The effect of compliance on the impact of mass drug administration for elimination of lymphatic filariasis in Egypt

We studied effects of compliance on the impact of mass drug administration (MDA) with diethylcarbamazine and albendazole for lymphatic filariasis (LF) in an Egyptian village. Baseline microfilaremia (mf) and filarial antigenemia rates were 11.5% and 19.0%, respectively. The MDA compliance rates were excellent (> 85%). However, individual compliance was highly variable; 7.4% of those surveyed after five rounds of MDA denied having ever taken the medications and 52.4% reported that they had taken all five doses. The mf and antigenemia rates were 0.2% and 2.7% in those who reported five doses of MDA and 8.3% and 13.8% in those who reported zero doses. There was no significant difference in residual infection rates among those who had taken two or more doses. These results underscore the importance of compliance for LF elimination programs based on MDA and suggest that two ingested doses of MDA are as effective as five doses for reducing filariasis infection rates

Evidence and gap map of studies assessing the effectiveness of interventions for people with disabilities in low‐and middle‐income countries

Background: There are approximately 1 billion people in the world with some form of disability. This corresponds to approximately 15% of the world's population (World Report on Disability, 2011). The majority of people with disabilities (80%) live in low- and middle-income countries (LMICs), where disability has been shown to disproportionately affect the most disadvantaged sector of the population. Decision makers need to know what works, and what does not, to best invest limited resources aimed at improving the well-being of people with disabilities in LMICs. Systematic reviews and impact evaluations help answer this question. Improving the availability of existing evidence will help stakeholders to draw on current knowledge and to understand where new research investments can guide decision-making on appropriate use of resources. Evidence and gap maps (EGMs) contribute by showing what evidence there is, and supporting the prioritization of global evidence synthesis needs and primary data collection. Objectives: The aim of this EGM is to identify, map and describe existing evidence of effectiveness studies and highlight gaps in evidence base for people with disabilities in LMICs. The map helps identify priority evidence gaps for systematic reviews and impact evaluations. Methods: The EGM included impact evaluation and systematic reviews assessing the effect of interventions for people with disabilities and their families/carers. These interventions were categorized across the five components of community-based rehabilitation matrix; health, education, livelihood, social and empowerment. Included studies looked at outcomes such as, health, education, livelihoods, social inclusion and empowerment, and were published for LMICs from 2000 onwards until January 2018. The searches were conducted between February and March 2018. The EGM is presented as a matrix in which the rows are intervention categories (e.g., health) and subcategories (e.g., rehabilitation) and the column outcome domains (e.g., health) and subdomains (e.g., immunization). Each cell lists the studies for that intervention for those outcomes, with links to the available studies. Included studies were therefore mapped according to intervention and outcomes assessed and additional filters as region, population and study design were also coded. Critical appraisal of included systematic review was done using A Measurement Tool to Assess Systematic Reviews’ rating scale. We also quality-rated the impact evaluation using a quality assessment tool based on various approaches to risk of bias assessment. Results: The map includes 166 studies, of which 59 are systematic reviews and 107 impact evaluation. The included impact evaluation are predominantly quasiexperimental studies (47%). The numbers of studies published each year have increased steadily from the year 2000, with the largest number published in 2017.The studies are unevenly distributed across intervention areas. Health is the most heavily populated area of the map. A total of 118 studies of the 166 studies concern health interventions. Education is next most heavily populated with 40 studies in the education intervention/outcome sector. There are relatively few studies for livelihoods and social, and virtually none for empowerment. The most frequent outcome measures are health-related, including mental health and cognitive development (n = 93), rehabilitation (n = 32), mortality and morbidity (n = 23) and health check-up (n = 15). Very few studies measured access to assistive devices, nutrition and immunization. Over half (n = 49) the impact evaluation come from upper-middle income countries. There are also geographic gaps, most notably for low income countries (n = 9) and lower-middle income countries (n = 34). There is a fair amount of evidence from South Asia (n = 73) and Sub-Saharan Africa (n = 51). There is a significant gap with respect to study quality, especially with respect to impact evaluation. There appears to be a gap between the framing of the research, which is mostly within the medical model and not using the social model of disability. Conclusion: Investing in interventions to improve well-being of people with disabilities will be critical to achieving the 2030 agenda for sustainable development goals. The EGM summarized here provides a starting point for researchers, decision makers and programme managers to access the available research evidence on the effectiveness of interventions for people with disabilities in LMICs in order to guide policy and programme activity, and encourage a more strategic, policy-oriented approach to setting the future research agenda

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4–52·0). The TFR decreased from 4·7 livebirths (4·5–4·9) to 2·4 livebirths (2·2–2·5), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83·8 million people per year since 1985. The global population increased by 197·2% (193·3–200·8) since 1950, from 2·6 billion (2·5–2·6) to 7·6 billion (7·4–7·9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9–1·2) in Cyprus to a high of 7·1 livebirths (6·8–7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0·08 livebirths (0·07–0·09) in South Korea to 2·4 livebirths (2·2–2·6) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high of 3·1 livebirths (3·0–3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill & Melinda Gates Foundation

Population and fertility by age and sex for 195 countries and territories, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017

BACKGROUND: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. METHODS: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10-54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10-14 years and 50-54 years was estimated from data on fertility in women aged 15-19 years and 45-49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories

Ultrastructure of the Interstitial Tissue in the Testis of the Egyptian Dromedary Camel (Camelus dromedarius)

The ultrastructural examination of the testicular interstitial tissue of Egyptian dromedary camel was performed to observe the seasonal changes. The activity of the interstitial tissue increased largely in spring. This was indicated by the large number of mature Leydig cells and two to three layers of myofibroblasts around the basal laminae of the seminiferous tubules with large blood vessels in the interstitial tissue. The testicular activity was moderate in winter as indicated by the lower number of immature Leydig cells. The lowest activity was in summer when Leydig cells became inactive with pyknotic nuclei. The cells of interstitial tissue lost their junctions with each other, leaving large intercellular spaces and myofibroblasts transformed to fibrocytes. The testicular activity began again to increase in autumn. The testicular activity of camel, however, did not stop in any season of the year, because even in non-breeding seasons a part of the interstitial tissue of the testis was active

Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters

Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505±1.0971e-17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505±9.6235e-14 and 0.0084505±2.5235e-9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563

Whole‐exome sequencing of T‐B+ severe combined immunodeficiency in Egyptian infants, JAK3 predominance and novel variants

Severe combined immunodeficiency (SCID) is fatal if not treated with immune reconstitution. In Egypt, T‐B+ SCID accounts for 38·5% of SCID diagnoses. An accurate genetic diagnosis is essential for choosing appropriate treatment modalities and for offering genetic counseling to the patient’s family. The objectives of this study were to describe the clinical, immunological and molecular characteristics of a cohort of twenty Egyptian patients with T‐B+ SCID. The initial diagnosis (based on clinical features and flow cytometry) was followed by molecular investigation (whole‐exome sequencing). All patients had the classic clinical picture for SCID, including failure to thrive (n = 20), oral candidiasis (n = 17), persistent diarrhea (n = 14), pneumonia (n = 13), napkin dermatitis (n = 10), skin rash (n = 7), otitis media (n = 3) and meningitis (n = 2). The onset of manifestations was at the age of 2·4 ± 1·6 months and diagnosis at the age of 6·7 ± ·5 months, giving a diagnostic delay of 4·3 months. JAK3 gene variants were most frequent (n = 12) with three novel variants identified, followed by IL2Rγ variants (n = 6) with two novel variants. IL7Rα and CD3ε variants were found once, with a novel variant each. T‐B+NK− SCID accounted for approximately 90% of the Egyptian patients with T‐B+SCID. Of these T‐B+NK− SCID cases, 60% were autosomal recessive syndromes caused by JAK3 mutations and 30% were X‐linked syndromes. It might be useful to sequence the JAK3 gene (i.e. targeted Sanger sequencing) in all T‐B+ SCID patients, especially after X‐linked SCID has been ruled out. Hence, no more than 10% of T‐B+ SCID patients might require next‐generation for a molecular diagnosis