Malaria control intervention coverage increased nationwide in Malawi during 2000–2010. Trends in intervention coverage were assessed against trends in malaria parasite prevalence, severe anemia (hemoglobin < 8 g/dL), and all-cause mortality in children under 5 years of age (ACCM) using nationally representative household surveys. Associations between insecticide-treated net (ITN) ownership, malaria morbidity, and ACCM were also assessed. Household ITN ownership increased from 27.4%  in 2004 to 56.8% (95% CI = 55.6–58.1) in 2010. Similarly intermittent preventive treatment during pregnancy coverage increased from 28.2% (95% CI = 26.7–29.8) in 2000 to 55.0% (95% CI = 53.4–56.6) in 2010. Malaria parasite prevalence decreased significantly from 60.5% (95% CI = 53.0–68.0) in 2001 to 20.4% (95% CI = 15.7–25.1) in 2009 in children aged 6–35 months. Severe anemia prevalence decreased from 20.4% (95% CI: 17.3–24.0) in 2004 to 13.1% (95% CI = 11.0–15.4) in 2010 in children aged 6–23 months. ACCM decreased 41%, from 188.6 deaths per 1,000 live births (95% CI = 179.1–198.0) during 1996–2000, to 112.1 deaths per 1,000 live births (95% CI = 105.8–118.5) during 2006–2010. When controlling for other covariates in random effects logistic regression models, household ITN ownership was protective against malaria parasitemia in children  and severe anemia . After considering the magnitude of changes in malaria intervention coverage and nonmalaria factors, and given the contribution of malaria to all-cause mortality in malaria-endemic countries, the substantial increase in malaria control interventions likely improved child survival in Malawi during 2000–2010. Plasmodium falciparum is endemic throughout Malawi.1 Malaria transmission is stable with seasonal peaks related to the rainfall during November to April.2 The Ministry of Health (MoH) in Malawi reported that malaria accounted for 40% of all outpatient visits and 40% of hospital deaths in 2001, and was the cause of workforce losses (up to 25 days of work lost per year on average).3 Malaria was also the leading cause (39%) of hospital admissions in children less than 5 years of age.3Malaria caused by 4 The Malawi Government and other funding partners contributed funding for malaria control as well during this time.5 The Malawi Government and its national and international partners have shown a strong continued commitment to malaria control and prevention, which is reflected in three successive 5-year Malaria Strategic Plans since 2000.3,6,7There have been substantial increases in funding for malaria control in Malawi that started around 2006 and which came mainly from the Global Fund to Fight AIDS, Tuberculosis and Malaria  and the President’s Malaria Initiative, in total amounts of $42.2 and $83.1 million, respectively, from 2006 to 2010.8 and the commercial sector to deliver insecticide-treated nets (ITNs) and had the first national ITN program in sub-Saharan Africa by 2003.7 The Malawi MoH has recommended using sulfadoxine-pyrimethamine (SP) for malaria intermittent preventive treatment during pregnancy (IPTp) since 1993,9,10 long before the World Health Organization (WHO) recommended IPTp in 2002.5 Over the years, the Malawi Government changed malaria treatment policies in response to the emergence of antimalarial drug resistance. In 1993, Malawi was the first country in sub-Saharan Africa to replace chloroquine with SP as the first-line treatment of uncomplicated malaria. In 2007, Malawi changed from SP as the recommended first-line antimalarial to artemether-lumefantrine (AL), an artemisinin-based combination therapy, following significant evidence of malaria parasite resistance to SP.11 These key milestones are summarized in Malawi has been at the forefront of the introduction of malaria control interventions in sub-Saharan Africa. The country initially used social marketing Given the increased investments, changes in malaria control polices, and early adoption of interventions in Malawi, this study evaluated the impact of malaria control efforts on malaria morbidity and all-cause childhood mortality (ACCM) during 2000–2010.13 The rationale for using this evaluation design with ACCM as the primary impact measure is discussed in depth elsewhere.13–15 The RBM MERG recommended using ACCM as a primary impact measure where baseline transmission intensity is moderate or high .13 This evaluation design was strengthened by using logistic regression models to assess the strength of association between household ITN ownership and malaria parasitemia and severe anemia among child household members during the evaluation period, two malaria morbidity measures which are in the causal pathway between malaria control interventions and changes in ACCM  Partnership’s Monitoring and Evaluation Reference Group (MERG) .13 The r in ACCM . MultivaSupplemental Table 1. The Demographic and Health Survey (DHS), Multiple Indicator Cluster Survey (MICS), and Malaria Indicator Survey (MIS) use a two-stage survey design, typically with the most recent census as the sampling frame for enumeration units. The DHS Program17 and United Nations Children’s Fund MICS18 websites and survey reports provide more detailed information on sampling of these surveys. The 2010 DHS results for intervention coverage and severe anemia were used for trend analysis instead of the 2010 MIS results due to seasonal comparability with the other surveys used in this evaluation. DHS field work was conducted in the low malaria transmission season and the MIS in the high malaria transmission season.A list of population-based evaluation indicators and data sources are shown in We calculated the proportion of women with a live birth 0–2 years before the survey who received at least two doses of SP for IPTp. Data on the source of SP medications were only collected in the 2004 and 2010 DHS, so source was not included in the calculation of this indicator.19 in children aged 6–59 months who slept in a sampled household the night before the survey was calculated using DHS data. Hemoglobin values were adjusted for altitude according to the standard methodology used by the DHS.20,21Severe anemia prevalence (hemoglobin concentration < 8 g/dL)22,23 The 2001 NMS was sampled for children 6–35 months old, whereas the 2009 survey was sampled for children 6–59 months old. The 2009 survey was reanalyzed to generate estimates for children 6–35 months of age for comparison purposes. The NMS were conducted in the low transmission season. Malaria parasite prevalence (microscopy-based) data were also obtained for children 6–59 months of age from the 2010 MIS (field work March to April 2010) during the peak malaria transmission season.The National Micronutrient Surveys (NMSs) are nationally representative cross-sectional household surveys, in which data were collected on severe anemia (Hb < 8 g/dL) and parasitemia (microscopy-based) in children 6–35 months of age in 2001 and 2009.24 and used in logistic regression models to assess the association between ITN ownership and parasitemia or severe anemia. These surveys measured malaria parasitemia (by microscopy) and severe anemia in children 6–30 months of age in six districts in 2005 and 2006 and in eight districts in 2007, 2008, and 2009. These surveys were conducted in April of each year, which represents the latter portion of the rainy season and therefore the peak of the malaria transmission season. The six districts surveyed in 2005 and 2006 included four from Southern Region , and one each from Central Region (Lilongwe) and Northern Region (Rumphi). The 2006 parasitemia data were not available for this study and therefore 2006 data were not included in the logistic regression models discussed below. In 2007, 2008, and 2009, an additional district was sampled in both the Central Region (Nkhotakota) and Northern Region (Karonga).Sub-national data on severe anemia (Hb < 8 g/dL) and malaria parasite prevalence estimates were obtained from anemia and parasitemia surveys,http://data.worldbank.org/country/malawi).25Contextual factor data including information on household attributes, asset ownership, women’s education, human immunodeficiency virus (HIV) prevalence, and maternal and child health interventions were obtained from the DHS, MICS, and MIS surveys unless otherwise indicated. Gross domestic product (GDP) per capita purchasing power parity (PPP) data were obtained from the World Bank /Earth Resources Observation and Science Center, Moderate Resolution Imaging Spectroradiometer satellite.21 Mortality rates were not calculated from the 2006 MICS because these 5-year estimates overlap with the estimates from the 2004 and 2010 DHS surveys making it difficult to detect changes between the surveys. ACCM estimates were stratified by urban and rural residence (as defined by DHS) and malaria risk zones. Malaria risk zones were determined by overlaying the cluster locations from the 2010 DHS on a 2007 map of estimated prevalence rates of P. falciparum infection in children 2–10 years of age (PfPR2–10) from the Malaria Atlas Project.29 Terciles of PfPR2–10 values were then created at the cluster level. Malaria parasite prevalence ranges for the three risk categories included 0–33.7% (lower); 33.8–45.2% (medium); and 45.3–100% (higher). Disaggregated annual estimates of ACCM with 95% confidence intervals (CIs), for the 1990–2010 period were calculated from the 2000 (1991–1999) and 2010 (2000–2010) DHS data following a similar method as for the 5-year mortality rates, but limited to 1-year intervals.Five-year all-cause mortality rates were calculated from the 2000, 2004, and 2010 DHS using a synthetic cohort life table approach described in detail in the Guide to DHS Statistics.30–32Malaria control intervention coverage and severe anemia prevalence estimates were stratified by sex, residence , wealth quintiles, and mother’s education . Severe anemia prevalence was disaggregated by malaria risk terciles and by age groups (6–23 months and 24–59 months), as studies have shown the impact of malaria control interventions on malaria morbidity in these age groups to be different in malaria-endemic countries.Statistical significance for differences was determined by assessing the 95% CIs around each of the survey estimates. Nonoverlapping CIs for estimates from two survey years were deemed statistically significant. All analyses were performed using Stata 12 .33 The models included a random intercept for each enumeration area. The models assessed the odds of malaria parasitemia or severe anemia for children aged 6–30 months after controlling for survey year and child, household, region, and meteorological factors. Differences in meteorological factors (at the district level) preceding each survey  were controlled for by including anomalies from the 5-year means for February and March rainfall and minimum temperature. Districts were collapsed into regional categories . An interaction term for survey year and region was included in the models. Malaria infection  was included as a covariate in the severe anemia model.Separate multivariable, random effects logistic regression models from the sub-national survey data predicting the outcomes of malaria parasitemia and severe anemia were conducted using household ITN ownership, defined as at least one ITN in the household, as the primary ITN exposure variable. Although the proportion of children sleeping under an ITN might have been used as an indicator of ITN exposure, it has been shown that the pooled effect of children sleeping under an ITN the previous night or household ITN ownership on the prevalence of parasitemia are of similar magnitude and not significantly different.Supplemental Figure 1 for maps of district-level household ITN ownership).Household ownership of at least one ITN rose significantly from 27.4% (95% CI = 25.7–29.0) in 2004 to 56.8% (95% CI = 55.6–58.1) in 2010 . HousehoSupplemental Tables 2 and 3). ITN use also improved among the total population, increasing from 13.8% (95% CI = 12.6–15.1) in 2004 to 29.0% (95% CI = 28.0–30.1) in 2010  increased substantially between 2000 and 2010 . ITN use in 2010 .Indoor residual spraying (IRS) was implemented sub-nationally in Malawi. Starting as a pilot in 2007, it was then expanded to seven districts along Lake Malawi and in the Shire Valley. At the time of the 2010 DHS, only households in Nkhotakota District had been sprayed during the year, resulting in 59.4% of the households in Nkhotakota District having been sprayed with IRS in the past 12 months .Supplemental Table 4).The proportion of women with a live birth in the 2 years preceding the survey who received at least two doses of SP during pregnancy increased from 28.2% (95% CI = 26.7–29.8) to 55.0% (95% CI = 53.4–56.6) between 2000 and 2010 . IPTp coSupplemental Table 5). Of all children less than 5 years of age who experienced fever in the prior 2 weeks, use of any antimalarial increased over the study period from 27.1% (95% CI = 25.3–28.9) in 2000 to 43.4% (95% CI = 41.4–45.4) in 2010. The proportion of febrile children treated with a recommended (first-line) antimalarial drug did not change between 2000 (23.3% [95% CI = 21.6–25.0]) and 2006 (20.2% [95% CI = 18.7–21.8]); however, an increase occurred between 2006 and 2010 (36.4% [95% CI = 34.4–38.4]) . SP was the recommended first-line antimalarial in 2000, 2004, and 2006 and ACTs (AL) in 2010.Although overall care seeking for fever in children under five has increased over the decade from 35.3% (95% CI = 33.1–37.5) to 65.4% (95% CI = 63.6–67.1), timely care seeking (within 24 hours of fever onset) has increased only slightly, albeit a statistically significant change (26.4% [95% CI = 24.6–28.3] in 2000 and 32.3% [95% CI = 30.7–34.0] in 2010) . Care se4–38.4]) . There wIn the Malawi NMSs, conducted during the low transmission season, malaria parasite prevalence in children 6–35 months of age decreased from 60.5% (95% CI = 53.0–68.0) in 2001 to 20.4% (95% CI = 15.7–25.1) in 2009 . MalariaSupplemental Table 7). Declines in severe anemia were also seen in the NMS, where severe anemia in children 6–35 months declined significantly from 17.7% (95% CI = 13.5–21.8) in 2001 to 7.3% (95% CI = 4.8–9.8) in 2009 .The prevalence of severe anemia did not decrease significantly between 2004 (10.6% [95% CI = 9.1–12.4]) and 2010 (8.7% [95% CI = 7.6–10.0]) in children aged 6–59 months using the DHS surveys. However, significant decreases were evident in subgroups of younger children, who were at higher risk for severe anemia than older children. Severe anemia prevalence in children 6–23 months of age declined from 20.4% (95% CI = 17.3–24.0) in 2004 to 13.1% (95% CI = 11.0–15.4) in 2010, a relative change of 36% , whereasSupplemental Table 8), with the smallest and largest reductions in the neonatal (26% reduction) and child (47% reduction) mortality rates, respectively. Significant declines were observed in ACCM among children from rural areas, but not urban areas  were examined. The GDP per capita (PPP) remained relatively constant between 2000 and 2006 , but then rose from 634 in 2006 to 749 in 2009, an 18.1% relative increase.25 Significant improvements were seen in access to an improved water source, increasing from 65.2% (95% CI = 62.1–68.2) in 2000 to 79.7% (95% CI = 77.7–81.5) in 2010; households with an improved roof, increasing from 25.8% (95% CI = 24.5–27.3) in 2006 to 35.0% (95% CI = 32.9–37.0) in 2010; electricity, increasing from 4.8% (95% CI = 3.6–6.4) in 2000 to 8.7% (95% CI = 7.6–9.9) in 2010; and telephones, increasing from 5.1% (95% CI = 3.8–6.9) in 2004 to 39.3% (95% CI = 37.6–41.1) in 2010  in 2000 to 68.9% (95% CI = 67.8–70.0) in 2010 and the percentage of births in a health facility also increased significantly from 55.3% (95% CI = 52.7–57.9) in 2000 to 73.2% (95% CI = 71.3–74.9) in 2010 . HoweverSupplemental Table 9). Early initiation of breastfeeding worsened over the evaluation period declining from 72.1% (95% CI = 70.7–73.5) in 2000 to 67.0% (95% CI = 66.3–67.7) in 2010, whereas exclusive breastfeeding increased from 44.2% (95% CI = 40.7–47.7) in 2000 to 71.4% (95% CI = 68.0–74.6) in 2010 and Vitamin A supplementation improved from 70.6% (95% CI = 69.0–72.1) in 2000 to 85.5% (95% CI = 84.6–86.4) in 2010 . Treatment seeking for both ARI (from 26.7% [95% CI = 24.4–29.1] to 65.4% [95% CI = 62.9–67.9]) and diarrhea (from 28.3% [95% CI = 25.7–31.2] to 62.4% [95% CI = 60.0–64.7]) improved  from 2000 to 2010  significantly improved from 70.1% (95% CI = 67.2–72.8) to 80.9% (95% CI = 78.9–82.8) between 2000 and 2010  and prevention of mother-to-child transmission (PMTCT) coverage both increased during the evaluation period. HIV-positive pregnant women receiving ARVs increased from 2,179 to 33,200 between 2004 and 2009.38 According to UNAIDS, PMTCT coverage during pregnancy and delivery was 24% in 2009 and 53% in 2011.39Among women aged 15–49 years, HIV prevalence did not change between 2004 and 2010 with an estimated 13% infected according to the DHS. Similarly, new child HIV infections remained relatively stable at 20,000 cases in 2000 and 19,000 cases in 2010.4,28 Additional meteorological analyses are presented in the accompanying article by Thomson and others.28Meteorological data for rainfall and temperature were assessed over the evaluation period. Rainfall and temperature patterns do not suggest that any long-term climate differences existed over the period of malaria control scale-up.In logistic regression models, lower household wealth status (quintile), older age (in months), not having an ITN in the home, and survey year by region  interaction were all significant predictors of malaria parasite prevalence . Mean raITN ownership was similarly protective against severe anemia in children 6–30 months of age. Lower household wealth status (quintile), younger age (in months), and not having an ITN in the home were all significant predictors of severe anemia . ChildreThis evaluation found a 41% reduction in ACCM in Malawi during 2000–2010, substantial increases in household ITN ownership and use in children under five, reductions in malaria parasitemia and severe anemia, and insufficient changes in socioeconomic factors and other determinants known to influence child health to fully explain the decline in child mortality. Consequently, the evaluation indicates that malaria interventions contributed to the decline in ACCM in Malawi during 2000–2010. The association of individual ITN ownership with reduced odds of parasitemia and severe anemia further strengthens this conclusion.Supplemental Figure 3 for estimates from the United Nations Interagency Group for Child Mortality Estimation34 and the Institute for Health Metrics and Evaluation).35 The magnitude of ACCM decline was greater in the medium and higher malaria risk areas compared with the lower malaria risk areas, which is consistent with what would be expected if part of the declines in ACCM were malaria-related given children in these areas had the greatest potential to benefit from increased malaria intervention coverage.40,41 Similarly, the relative decline in ACCM was greater in rural areas (43%) than urban areas (10%). Rural areas are where most of the malaria transmission occurs and most (84%)42 of the population lives. However, the largest decline  took place between 1996–2000 and 2000–2004, a period preceding the largest increase in malaria control intervention coverage in Malawi. However, malaria control intervention coverage was low but not insignificant between 2000 and 2004. For example, an ITN program was underway in all districts (household ownership of at least one ITN was 27% in 2004) as well as IPTp roll out (two doses of SP was 28% in 2000 and 47% in 2004). These early declines in ACCM are therefore likely due in part to the initial scaling up of malaria control interventions, along with increases in other child survival interventions and socioeconomic improvements. In the 2004–2010 period, when malaria control was intensified, mortality continued to decline.The 41% decline in ACCM observed in our evaluation during 2000 and 2010 is comparable to the mortality declines over this period shown by other investigators using different mortality estimation methods  population use of ITNs can achieve community benefits (change in EIR) of ITN use beyond individual protection alone.45 Thus, the increase in coverage of ITNs provided individual protection to the user and likely community protection as well. Malawi used IRS as another vector control measure in addition to ITNs; however, in this national-level impact analysis, IRS was unlikely to play a significant role given only a 2% national coverage in 2010.46 IRS could have had important sub-national effects due to the high coverage in targeted districts.Malaria control intervention coverage increased nationally during 2000–2010. ITNs as the primary malaria prevention tool were implemented in Malawi using a variety of delivery methods, including social marketing, commercial sector, routine distribution at ANC and EPI clinics, and mass distribution campaigns .4 A soci5,47 Thus, given the protective effect of IPTp on low birth weight,48 increasing IPTp coverage could have improved birth outcomes leading to reductions in neonatal mortality before the study period and continued to contribute to reductions in these outcomes over the evaluation period. However, given the minimal improvements in neonatal mortality seen here  and the increases observed in the proportion of babies born with low birth weight , the effect of IPTp may have been mitigated by other contextual factors, for example a reduction in women receiving four ANC visits between 2000 and 2010 (19% relative reduction). In addition, there was an increase in the prevalence of P. falciparum mutations that reduce the effectiveness of SP used for IPTp in Malawi.49At the time of this evaluation in 2010, Malawi had one of the highest IPTp coverage (55%) levels in sub-Saharan Africa, up from 28% in 2000.50,51 Studies of ACTs have demonstrated higher rates of parasite clearance and better treatment outcomes compared with SP and cholorquine.52,53 Among children less than 5 years of age with fever, treatment with a first-line antimalarial increased from 23% in 2000 to 36% in 2010 and treatment with a first-line antimalarial within 24 hours increased slightly from 19% in 2000 to 24% in 2010. There are limitations to using household survey data to assess malaria case management, including problems with recall by the mothers or caretakers. In addition, the scaling up of diagnostics could have blunted the true coverage estimates of appropriate malaria case management; however, the RDT roll out occurred after the end of the evaluation period. Overall, it can be concluded that treatment of fevers increased and there was increased use of more effective antimalarial treatments over the evaluation period, which may have contributed to a decline in ACCM.Care seeking for fevers from formal health providers in children less than 5 years of age increased (35% in 2000 to 65% in 2010). The majority (80%) of this increase in care seeking for fever occurred in the later part of the decade between 2004 and 2010. ACTs replaced SP as the first-line antimalarial drug in 2007, midway through the evaluation period.54,55 The 2012 and 2014 estimates were in the high malaria transmission season (rainy season) or just following and were lower than the 2010 MIS estimate, also conducted in the high transmission season. The prevalence in the NMS and MIS are not directly comparable due to survey design and fieldwork being conducted in different malaria transmission seasons, which poses challenges in interpreting trends in malaria parasite prevalence during and beyond the evaluation period. As a result, we included the logistic regression models to further explore the impact of malaria interventions  on malaria parasitemia and severe anemia.Declines were observed in malaria parasitemia and severe anemia, which lie along the causal pathway between malaria control intervention increases and reductions in malaria mortality . The ana56 Thus, the impact of malaria control interventions on malaria-related syndromes would be expected to be larger in younger age groups (under 2 years of age) compared with those 4 or 5 years of age.18,57 Severe anemia in children in the subgroup 6–23 months of age decreased significantly (36% relative reduction) between 2004 and 2010 based on DHS data, whereas there was no decline in severe anemia in children 24–59 months of age. Children 6–23 months of age are at a higher risk of severe malaria-related outcomes, such as anemia and mortality, than children 24–59 months of age.30,31 In addition, the relative contribution of malaria to the overall anemia burden in young children would be expected to be greater in areas of medium to higher malaria transmission; thus, control of parasite transmission would be expected to reduce anemia in these populations.19 In line with these expectations, declines in severe anemia in the 6- to 23-month age group varied based on malaria risk zone; severe anemia declined by 46% in higher risk areas and by 44% in medium risk areas as compared with 12% in lower risk areas. Thus, the observed trends are consistent with expected changes in anemia due to malaria control interventions with respect to both age and malaria endemicity.In settings of high malaria endemicity, the burden of disease and the impact of malaria control are largely concentrated in infancy and early childhood.37 particularly for evaluations that were conducted when rapid changes were under way in many other aspects of health services.36 Numerous socioeconomic factors and maternal and child health interventions were assessed for their potential impact on ACCM. Relative increases were observed in many of these factors  including GDP (18.1% relative increase), care seeking for diarrhea (120%) and acute respiratory illness (145%), breastfeeding (61.5%), and deliveries in a health facility (32.4%), which likely contributed to reductions in ACCM. Nutritional improvements were also seen with decreases in stunting (13%), underweight (38%), and wasting (37%). Sustained high coverage of BCG, measles, DPT3, and polio3 immunizations were observed during the evaluation period with coverage at levels exceeding 80% throughout the period. Coverage with these vaccines undoubtedly saved many lives over the evaluation period, but the further increases in coverage of these vaccines are unlikely to have had a large impact on the change in mortality in children less than 5 years of age. One exception may be the increase in measles immunization coverage (83% in 2000 to 93% in 2010); measles is highly contagious, so immunization coverage levels of at least 90% need to be maintained to confer population-level protection.58 Also, Haemophilus influenza (b) and Hepatitis B immunizations were added to the EPI package, as a component of the pentavalent vaccine DPT3-HBV-Hib, during the evaluation period (2002); thus coverage of these two vaccinations rose from near-zero in 2000 to 93% in 2010, and the implementation of the Hib vaccine in particular likely contributed to the declines in ACCM. Hib is the main cause of nonepidemic bacterial meningitis in children less than 12 months of age and is also a major cause of pneumonia.59 It is estimated that three doses of the Hib vaccine can prevent 38–43% of the childhood meningitis mortality.59Appropriate considerations of contextual factors are essential for ensuring the internal and external validity of evaluations of large-scale health programs,4,28 Rainfall and temperature patterns do not suggest that any long-term climate differences existed over the period of malaria control scale-up that would have independently led to substantially different patterns of malaria morbidity and mortality at the end of the decade versus the start.4 However, analysis of meteorological data in Malawi was limited to a national level and may have missed the sub-national variation in climate factors.28Meteorological factors were considered over the evaluation period.6 Given that changes in fundamental determinants often act through the proximate determinants (examined here) to impact childhood morbidity and mortality, the focus here is on the proximate determinants.In addition to changes in the contextual factors highlighted here, there were improvements to the overall health system, including health financing, which could have impacted ACCM. For example, the Government of Malawi developed a Sector Wide Approach program, which included the provision of an essential healthcare package.To mitigate limitations inherent in plausibility based evaluation designs , multivariable models were used to assess the association of one malaria intervention (ITN ownership) with malaria parasitemia, severe anemia, and child survival. In sub-national district analysis between 2005 and 2009, household ownership of ITNs was protective against malaria parasitemia and severe anemia in children 6–30 months of age, controlling for meteorological factors, geographic location, household wealth, and child’s age; odds of parasitemia were 19% lower and odds of severe anemia were 18% lower in children living in households with at least one ITN compared with those without ITNs.16 It should be noted that the Cox proportional hazards model assesses individual level associations, which could be influenced by residual confounding and may not translate directly to population level impact. However, given there are numerous controlled studies that have assessed the association between ITNs and malaria morbidity and mortality,8,44 it was important to assess the association in Malawi under programmatic conditions where distribution of ITNs was not under the control of a study, but relied on the routine distribution systems operating in country .We also found that household ITN ownership was associated with increased child survival using a Cox proportional hazards model and a district-level platform model presented in the accompanying paper by Florey and others.16 In addition, ITN ownership was found to be protective against malaria morbidity measures (severe anemia and parasitemia in children 6–30 months of age). Taken together this evidence suggests that malaria control interventions in Malawi likely contributed to reductions in ACCM during 2000–2010. Reductions in ACCM will likely continue as intervention coverage intensifies and the overall malaria control program increases efficiencies in service delivery.Malawi has made progress in improving coverage of malaria prevention and treatment interventions during the evaluation period. Part of the decline in all-cause mortality, particularly the declines earlier in the evaluation period could be due to the improvements in the coverage of socioeconomic factors and other child health interventions as well as the initial expansion of malaria control interventions. In addition, it is likely that the decline in all-cause mortality among children less than 5 years of age was in part due to a reduction in malaria-specific mortality, particularly later in the evaluation period. Multivariable models support this claim; ITN ownership is associated with improved child survival in Malawi.Supplemental Tables.


Tropical freshwater environments, like rivers, are important reservoirs of microbial life. This study employed metagenomic sequencing to survey prokaryotic microbiota in the Solimões, Purus, and Urucu Rivers of the Amazon Basin in Brazil. We report a rich and diverse microbial community. Actinobacteria, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Crenarchaeota. Some marine clades, e.g., Ilumatobacter, were also reported (The Amazon Basin spans eight countries and occupies approximately 40% of the South American continent . This bareported .The runoff from rivers in this region account for nearly 15% of the world’s runoff from rivers. There are two well-established seasons (dry and flood) as described in references 6–9–Because of the presence of noncultivable microorganisms in environmental samples, it is impossible to isolate many of these organisms and difficult to recreate environmental microbiotas in the laboratory. Thus, the most appropriate approach to gain details regarding the community structure is metagenomics. Despite its importance, data on the microbial diversity of the Upper Amazon Basin are very limited. Therefore, this study aimed to form a landscape of the most frequent species in this environment.We sequenced total DNA extracted from freshwater samples collected from different rivers in the Upper Amazon Basin. These samples were collected in September 2008 (dry season) from the Solimões , Purus , and Urucu  Rivers. Sampling and total DNA extractions were performed as previously described ; bases wProteobacteria (abundance percentages of 34.9 to 54.5%), Actinobacteria (14.9 to 24.3%), Cyanobacteria (1.9 to 24.3%), Bacteroidetes (2.0 to 6.6%), Planctomycetes (1.8 to 8.2%), and Firmicutes (2.5 to 3.2%). Archaea were mainly assigned to Euryarchaeota (0.2 to 0.5%) and Thaumarchaeota (0 to 1.6%). Alpha diversity was estimated to be between 346 and 620 species. Functional annotations revealed that a considerable portion of reads were associated with respiration (3.4 to 3.7%), photosynthesis (0.2 to 0.8%), and phages, prophages, transposable elements, and plasmids (1.9 to 2.2%). The present metagenomic project holds important insights on the metabolic potential of microbial communities from tropical freshwater rivers.Taxonomic assignment was made using the MG-RAST server  and reveSRR1514963 (Solimões River), SRR1515032 (Purus River), and SRR1518285 (Urucu River).The sequences obtained in this project have been deposited in the NCBI Short Read Archive under the following accession numbers:


Recently, the prevalence of chromosomal abnormalities (CA) increased as the increasing proportion of mothers with advanced age. We aimed to explore the prevalence of CA in relation to maternal age and perinatal features.A retrospective study was performed based on provincial birth defects surveillance data. The relative risk (RR) and 95% confidence interval (CI) were used to calculate maternal age-specific rates of CA. Socio-demographic characteristics of mothers and perinatal features were listed.x2line-trend =52.69, p < 0.001). During this period, the prevalence for CA per 10,000 births among women over 35 years old increased from 15.34 in 2011 to 33.82 in 2015 . The RR for overall CA, trisomy 21(T21), trisomy 18(T18) and others in mothers 35 years or older were 6.64 (95% CI 5.55 ~ 7.93), 6.83 (95% CI 5.63 ~ 8.30), 4.06 (95% CI 2.09 ~ 7.90) and 7.54 (95% CI 4.02 ~ 14.11) respectively in comparison to mothers aged 25–29 years old. The stillbirths rate for total CA was 76.45%. T21 and T18 were strongly associated with multiple anomalies, especially congenital heart abnormalities.The total prevalence of CA was 6.38 per 10,000 births, which increased per 10,000 births linearly from 4.02 in 2011 to 9.13 in 2015 (The prevalence of CA increased as maternal age increased. Cases with CA were associated with other congenital defects and high mortality risk. Chromosomal abnormalities are a significant public health issue due to their adverse impacts on pregnancy outcomes. The most frequent chromosomal abnormalities in newborns have been trisomy-related—such as trisomy 21, 18, and 13—and sex chromosomal abnormalities. Recently, an upward trend in the prevalence of chromosomal abnormalities had been widely reported in relation to the increasing proportion of births to older mothers . In EuroChromosomal abnormalities are strongly associated with adverse pregnancy outcomes, such as early fetal loss, stillbirths and combined with other congenital birth defects , 8, 9. AIn China, the latest national study showed the percentage of mothers over 35 years of age increased from 2.96% in 1996 to 8.56% in 2007 . AdditioZhejiang province is located in the eastern part of China with well-developed economy and high-coverage of prenatal screening. The most recent research in Zhejiang showed that 18.62% children exhibited chromosomal abnormalities among a sample of 4,129 suspected subjects . This stThis is a retrospective study based on a hospital-based Birth Defect Surveillance System in Zhejiang province. The surveillance system consists of 91 delivery hospitals and covers about one-third of all births in this province. All births in monitored hospitals were assessed within seven days. Cases identified with congenital abnormalities at any gestational age were registered regardless of whether it was a live birth or the pregnancy was terminated due to birth defects. The identification of congenital abnormalities was performed by experts from the medical fields of genetics, obstetrics, pediatrics, pathology and laboratory clinics. Data were collected based on prenatal screening and clinical reports. Quality control was performed annually by local experts each year.In Zhejiang province, antenatal screening of all pregnant women is routinely offered. In the first-trimester, screening by maternal serum (free beta human chorionic gonadotropin β-hCG), pregnancy-associated plasma protein-A (PAPP-A) and ultrasound marker  are offered. These screenings serve to identify the risk for T21, T18 and other major chromosomal abnormalities. In the second trimester, prenatal screening by human alpha-fetoprotein (hAFP), human chorionic gonadotropin (HCG), estriol (uE3) and ultrasound screening are provided. Amniocentesis and chorionic villous sampling (CVS) are usually conducted at 16–20 weeks of gestation for pregnancies which are considered to be at high-risk for chromosomal abnormalities either because of abnormal results from the prenatal screening, maternal characteristics  or family histories. Non-invasive prenatal testing (NIPT) is available but not widely used as of 2013. Therefore, NIPT is selective to mothers who meet the requirements. All cases with chromosomal abnormalities were diagnosed by karyotype analysis through amniocentesis or newborn serum screening. The data were linked to maternal obstetric and newborn records, and recorded throughout the network. In this study, a total of 818 cases with chromosomal abnormalities were detected during the period from 2011 to 2015. Due to a missing subtype of karyotypes for seven of the cases, only 811 cases were included in this study.P-values below 0.05 were considered significant. SPSS 16.0 software was used to analyze the data.The occurrence of chromosomal abnormalities was calculated by the number of cases for all subtypes of chromosomal abnormalities per 10,000 births (live births and stillbirths). Trends in the prevalence of chromosomal abnormalities were determined by line chi-square tests. The relative risk (RR) and 95% confidence interval (CI) for chromosomal abnormalities were analyzed for the total population and mothers over 35 years of age. Additionally, the presence of abnormal categories between mothers over 35 years of age was compared to mothers ages 25 through 29. All Eight hundred-eleven cases of infants with chromosomal abnormalities were born to a total of 809 mothers, including two sets of twins during this period. The average age of these mothers was 31.83 ± 6.07 years old, ranging from 16 to 47 years old. Among abnormal cases, 328 were female, 429 were male and 54 were of undetermined sex . At the same time, the prevalence for chromosomal abnormalities per 10,000 births among women over 35 years old also increased from 15.34 in 2011 to 33.82 in 2015 . The RR for chromosomal abnormalities proved significantly higher with advanced maternal age in comparison to the general population each year  in 2011 to 10.08%  in 2015. During 2011–2015, the overall prevalence for chromosomal abnormalities was 6.38 per 10,000 births . The total prevalence for chromosomal abnormalities per 10,000 births increased in an obvious and linear fashion from 4.02 in 2011 to 9.13 in 2015  in mothers 35 years or older were 6.64 (95% CI 5.55 ~ 7.93), 6.83 (95% CI 5.63 ~ 8.30), 4.06 (95% CI 2.09 ~ 7.90) and 7.54 (95% CI 4.02 ~ 14.11) respectively in comparison to the reference group (aged 25 ~ 29 years old) , the proportion of mothers aged 35 years or older increased from 13% in 1990 to 19% in 2009, and accompanied an increase in trisomy-affected pregnancies [In our study, women with advanced age were at higher risk of chromosomal abnormalities. In regard to all births, the RR of chromosomal abnormalities tended to increase by nearly four-fold in older women. In comparison to mothers in the 25–29 years old group, the risk for chromosomal abnormalities by subtype analysis among mothers over 35 years was four to seven times greater. The results have been evidenced worldwide, although there were some differences in the determination of advanced maternal age and sample population. In a study conducted in Korean, for every year of age increase, the odds ratio increased by 1.18 times for T21, by 1.18 times for T18, and by 1.16 times for fetal aneuploidies . In the The general average incidence of chromosomal abnormalities in our study was much lower than the corresponding data in European countries, where chromosomal abnormalities accounted for 43.8 of every 10,000 births . As the T18 syndrome is commonly known as Edwards syndrome. It is caused by the presence of an extra chromosome; 18. The incidence of T18 ranged from approximately 0.50 to 0.70 per 1000 births and about 0.1 per live 1000 births , 21, 24.In our study, the overall stillbirths rate was 76.45%. A slightly higher rate of cases with T18, T13 and SCAs suffered fetal death in comparison to T21. According to EUROCAT, termination for chromosomal abnormalities hovered around 70% for T18 and T13, and was 47% for T21. The influence of legal concerns and religious beliefs might partly explain the different birth decisions with regard to the termination of pregnancies that presented abnormalities. We found that 8.50% of the abnormal cases were concurrent with multiple congenital abnormalities. Of these, 7.02% of the chromosomal abnormalities were associated with cardiovascular malformation. In a European study, 80% and 57% of babies with T18 and T13 respectively had a cardiac anomaly [It is the first full-scale study about the clinical epidemiology of chromosomal abnormalities in relation to maternal age, perinatal features in Zhejiang province of China. Since the fertility policy in China has changed, and reproductive habits in various countries around the world have also undergone radical modifications, it is necessary to take into consideration the age-related chromosomal anomalies. The main point of this article is not that the prevalence of births with CA have increased, due to maternal age increase, which is a well established knowledge, but the fact that this should be taken into consideration by operators involved in medical decisions and health-related policy making.Two limitations in our study should be noted. First, in a hospital-based birth defect surveillance program, cases born outside monitored hospitals or not identified within seven days of birth were not registered. Second, not all mothers who experienced spontaneous abortion necessarily test for chromosomal abnormalities unless they intended to become pregnant. This could lead to an underestimate of the prevalence of total chromosomal abnormalities and early fetal loss. Therefore, the linking of multiple databases, the identification of miscarriages and post-natal cases should be pursued further in future studies.


As atrial fibrillation (AF) is often asymptomatic, it may remain undiagnosed until or even after development of complications, such as stroke. Consequently the observed prevalence of AF may underestimate total disease burden.2-specific stroke probability for each patient, adjusting for age and gender composition based on United States census data.To estimate the prevalence of undiagnosed AF in the United States, we performed a retrospective cohort modeling study in working age (18–64) and elderly (≥65) people using commercial and Medicare administrative claims databases. We identified patients in years 2004–2010 with incident AF following an ischemic stroke. Using a back-calculation methodology, we estimated the prevalence of undiagnosed AF as the ratio of the number of post-stroke AF patients and the CHADS2 score ≥1, and 56% had CHADS2 score ≥2.The estimated prevalence of AF (diagnosed and undiagnosed) was 3,873,900  elderly and 1,457,100  working age adults, representing 10.0% and 0.92% of the respective populations. Of these, 698,900 were undiagnosed: 535,400  elderly and 163,500  working age adults, representing 1.3% and 0.09% of the respective populations. Among all undiagnosed cases, 77% had a CHADSUsing a back-calculation approach, we estimate that the total AF prevalence in 2009 was 5.3 million of which 0.7 million (13.1% of AF cases) were undiagnosed. Over half of the modeled population with undiagnosed AF was at moderate to high risk of stroke. Atrial fibrillation (AF) is a major cause of ischemic stroke, but often has minimal or no symptoms and therefore can be difficult to diagnose . CurrentIn this paper, we propose an indirect back-calculation method to estimate the prevalence of undiagnosed AF. Back-calculation is a process whereby generally unobservable features of an event  can be inferred. The methodology was first developed in the late 1980s for obtaining short-term projections of Acquired Immunodeficiency Syndrome . For theWe performed a retrospective cohort study using 2004–2010 health insurance claims data from a commercial claims dataset representing a number of large, self-insured companies and administrative claims data from Medicare. The commercial claims data were used to study AF prevalence in the pre-Medicare, working adult population (aged 18–64 years); Medicare Limited Data Set was used to study the prevalence of AF in older adults (age ≥65 years). Data from commercial and Medicare administrative claims databases were fully anonymized before the authors accessed them. Our sample was restricted to U.S. residents aged ≥18 years with continuous enrollment for at least 12 month before and 15 months after the fourth quarter of 2009.We estimated the prevalence of undiagnosed AF using a four-step back-calculation methodology  7]. Ste. Ste7]. Step 2 identified patients who: (i) had no AF diagnosis between 2004–2009 while enrolled, (ii) had a stroke in 2010, and (iii) were subsequently diagnosed with non-valvular AF within 3 months after the stroke (“observable” cases of undiagnosed AF). Patients were classified as having an ischemic stroke if there was an inpatient or emergency room claim with a primary ICD-9-CM code of 433.01, 433.11, 433.21, 433.31, 433.81, 433.91; 434 or 436. This approach to identify patients with ischemic stroke has been previously validated with a sensitivity of 74%, specificity of 95%, and a positive predictive value of 88% . As admi2 scores. We measured each patient’s CHADS2 score based on whether the patient had at least one claim for congestive heart failure (ICD-9 codes 428.x), hypertension , diabetes (ICD-9-CM codes 250.x), or a previous stroke . Furthermore, we used CHADS2 rather than CHA2DS2-VASc scores as there were very few people in the working-age population with low CHA2DS2-VASc scores who experienced AF after a stroke, leading to highly unstable estimates. Then we approximated stroke risks for patient with stroke and followed by an AF diagnosis using all-cause stroke risk by CHADS2 score among patients with diagnosed AF but not on anticoagulation from the literature [Step 3 assigned stroke risk to patients with AF based on CHADSterature . As a se2 score) of undiagnosed AF from Step 2 by the all-cause CHADS2-specific stroke probabilities determined in Step 3 to estimate the total number of undiagnosed AF cases.Step 4 divided the number of observable cases  higher than patients who were first diagnosed with AF within 3 months after the stroke (Patients with observed (diagnosed) AF were older, more likely to be male, and had higher CHADSthout AF . Among ee stroke .For working adults aged 18–64, the prevalence of diagnosed AF in our data was 0.83% (95% CI: 0.79%-0.86%), whereas 8.70% (95% CI: 8.62%-8.78%) of elderly adults had diagnosed AF . The latApplying the back-calculation methodology, we estimated that 163,500  working age adults had undiagnosed AF. Although the prevalence of undiagnosed AF among working age adults was rare overall , 11.2% of all persons with AF in the working age population were undiagnosed. We estimated that 1.32%  or 535,400  elderly patients had undiagnosed AF. Among the elderly, 13.8% of persons with AF were undiagnosed.2 score ≥2, and 77% had CHADS2 ≥1  of AF in the U.S. was estimated to be 5.33 million (2.4% of adults) comprised of 1,457,100  working age adults and 3,873,900  patients aged ≥65 years representing 0.92% and 10.0% of these populations, respectively. Of the total 5.33 million patients with AF, 698,900 (13.1%) were undiagnosed. Although males were more likely to have AF, a larger proportion of female patients with AF were undiagnosed. Similar to patients with diagnosed AF, undiagnosed AF increased with age. Overall, 56% of undiagnosed AF cases had a CHADSHADS2 ≥1 .2-specific stroke incidence rates derived from our datasets of patients with diagnosed AF, our estimated prevalence of undiagnosed AF increased. Under this alternative assumption, the estimated prevalence of undiagnosed AF was 0.85 million  among working age individuals and 2.0 million  among the elderly, a total of 2.85 million individuals. When we defined stroke in the claims data using ischemic stroke, hemorrhagic stroke, or TIA, the prevalence of undiagnosed AF was 0.20% (95% CI: 0.02%-0.48%) for working age adults the 2.88% (95% CI: 2.55%-3.32%) for elderly adults. In our third sensitivity analysis using CHA2DS2-VASc scores to predict stroke risk rather than CHADS2 scores, we found that the results for our commercial population were unstable due to the small sample size of patients with stroke and the increased number of potential scores . Among the elderly individuals, however, we estimated that 1.22 million  of people aged 65 years and older had undiagnosed atrial fibrillation.When we applied CHADSThe estimated prevalence of undiagnosed AF in the U.S. in 2009 was 700,000 while the total prevalence of AF was 5.3 million. One in eight patients with AF was undiagnosed. Furthermore, over half of the population with undiagnosed AF was at moderate to high risk of stroke.Our total prevalence estimate of 5.3 million  for 2009 is markedly higher than prior projections of 2.2–2.7 million (1.0–1.2%) in 2009–2010 from the American Heart Association (AHA) Statistical Update  and estiPrior estimates of undiagnosed AF prevalence come primarily from AF screening studies, reporting 1.7% in a general European population  and 2.3–2 risk factors, in whom prevalence is substantially higher. Our results indicated that the prevalence of undiagnosed AF increases from 0.11% of the population of patients with a CHADS2 score of zero to 0.73% of the population of patients with a CHADS2 score of 2 and up to 0.95%-1.39% of the population of patients with a CHADS2 score between 3 and 6. At the same time, our data indicated that routine full population screening may be of low yield, as the younger working age population had an undiagnosed AF prevalence of 0.09%.These data show appreciable rates of undiagnosed AF such that targeted screening strategies could prove beneficial, particularly in subgroups of older (age ≥65) patients or in patients with multiple CHADSThere are several limitations to our analysis. First, our back-calculation methodology assumed that all AF detected in the three months after ischemic stroke was present prior to stroke. We believe this to be reasonable and biologically plausible, and our estimates showed good calibration to known AF prevalence estimates. To address the case where AF appears spontaneously after stroke, we did adjust our prevalence estimates based on the baseline AF incidence in our data. Second, our model may have underestimated the prevalence of undiagnosed AF since a number of people with AF have silent strokes that would not be captured by claims data, have short episodes of AF that may not meaningfully increase stroke risk, or may have cryptogenic strokes that were in fact due to undiagnosed AF. Third, estimates were modeled from data sources that may not be truly representative of the U.S. population . Fourth, our model did not account for differences in stroke event rates by race or ethnicity and we assumed that stroke incidence rates among patients with undiagnosed AF were similar to those of patients with diagnosed AF. Fifth, data and funding available to the authors at the time of research was only through 2010. Finally, the estimates presented rely on the accuracy of the back-calculation model; population AF screening studies will be required to better estimate point prevalence of undiagnosed AF.Based on a model of back-calculating disease prevalence from ischemic stroke events, we estimate that the total AF prevalence in 2009 was approximately 5.3 million persons, which is substantially higher than prior estimates based on cohort and population studies. Of these patients with AF, about 700,000 persons were undiagnosed—about one in eight patients—corresponding to 0.31% of the total US adult population.


The prevalence of autoimmune thyroiditis (AIT) and papillary thyroid carcinoma (PTC) is rising in children and adolescents, and the coincidence of AIT and PTC is as high as 6.3–43%.To investigate the ultrasound manifestation of AIT in relation to PTC development in paediatric patients.179 paediatric patients , mean (SD) age: 13.9 (3.03) years diagnosed with AIT and referred for ultrasound evaluation. Eight patients were diagnosed with PTC .Retrospective analysis of thyroid ultrasound scans of patients diagnosed with AIT. Thyroid and autoimmune status was assessed based on TSH, fT4, fT3 and increased aTPO and/or aTG and/or TRAB levels. In patients with PTC, total thyroidectomy was performed.Analysis of thyroid US scans revealed that the following five ultrasound variants of AIT were observed in 179 patients: the most common in 35.2%—diffuse thyroiditis with hypoechogenic background and normoechogenic parenchyma, in 30.2%—diffuse thyroiditis with irregular background, in 18.9% nodular variant with normoechogenic background, in 11.7%—micronodulations and in 3.9%—diffuse hypoechogenic background. Eight cases of PTC were diagnosed in nodular variant of AIT with normoechogenic irregular background.Patients with AIT and nodular variant with normoechogenic irregular background of the thyroid gland on US scans are in the risk group of developing PTC and should be followed up with regular neck US assessment. The prevalence of chronic autoimmune thyroiditis (AIT) has been assessed as 0.3–2% in children and 4–9.6% in adolescents and is rising , 2. The Papillary thyroid carcinoma (PTC) accounts for 90% or more of all childhood differentiated thyroid carcinoma (DTC) cases . The preIn the last years, there is an increase of coincidence of AIT and PTC in children and adolescents. A coincidence of AIT and PTC is ranging between 6.3 and 43%, depending on the patients selection –12. Oh eAccording to the current paediatric guidelines neck US in children with autoimmune thyroid disease should be performed at least every 12 months , 15. KnoThe aim of the present study is a trial to classify US thyroid scans in AIT patients and to assess characteristic features of thyroid presentation in patients with PTC and AIT.The study included 179 paediatric patients  diagnosed with AIT and referred for ultrasound evaluation in the Out-Patient Endocrine Department between years 2015 and 2016. The mean age of the patients was 13.9 years range 5–18 years). Eight patients were diagnosed with papillary thyroid carcinoma  and fT4 close to low normal range (fT4 n: 10–25 pmol/l). Overt hypothyroidism was diagnosed if TSH was above the upper normal range and fT4 was below the low normal range. Overt hyperthyroidism was diagnosed if TSH was suppressed and fT4 and fT3 above the upper normal range (fT3 n: 3.8–6.8 pmol/l). AIT was diagnosed in euthyroid patients with an increased aTPO and/or aTG and/or TRab antibody levels and typical features of chronic autoimmune thyroiditis on thyroid ultrasound scans.The analyzed data were as follows: age at diagnosis, gender, thyroid status , autoantibodies levels  and US features of the AIT. Autoimmune thyroid disease (AITD) was diagnosed based on clinical  and hormonal  parameters as well as typical features of chronic autoimmune thyroiditis on thyroid ultrasound scans and increased aTPO, and/or aTG and/or TRab levels. Compensated  hypothyroidism was diagnosed if TSH was above the upper normal range . Thyroid US was performed using a high-resolution Voluson 730, GE Medical System with an 8- to 12-MHz linear transducer. The US examination was performed in the longitudinal and axial planes. Scans indicating background parenchymal echogenicity were reviewed retrospectively. Normal thyroid parenchyma (normoechogenic background) was defined as demonstrating homogenous echogenicity and relative hyperechogenicity compared with the adjacent sternohyoid, sternothyroid, omohyoid and sternocleidomastoid muscles. The abnormal parenchymal features of the thyroid gland, including irregular echotexture, micronodularity, and diffuse or focal hypoechogenic lesions and nodules on gray-scale US, were evaluated. FNAB was performed in 34 patients with a nodule found on US.U test and ANOVA tests were used. For comparison of multiple groups, ANOVA Kruskal–Wallis test was used. The level of significance was set at p < 0.05. Calculations were performed using the STATISTICA 10.0 PL software (Poland).To compare the two groups, the two-sided Mann–Whitney Analysis of the thyroid ultrasound scans from the last 2 years revealed that the following five ultrasound variants of autoimmune thyroiditis were observed in the study group: the most common in 35.2% 63/179)—diffuse thyroiditis with hypoechogenic background and normoechogenic parenchyma . FNAB was performed in all patients. In 26/34 patients, FNAB result was assessed as benign (Bethesda II) [In patients with nodular variant of AIT with normoechogenic irregular background goiter was found in 24/34 patients (70.6%). In this variant of AIT, the mean nodule size was 6.4 mm (2–26 mm). Uninodularity was found in 30 patients (88.2%) and multinodularity in 4 (esda II) . PTC wasesda II) . In patiTSH range in different ultrasound classes is presented in Table In diffuse thyroiditis with hypoechogenic background and normoechogenic parenchyma variant 69.8% (44/63) patients were hypothyroid at diagnosis (59.1% with an overt and 40.9% with compensated hypothyroidism), 17.5% (11/63) were euthyroid and 12.7% (8/63) hyperthyroid. Significantly higher aTPO and aTG levels were observed in diffuse thyroiditis with hypoechogenic background and normoechogenic parenchyma variant when compared with other AIT variants and aTPO level was also significantly higher than in PTC group.In diffuse thyroiditis with irregular background, 37% (20/54) were hypothyroid (55% with compensated and 45% with an overt hypothyroidism), 35.2% (19/54) euthyroid and 27.8% (15/54) hyperthyroid.n: 0.4–4.0), raised fT3-13.4 pmol/l (n: 3–8.1) and fT4-31.6 pmol/l (n: 10–25). TRab level was 5.2 IU/ml (N < 1.0) at diagnosis and 1.0 IU/ml a year later at the time of PTC diagnosis. This patient was treated with decreasing doses of antithyroid drug (thiamasole) until the surgery. The nodule was 13 mm in size, hypoechogenic with microcalcifications, irregular border and increased vascularisation  were euthyroid, 35% (12/34) hypothyroid (83.3% with compensated and 16.7% with an overt hypothyroidism) and 11.8% (4/34) hyperthyroid. Analysis of patients with nodular variant and normoechogenic irregular background revealed that PTC was diagnosed in 27.8% (5/18) of euthyroid, 25% (1/4) of hyperthyroid, and 16.6% (2/12) of hypothyroid patients in this group . On the other hand, thyroid status analysis of patients with coexistence of AIT and PTC revealed that 62.5% (5/8) PTC patients were euthyroid, 25% (2/8) with compensated hypothyroidism and 1 patient was hyperthyroid. The patient with PTC and thyrotoxicosis was diagnosed at the age of 16.8 years. The cause of referral to the endocrine center was goiter and TSH < 0.02 uIU/ml (One of the patients with papillary thyroid carcinoma had a diffuse sclerosing variant of PTC with very high unmeasurable aTPO (>9000 IU/ml) and aTG (>8000 IU/ml) levels. After excluding this patient from the PTC group, the mean levels of autoantibodies were as follows: aTPO was 1362.2 IU/ml [range 2081.9–5911.2 IU/ml) and aTG-129.4 IU/ml (range 127.9–300 IU/ml). After the surgery in all PTC patients aTg level was negative.In micronodulation variant 52.4% (11/21) were euthyroid, 42.8% (9/21) hypothyroid (77.8% with compensated and 22.2% with an overt hypothyroidism) and 4.8% (1/21) hyperthyroid.Seven patients (3.9%) with positive but relatively low levels of aTPO and aTG levels with diffuse hypoechogenic background on US scans were euthyroid and during further monitoring 3 of them presented a normalization of US echogenicity and aTPO and aTG levels (data not shown). We suspect that this variant corresponds to a transient thyroiditis or to an early phase of AIT.The analysis of US features seen on the thyroid scans revealed that the following five US variants of autoimmune thyroiditis were observed in our group of 179 paediatric patients: the most common in 35.2% diffuse thyroiditis with hypoechogenic background and normoechogenic parenchyma, in 30.2% diffuse thyroiditis with irregular background, in 18.9% nodular variant with normoechogenic irregular background, in 11.7% micronodulations, and in 3.9% diffuse hypoechogenic background. These ultrasound autoimmune thyroiditis features were already described in previous studies  but presIn our study group, we have found papillary thyroid carcinoma in 8 patients (8/179 cases) that constituted 4.5% of the whole group. All eight cases of papillary thyroid carcinoma were diagnosed in nodular variant of AIT with normoechogenic irregular background. 23.5% of patients (8/34 cases) with this variant of AIT developed PTC. In 5 patients with PTC malignant nodules were hypoechogenic, in 2 patients isoechogenic with hypoechogenic “halo” and in 1 patient with diffuse sclerosing PTC variant, tiny disseminated hyperechogenic septae and multiple microcalcifications fulfilling whole thyroid gland were seen on US imaging. In all cases, the echogenicity of thyroid parenchyma was increased when related to the adjacent sternothyroid, sternohyoid, sternocleidomastoid or omohyoid muscles. Our results are supported by previous work presenting nodular form of Hashimoto’s thyroiditis within a sonographic background of normal thyroid parenchyma . UsuallyPapillary thyroid carcinoma (PTC) accounts for 90% or more of all childhood DTC cases . AccordiThe association between Hashimoto’s thyroiditis and thyroid cancer has been debated and remains an active area of research and controversy , 22–24. Keskin et al.  analyzedFrom histopathological perspective, it is important to distinguish between diffuse lymphocytic infiltration and focal peritumoral lymphocytic thyroiditis . HashimoThe analysis of the thyroid status revealed that in our group 47.5% patients were hypothyroid (54.1% with compensated and 45.9% with an overt hypothyroidism), 36.8% euthyroid and 15.6% hyperthyroid at diagnosis. In accordance with other studies presenting that the majority of AIT children was euthyroid at diagnosis, 62.5% of patients in our group did not receive thyroxine treatment at diagnosis (euthyroid and compensated hypothyroid patients) , 27.The ultrasound variant with diffuse thyroiditis and hypoechogenic background and normoechogenic parenchyma is also defined an atrophic form but the majority of children with this variant presents with goiter , 27. In As expected and in accordance to other studies , abnormaOn the other hand, patients with nodular variant and normoechogenic irregular background had significantly lower TSH, aTPO and aTG levels than patients with most common AIT variant. The majority of patients with nodular variant as well as with PTC did not receive a thyroxine therapy at diagnosis. This might be in agreement with Paparodis et al.  finding A limitation of the present study is a relatively small number of patients. Multi-center transition studies involving both paediatric and adult patients are needed to evaluate disease and thyroid ultrasound data especially in the light of previous recommendations that thyroid US scans in children with AIT as not changing the treatment, clinical course or outcome, should not be indicated routinely  and currWhat our study adds is a proposal to place children with nodular variant and normoechogenic irregular background in the PTC development risk group. Additionally, presented ultrasound classification of AIT variants is relatively novel in paediatric population and represents good basis for further exploration of PTC susceptibility.Patients with AIT and nodular variant with normoechogenic irregular background of the thyroid gland on US scans are in the risk group of developing PTC and should be followed up with regular neck US assessment.


Visual impairment and blindness are major public health problems in developing countries where there is no enough health-care service.  To determine the prevalence of visual impairment among school children.  A school-based cross-sectional study was conducted between 15 June 2015 and 30 November 2015 at Arada subcity primary schools, Addis Ababa, Ethiopia. Two schools were selected randomly, and 378 students were screened from grades 1 to 8 using systematic random sampling method. Snellen chart was used for visual acuity test. Students who had visual acuity of ≤6/12 were further examined by an ophthalmologist to diagnose the reason for low vision. Data was analyzed using SPSS version 20.  A total of 378 students were screened, and 192 (50.8%) were females and the remaining 186 (49.2%) were males. The prevalence of visual impairment (VA) of ≤6/12 on either eye was 5.8%, VA < 6/18 on either eye was 1.1%, and VA < 6/18 on the better eye was 0.53%. In this study, color blindness  was significantly associated with visual acuity impairment.  The prevalence of visual impairment among school children in the study area was 5.8% and school screening is recommended. Visual system is one of our most important sensory systems. It is the primary means of integration between individuals and the external environments. Vision results from the entrance of light into the eye and the interpretation of this stimulus by the brain .Low vision is defined as a visual acuity of less than 6/18, but equal to or better than 3/60, or a corresponding visual field loss of less than 20 degrees in the better eye with the best possible correction. On the other hand, blindness refers visual acuity of less than 3/60 or a corresponding field loss of less than 10 degrees in the better eye with the best possible correction [It has been estimated that 75–90% of all learning in the classroom comes to the students either wholly or partially via the visual pathway . TherefoThe major causes of low vision and blindness includes cataract, refractive error, and trachomatous corneal opacity. Major causes of low vision and blindness are either preventable or treatable –9. AccorSub-Saharan Africa has an estimated 5-6 million blind and 16–18 million persons with low vision. Around 60% of them live in twenty African countries including Botswana, Eritrea, Ethiopia, Gambia, Ghana, Kenya, Lesotho, Liberia, Malawi, Mauritius, Namibia, Nigeria, Seychelles, Sierra Leone, South Africa, Swaziland, Uganda, the United Republic of Tanzania, Zambia, and Zimbabwe , 14.The eye problem in Ethiopia is among the major public health challenges. It poses huge economic and social impacts for the affected individuals, the society, and the nation at large .The prevalence of low vision in Ethiopia is 3.7% with considerable regional variations. The large proportion of this problem (91.2%) is due to avoidable (either preventable or treatable) causes , 16. HowTherefore, the main aim of this study was to determine the prevalence of visual acuity impairment among school children in Arada subcity, Addis Ababa, Ethiopia. It gives an evidence of the need of school screening for Ethiopia.P) was taken to be 3.7% from the previous study [The study was conducted from June 10 to November 30, 2015 in two primary schools; the Holy Trinity Cathedral Primary School and the Zeray Deres Primary School in Arada subcity, Addis Ababa, Ethiopia. Two schools were selected randomly from 9 primary schools in the subcity, and 378 students were screened from grades 1 to 8 using systematic random sampling method. The total sample size was estimated using a single population proportion formula; the prevalence of low vision in Ethiopia  with their 95% confidence interval (CI) and to test the significant associations between the risk factors and visual acuity impairment. A Ethical approval was obtained from Addis Ababa University Research and Ethical Review Board. Written and informed consent was obtained from parents or guardians of the children and confidentiality was maintained.A total of 378 students participated in the study. Among these, 255 (67.5%) were from the Holy Trinity Cathedral Primary School and 123 (32.5%) were from the Zeray Deres Primary School. Their age was from 5 to 16 years with mean age of 11.05 ± 2.58. One hundred sixty-two (42.9%) were from 1st to 4th grade and 216 (57.1%) were from 5th to 8th grade. The frequencies of females and males were 192 (50.8%) and 186 (49.2%), respectively .Regarding age distribution, the minimum age was 5 and the maximum age was 16 years old. Majority of the participants, 60 (16%), were age of 14 .With regard to educational level of the parents of the visually impaired students, only 4 (18%) had college diploma and above. In terms of income, large proportion, 19 (86.4%), of the parents of the visually impaired students have low income (≤2500 Ethiopian birr) .From the total participants, 22 (5.8%) were visually impaired (VA ≤ 6/12 in either eye) and 356 (94.2%) of them were normal (VA > 6/12 in the worse eye) .P = 0.38)  and 10 (45.5%), respectively . However = 0.38) . Among t = 0.38) .Color vision  and low grade level  were significantly associated with visual acuity impairment .As per the ICD-9 guideline, the study showed that 5.8% of the total participants had visual acuity impairment .From the total visually impaired children, 18 (4.7%) had visual acuity of 6/12–6/18 and all of them had bilateral visual problem. The remaining 4 (1%) had visual acuity of <6/18–6/60. Two (0.5%) of them had bilateral visual problem, and the remaining 2 (0.5%) had unilateral visual impairment .Visual acuity impairment was caused by different factors. Refractive error was the leading cause of visual impairment in this study which accounts for 17/22 (77.3%) of the causes. Cataract, severe allergy, amblyopia, and strabismus each accounts for 1/22 (4.5%) of the causes. The cause of abnormal vision of one student was not known because he was absent at the time of examination by an ophthalmologist .According to this study, the prevalence of visual impairment VA ≤ 6/12 in either eye was 22 (5.8%), VA < 6/18 in either eye was 4 (1.1%), and VA < 6/18 in the better eye was 2 (0.53%). This finding is less than the study done nationally in Ethiopia which was 3.7% . The lowP > 0.05). This may be due to the fact that in this study, the sample size comprised of a small age difference which was between 5 and 16 years old with mean age 11.05 ± 2.58; therefore, the association between age and reduction of visual acuity may not be seen. The result is in line with another study done in Malaysia which states that age factor was not associated with reduction of visual acuity [The study done in Malaysia and Indonesia , 19 showl acuity .P = 0.38).In this study, the prevalence of visual acuity impairment was slightly higher among females (3.2%) as compared to males (2.6%). This is supported by the study done in India . This miDifferent studies showed a significant association between visual acuity impairment in children with income, educational background, and visual status of parents. A study done in South Africa  showed tRegarding educational background of parents, only 4 (18.1%) had college diploma and university degree. More than 81% of them had no higher education. Studies done in India, China, and Nepal reported that visual problems were found around three times more in those who have no schooling than those who have schooling , 23, 24.In our study, almost half of the parents of the visually impaired children had visual problems including myopia, hyperopia, color blindness, and undefined blindness. Causes of low vision like refractive errors can be inherited. A study done by Mutti et al. stated that the risk of inheriting impaired visual acuity was increased if parents had similar problems .P < 0.01). This implies that being color vision defective is more at risk to individuals with visual impairment than to individuals who had normal color vision. This is due to the fact that missing, dead, or damaged cone cells result in loss of both acuity and color perception at the same time [In this study, 31.8% of individuals who had VA ≤ 6/12 had color vision defect. There was a strong association between visual acuity impairment and color blindness , VA < 6/18 in either eye was 4 (1.1%), and VA < 6/18 in the better eye was 2 (0.53%). School screening is recommended for early detection and possible management.


P = .04). Effective prevention and intervention approaches are needed to lower diabetes prevalence among US veterans and ultimately improve their health status.Diabetes is a highly prevalent chronic disease among US adults, and its prevalence among US veterans is even higher. This study aimed to examine the prevalence of and trends in diabetes in US veterans by using data from the US National Health and Nutrition Examination Survey from 2005 through 2014. The overall prevalence of diabetes and undiagnosed diabetes was 20.5% and 3.4%, respectively, and increased from 15.5% in 2005–2006 to 20.5% in 2013–2014 ( Diabetes was the seventh leading cause of death in the United States in 2013  patients ,5. The oWe used data from 5 NHANES cycles conducted from 2005 through 2014. NHANES uses a stratified multistage probability sampling approach to obtain representative samples from 50 states and the District of Columbia. Veteran status was self-identified through participant household interviews. The unweighted sample size for 2013–2014 was 491 and ranged from 472 to 685 for each cycle from 2005 through 2012.1c of 6.5% or higher, 2) fasting plasma glucose of 126 mg/dL or higher, 3) a 2-hour plasma glucose of 200 mg/dL or higher, or 4) a diagnosis of diabetes by a physician or other health care provider (Diabetes in a participant was defined as having at least 1 of 4 conditions: 1) a glycated hemoglobin ADemographic variables were age, sex, and race/ethnicity . Age was sorted into 3 groups: 20 to 44 years, 45 to 64 years, and 65 years or older. Socioeconomic status included poverty level and education level. Poverty level is a ratio of annual income to the federal poverty level (FPL) adjusted for the number of people in the household and where they lived. We categorized poverty level into 3 groups: less than 100% of FPL, 100% to less than 300% of FPL, and 300% or more of FPL. Education was categorized by years of education: less than 12 years, 12 years, and more than 12 years.2 test measured the bivariate association of diabetes and each exploratory variable. We used the Cochran-Armitage trend test to assess prevalence of diabetes time-trends from 2005 through 2014, and the proportional test was used to compare prevalence differences. Significance was set at P less than .05. All analyses were performed with SAS version 9.4 .Rao-Scott χP = .005) and education (P = .03) were significantly associated with the odds of diabetes. The highest prevalence of diabetes (25.7%) and obesity (43.5%) was observed among Hispanic veterans.The overall pooled weighted prevalence of diabetes in NHANES for 2013–2014 was 20.5% , and the prevalence of undiagnosed diabetes was 3.4% , . DiabeteP = .04 for trend test) and peaked in 2009–2010 (22.6%), (P = .04 for trend test). The prevalence of diabetes among veterans who had less than 12 years of education increased from 21.9% in 2005–2006 to 33.5% in 2013–2014 (P = .04 for trend test). Among veterans with more than 12 years of education, the prevalence increased from 12.3% in 2005–2006 to 19.9% in 2013–2014 (P = .03 for trend test). The increase in age-standardized prevalence was not significant for any of the 3 age groups.The overall prevalence trend of diabetes increased from 15.5% in 2005–2006 to 20.5% in 2013–2014 ((22.6%), . The preDiabetes is more prevalent among US veterans than among the general population ,5. This Diabetes is preventable for many people and treatable, but people with undiagnosed diabetes have delays in receiving effective treatment and, thus, are more likely to develop complications such as coronary artery diseases and retinal microvascular diseases than people with diabetes who receive treatment . HoweverOverall prevalence of diabetes in the NHANES participants increased from 15.5% in 2005–2006 to 20.0% in 2007–2008 and remained stable at around 20.0% in NHANES surveys from 2009 through 2014. This change is partially because fewer people aged 65 years or older were included in the 2005–2006 survey 37.8%) than that in the 2007–2014 surveys (>40%). Unlike previously reported findings (7.8% thanOur study had limitations. Statistical tests cannot be applied to data for female veterans because NHANES reported few or no female veterans with the disorder. In addition, NHANES data do not distinguish between type 1 and type 2 diabetes or gestational diabetes in the NHANES participants.Certain factors limit the accuracy of the estimated prevalence of diabetes in US veterans when using VA databases. First, based on the eligibility criteria and capability of VA facilities, in fiscal year 2014 less than 30% of the total veteran population sought VA health care, and more than 70% of veterans sought care outside the VA system even though some of them were enrolled in the VA system . NHANES


Background: The vast majority of deaths in the health and Kombewa demographic surveillance system (HDSS) study area are not registered and reported through official systems of vital registration. As a result, few data are available regarding causes of death in this population.Objectives: To describe causes of death among residents of all ages in the Kombewa HDSS, located in rural Western Kenya.Methods: Verbal autopsy (VA) interviews at the site were conducted using the modified 2007 and later 2012 standardized WHO questionnaires. Assignment of causes of death was made using the InterVA-4 model version 4.02. Cox regression model, adjusted for sex, was built to evaluate the influence of age on mortality.Results: There were a total of 5196 deaths recorded between 2011 and 2015 at the site. VA interviews were successfully completed for 3903 of these deaths (75.1%). Mortality rates were highest among neonates HR = 38.54 (<0.001) and among Infants HR = 2.07 (<0.006) in the Kombewa HDSS. Among those deaths in which VA was performed, the top causes of death were HIV/AIDS (12.6%), Malaria (10.3%), Pneumonia (10.1%), Acute abdomen (7.0%), Stroke (5.2%) and TB (4.9%) for the whole population in general. Stroke, acute abdomen heart diseases and Pneumonia were common causes of death (CODs) among the elderly over the age of 65.Conclusions: The analysis established the main CODs among people of all ages within the area served by the Kombewa HDSS. We hope that information generated from this study will help better address preventable deaths in the surveyed community as well as help mitigate negative health impacts in other rural communities throughout the Western Kenya region. Reliable and timely cause specific mortality data form the basis for health care planning, resource allocation and evaluating impact of interventions –3. ConveA HDSS longitudinally monitors births, deaths, causes of death, migration, and other health and socio-economic indicators within a defined population. These data are useful in providing data complementary to facility-based reporting systems and supplementary to large scale surveys (such as census surveys), which are usually conducted after long intervals . In such2 along the north-eastern shores of Lake Victoria.Data analyzed for this study were collected under the Kombewa HDSS, run by the KEMRI/Walter Reed Project in a rural portion of Kisumu County, Western Kenya. Details of the Kombewa HDSS catchment area have been described in detail elsewhere . In brieThe HDSS is located about 40 km West of Kisumu City, which is the administrative capital of Kisumu County. It boarders the KEMRI/CDC HDSS to the west, in the neighboring Siaya County . A dynamDeaths of HDSS residents were collected routinely by trained field workers during biannual house visits. Residents in the HDSS are defined as all persons residing in the study area for 6 months or more, excluding transient residents and visitors. In addition to the routine house visits, a team of dedicated ‘village reporters,’ largely drawn from a pool of Ministry of Health (MOH) trained Community Health Volunteers (CHVs), provided death notification within 7 days of an event. The notified events were thereafter verified and registered into the database by a team of HDSS field staff. Recorded deaths were then followed up with a standardized VA interview by specially trained lay interviewers to record events surrounding death. VA interviews were conducted using the modified 2007 then the 2012 standardized WHO questionnaires recommended by INDEPTH for deaths occurring in the HDSS ,16. The Assignment of cause of death (COD) was made using the InterVA-4 model version 4.02. A detailed description of the InterVA-4 model has been given elsewhere . In brieCause-specific mortality rates were calculated as the number of cause-specific deaths per 1000 person-years of follow-up and per 1000 live births . The Cox regression model, adjusted for sex, was built to evaluate the influence of age on mortality. Annual mortality rates over the five year period were also calculated for the assessment of cause-specific mortality trends. P-value of <0.05 was considered significant. All analyses were performed using STATA version 12 .The analysis presented in this paper is based on follow-up of 650 919.2 person-years during 2011–2015. There were a total of 5196 registered deaths between 2011 and 2015 at the site. For 3903 of these deaths (75.1%) VA interviews were successfully completed. Among those deaths in which a VA was performed, 62.1% occurred at home and 37.9% occurred in a health facility. Most neonatal deaths (63.2%) occurred in hospitals while more of the elderly died at home (71.7%). The crude death rate for the site was 8.0 per 1000 person years with higher rates for males [Mortality rate ratio (95% CI):1.20 (1.14–1.27)]. Overall, 11,093 live births were registered between 2011 and 2015 in the Kombewa HDSS (5377 boys and 5716 girls). In the same period, the overall infant mortality rate  was 35.0 deaths per 1000 live births. The overall under five mortality rate  was 85.0 deaths per 1000 live births. The infant mortality rate and under five mortality rate for the site over the five year period are presented below .Figure 2The top causes of death for the 3903 deaths (75.1%) with VA interviews were HIV/AIDS (12.6%), Malaria (10.3%), pneumonia (10.1%), acute abdomen (7.0%), stroke (5.2%), and pulmonary Tuberculosis (PTB), 4.9%. Deaths that could not be assigned a cause of death (indeterminate) translated to 12.9% of all deaths. Other top causes of death in the Kombewa HDSS included stomach cancers (4.0%), cardiac diseases (3.1%), unspecified cancers (2.2%) and diarrhea (2.2%). Among neonates (0–28 days), unspecified neonatal causes (22.1%), birth asphyxia (20.6%), prematurity (16.2%), neonatal pneumonia (11.8%), and congenital malformation (2.9%) were the top causes of death. Preterm deaths accounted for 20.6% of female deaths and 11.8% of male deaths in this age group. The leading causes of death among neonates (0–28 days) are presented in Infant mortality rate for the site was 35.3 per 1000 live births. The infant mortality rate amongst females was 33.9 per 1000 live births and 36.8 per 1000 among males for this age category. The leading causes of death among infants outside of the neonatal period were pneumonia (29.3%), malaria (25.1%), HIV/AIDS (10.7%), diarrhea (9.3%), and measles (3.3%). The Hazard Ratio for communicable diseases  was >1.80 for this age category, Malaria was the leading cause of death in children aged 1–4 years, accounting for 37.4% of all deaths with VA interviews in this category. The other top causes of death included pneumonia (16.7%), HIV/AIDS (13.3%), and diarrhea (8.5%). Pneumonia accounted for 19.3% of deaths among females and 14% of deaths in males in this age group. Indeterminate causes of death accounted for 10.4% in this age category. The leading causes of death among children under the age of 5 years are presented in The overall mortality rate for the age group 5–14 years was 1.5 per 1000 p-y. Malaria was the leading cause of death (28.0%), followed by pneumonia (14.5%), HIV/AIDS (11.6%), acute abdomen 5.3%, and road traffic accidents (4.3%)for this age group. The rates of PTB and diabetes were significantly high in this age category (HR = 2.29 (0.002) and 2.78 (0.012), respectively). Indeterminate COD accounted for 9.7% of deaths for this age group. The leading causes of death among the age group 5–14 years are presented in The overall mortality rate for the age group 15–49 years was 5.3 per 1000 p-y. This age group accounted for 28.0% of all deaths for which VAs were available. HIV/AIDS was the leading cause of death (23.5%), followed by pneumonia (7.8%), PTB (7.8%), acute abdomen (6.1%), and malaria (5.9%) among persons aged 15–49 years. NCDs such as stomach cancers [HR = 1.53 (0.130)], cardiac diseases [HR = 1.85 (0.035)], and diabetes [HR = 2.01 (0.038)] were significantly higher in this age category, The overall mortality rate for the age group 50–64 years was 13.8 per 1000 p-y representing 14.0% of all deaths for which VA interviews were available. The lead cause of death among this age group was HIV/AIDS (14.5%), followed by acute abdomen (9.9%), pneumonia (9.0%), digestive neoplasm (8.3%), and stroke (7.5%). NCDs  were significantly high in this age category (HR = 1.18–0.531), The overall mortality rate for the age group 65+ was 53 per 1000 p-y. The leading COD were stroke (9.7%), acute abdomen (9.5%), cardiac diseases (7.3%), pneumonia (7.1%), and stomach cancers (6.0%). Deaths due to diabetes were significantly higher in this age category [HR = 1.04 (0.909)], The causes of death among children under the age of five  are presented below .The specific causes were then broadly categorized into non-communicable diseases (NCDs), communicable diseases (CDs), neonatal causes, maternal causes, indeterminate, and external causes (injuries). When grouped according to broad causes, CDs were the leading cause of death overall (43%), followed by NCDs (34%), indeterminate (13%), external (7%), neonatal (2%), and maternal causes (1%) .Figure 5CDs represented the most frequent cause of death in the Kombewa HDSS. Overall, 43.1% of the deaths subjected to InterVA-4 were due to CDs, resulting in a mortality rate of 2.6 deaths per 1000 person-years. Among infants, CDs accounted for 80.0% of all causes of infant deaths. The most prominent CDs for this age category were pneumonia, malaria and HIV . CDs werNCDs accounted for 34.4% of all deaths, with an overall mortality rate of 2.1 deaths per 1000 person-years. NCDs were most prominent in the age groups 50–64 and 65+ years, accounting for 46.1 and 57.8% of all causes of death within these age categories, respectively. The most prominent causes of NCDs within the age groups 50–64 and 65+ years were stomach cancers, stroke, acute abdomen, and unspecified NCDS .Deaths due to maternal and neonatal conditions accounted for 2.1% of all analyzed deaths. The main and typically neonatal conditions were unspecified neonatal causes (25.0%), asphyxia (20.6%), preterm (16.3%), and pneumonia (11.8%) of all examined neonatal deaths. The main causes of maternal deaths linked with pregnancy were sepsis, hemorrhage, and hypertension . MaternaThere were a total of 289 deaths (7.4%) due to injuries during the reporting period, with males accounting for 65.4% of deaths due to external causes. Death due to injuries was most prominent in the age groups 5–14 years  and in the age group 15–49 . As summarized in Mortality rates were highest among neonates HR = 38.54 (<0.001) and among infants HR = 2.07 (<0.006) in the Kombewa HDSS . The cumIn the population of the Kombewa HDSS, communicable diseases are the leading cause of death, accounting for 43.1% of deaths recorded between 2011 and 2015 see . Among tHIV/AIDS was the leading cause of death in the study population, accounting for 12.6% of all deaths see . The HDSMalaria remains a major public health concern, especially among children under the age of five years. Within the Kombewa HDSS population, malaria was the second leading cause of death, accounting for 10.3% of all deaths. Rates of death attributed to malaria were largely consistent with those in the Kisumu HDSS , and were higher, as expected, than rates of death attributed to malaria in Nairobi and Kilifi  . Kisumu The HDSS platform is very important for health policy evaluation and has many potential applications. For example, the site participated in several multicenter clinical trials of the RTS,S/AS01 malaria vaccine, including the large phase 3 trial in African children in which episodes of both clinical and severe malaria in children receiving the vaccine at 5–17 months of age were reduced by approximately 50% . The RTSOf particular interest are external causes of death, such as road traffic accidents, which accounted for 7.4% of all deaths. Road traffic accidents, accounted for 23.5% (68/289) of all external causes of death see . This reSuicide accounted for 21.1% of the 289 deaths due to injuries in the Kombewa HDSS. These deaths were more common in the 15–49 year age group; similar results were reported in the Kisumu HDSS, Kilifi, and Nairobi. It is unclear what the driving force behind this is, however, drug and alcohol use, stigmatization due to HIV/AIDS, or high unemployment rates may contribute. Assault cases accounted for 16.6% of all external causes of deaths. The age group 15–49 accounted for 68.8% of all deaths due to assault.NCDS have been shown to be a major contributor of disease burden in the developing world, especially among adults . In the Timely and reliable statistics on deaths are necessary for population health assessment, epidemiological research, health planning, and programme evaluation. Longitudinal data from population-based surveillance, such as a HDSS, provide crucial time-trend age-specific, cause- specific mortality data that can be pivotal in addressing public health concerns and supporting research activities. This dataset provides opportunities for a wide range of more detailed analyses based on the different categories of public health interest such as childhood causes of death, pregnancy related mortality, and NCDs among others.