The impact of conventional and organic farming on soil biodiversity conservation: a case study on termites in the long-term farming systems comparison trials in Kenya

A long-term experiment at two trial sites in Kenya has been on-going since 2007 to assess the effect of organic and conventional farming systems on productivity, profitability and sustainability. During these trials the presence of significant numbers of termites (Isoptera) was observed. Termites are major soil macrofauna and within literature they are either depict as ‘pests’ or as important indicator for environmental sustainability. The extent by which termites may be managed to avoid crop damage, but improve sustainability of farming systems is worthwhile to understand. Therefore, a study on termites was added to the long-term experiments in Kenya. The objectives of the study were to quantify the effect of organic (Org) and conventional (Conv) farming systems at two input levels (low and high) on the abundance, incidence, diversity and foraging activities of termites. The results showed higher termite abundance, incidence, activity and diversity in Org-High compared to Conv-High, Conv-Low and Org-Low. However, the termite presence in each system was also dependent on soil depth, trial site and cropping season. During the experiment, nine different termite genera were identified, that belong to three subfamilies: (i) Macrotermitinae (genera: Allodontotermes, Ancistrotermes, Macrotermes, Microtermes, Odontotermes and Pseudocanthotermes), (ii) Termitinae (Amitermes and Cubitermes) and (iii) Nasutitiermitinae (Trinervitermes). We hypothesize that the presence of termites within the different farming systems might be influenced by the types of input applied, the soil moisture content and the occurrence of natural enemies. Our findings further demonstrate that the organic high input system attracts termites, which are an important, and often beneficial, component of soil fauna. This further increases the potential of such systems in enhancing sustainable agricultural production in Kenya

Oral contraceptives and intrauterine devices as risk factors for breast and cervical cancers: a systematic review

Breast and cervical cancers have commandingly become major public health threats across the world. While studies have reported on the nexus between the use of oral contraceptives (OCs) and intrauterine devices (IUDs) as risk factors for breast and cervical cancers, there exists a paucity of explicit data on the nature of the association. Authors report the effect of oral contraceptives and the use of IUDs on the development of breast and cervical cancers. Several databases (Cochrane Library, Google Scholar and PubMed) were searched using well-specified criteria and a total of 15 papers selected. Meta-analyses, systematic reviews and studies that used cross-sectional designs were excluded from the review. Three and twelve cohort and case-control studies were reviewed respectively. Four of these studies reported an increased association between oral contraceptives and the risk of cervical cancer while nine showed positive correlation between oral contraceptives and risk of breast cancer. One study showed association between levonogestrel IUDs and risk of breast cancer while the other study did not show association between both levonogestrel and copper IUDs with risk of breast cancer. Use of copper IUDs was associated with diminishing risk of cervical cancer. Overall, use of oral contraceptives upsurges risk of breast and cervical cancers especially when used for longer periods of time. Further studies should therefore be done to understand the mechanisms of action of oral contraceptives and IUDs on the development of both cancers

Switch from 200 to 350 CD4 baseline count: what it means to HIV care and treatment programs in Kenya

Introduction: With the increasing population of infected individuals in Africa and constrained resources for care and treatment, antiretroviralmanagement continues to be an important public health challenge. Since the announcement of World Health Organization recommendation andguidelines for initiation of antiretroviral Treatment at CD4 count below 350, many developing countries are adopting this strategy in their countryspecific guidelines to care and treatment of HIV and AIDS. Despite the benefits to these recommendations, what does this switch from 200 to 350CD4 count mean in antiretroviral treatment demand? Methods: A Multi-centre study involving 1376 patients in health care settings in Kenya. CD4count was carried out by flow cytometry among the HIV infected individuals in Kenya and results analyzed in view of the In-country and the newCD4 recommendation for initiation of antiretroviral treatment. Results: Across sites, 32% of the individual required antiretroviral at <200 CD4Baseline, 40% at <250 baseline count and 58% based on the new criteria of <350 CD4 Count. There were more female (68%) than Male(32%).Different from <200 and <250 CD4 baseline criteria, over 50% of all age groups required antiretroviral at 350 CD4 baseline. Age groupsbetween 41-62 led in demand for ART. Conclusion: With the new guidelines, demand for ARVs has more than doubled with variations notedwithin regions and age groups. As A result, HIV Care and Treatment Programs should prepare for this expansion for the benefits to be realized.Key words: CD4, New criteria, HIV, AIDS, care and treatment, ARV initiatio

Termite-induced injuries to maize and baby corn under organic and conventional farming systems in the Central Highlands of Kenya

Open Access Journal; Published online: 22 Oct 2019Termite-induced injuries to maize and baby corn were evaluated in on-going comparison experiments on organic and conventional farming systems at two trial sites in the Central Highlands of Kenya (Chuka and Thika). The farming systems were established in 2007 at two input levels: Low input level, representing subsistence farming (Conv-Low, Org-Low) and high input level, representing commercial farming (Conv-High, Org-High). Termite-induced injuries to maize and baby corn, such as tunneling the stem or lodging the whole plant were assessed over two cropping seasons. The lodging occurred exclusively at Thika. It first became apparent in the Org-Low system, with most of lodging occurring during the vegetative stage. Baby corn grown under high input systems showed increasing lodging from the late vegetative crop stage and peaked before the final harvest. Tunneling was recorded at both sites, but was generally below 5%, with no significant differences between the farming systems. Overall, the injury patterns caused by termites appear to be a function of the plant growth stage, termite colony activities, trial site, and the types and levels of fertilizer input. Thus, the management practice used in each farming system (organic or conventional) might have greater influence on crop injuries than the type of farming system itself or the termite abundance within each system

Evaluation of Genotype x Environment Interaction and Stability of Grain Yield and Related Yield Components in Pearl Millet (Pennisetum glaucum (L.) R.Br.)

Thirty six pearl millet genotypes were evaluated in randomized complete block design with two replications during 2011/2012 at two locations to study the magnitude of genotype by environment interaction for yield and yield related traits and identify the most stable high yielding genotypes. ANOVA of data at individual locations revealed significant differences among genotypes at Marigat and Koibatek for all yield components. Combined mean analysis of variance showed that the Genotype and location main effects and the genotype by environment interaction were highly significant (P≤0.01) for grain yield and other traits, indicating differential response of genotypes across testing locations and the need for stability analysis. Marigat was the most suitable environment and gave highest mean grain yield of 3620 kg/ha. The lowest yield 870 Kg/ha was recorded at Koibatek. Genotypes EUP 32, EUP 35, EUP 19 and EUP 10 produced high mean yield of 3530, 3080, 2690 and 2590 kg/ha respectively. The lowest grain 1290 kg/ha was obtained from genotype EUP 4.Based on the parameters of stability, three stable (widely adapted) and high yielding genotypes (EUP 34, EUP 18, and EUP 9) were identified. They also out-yield the standard open pollinated variety (OPV) check, Kat PM2. Genotypes EUP 32 was the highest yielding across all sites followed by EUP 35 and could be recommended for further multi-location evaluation in warmer environment and possible release for commercial production. The findings of this study showed that pearl millet hybrids have high potential for commercial production in Kenya than the OPVs. The ANOVA results showed that the effects of environments, genotypes and genotype x environment interaction (GE) were important in trait expression and performance of genotypes. In addition, it was observed that amount of rainfall received at both vegetative and post-anthesis phases and temperature had an effect on grain yield. The GGE biplot analysis characterised the environments in terms of stability and productivity, where Marigat was the best for grain yield; implying that environment-specific selection should be adopted. Genotypes EUP 34, EUP 18, and EUP 9 were the best performing since they out yielded the standard OPV check. These stable high yielding genotypes can be evaluated further in varied agro-ecologies and recommended for release as commercial hybrid varieties in ASALs of Kenya

HIV-1 subtype and viral tropism determination for evaluating antiretroviral therapy options: an analysis of archived Kenyan blood samples

<p>Abstract</p> <p>Background</p> <p>Infection with HIV-1 is characterized by genetic diversity such that specific viral subtypes are predominant in specific geographical areas. The genetic variation in HIV-1 <it>pol </it>and <it>env </it>genes is responsible for rapid development of resistance to current drugs. This variation has influenced disease progression among the infected and necessitated the search for alternative drugs with novel targets. Though successfully used in developed countries, these novel drugs are still limited in resource-poor countries. The aim of this study was to determine HIV-1 subtypes, recombination, dual infections and viral tropism of HIV-1 among Kenyan patients prior to widespread use of antiretroviral drugs.</p> <p>Methods</p> <p>Remnant blood samples from consenting sexually transmitted infection (STI) patients in Nairobi were collected between February and May 2001 and stored. Polymerase chain reaction and cloning of portions of HIV-1 <it>gag</it>, <it>pol </it>and <it>env </it>genes was carried out followed by automated DNA sequencing.</p> <p>Results</p> <p>Twenty HIV-1 positive samples (from 11 females and 9 males) were analyzed. The average age of males (32.5 years) and females (26.5 years) was significantly different (p value < 0.0001). Phylogenetic analysis revealed that 90% (18/20) were concordant HIV-1 subtypes: 12 were subtype A1; 2, A2; 3, D and 1, C. Two samples (10%) were discordant showing different subtypes in the three regions. Of 19 samples checked for co-receptor usage, 14 (73.7%) were chemokine co-receptor 5 (CCR5) variants while three (15.8%) were CXCR4 variants. Two had dual/mixed co-receptor use with X4 variants being minor population.</p> <p>Conclusion</p> <p>HIV-1 subtype A accounted for majority of the infections. Though perceived to be a high risk population, the prevalence of recombination in this sample was low with no dual infections detected. Genotypic co-receptor analysis showed that most patients harbored viruses that are predicted to use CCR5.</p

Health disparities across the counties of Kenya and implications for policy makers, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND:The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 provided comprehensive estimates of health loss globally. Decision makers in Kenya can use GBD subnational data to target health interventions and address county-level variation in the burden of disease. METHODS:We used GBD 2016 estimates of life expectancy at birth, healthy life expectancy, all-cause and cause-specific mortality, years of life lost, years lived with disability, disability-adjusted life-years, and risk factors to analyse health by age and sex at the national and county levels in Kenya from 1990 to 2016. FINDINGS:The national all-cause mortality rate decreased from 850·3 (95% uncertainty interval [UI] 829·8-871·1) deaths per 100 000 in 1990 to 579·0 (562·1-596·0) deaths per 100 000 in 2016. Under-5 mortality declined from 95·4 (95% UI 90·1-101·3) deaths per 1000 livebirths in 1990 to 43·4 (36·9-51·2) deaths per 1000 livebirths in 2016, and maternal mortality fell from 315·7 (242·9-399·4) deaths per 100 000 in 1990 to 257·6 (195·1-335·3) deaths per 100 000 in 2016, with steeper declines after 2006 and heterogeneously across counties. Life expectancy at birth increased by 5·4 (95% UI 3·7-7·2) years, with higher gains in females than males in all but ten counties. Unsafe water, sanitation, and handwashing, unsafe sex, and malnutrition were the leading national risk factors in 2016. INTERPRETATION:Health outcomes have improved in Kenya since 2006. The burden of communicable diseases decreased but continues to predominate the total disease burden in 2016, whereas the non-communicable disease burden increased. Health gains varied strikingly across counties, indicating targeted approaches for health policy are necessary. FUNDING:Bill & Melinda Gates Foundation

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of 'leaving no one behind', it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. METHODS: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator