

2,000,000

N

r

o

f

w

o

m

e

n

w

i

t

h

H

I

V

Year

Data

Model

0%10%20%30%40%50%60%70%80%90%100%

15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54

Prevalen

ce

Age Groups

Age-specificprevalence

Data- MenModel- MenData- Women

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

15-1920-2425-2930-3435-3940-4445-4950-54

P

r

e

v

a

l

e

n

c

e

AgeGroups

Age-specificprevalence

Data-Men

Model-Men

Data-Women

0

200,000

400,000

600,000

800,000

1,000,000

Numbe

rofp

eople

Year

NumberofpeopleonART- bothsexes

DataModel

0

200,000

400,000

600,000

800,000

1,000,000

N

u

m

b

e

r

o

f

p

e

o

p

l

e

Year

NumberofpeopleonART-bothsexes

Data

Model

0

200,000

400,000

600,000

800,000

1,000,000Nu

mbe

rofp

eople

Year

NumberofpeopleonART- men

DataModel

0

200,000

400,000

600,000

800,000

1,000,000

N

u

m

b

e

r

o

f

p

e

o

p

l

e

Year

NumberofpeopleonART-men

Data

Model

0

200,000

400,000

600,000

800,000

1,000,000Nu

mbe

rofp

eople

Year

NumberofpeopleonART- men

DataModel

0

200,000

400,000

600,000

800,000

1,000,000

N

u

m

b

e

r

o

f

p

e

o

p

l

e

Year

NumberofpeopleonART-men

Data

Model

0

200,000

400,000

600,000

800,000

1,000,000Nu

mbe

rofp

eople

Year

NumberofpeopleonART- children

DataModel

0

200,000

400,000

600,000

800,000

1,000,000

N

u

m

b

e

r

o

f

p

e

o

p

l

e

Year

NumberofpeopleonART-children

Data

Model

0

100,000

200,000

300,000

400,000

500,000

600,000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Numbe

rofp

eople

Year

ARTbyCD4count- Men

>500 350-500 250-350 200-250 100-200 50-100 <50

>500 350-500 250-350 200-250 100-200 50-100 <50

0

100,000

200,000

300,000

400,000

500,000

600,000

200420052006200720082009201020112012201320142015

N

u

m

b

e

r

o

f

p

e

o

p

l

e

Year

ARTbyCD4count-Men

>500350-500250-350200-250100-20050-100<50

>500350-500250-350200-250100-20050-100<50

0

100,000

200,000

300,000

400,000

500,000

600,000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Numbe

rofpe

ople

Year

ARTbyCD4count- Women

>500 350-500 250-350 200-250 100-200 50-100 <50

>500 350-500 250-350 200-250 100-200 50-100 <50

0

100,000

200,000

300,000

400,000

500,000

600,000

200420052006200720082009201020112012201320142015

N

u

m

b

e

r

o

f

p

e

o

p

l

e

Year

ARTbyCD4count-Women

>500350-500250-350200-250100-20050-100<50

>500350-500250-350200-250100-20050-100<50

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-49 50-59 >60

Prevalen

ce

AgeGroups

Asthma

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-4950-59 >60

P

r

e

v

a

l

e

n

c

e

AgeGroups

Asthma

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-49 50-59 >60

Prevalen

ce

AgeGroups

Depression

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-4950-59 >60

P

r

e

v

a

l

e

n

c

e

AgeGroups

Depression

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-49 50-59 >60

Prevalen

ce

AgeGroups

Diabetes

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-4950-59 >60

P

r

e

v

a

l

e

n

c

e

AgeGroups

Diabetes

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-49 50-59 >60

Prevalen

ce

AgeGroups

Hypertension

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-4950-59 >60

P

r

e

v

a

l

e

n

c

e

AgeGroups

Hypertension

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-49 50-59 >60

Prevalen

ce

AgeGroups

CKD

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-4950-59 >60

P

r

e

v

a

l

e

n

c

e

AgeGroups

CKD

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-49 50-59 >60

Prevalen

ce

AgeGroups

CKD

Data

Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

18-4950-59 >60

P

r

e

v

a

l

e

n

c

e

AgeGroups

CKD

Data

Model

Hypertension

Diabetes

Stroke

Depression

Asthma

HIV & ART

Age and Sex

Breast cancer

Cervical cancer

Oesophageal cancer

Liver cancer

Prostate Cancer

Kidney disease

Other Cancer

Colorectal cancer

Mortality

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Num

bero

fpeo

ple(1,000)

Year

Data

Model

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

19501955196019651970197519801985199019952000200520102015

N

u

m

b

e

r

o

f

p

e

o

p

l

e

(

1

,

0

0

0

)

Year

Data

Model