Mapping the Current and Future Noncommunicable Disease Burden in Kenya by Human Immunodeficiency Virus Status: A Modeling Study.

BACKGROUND: The noncommunicable disease (NCD) burden in Kenya is not well characterized, despite estimates needed to identify future health priorities. We aimed to quantify current and future NCD burden in Kenya by human immunodeficiency virus (HIV) status. METHODS: Original systematic reviews and meta-analyses of prevalence/incidence of cardiovascular disease (CVD), chronic kidney disease, depression, diabetes, high total cholesterol, hypertension, human papillomavirus infection, and related precancerous stages in Kenya were carried out. An individual-based model was developed, simulating births, deaths, HIV disease and treatment, aforementioned NCDs, and cancers. The model was parameterized using systematic reviews and epidemiological national and regional surveillance data. NCD burden was quantified for 2018-2035 by HIV status among adults. RESULTS: Systematic reviews identified prevalence/incidence data for each NCD except ischemic heart disease. The model estimates that 51% of Kenyan adults currently suffer from ≥1 NCD, with a higher burden in people living with HIV (PLWH) compared to persons not living with HIV (62% vs 51%), driven by their higher age profile and partly by HIV-related risk for NCDs. Hypertension and high total cholesterol are the main NCD drivers (adult prevalence of 20.5% [5.3 million] and 9.0% [2.3 million]), with CVD and cancers the main causes of death. The burden is projected to increase by 2035 (56% in persons not living with HIV; 71% in PLWH), with population growth doubling the number of people needing services (15.4 million to 28.1 million) by 2035. CONCLUSIONS: NCD services will need to be expanded in Kenya. Guidelines in Kenya already support provision of these among both the general and populations living with HIV; however, coverage remains low.

Barriers Influencing Linkage to Hypertension Care in Kenya: Qualitative Analysis from the LARK Hypertension Study.

BACKGROUND: Hypertension, the leading global risk factor for mortality, is characterized by low treatment and control rates in low- and middle-income countries. Poor linkage to hypertension care contributes to poor outcomes for patients. However, specific factors influencing linkage to hypertension care are not well known. OBJECTIVE: To evaluate factors influencing linkage to hypertension care in rural western Kenya. DESIGN: Qualitative research study using a modified Health Belief Model that incorporates the impact of emotional and environmental factors on behavior. PARTICIPANTS: Mabaraza (traditional community assembly) participants (n = 242) responded to an open invitation to residents in their respective communities. Focus groups, formed by purposive sampling, consisted of hypertensive individuals, at-large community members, and community health workers (n = 169). APPROACH: We performed content analysis of the transcripts with NVivo 10 software, using both deductive and inductive codes. We used a two-round Delphi method to rank the barriers identified in the content analysis. We selected factors using triangulation of frequency of codes and themes from the transcripts, in addition to the results of the Delphi exercise. Sociodemographic characteristics of participants were summarized using descriptive statistics. KEY RESULTS: We identified 27 barriers to linkage to hypertension care, grouped into individual (cognitive and emotional) and environmental factors. Cognitive factors included the asymptomatic nature of hypertension and limited information. Emotional factors included fear of being a burden to the family and fear of being screened for stigmatized diseases such as HIV. Environmental factors were divided into physical (e.g. distance), socioeconomic (e.g. poverty), and health system factors (e.g. popularity of alternative therapies). The Delphi results were generally consistent with the findings from the content analysis. CONCLUSIONS: Individual and environmental factors are barriers to linkage to hypertension care in rural western Kenya. Our analysis provides new insights and methodological approaches that may be relevant to other low-resource settings worldwide.

The impact of scaling up cervical cancer screening and treatment services among women living with HIV in Kenya: a modelling study.

Introduction: We aimed to quantify health outcomes and programmatic implications of scaling up cervical cancer (CC) screening and treatment options for women living with HIV in care aged 18-65 in Kenya. Methods: Mathematical model comparing from 2020 to 2040: (1) visual inspection with acetic acid (VIA) and cryotherapy (Cryo); (2) VIA and Cryo or loop excision electrical procedure (LEEP), as indicated; (3) human papillomavirus (HPV)-DNA testing and Cryo or LEEP; and (4) enhanced screening technologies (either same-day HPV-DNA testing or digitally enhanced VIA) and Cryo or LEEP. Outcomes measured were annual number of CC cases, deaths, screening and treatment interventions, and engaged in care (numbers screened, treated and cured) and five yearly age-standardised incidence. Results: All options will reduce CC cases and deaths compared with no scale-up. Options 1-3 will perform similarly, averting approximately 28 000 (33%) CC cases and 7700 (27%) deaths. That is, VIA screening would yield minimal losses to follow-up (LTFU). Conversely, LTFU associated with HPV-DNA testing will yield a lower care engagement, despite better diagnostic performance. In contrast, option 4 would maximise health outcomes, averting 43 200 (50%) CC cases and 11 800 (40%) deaths, given greater care engagement. Yearly rescreening with either option will impose a substantial burden on the health system, which could be reduced by spacing out frequency to three yearly without undermining health gains. Conclusions: Beyond the specific choice of technologies to scale up, efficiently using available options will drive programmatic success. Addressing practical constraints around diagnostics' performance and LTFU will be key to effectively avert CC cases and deaths.

Recommendations for the use of mathematical modelling to support decision-making on integration of non-communicable diseases into HIV care.

INTRODUCTION: Integrating services for non-communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale-up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD-HIV integration, use Kenya as a case-study to highlight how modelling has supported wider policy formulation and decision-making in healthcare and to collate stakeholders' recommendations on use of models for NCD-HIV integration decision-making. DISCUSSION: Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost-effective, practical and achieve rapid coverage scale-up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost-effective and sustainable policy option for countries with large HIV programmes and small, un-resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD-HIV integration. Modelling has successfully been used to inform health decision-making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost-benefit analysis for integration and (v) evaluating health system capacity needs. CONCLUSIONS: Modelling can and should play an integral part in the decision-making processes for health in general and NCD-HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision-making will depend on several factors including policy makers' comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability.

The influence of mobility among high-risk populations on HIV transmission in Western Kenya.

Western Kenya suffers a highly endemic and also very heterogeneous epidemic of human immunodeficiency virus (HIV). Although female sex workers (FSW) and their male clients are known to be at high risk for HIV, HIV prevalence across regions in Western Kenya is not strongly correlated with the fraction of women engaged in commercial sex. An agent-based network model of HIV transmission, geographically stratified at the county level, was fit to the HIV epidemic, scale-up of interventions, and populations of FSW in Western Kenya under two assumptions about the potential mobility of FSW clients. In the first, all clients were assumed to be resident in the same geographies as their interactions with FSW. In the second, some clients were considered non-resident and engaged only in interactions with FSW, but not in longer-term non-FSW partnerships in these geographies. Under both assumptions, the model successfully reconciled disparate geographic patterns of FSW and HIV prevalence. Transmission patterns in the model suggest a greater role for FSW in local transmission when clients were resident to the counties, with 30.0% of local HIV transmissions attributable to current and former FSW and clients, compared to 21.9% when mobility of clients was included. Nonetheless, the overall epidemic drivers remained similar, with risky behavior in the general population dominating transmission in high-prevalence counties. Our modeling suggests that co-location of high-risk populations and generalized epidemics can further amplify the spread of HIV, but that large numbers of formal FSW and clients are not required to observe or mechanistically explain high HIV prevalence in the general population.

Age patterns and sex ratios of adult mortality in countries with high HIV prevalence.

OBJECTIVE: To compare the 2016 United Nations Programme on HIV/AIDS (UNAIDS) modelled estimates of adult mortality in sub-Saharan Africa to empirical estimates. DESIGN: Age-specific mortality rates were obtained from nationally representative sibling survival data, recent household deaths and vital registration, and directly compared with UNAIDS estimates. Orphanhood prevalence derived from UNAIDS mortality estimates was compared with survey and census reports on the survival of children's parents. METHODS: Age-specific mortality rates for adults aged 15-59 years were calculated from Demographic and Health Surveys and deaths reported in censuses or vital registration, adjusted for underreporting, whenever possible. Proportions of orphans were extracted from censuses and surveys for children aged 5-9 years. RESULTS: UNAIDS estimates were significantly higher than sibling mortality estimates, except among men in countries with very high HIV prevalence. There was a better agreement between rates based on household deaths or vital registration and model outputs. Sex ratios (M/F) of adult mortality were lower in UNAIDS estimates. The modelled orphan prevalence was significantly higher than in surveys and censuses, again with the exception of paternal orphans in countries with very high HIV prevalence. Ratios of paternal-to-maternal orphans were lower in the UNAIDS model than surveys and censuses. Among women, increases in mortality due to AIDS were more concentrated in the age range 25-50 years in model outputs, as compared with empirical estimates. CONCLUSION: Discrepancies in levels, sex ratios and age patterns of adult mortality between empirical and UNAIDS estimates call for additional data quality assessments and improvements in estimation methods.