Hiv transmission in kenya-Kenya Country Profile

Disease spreads as a result of people moving and coming in contact with each other. Thus the mobility patterns of individuals are crucial in understanding disease dynamics. Here we study the impact of human mobility on HIV transmission in different parts of Kenya. We build an SIR metapopulation model that incorporates the different regions within the country. We parameterise the model using census data, HIV data and mobile phone data adopted to track human mobility.

Hiv transmission in kenya

Bystatements were finally made about the problem and how the situation should have been addressed sooner. A meeting at the International AIDS Conference, being held from 23 to Naked sacrifice July in Amsterdam, Netherlands, has showcased how Kenya is responding to the challenges and opportunities on the way towards validation of the elimination Hif mother-to-child transmission of HIV in a high-burden context. No identifiable patient information was included in the database, but facility-specific patient identification numbers were included. Keeling M, Danon L. Similarly, we do not model precautions taken by individuals who might avoid having sexual relationships with people coming from the highly infected areas [ 6162 ]. Methods Med. Statistical analysis Demographic data were summarized with Hiv transmission in kenya statistics.

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Kneya Kingdom Russia Ukraine. Overall, we observed slightly higher odds of positivity in female infants compared to male infants Hiv transmission in kenya birth to six gransmission. Definition and estimation of an actual reproduction number describing past infectious disease transmission: application to Shaved pink virgins epidemics among homosexual men in Denmark, Norway and Sweden. Margevicius R and Joshi H. For example, after 8 years without human mobility, Wajir region has 2, infectious individuals, while including human mobility gives 2, infectious individuals. For example, policies should concentrate on educating people working in urban areas to take care in their sexual kehya while in the urban area by promoting safe sex, encouraging condom use, reducing the number of sex partners or reducing risky behaviours like having sex with sex workers [ 121561 ]. Mathematical models that capture spatial scale for the spread of diseases dynamics are usually referred to as metapopulation models. The proportion of mothers Hjv on ART during pregnancy among those with treatment data decreased dramatically over Hiv transmission in kenya, from Variables that were significantly associated with infant HIV infection in univariate models were included in a multivariate logistic regression model. Through the air.

International Journal of Applied and Computational Mathematics.

  • Disease spreads as a result of people moving and coming in contact with each other.
  • HIV risk factors were assessed using multivariable logistic regression.
  • Several issues addressed in the report include gender based prevalence rates, new infections county and point of entry into the database used to dispense ART Antiretroviral therapy.
  • Kenya has a severe, generalized HIV epidemic, but in recent years, the country has experienced a notable decline in HIV prevalence, attributed in part to significant behavioral change and increased access to ART antiretroviral drugs.
  • Myths persist about how HIV is transmitted.
  • CDC also provides technical leadership in the implementation of high-quality laboratory services in Kenya through infrastructure improvement, capacity building of health workers, and optimization of the laboratory-clinical interface.

Cross-sectional analysis of national early infant diagnosis EID database. HIV risk factors were assessed using multivariable logistic regression. Greater odds of infection were observed in females OR: 1. In —, 9. Infants exposed to all three risky practices had a seven-fold higher odds of HIV infection compared to those exposed to recommended practices.

Unfortunately, barriers to healthcare and sub-optimal infant feeding and care practices still exist in resource-limited settings and continue to propagate transmission of HIV from mothers to infants.

Identifying infected infants early and initiating them on ART as soon as possible after diagnosis is essential to slow the progression from HIV infection to AIDS and prolong the life of the patient[ 1 ]. However, gaps remain.

Access to care is poorest in rural areas. Better understanding of these gaps and the remaining risk factors for HIV infection among infants who get tested is essential to eliminate MTCT in Kenya and neighboring countries.

This study used nearly a decade of nationwide data from a national HIV laboratory database of over , EID samples to assess the risk factors for HIV transmission in infants and to identify important predictors of HIV infection over time. As of , there was targeted testing of HIV-exposed symptomatic infants; in —, as more resources became available for testing, the guidelines changed to test all HIV-exposed infants.

If positive, an EID test was recommended. If the antibody test was positive at 9 months, the infant received a confirmatory EID test. In , the algorithm was updated to recommend an EID test at 6 weeks or first contact after 6 weeks[ 10 ].

Seven laboratories form the testing network and laboratory request forms are compiled into a national database across the laboratories. Thus, the database covers nearly all infants receiving EID testing in the country. Mean turnaround time from sample collection to results dispatch from the laboratory has changed over time but was 17 days as of July [ 11 ]. This cross-sectional analysis was based on a retrospective review of all data stored in the national HIV database between January and July Briefly, samples were collected under sterile conditions from infants using either a heel prick or finger prick depending on the age and weight of the infant.

DBS filter papers were labeled and dried separately on a drying rack overnight. They were then packaged using glycine envelopes and sealed plastic bags under sterile conditions and sent to the testing laboratory by a courier system accompanied by a laboratory request form.

All positive samples were retested to confirm their status while a request for recollection of a new sample was made in cases where the test failed or number of blood spots was not sufficient to allow repeat confirmatory testing for positive samples.

The database was retrospectively accessed to extract the relevant data and run these analyses. A total of , samples were successfully collected from infants visiting health facilities across all regions in Kenya between January and July and tested in seven national laboratories.

Due to improper DBS sample collection, packaging or labeling, samples were rejected and were therefore excluded from the analysis. Patients with missing data on other predictor variables were included in a missing indicator category. Demographic data were summarized with descriptive statistics. The primary outcome was infant HIV status infected vs.

We examined predictors of infant HIV status, including breastfeeding, age, prophylaxis, antiretroviral therapy and portal of entry. Variables that were significantly associated with infant HIV infection in univariate models were included in a multivariate logistic regression model. Finally, we combined all behavioral risk factors identified for infant HIV infection into one variable and assessed the increased odds of infection in an infant with multiple risk factors.

The models accounted for clustering by health facilities using the vce cluster function for statistical analysis. The interaction between age and sex was examined and a Wald p-value for interaction was calculated.

Kenya has a national quality assurance program for the seven molecular laboratories. All seven labs are enrolled and participate in the CDC Atlanta proficiency testing program. They also participate in a quarterly inter-laboratory EQA programme.

The need for participant consent was waived. No identifiable patient information was included in the database, but facility-specific patient identification numbers were included.

Patient characteristics and univariate and multivariate logistic regression analyses are presented in Table 1. Of the , infants in the dataset, 8. Data on sex was unavailable for The median age at testing was 1. Age at testing did not differ substantially between the sexes. Exclusive breastfeeding EBF was provided to Mixed breastfeeding was practiced by 9. In addition, mixed breastfeeding decreased over time.

Approximately Univariate and multivariable logistic regression accounting for clustering by health facility. The multivariable model was adjusted for all factors in this table.

The missing indicator method was used to account for missing data. Abbreviations: AZT: zidovudine. Sd: single dose. NVP: nevirapine. HAART: highly active antiretroviral therapy. In multivariate analyses, the odds ratio for HIV infection in females was 1. Compared to infants being tested at 6 weeks to 2 months the majority of infants , the odds ratio of testing positive for HIV infection was 1. However, most of those infants were listed as testing at 0 months, which may have represented a data entry error; sensitivity analyses in which these infants were considered to be missing age showed an attenuated association in this age group [OR changed to 1.

The percentage testing positive for infection was the lowest at 6 weeks to 2 months when the largest number of infants were tested, and then increased with age to Compared to infants being tested at 6 weeks to 2 months, the odds ratio of testing positive for HIV infection was 1.

Infants whose mothers were not taking ART had nearly double the odds of the HIV infection when compared to those whose mothers were on treatment multivariable-adjusted OR 1. For infants not on any prophylaxis, the odds ratio for HIV infection was 2.

The percentage of infants on nevirapine for 6 weeks that tested positive was 4. Infants who were categorized as mixed breastfed had 1.

There was not a statistically significant difference in HIV infection between exclusively breastfeeding and not breastfeeding. The highest yield for detecting infected patients was observed in the pediatric ward and the outpatient department. For infants who sought health services at the pediatric ward, the odds ratio of HIV infection was 3. However, given the difference in the volume of patients tested in each entry point, only 3.

Table 2 shows multivariable-adjusted model results stratified by early and recent time period. The percentage of infants receiving an EID test who tested positive for HIV infection decreased over time; in —, positivity was Most associations were consistent over time, although some associations varied in their strength. Multivariable logistic regression accounting for clustering by health facility. A separate model was run for each time period. The multivariable model was adjusted for all factors in this table plus test year.

The missing indicator method was used to account for missing data; to save space, missing categories are not shown. The proportion of mothers not on ART during pregnancy among those with treatment data decreased dramatically over time, from Infant prophylaxis was not widely available in — so data was not routinely collected, but between — and — the proportion of infants receiving no prophylaxis decreased from Mixed breastfeeding decreased from Not breastfeeding appeared protective compared to exclusive breastfeeding in — OR: 0.

Finally, associations between entry point and HIV have changed over time, particularly in that the pediatric ward in more recent years had an extremely high yield for identifying infected patients. There were no substantial differences in the study findings when patients with age listed as a negative value or greater than or equal to 2 years were included in the analysis with age listed as missing.

For this study, we analysed a comprehensive national dataset from Kenya covering nearly a decade to describe the determinants of HIV status in infants below 2 years of age at testing.

Exposure to risky practices mother not on HAART, no infant prophylaxis, mixed breastfeeding was associated with a seven-fold higher odds of HIV-positivity compared to exposure to recommended practices mother on HAART, infant on nevirapine for six weeks, no breastfeeding. Most importantly, many infants were not identified as infected until older ages and received sub-optimal prevention practices lack of HAART for mother, infant prophylaxis, exclusive breastfeeding or exclusive replacement feeding.

Yet hundreds to thousands of infants are still becoming infected each year because their mothers are not enrolled in care. Barriers to testing and care include distance to health facility and transportation costs, facility inefficiencies, such as stock-outs and long wait times, and persistent shamefulness and stigma[ 15 ].

Expanded testing efforts in adolescent and early adult women and increased HIV self-testing availability may help to expand case-finding in women of childbearing age.

HIV-infected women who deliver outside of facilities tend to have lower income and be less educated and less likely to be on treatment[ 18 ], meaning that their infants are at especially high risk. Pilot interventions that have been shown to improve PMTCT program coverage, retention, and quality include mHealth tools[ 19 ] such as SMS[ 20 ], rapid results initiatives[ 21 ], systems engineering approaches[ 22 ], and efforts to reduce health provider absenteeism[ 23 ]; these could be considered in areas struggling with program performance.

In addition, there likely remain inconsistencies with health provider care among women and infants enrolled in PMTCT programs. A case-control study in Western Kenya observed that Infants were more likely to be infected with HIV if their provider did not follow maternal and infant ART guidelines[ 24 ].

Ensuring reinforcement of guidelines through periodic re-trainings and supportive supervision, as well as strong supply chain management systems will be important to continue to strengthen the PMTCT program. We observed HIV infections in some infants despite their mothers being on HAART; the percentage of infected infants in this category declined over time as drug regimens changed to 4.

Possible reasons for transmission in these infants include late treatment initiation in mothers, treatment nonadherence, lower efficacy on certain treatments, drug resistance, and stock-outs.

This database does not contain information on these factors and therefore we are only able to speculate on the relative contribution of each one. Some mothers had interrupted HAART, indicating that non-adherence to treatment may be another barrier to care and testing.

Strong adherence support programs are also important to minimize transmission during pregnancy. Innovative methods including community adherence groups[ 25 ] and transport reimbursement for low-income patients[ 26 ] may help to improve drug adherence and retention in PMTCT programs. New treatment regimens for mothers and prophylaxis options for infants will likely influence transmission rates in the future.

Contact between broken skin, wounds, or mucous membranes and HIV-infected blood or blood-contaminated body fluids. These results contrast with some other studies that report a positive correlations between mobility and the HIV prevalence [ 19 , 54 ]. Naresh R, Sharma D. Fowler MG. This is because vaginal fluid and blood can carry HIV. This also means that comparisons between groups cannot be interpreted as nationally representative, do not necessarily represent transmission, and are in part a product of testing coverage.

Hiv transmission in kenya

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Disease spreads as a result of people moving and coming in contact with each other. Thus the mobility patterns of individuals are crucial in understanding disease dynamics. Here we study the impact of human mobility on HIV transmission in different parts of Kenya. We build an SIR metapopulation model that incorporates the different regions within the country.

We parameterise the model using census data, HIV data and mobile phone data adopted to track human mobility. We found that movement between different regions appears to have a relatively small overall effect on the total increase in HIV cases in Kenya.

However, the most important consequence of movement patterns was transmission of the disease from high infection to low prevalence areas. Mobility slightly increases HIV incidence rates in regions with initially low HIV prevalences and slightly decreases incidences in regions with initially high HIV prevalence.

We discuss how regional HIV models could be used in public-health planning. This paper is a first attempt to model spread of HIV using mobile phone data, and we also discuss limitations to the approach.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. The basic approach is to describe the population in terms of three states: those who have not contracted HIV, susceptible , those who have HIV and remain sexually active, infectious and those individuals who are no longer engaged in spreading the disease, removed.

This style of SIR model has been widely used to capture the dynamics of diseases, by for example [ 1 — 5 ] and in many other articles. For HIV, the SIR model is also applied, but with the assumption that infected individuals do not recover from the disease but rather stop being infectious [ 6 — 10 ]. SIR models consider the dynamics of diseases to depend only on the individual status like susceptible, infected or removed.

Under the standard SIR model, the transmission rates, infection periods, contact patterns and removed rates do not account for the spatial spread of the diseases.

In this standard version, they ignore variations in demographic, social, cultural, economic and geographic factors [ 4 , 11 ]. In reality, however, the spread of HIV is highly associated with geographic factors, such as population mobility, as well as the accessibility of and the proximity between infected areas [ 12 ]. Human mobility can be seasonal, short term and long term [ 13 , 14 ].

Several studies have shown that migrant workers in urban areas can spread HIV to rural areas [ 15 , 16 ]. For example, in South Africa, men who had moved away from a rural area to an urban area were twice as likely to have HIV than those still living in the rural area [ 12 ]. These migrant workers would regularly return to the rural area, potentially spreading the virus. Likewise, areas associated with high human mobility such as commercial farms and agricultural estates, mining areas, business centres and residential areas along busy roads have been connected to an increase in HIV infections [ 17 — 21 ].

Not all migrations appear to increase HIV transmission, and other studies have reported that the rural-urban migrations have little or no link to HIV risk [ 22 , 23 ]. Overall, however, it appears that short-term human mobility, for example return visits from work to home town, are associated with high-risk behaviour. The migration to urban areas leads to an increased chance of interacting with individuals who are at higher-risk of being HIV infected, like sex workers [ 13 , 14 , 24 ].

Return visits to a home town then provide further spread of the disease. Mathematical models that capture spatial scale for the spread of diseases dynamics are usually referred to as metapopulation models. In metapopulation models, the area under study is divided into different regions according to geographic positions.

The distinct regions could be cities, towns or villages. The regions are connected by people travelling between them. Various studies have incorporated metapopulations in disease dynamics [ 25 — 30 ]. For example, Sattenspiel and Dietz [ 31 ], used a metapopulation model to show that increase in human mobility was associated with an increase of the spread of measles in the West Indian island of Dominica.

Travel networks have very complex influence on disease dynamics, since they may fuel or may help disease extinction [ 29 ]. For example, Arino et al [ 32 ] used a two-region metapopulation model to study the influence of travel rates with respect to the stability of influenza. They observed that, for isolated regions, the disease in one of the regions approached the disease-free equilibrium while in the other region it approached the endemic. Introducing very small movements between the regions, the disease in all regions approached the endemic state.

On increasing the movements rates, influenza died out in all the regions. They reported that, migrations when coupled to increased risk behaviour, have a causal effect on the increase of HIV. In a modelling study, Smith? However, the extent to which human mobility impacts the transmission of HIV infections still needs to be studied further.

Once again, it appears that there is a need to develop metapopulation models that are well-designed in order to clearly explain the impact of human mobility. We modify the metapopulation model of Keeling and Rohani [ 35 ]. This model does not include a removed class, as is typical in HIV models.

We thus introduce the removed class, which incorporates individuals who are in AIDS status and are no longer transmitting the disease. The model is parametrised using demographic data from Kenya at the time of the census [ 36 ] and the world bank data [ 37 ]. The rates of moving between regions is estimated from mobile phone data collected by Wesolowski et al [ 38 ].

These data allow us to set up a full metapopulation HIV model for Kenya. Our study builds on the work of Wesolowski et al [ 38 ], in analysing the impact of human mobility on malaria in Kenya. They used mobile phone data to track the regional travel of people between June and June Every call or message made by each user was mapped to one of 11, cell towers located in different regions in the country.

From the call, an individual was assigned a primary settlement where they spent the majority of their night time. Using the primary settlements, they estimated the average monthly regional travel. We use their monthly mobility rate to study the impact of human mobility on HIV infections in Kenya.

The exposed period from time of HIV infection to a stage when infected individual becomes infectious is very short, so that when an individual acquires infections, immediately becomes infectious [ 40 — 43 ]. For this reason, we use the SIR model. The dynamics of each sub-population includes only individuals aged 15—64 years only, because this is the group of individuals who are sexually active, hence susceptible to HIV infections.

To include different regions in our model, we define S ij , I ij , R ij and N ij to be respectively the number of Susceptible, Infectious, Removed and total adult individuals currently visiting region i but who live in region j. For example, infected individuals working and spending most of their time in region j and going back to visit their family in region i are denoted I ij.

We assume a homogeneous mixing of individuals within the regions, meaning that any infectious individual has the same probability of transmitting the disease to any susceptible individual in the population.

We define l ji to be the per individual rate per year of moving from region i to region j. We assume that r is the rate of return from visits to another region, which is assumed to be independent of the regions travelled between. We define to be the total adult population who are currently in region i.

We assume that the basic parameters governing the effects of the disease and population demographics are the same in all regions. We further assume that individuals return to their home region before departing for another region and there is no permanent migration and emigration between the sub-populations, so that individuals travel to other sub-population occasionally.

In addition, we assume that the recruitment into classes occurs within home regions; i. The equation describing the dynamics of the susceptible individuals S ii in region i is given by 1. Susceptible individuals from region j who are current in region i are recruited by the rate which is governed by the number of people who move out from region j , at the rate l ij S jj and those returning to regional j at the rate rS ij individuals per year respectively.

Similarly, the equation describing the dynamics of the susceptible individuals S ij in region i is given by 2. The infectious population I ii is recruited at a rate and those returning at a rate individuals per year respectively. Together this gives 3.

The infectious population I ij is recruited by those acquiring HIV infections in region i at the rate and from those coming for a visit at the rate l ij I jj individuals per year respectively. The non-linear ordinary differential equation describing the dynamics of the infectious individuals I ij in region i is given by 4. The non-linear ordinary differential equation describing the dynamics of the removed individuals R i in region i is given by 5.

The non-linear ordinary differential equation describing the dynamics of the infectious individuals R ij in region i is given by 6 The model is summarised in Fig 1. For clarity, we consider individuals travelling to another region after returning home first. To compute the basic reproduction number R 0 of the system of equations from Eqs 1 — 6 , we only consider equations of the states that include the infected individuals. These equations are referred to as the infected system [ 44 ]. In our model, we have assumed that the susceptible and the removed classes do not contribute to the transmission of HIV.

The only class involved in the disease transmission is the infectious class. We therefore write the the system that represents the infectious individuals in a given region and those commuting between these regions as follows: 7.

The computation of R 0 by the next-generation operator begins with equations of the system that involve the transmission part describing the production of new infections and then with those involve transition part, describing changes in state among the infected individuals [ 45 ]. If the sum of the entries in each column is positive, then A is non-singular m-matrix [ 46 , 47 ]. In this case, the basic reproduction numbers can not be written explicitly. However, it can be observed that the basic reproduction number depends on the travel rates, demographic and the epidemic parameters.

Therefore, given the set of parameter values and the travel rates, R 0 can be computed numerically. We start with the disease-related parameters, which are assumed to be the same for all regions.

The average life expectancy at birth of Kenya from to is about We then look at the population and movement parameters. S2 Table gives the adult population for [ 36 ] and the estimate of people infected with HIV in 20 regions of Kenya for [ 39 ]. The rate at which individuals from region j visit region i , denoted by l ij individuals per year, is estimated from the monthly average number of trips per individuals over the course of the year see S3 Table.

The data is then divided by to get the average number of trips for an individual per year. Mobile phone data does not allow us to estimate this parameter more accurately, so we take this estimate as a starting point. A challenge is to estimate the spread of the infection in various regions. In the absence of regional variation, this can be estimated from the basic reproduction number R 0.

Hiv transmission in kenya

Hiv transmission in kenya