The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

As antiretroviral treatment (ART) coverage for people living with HIV (PLHIV) increases, HIV programmes require up-to-date information about evolving HIV risk behaviour and transmission risk, including those with low-level viremia (LLV; >50 to ≤1000 copies/mL), to guide prevention priorities. We aimed to assess differences in sexual risk behaviours, distribution of viral load (VL) and proportion of transmission across PLHIV subgroups. We analysed data from Population-based HIV Impact Assessment surveys in 14 sub-Saharan African countries during 2015-2019. We estimated adjusted prevalence ratios (aPR) of self-reported HIV high-risk behaviour (multiple partners and condomless sex) across cascade stages via generalised estimation equations. We modelled the proportions of transmission from each subgroup using relative self-reported sexual risk, a Hill function for transmission rate by VL, and proportions within cascade stages from surveys and UNAIDS country estimates for 2010-2020. Compared to PLHIV with undetectable VL (≤50 copies/mL), undiagnosed PLHIV (aPR women: 1.28 [95% CI: 1.08-1.52]; men: 1.61 [1.33-1.95]) and men diagnosed but untreated (2.06 [1.52-2.78]) were more likely to self-report high-risk sex. High-risk behaviour was not significantly associated with LLV. Mean VL was similar among undiagnosed, diagnosed but untreated, and on ART but non-suppressed sub-groups. Across surveys, undiagnosed and diagnosed but untreated contributed most to transmission (40-91% and 1-41%, respectively), with less than 1% from those with LLV. Between 2010 and 2020, the proportion of transmission from individuals on ART but non-suppressed increased. In settings with high ART coverage, effective HIV testing, ART linkage, and retention remain priorities to reduce HIV transmission. Persons with LLV are an increasing share of PLHIV but their contribution to HIV transmission was small. Improving suppression among PLHIV on ART with VL ≥1000 copies/mL will become increasingly important.

The UNAIDS 95-95-95 targets are an important metric for guiding national HIV programs and measuring progress towards ending the HIV epidemic as a public health threat by 2030. Nevertheless, as proportional targets, the outcome of reaching the 95-95-95 targets will vary greatly across, and within, countries owing to the geographic diversity of the HIV epidemic. Countries and subnational units with a higher initial prevalence and number of people living with HIV (PLHIV) will remain with a larger number and higher prevalence of virally unsuppressed PLHIV-persons who may experience excess morbidity and mortality and can transmit the virus to others. Reliance on achievement of uniform proportional targets as a measure of program success can potentially mislead resource allocation and progress towards equitable epidemic control. More granular surveillance information on the HIV epidemic is required to effectively calibrate strategies and intensity of HIV programs across geographies and address current and projected health disparities that may undermine efforts to reach and sustain HIV epidemic control even after the 95 targets are achieved.

Background: Adverse childhood experiences (ACEs) are a global public health concern. Many children experience multiple ACEs. Patterning of multiple ACEs may change over time.Objective: To assess latent classes of ACEs among male and female youth in Kenya and evaluate whether ACEs latent classes changed between surveys conducted in 2010 and 2019.Participants and setting: We used data from Kenya Violence Against Children and Youth Survey, a repeated nationally representative survey of male and female youth aged 13-24: 2010 (nf = 1227; nm = 1456) and 2019 (nf = 1344; nm = 788).Methods: Latent class analysis was used to estimate clustering of seven ACEs: orphanhood, experiencing physical intimate partner violence, physical violence by a parent/caregiver, physical violence by an adult community member, forced first sex, emotional (EV) and sexual violence (SV), stratified by sex and time.Results: For females in 2010, identified classes included (1) SV only, (2) household and community physical violence (PV), EV and SV, (3) household and community PV only, (4) low ACEs, and (5) EV only. In 2019, classes included (1) SV only, (2) household and community PV only, and (3) low ACEs. Among males in 2010, the four-class model included (1) household and community PV with EV, (2) low ACEs, (3) household and community PV with SV, and (4) household and community PV only. In 2019, identified classes included (1) orphanhood and SV, (2) orphanhood and PV, (3) low ACEs, and (4) household and community PV only. For both males and females, across the two survey years, some classes demonstrated continuity (low ACEs and caregiver and community PV for both males and females, and SV for females). Orphanhood emerged as relevant to the ACEs latent class structure in 2019 compared to 2010 among males.Conclusion: Prevalence and changes in latent classes between 2010 and 2019 can point toward priority areas and subgroups for violence prevention and response in Kenya.

Nationally representative surveys provide an opportunity to assess trends in recent human immunodeficiency virus (HIV) infection based on assays for recent HIV infection. We assessed HIV incidence in Kenya in 2018 and trends in recent HIV infection among adolescents and adults in Kenya using nationally representative household surveys conducted in 2007, 2012, and 2018. To assess trends, we defined a recent HIV infection testing algorithm (RITA) that classified as recently infected (<12 months) those HIV-positive participants that were recent on the HIV-1 limiting antigen (LAg)-avidity assay without evidence of antiretroviral use. We assessed factors associated with recent and long-term (>= 12 months) HIV infection versus no infection using a multinomial logit model while accounting for complex survey design. Of 1,523 HIV-positive participants in 2018, 11 were classified as recent. Annual HIV incidence was 0.14% in 2018 [95% confidence interval (CI) 0.057-0.23], representing 35,900 (95% CI 16,300-55,600) new infections per year in Kenya among persons aged 15-64 years. The percentage of HIV infections that were determined to be recent was similar in 2007 and 2012 but fell significantly from 2012 to 2018 [adjusted odds ratio (aOR) = 0.31, p < .001]. Compared to no HIV infection, being aged 25-34 versus 35-64 years (aOR = 4.2, 95% CI 1.4-13), having more lifetime sex partners (aOR = 5.2, 95% CI 1.6-17 for 2-3 partners and aOR = 8.6, 95% CI 2.8-26 for >= 4 partners vs. 0-1 partners), and never having tested for HIV (aOR = 4.1, 95% CI 1.5-11) were independently associated with recent HIV infection. Although HIV remains a public health priority in Kenya, HIV incidence estimates and trends in recent HIV infection support a significant decrease in new HIV infections from 2012 to 2018, a period of rapid expansion in HIV diagnosis, prevention, and treatment.

ObjectivesTo investigate factors associated with hospital length of stay (LOS) in patients admitted with suspected malaria using a competing risk approach.SettingCounty government referrals and major faith-based hospitals in Kenya in 2018.DesignSecondary analysis of a cross-sectional survey data.ParticipantsData were extracted from 2396 medical records of patients admitted with suspected malaria at 90 hospitals.Outcome measuresLOS, defined as time to discharge, was the primary event of interest, and time to death was the competing event against patient factors assessed during admission and hospitalisation.ResultsAmong the patients analysed, 2283 were discharged, 49 died and 64 were censored. The median LOS was 4 days (IQR: 3–6 days). The cumulative incidence of discharge significantly decreased (p<0.05) by 12.7% (subdistribution-HR (SDHR): 0.873; 95% CI 0.789 to 0.967) when the respiratory rate was assessed, by 14.1% (SDHR 0.859; 95% CI 0.754 to 0.978) when oxygen saturation was monitored, by 23.1% (SDHR 0.769; 95% CI 0.709 to 0.833) and 23.4% (SDHR 0.766; 95% CI 0.704 to 0.833) when haemoglobin/haematocrit and glucose/random blood sugar were performed, respectively, and by 30.4% (SDHR 0.696; 95% CI 0.626 to 0.774) when patients had at least one clinical feature of severe malaria. Conversely, patients with confirmed severe malaria and those treated with injectable artesunate had a significantly increased cumulative incidence of discharge by 21.4% (SDHR 1.214; 95% CI 1.082 to 1.362) and 33.9% (SDHR 1.339; 95% CI 1.184 to 1.515), respectively.ConclusionsFactors of inpatient clinical processes that influence hospital LOS were identified. These can be targeted during quality improvement interventions to enhance health service delivery in Kenya. Early recognition and appropriate management of the signs of malaria severity could greatly affect beneficial outcomes. Strengthening clinical practices and nursing care according to national case management guidelines should be a priority for malaria control managers in Kenya.

Objectives This study applied a Bayesian hierarchical ecological spatial model beyond predictor analysis to test for the best fitting spatial effects model to predict subnational levels of health workers’ knowledge of severe malaria treatment policy, artesunate dosing, and preparation. Setting County referral government and major faith-based hospitals across 47 counties in Kenya in 2019. Design and participants A secondary analysis of cross-sectional survey data from 345 health workers across 89 hospitals with inpatient departments who were randomly selected and interviewed. Outcome measures Three ordinal outcome variables for severe malaria treatment policy, artesunate dose and preparation were considered, while 12 individual and contextual predictors were included in the spatial models. Results A third of the health workers had high knowledge levels on artesunate treatment policy; almost three-quarters had high knowledge levels on artesunate dosing and preparation. The likelihood of having high knowledge on severe malaria treatment policy was lower among nurses relative to clinicians (adjusted OR (aOR)=0.48, 95% CI 0.25 to 0.87), health workers older than 30 years were 61% less likely to have high knowledge about dosing compared with younger health workers (aOR=0.39, 95% CI 0.22 to 0.67), while health workers exposed to artesunate posters had 2.4-fold higher odds of higher knowledge about dosing compared with non-exposed health workers (aOR=2.38, 95% CI 1.22 to 4.74). The best model fitted with spatially structured random effects and spatial variations of the knowledge level across the 47 counties exhibited neighbourhood influence. Conclusions Knowledge of severe malaria treatment policies is not adequately and optimally available among health workers across Kenya. The factors associated with the health workers’ level of knowledge were cadre, age and exposure to artesunate posters. The spatial maps provided subnational estimates of knowledge levels for focused interventions.

Background Previous research has shown a high prevalence of violence among young people in Kenya. Violence is a known risk factor for HIV acquisition and these two public health issues could be viewed as a syndemic. In 2010, Kenya became the third country to implement the Violence Against Children and Youth Survey (VACS). The study found a high prevalence of violence in the country. Led by the Government of Kenya, stakeholders implemented several prevention and response strategies to reduce violence. In 2019, Kenya implemented a second VACS. This study examines the changes in violence and risk factors for violence and HIV between 2010 and 2019. Methods The 2010 and 2019 VACS used a similar sampling approach and measures. Both VACS were cross-sectional national household surveys of young people aged 13-24 years, designed to produce national estimates of physical, sexual, and emotional violence. Prevalence and changes in lifetime experiences of violence and risk factors for violence and HIV were estimated. The VACS uses a three-stage cluster sampling approach with random selection of enumeration areas as the first stage, households as the second stage, and an eligible participant from the selected household as the third stage. The VACS questionnaire contains sections on demographics, risk and protective factors, violence victimisation, violence perpetration, sexual behaviour, HIV testing and services, violence service knowledge and uptake, and health outcomes. For this study, the main outcome variables were violence victimisation, context of violence, and risk factors for violence. All analyses were done with the entire sample of 13-24-year-olds stratified by sex and survey year. Findings The prevalence of lifetime sexual, physical, and emotional violence significantly declined in 2019 compared with 2010, including unwanted sexual touching, for both females and males. Experience of pressured and forced sex among females also decreased between the surveys. Additionally, significantly more females sought and received services for sexual violence and significantly more males knew of a place to seek help in 2019 than in 2010. The prevalence of several risk factors for violence and HIV also declined, including infrequent condom use, endorsement of inequitable gender norms, endorsement of norms justifying wife beating, and never testing for HIV. Interpretation Kenya observed significant declines in the prevalence of lifetime violence and some risk factors for violence and HIV, and improvements in some service seeking indicators between 2010 and 2019. Continued prioritisation of preventing and responding to violence in Kenya could contribute to further reductions in violence and its negative outcomes. Other countries in the region that have made substantial investments and implemented similar violence prevention programmes could use repeat VACS data to monitor violence and related outcomes over time. Copyright Published by Elsevier Ltd.

Abstract Background For assessing the HIV epidemic in Kenya, a series of independent HIV indicator household-based surveys of similar design can be used to investigate the trends in key indicators relevant to HIV prevention and control and to describe geographic and sociodemographic disparities, assess the impact of interventions, and develop strategies. We developed methods and tools to facilitate a robust analysis of trends across three national household-based surveys conducted in Kenya in 2007, 2012, and 2018. Methods We used data from the 2007 and 2012 Kenya AIDS Indicator surveys (KAIS 2007 and KAIS 2012) and the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA 2018). To assess the design and other variables of interest from each study, variables were recoded to ensure that they had equivalent meanings across the three surveys. After assessing weighting procedures for comparability, we used the KAIS 2012 nonresponse weighting procedure to revise normalized KENPHIA weights. Analyses were restricted to geographic areas covered by all three surveys. The revised analysis files were then merged into a single file for pooled analysis. We assessed distributions of age, sex, household wealth, and urban/rural status to identify unexpected changes between surveys. To demonstrate how a trend analysis can be carried out, we used continuous, binary, and time-to-event variables as examples. Specifically, temporal trends in age at first sex and having received an HIV test in the last 12 months were used to demonstrate the proposed analytical approach. These were assessed with respondent-specific variables (age, sex, level of education, and marital status) and household variables (place of residence and wealth index). All analyses were conducted in SAS 9.4, but analysis files were created in Stata and R format to support additional analyses. Results This study demonstrates trends in selected indicators to illustrate the approach that can be used in similar settings. The incidence of early sexual debut decreased from 11.63 (95% CI: 10.95–12.34) per 1,000 person-years at risk in 2007 to 10.45 (95% CI: 9.75–11.2) per 1,000 person-years at risk in 2012 and to 9.58 (95% CI: 9.08–10.1) per 1,000 person-years at risk in 2018. HIV-testing rates increased from 12.6% (95% CI: 11.6%–13.6%) in 2007 to 56.1% (95% CI: 54.6%–57.6%) in 2012 but decreased slightly to 55.6% [95% CI: 54.6%–56.6%) in 2018. The decrease in incidence of early sexual debut could be convincingly demonstrated between 2007 and 2012 but not between 2012 and 2018. Similarly, there was virtually no difference between HIV Testing rates in 2012 and 2018. Conclusions Our approach can be used to support trend comparisons for variables in HIV surveys in low-income settings. Independent national household surveys can be assessed for comparability, adjusted as appropriate, and used to estimate trends in key indicators. Analyzing trends over time can not only provide insights into Kenya’s progress toward HIV epidemic control but also identify gaps.

COVID-19 mitigation measures such as curfews, lockdowns, and movement restrictions are effective in reducing the transmission of SARS-CoV-2; however, these measures can enable sexual violence. We used data from the Kenya Health Information System and different time-series approaches to model the unintended consequences of COVID-19 mitigation measures on sexual violence trends in Kenya. We found a model-dependent 73%–122% increase in reported sexual violence cases, mostly among persons 10–17 years of age, translating to 35,688 excess sexual violence cases above what would have been expected in the absence of COVID-19–related restrictions. In addition, during lockdown, the percentage of reported rape survivors receiving recommended HIV PEP decreased from 61% to 51% and STI treatment from 72% to 61%. Sexual violence mitigation measures might include establishing comprehensive national sexual violence surveillance systems, enhancing prevention efforts during school closures, and maintaining access to essential comprehensive services for all ages and sexes.

C OVID-19 mitigation measures such as curfews, lockdowns, and travel restrictions reduce disease transmission, but these measures also disrupt economic activities and social networks, and hinder access to health and social services (1,2).Mass disruption of socioeconomic activities often has unintended consequences, including an increase in sexual violence and prolonged exposure to abusers, while concomitantly limiting survivors' access to and the availability of medical and social services (2-5).A COVID-19 case was confirmed in Kenya on March 13, 2020.The government rolled out a series of measures to contain the spread of COVID-19 and mitigate its impacts on March 15, 2020.These measures included school closures, movement restrictions, curfews, rescheduling of clinical services, and reassignments of health workers to COVID-19 case management (Appendix Figure 1, https://wwwnc.cdc.gov/EID/article/28/13/22-0394-App1.pdf).In May 2020, the United Nations Population Fund warned that an additional 31 million cases of sexual and gender-based violence would be seen globally during implementation of COVID-19 mitigation measures and called on governments to be alert to these dangers (6).In July 2020, one study found that patterns of sexual violence against children in Kenya had changed and that the average age of survivors declined from 16 to 12 years (H.D. Flowe et al., unpub.data, https:// doi.org/10.31234/osf.io/eafwu).That study also found that 76% of offenses occurred during the day and coincided with normal school hours.Another study during the lockdown noted that 78% of perpetrators were known to the victim, either family members or neighbors (7).These studies were not designed to quantify national estimates of sexual violence, but they attest to the heightened exposure of women and girls to sexual violence.To determine whether sexual violence increased in Kenya during the COVID-19 pandemic, we examined trends in reported sexual violence cases in Kenya during January 2015-June 2021.

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.

Background As countries make progress towards HIV epidemic control, there is increasing need to identify finer geographic areas to target HIV interventions. We mapped geographic clusters of new HIV diagnoses, and described factors associated with HIV-positive diagnosis, in order to inform targeting of HIV interventions to finer geographic areas and sub-populations. Methods We analyzed data for clients aged > 15 years who received home-based HIV testing as part of a routine public health program between May 2016 and July 2017 in Siaya County, western Kenya. Geospatial analysis using Kulldorff’s spatial scan statistic was used to detect geographic clusters (radius < 5 kilometers) of new HIV diagnoses. Factors associated with new HIV diagnosis were assessed in a spatially-integrated Bayesian hierarchical model. Results Of 268,153 clients with HIV test results, 2906 (1.1%) were diagnosed HIV-positive. We found spatial variation in the distribution of new HIV diagnoses, and identified nine clusters in which the number of new HIV diagnoses was significantly (1.56 to 2.64 times) higher than expected. Sub-populations with significantly higher HIV-positive yield identified in the multivariable spatially-integrated Bayesian model included: clients aged 20–24 years [adjusted relative risk (aRR) 3.45, 95% Bayesian Credible Intervals (CI) 2.85–4.20], 25–35 years (aRR 4.76, 95% CI 3.92–5.81) and > 35 years (aRR 2.44, 95% CI 1.99–3.00); those in polygamous marriage (aRR 1.84, 95% CI 1.55–2.16), or separated/divorced (aRR 3.36, 95% CI 2.72–4.08); and clients who reported having never been tested for HIV (aRR 2.35, 95% CI 2.02–2.72), or having been tested > 12 months ago (aRR 1.53, 95% CI 1.41–1.66). Conclusion Our study used routine public health program data to identify granular geographic clusters of higher new HIV diagnoses, and sub-populations with higher HIV-positive yield in the setting of a generalized HIV epidemic. In order to target HIV testing and prevention interventions to finer granular geographic areas for maximal epidemiologic impact, integrating geospatial analysis into routine public health programs can be useful.

Background: In 2010, Kenya became the third country to implement the Violence Against Children and Youth Survey (VACS). The study found high prevalence of violence in the country. Led by the Government of Kenya, stakeholders implemented several prevention and response strategies to reduce violence. In 2019, Kenya implemented a second VACS. This study examined changes in violence and risk factors for violence between 2010 and 2019.Methods: The 2010 and 2019 VACS used a similar sampling approach and measures. Both were national household surveys of persons aged 13–24 years. Prevalence and changes in lifetime experiences of violence and risk factors for violence were estimated.Findings: The prevalence of lifetime sexual, physical, and emotional violence significantly declined in 2019 compared to 2010 for both females and males. Additionally, significantly more female survivors sought and received services for sexual violence and significantly more males knew of a place to seek help in 2019 than in 2010. Prevalence of several risk factors for violence also declined. For example, fewer females endorsed at least one inequitable gender norm and fewer males endorsed at least one inequitable gender norms and norms endorsing wife beating in 2019 compared to 2010.Interpretation: Kenya observed significant declines in the prevalence of lifetime violence victimization and some risk factors for violence between 2010 and 2019. Although low, some service seeking and receipt indicators also improved between 2010 and 2019. Prioritizing services and their uptake among survivors of childhood violence in Kenya will be important.Funding: The U.S. President's Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention (CDC) under the terms of cooperative agreements #5NU2GGH001520 and #1U2GGH001619.Declaration of Interest: The authors have no declared conflicts of interest.Ethical Approval: Protocol reviewed and approved by the Ethical Review Committee (ERC) of the Kenya Medical Research Institute and by the CDC Institutional Review Board (IRB).

Tungiasis is a neglected parasitic disease that significantly affects communities, especially in developing countries. This study developed a Bayesian severity of the jigger infestation model and its spatial counterpart. Putative determinants leading to different levels of infestation and the most affected areas were to be identified through the model. We collected data through a cross-sectional study with a multi-stage sampling design. A structured questionnaire was administered in each household to capture variables used for modelling jigger infestations. The severity of jigger infestation categorized for each individual was modelled against all the other predictor variables. It was also integrated with spatial data to determine the spatial distribution pattern of jigger infestation. A Bayesian multinomial logistic regression model was used to assess the association between various predictors and different infestation levels. Specifically, an ordered Bayesian Severity Hierarchical (OBSH) categorical model was obtained. This model was categorical based on the Counties (1-Nyeri, 2-Murang'a and 3-Kiambu). Results from this model showed that for a one-unit decrease in the poverty index at level 1 (individuals categorized as poor) there was about a 69% increase in the severity of jigger infestation. A one-unit increase in the percentage of clay in the soil increased the odds ratio of the severity of jigger infestation by a factor of 11.21 while a high percentage of nitrogen in the soil lowered the severity of infestation.  Severity of jigger infestation reduced from the baseline, Nyeri County to Kiambu County. It also increased with increasing altitude due to a decrease in nitrogen levels.

Estimating incidence from cross-sectional data sources is both important to the understanding of the HIV epidemic and challenging from a methodological standpoint. We develop a new incidence estimator that measures the size of the undiagnosed population and the amount of time spent undiagnosed in order to infer incidence and transmission rates. The estimator is calculated using commonly collected information on testing history and HIV status and, thus, can be deployed in many HIV surveys without additional cost. If ART biomarker status and/or viral load information is available, the estimator can be adjusted for biases in self-reported testing history. The performance of the estimator is explored in two large surveys in Kenya, where we find our point estimates to be consistent with assay-derived estimates, with much smaller standard errors.

Sub-Saharan Africa (SSA) is at a crossroad. Over the last decade, successes in the scale up of HIV care and treatment programs has led to a burgeoning number of people living with HIV (PLHIV) in care. At the same time, an epidemiologic shift has been witnessed with a concomitant rise in non-communicable diseases (NCD) related morbidity and mortality. Against low levels of domestic financing and strained healthcare delivery platforms, the NCD-HIV syndemic threatens to reverse gains made in care of people living with HIV (PLHIV). NCDs are the global health disruptor of the future. In this review, we draw three proposals for low and middle-income countries (LMICs) based on existing literature, that if contextually adopted would mitigate against impending poor NCD-HIV care outcomes. First, we call for an adoption of universal health coverage by countries in SSA. Secondly, we recommend leveraging on comparably formidable HIV healthcare delivery platforms through integration. Lastly, we advocate for institutional-response building through a multi-stakeholder governance and coordination mechanism. Based on our synthesis of existing literature, adoption of these three strategies would be pivotal to sustain gains made so far for NCD-HIV care in SSA.

Corresponding author 11 Email: otiende.verrah@students.jkuat.ac.ke; verrahodhiambo@gmail.com 12 VO, TO and HM conceived and designed the study. VO wrote the first draft of the manuscript. 13 VO and TO analyzed data. HM contributed to data analysis. All authors contributed to reviewing 14 literature, interpretation of the results and writing of the manuscript. All authors read and 15 approved the final manuscript. 16 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted January 27, 2020. ; https://doi.org/10.1101/2020.01.22.20018390 doi: medRxiv preprint

INTRODUCTION:People Living with HIV (PLHIV) bear a disproportionate burden of non-communicable diseases (NCDs). Despite their significant toll across populations globally, the NCD burden among key populations (KP) in Kenya remains unknown. The burden of four NCD-categories (cardiovascular diseases, cancer, chronic respiratory diseases and diabetes) was evaluated among female sex workers (FSWs) and men who have sex with men (MSM) at the Sex Workers Outreach Program (SWOP) clinics in Nairobi Kenya.METHODS:A retrospective medical chart review was conducted at the SWOP clinics among KP clients ≥15 years living with HIV enrolled between October 1, 2012 and September 30, 2015. The prevalence of the four NCD-categories were assessed at enrollment and during subsequent routine quarterly follow-up care visits as per the Ministry of Health guidelines. Prevalence at enrollment was determined and distributions of co-morbidities assessed using Chi-square and t-tests as appropriate during follow-up visits. Univariate and multivariate analysis were conducted to identify factors associated with NCD diagnoses.RESULTS:Overall, 1,478 individuals' records were analyzed; 1,392 (94.2%) were from FSWs while 86 (5.8%) were from MSM over the three-year period. FSWs' median age was 35.3 years (interquartile range (IQR) 30.1-41.6) while MSM were younger at 26.8 years (IQR 23.2-32.1). At enrollment into the HIV care program, most KPs (86.6%) were at an early WHO clinical stage (stage I-II) and 1462 (98.9%) were on first-line anti-retroviral therapy (ART). A total of 271, 18.3% (95% CI: 16.4-20.4%), KPs living with HIV had an NCD diagnosis in their clinical chart records during the study period. Majority of these cases, 258 (95.2%) were noted among FSWs. Cardiovascular disease that included hypertension was present in 249/271, 91.8%, of KPs with a documented NCD. Using a proxy of two or more elevated blood pressure readings taken < 12 months apart, prevalence of hypertension rose from 1.0% (95% CI: 0.6-1.7) that was documented in the charts during the first year to 16.3% (95% CI: 14.4-18.3) in the third year. Chronic respiratory disease mainly asthma was present in 16/271, a prevalence of 1.1% (95% CI: 0.6-1.8) in the study population. Cancer in general was detected in 10/271, prevalence of 0.7% (95% CI: 0.3-1.2) over the same period. Interestingly, diabetes was not noted in the study group. Lastly, significant associations between NCD diagnosis with increasing age, body-mass index and CD4 + cell-counts were noted in univariate analysis. However, except for categories of ≥ BMI 30 kg/m2 and age ≥ 45, the associations were not sustained in adjusted risk estimates.CONCLUSION:In Kenya, KP living with HIV and on ART have a high prevalence of NCD diagnoses. Multiple NCD risk factors were also noted against a backdrop of a changing HIV epidemic in the study population. This calls for scaling up focus on both HIV and NCD prevention and care in targeted populations at increased risk of HIV acquisition and transmission. Hence, KP programs could include integrated HIV-NCD screening and care in their guidelines.