This difference in multimorbidity NCD prevalence by HIV status is forecast to become more pronounced over the coming 20 years. The proportion of PLHIV diagnosed with at least one key NCD is projected to increase from 33% in 2015 to 59% in 2035, and proportion diagnosed with two or more from 5% in 2015 to 16% in 2035 (Fig. 3a). In the absence of additional NCD risk because of HIV infection, fewer (36%) would be expected to be diagnosed with at least one NCD. This contrasts with projected key NCD prevalence among HIV-negative, with 21% diagnosed with at least one NCD in 2035.
Amongst the NCDs evaluated by this model, the most prevalent NCDs between 2015 and 2035 are hypertension, CKD, depression and ever having been diagnosed with (any) cancer. Prevalence in 2015 is higher amongst PLHIV compared with uninfected persons and will increase steeply by 2035 compared with uninfected persons (Fig. 3c; for example, 25% of PLHIV are diagnosed with hypertension vs. 5.6% of uninfected persons in 2015 and 47.5% vs. 7.7% in 2035, 4.7% of PLHIV with CKD vs. 1.2% of uninfected persons in 2015 and 8% vs. 1.5% in 2035; Fig. 3c). The most prevalent cancers are cervical cancer (0.6% in 2015 vs. 0.8% in 2035) and breast cancer (0.4% in 2015 vs. 0.8% in 2035 in all people). As hypertension is a risk factor for other NCDs, including serious cardiovascular outcomes (i.e. strokes) and CKD, this increase contributes to escalating numbers of persons with multiple comorbidities. Age-standardized incidence rates per person suggest that the majority of these patterns will be driven by age (e.g. 0.16 and 0.16 in uninfected adults versus 0.21 and 0.16 in adult PLHIV in 2015 and 2035, respectively, for hypertension).
Although there is a predicted growth in prevalence of NCDs amongst PLHIV, most cases of NCDs will still be amongst HIV-negative persons (consistent with the fact that most people in the population are HIV-negative) and the total number of persons in Zimbabwe diagnosed with NCDs will nearly double or more in the coming 20 years (Fig. 3b). The number of people diagnosed with hypertension will increase from 1.33 million [1.04 million (78%) HIV-negative and 0.29 million (22%) PLHIV] in 2015 to 3.06 million [2.57 million (84%) HIV-negative and 0.49 million (16%) PLHIV]. Other than hypertension, the NCDs which affect the greatest number of people are CKD, increasing from 0.27 million in 2015 [0.22 million (80%) HIV-negative and 0.05 million (20%) PLHIV] to 0.59 million in 2035 [0.51 million (86%) HIV-negative and 0.08 million (14%) PLHIV] and depression 0.18 million in 2015 [0.15 million (85%) HIV-negative and 0.03 million (15%) PLHIV] to 0.44 million in 2035 (0.38 million (88%) HIV-negative and 0.05 million (12%) PLHIV].
Amongst people with at least two NCDs, a majority will suffer from hypertension with either CKD (21.2%), depression (16.6%), cancer (12.8%), diabetes (11.5%), asthma (10.0%) or stroke (4.7%; Fig. 3d), with trends similar by HIV status. Notable differences by HIV status include that, although the major contributing multimorbidity profiles amongst HIV-uninfected are composed of two NCDs, amongst PLHIV, a large proportion consist of three NCDs. These include hypertension and CKD with either depression (2.1% in 2015 and 2.3% in 2035), diabetes (0.7% in 2015 and 1.6% in 2035), cancer (0.5% in 2015 and 1.4% in 2035), or hypertension with depression and cancer (0.2% in 2015 and 1.5% in 2035; Fig. 3d).
Without changes in underlying risk factors, the burden of key NCDs in Zimbabwe is set to increase steeply in the coming 20 years, particularly in PLHIV. This will have major implications for healthcare provisions, requiring substantial planning for additional services. The increase in NCD burden will be driven by population growth, and amongst PLHIV by the rapidly ageing population of PLHIV (because of reductions in incidence and the success of ART scale-up) and, to a lesser extent, the cumulative exposure to HIV and ART. Our results suggest that by 2035, adult PLHIV will be nearly twice as likely to suffer from at least one key NCD and three times more likely to suffer from multiple key NCDs compared with HIV-negative persons, and that 15.2% of all key NCDs will be diagnosed in PLHIV, although they will contribute only 5.0% of the total Zimbabwean population.
The changing patterns of disease burden will exert increasing pressure on already fragile health systems in the country at all levels of care . Given resource constraints, the identification of cost-effective chronic disease service delivery models to manage the dual burden of HIV and NCDs will be critical to maintain the quality of health services in the country. In particular, there is a need to expand and strengthen the screening and treatment of hypertension, CKD, depression and cancers, as well as for prevention campaigns to be intensified. At the same time, the need to manage infectious diseases including tuberculosis, malaria and schistosomiasis (which also interact with HIV [25–27]), will remain in Zimbabwe for many years . Existing vertical infectious disease programmes potentially offer a platform to expand NCD services, but may be over-burdened. Service integration has the potential to both reduce costs (for providers and patients) associated with multiple visits and improve the quality of care – if carefully designed and sufficiently resourced, as to not damage existing health gains. Further research is needed to identify the feasibility of integrating additional services – for PLHIV and uninfected persons – into existing services and guide aspects such as monitoring and evaluation systems, targeted behavioural changes, demand generation for preventive services and linkage to care , in a way that strengthens rather than depletes current human resource capacity in the health sector.
Some important changes are already underway that may facilitate these efforts. HIV programs are shifting from vertical programs, focused on HIV diagnosis and treatment, to integrated care management, incorporating testing and treatment for other conditions and exploring community-based delivery . Recent World Health Organization (WHO) guidelines have promoted integrated and differentiated care, which increasingly seeks to minimise the requirement of PLHIV that are stably virally suppressed to attend clinics . As the PLHIV population ages, new protocols should carefully consider the extent to which patients with NCDs should have differential care, and balance the benefits of reduced treatment monitoring intensity against the benefits of risk group identification, screening and management of NCDs among PLHIV .
Our 2015 estimates are qualitatively similar to those reported in the 2015 cross-sectional study by Magodoro et al.. This study found a large burden of single (19.6%) and multiple (4.6%) morbidity amongst their study population of PLHIV, and that hypertension (10%), diabetes (2.1%), asthma (4.3%) and cancers (1.8%) contributed a large burden of NCD. Differences observed between their results and ours (e.g. single morbidity 19.6 vs. 28%, hypertension prevalence of 10 vs. 25%, asthma prevalence of 4.3 vs. 1.2%, diabetes prevalence of 2.1 vs. 1% and cancer prevalence 1.8 vs. 2.5% in their study compared with ours) may be in part because of the range of NCDs investigated, differences in NCD definitions, inclusion criteria for study participants and method used by Magodoro et al., who reviewed patients’ records for self-reported NCDs rather than clinical diagnosis.
This is the first model to forecast NCD burden in a low–middle-income setting with high HIV burden, with a focus on the burden amongst PLHIV in particular and to show how this is driven by clinical interactions, the evolving HIV epidemiology and maturing ART program. Our results are likely to be relevant for other HIV hyperendemic settings in Africa, which are undergoing rapid expansion of ART programs. Although further data collection and analysis on NCD trends in all countries is required, the results of this study provide an initial insight into the future NCD burden in a SSA country with a high HIV prevalence.
Despite these strengths, the model is limited by sparse data availability for age-specific NCD incidence or prevalence estimates in Zimbabwe. This restricts the extent to which the output could be tested and validated and may translate into conservative estimate of future NCD burden to the extent to which specific NCDs are not included. A major assumption of this study is that age-specific prevalence estimates in Zimbabwe will be similar to those reported in neighbouring countries, including South Africa, and that the increased likelihood for an individual to develop an NCD based on preexisting history of HIV or another NCD are translatable from high-income settings. The mechanisms driving increased NCD risk amongst PLHIV are complex and the relative contribution of various risk factors for NCDs in PLHIV is a topic of ongoing research. The model aims to capture the key factors relating to the HIV epidemic and attempts to incorporate the interdependence of NCDs and HIV in a manner consistent with best current understandings, but inevitably many layers of clinical interaction are not captured. For example, the model accounts for overall HIV-related risk for NCDs (e.g. related to inflammation of antiretroviral use), but in the absence of robust projections on how antiretroviral usage may change over time, we do not include the impact of individual antiretrovirals on NCDs . Similarly, the model does not account for potential changes in healthcare access (other than 90 : 90 : 90 scale-up) or structural changes, which may impact both NCD and HIV burden, for example, integrating screening and treatment services for NCDs in HIV clinics may reduce NCD burden. Most research output come from high-income settings , and although we have used those insights here, the extent to which those interactions will pertain to the Zimbabwean populations remains unclear. We hope that these analyses will catalyse further data collection and reporting, and as more data on age-specific NCD prevalence and risk factors for NCDs emerge from Zimbabwe, the model output can be updated.
The model does not account for changes in survival rates with NCDs, and does not simulate all NCDs that can affect the population of Zimbabwe. Nor does the model simulate communicable diseases and how increases in life expectancy could affect these or how reduction in fear of HVI transmission could increase risk behaviour and consequently, sexually transmitted diseases. However, the model does model all cancers, including AIDS-defining cancers, which may result in an underestimation of the proportion of cancers in HIV-positive compared with HIV-negative people. Furthermore, NCD data is based on diagnosed cases, and thus is likely to underestimate the true burden by excluding undiagnosed cases. These factors would tend to make the results a conservative estimate of the true NCD burden affecting Zimbabwe in the next 20 years. The model does not incorporate a transmission model, but rather simulates infection with HIV using incidence estimates . Consequently, there is no dynamic feedback between processes in this model and those HIV incidence projections.
Because of the lack of detailed data on lifestyle factors, such as smoking, alcohol consumption, diet and exercise and lack of robust projections on how these factors may change in the coming 20 years, the model assumes that the effect of lifestyle factors is uniform across the population and constant over time. If lifestyle-related risk factors are really restricted to a small proportion of the population in Zimbabwe, the model results may be overestimating or underestimating the number of people suffering from one or more NCD (overestimate in relation to smoking and alcohol consumption and underestimate in the case of healthy diet and exercise). As new data becomes available, the model results can be updated.
The profile of the PLHIV in Zimbabwe, and elsewhere in Africa, is changing, presenting new challenges in terms of meeting their healthcare needs. Failing to adapt could lead to deteriorating outcomes for PLHIV and further strain fragile health systems. Existing vertical HIV programs may provide opportunity for new NCD screening services for PLHIV and an example for uninfected persons. A priority for health systems research in the coming years must be to establish how these many competing priorities can be managed effectively with the limited resources that are available to them.
J.O. assisted with the model construction and code writing. A.V., N.P.F. and M.V. advised on all the healthcare system aspect of the interpretation of the output of this work. S.G. advised on HIV and NCD program aspects within Zimbabwe relating to the interpretation of the results. T.B.H. and M.S. conceptualized the study. M.S. developed the model, and carried out the analyses.
Funding: The study was funded by the Rush Foundation and the NIH (R21AG053093–02).
Conflicts of interest
There are no conflicts of interest.
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ageing; HIV; model; noncommunicable diseases; Zimbabwe
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