Aortic stiffness is an independent predictor of cardiovascular (CV) events and mortality. However, no data exists for the prognosis of combined aortic stiffness and myocardial ischemia. Using cardiac magnetic resonance (CMR) imaging, we assessed the association of aortic stiffness by pulse wave velocity (PWV), myocardial ischemia, and CV events in patients with known or suspected coronary artery disease (CAD).Velocity-encoded CMR was performed in 520 patients who had undergone adenosine stress CMR. The PWV was determined between the mid-ascending and mid-descending thoracic aorta. Patients were divided into 4 groups by PWV (higher or lower PWV) and myocardial ischemia (positive or negative ischemia). Combined CV events including mortality, acute coronary syndrome, heart failure, coronary revascularization, and stroke were analyzed among the 4 groups.p <  0.001). The group with a higher PWV and negative ischemia also was significantly associated with CV events . Groups with a lower PWV-positive ischemia and a higher PWV-negative ischemia showed no difference in terms of CV events . Patients with myocardial ischemia who had higher PWV demonstrated significantly higher event rates than those who had lower PWV . Multivariate analysis demonstrated that myocardial ischemia and PWV were independent predictors for combined CV events .The median follow-up period was 46.5 months, and the median PWV was 10.54 m/sec. Myocardial ischemia was positive in 199 patients (38.3%). The group with a higher PWV and positive ischemia had the most CV events (hazard ratio 8.94, Stress perfusion CMR provided prognostic utility in patients with known or suspected CAD. Adding aortic stiffness to stress perfusion CMR could improve risk assessment and prediction for future CV events. Aortic stiffness is one of the earliest detectable indicators of adverse structural and functional changes in the vessel wall . SeveralMeasurement of aortic stiffness can be performed by several methods, such as carotid-femoral pulse wave velocity (PWV) using a tonometer, computed tomography, or cardiac magnetic resonance (CMR) imaging. Each method has its advantages and disadvantages. Although carotid-femoral PWV is inexpensive and available, CMR is often preferred for several reasons. The advantages of PWV using CMR include the provision of cross-sectional images covering the desired length of the aorta, a high spatial resolution, direct measurement of the aorta length without geometric assumptions of the distance (in contrast to tonometer), a lack of ionizing radiation, and the ability to evaluate other aspects of the aorta  , 18. FurPWV assessment can be integrated in a comprehensive CMR study for coronary artery disease (CAD) evaluation. Adenosine stress CMR, a noninvasive test for the highly sensitive and specific diagnosis of CAD , provideThis study aimed to determine whether aortic stiffness by CMR-based PWV can add the prognostic value to myocardial ischemia for the prediction of future cardiovascular events.This retrospective, single-center study was approved by the center’s institutional ethics committee. A total of 520 patients referred for adenosine stress CMR as part of the diagnosis and risk stratification of CAD were consecutively enrolled at Siriraj Hospital  between October 2010 and February 2014. The inclusion criteria were males or females aged 18 or older who underwent adenosine stress CMR and aortic PWV, with a follow-up period of at least 12 months following the CMR examination. Patients with (1) an incomplete CMR examination or (2) diseases of the aorta which involved PWV measurement  were excluded.Detailed medical history and vital signs including blood pressure (BP) data were recorded from medical records. Further, BP was measured in a supine position by automated BP devices using appropriate cuff size, on the same day of CMR examination (approximately 10 min before scanning). The BP values were averaged from two measurements for each patient.The follow-up data were collected from clinical visits, medical records, and scripted telephone interviews. Demographic data, cardiovascular risk factors, adenosine stress CMR, and PWV results were also included. Based on their PWV and ischemia results, patients were divided into 4 groups as higher or lower PWV (using mean or median), and positive or negative ischemia.The primary outcome was to assess the combined cardiovascular outcomes  of the patients in each group. The secondary outcomes were identified as the results of each primary outcome and cardiovascular mortality. The predictors of the combined cardiovascular outcomes were analyzed. Since adenosine stress CMR results may influence decisions regarding revascularization and lead to periprocedural events or death, coronary revascularizations which occurred within 6 months of the CMR, or periprocedural events that occurred in the same admission, were not included for analysis.°; field of view (FOV) in x axis 240–360 mm; FOV in y axis 250–300 mm; slide thickness 8 mm; acquisition voxel size 1.75 × 1.75 mm; and reconstructed voxel size 0.64 × 0.64 mm.The CMR study was performed using a 1.5 T Philips Achieva XR scanner . After a scout image to locate the cardiac axis, an electrocardiogram (ECG)-triggered, breath-hold, black blood, single-shot sequence was acquired in the axial orientation for 30 slides; it covered the whole heart and thoracic aorta. The scanning parameters were echo time (TE) 24 milliseconds (ms); repetitive time (TR) 1400 ms; refocusing flip angle 90°; slide thickness 8 mm; FOV 270 mm; reconstructed FOV 320 mm; typical matrix size 2.55 × 2.6 mm; and reconstructed spatial resolution 1.3 × 1.3 mm. Continuous ECG monitoring was performed, and 1-min-interval BP and oxygen saturation monitoring were measured.The myocardial first-pass perfusion study was assessed immediately after an injection of 0.05 mmol/kg of gadolinium contrast agent  beginning at 3-min of adenosine 0.56 mg/kg infusion. The 3 short-axis slices of apical, mid, and basal left ventricular levels were acquired using an ECG-triggered, steady-state free precession, inversion-recovery, single-shot, turbo gradient-echo sequence. The image parameters were TE 1.32 ms; TR 2.6 ms; refocusing flip angle 50°; FOV in x axis 250 mm; FOV in y axis 210 mm; slide thickness 8 mm; typical matrix size 2.0 × 2.0 mm; reconstructed spatial resolution 1.12 × 1.12 mm; temporal resolution 10–20 ms; and velocity encoding 170 cm/s.The PWV image was acquired during the waiting period between the stress and viability studies, approximately 10 min after adenosine injection. The image was determined with the free-breathing, velocity encoded CMR (VE-CMR) technique as the through-plane flow in the mid-ascending and mid-descending thoracic aorta at the level of the pulmonary trunk. The imaging parameters were retrospective ECG-triggered; TE 3.1 ms; TR 5.3 ms; refocusing flip angle 12Sixteen myocardial segments were defined for perfusion analysis (in accordance with the standard recommendation of the American Heart Association), excluding the true apex . A myocaDedicated cardiovascular imaging software was applied for the PWV analysis. The contours of the mid-ascending and mid-descending thoracic aorta were manually drawn to achieve the flow (m/sec) at those 2 locations throughout all phases of the cardiac cycle. The corresponding flow-time curve was generated. The arrival time of the pulse wave was measured as the point of interception of the linear extrapolation of the baseline and the steep early systolic stage. The aortic path length was determined by multiplanar reconstruction of axial half-Fourier acquisition from the steady stage image. As to the reconstructed sagittal view, the path length was depicted as the centerline from the levels of the mid-ascending aorta to the mid-descending thoracic aorta, corresponding to the same level obtained in the VE-CMR.The PWV between the mid-ascending and mid-descending thoracic aorta was calculated using the following formula:When Δ x reflected the length of the aortic path between the mid-ascending and mid-descending thoracic aorta and Δ T represented the time delay between the arrival of the foot of pulse wave at those two corresponding levels Fig. . The anaTo assess the intra- and inter-observer reliability of the PWV measurements by CMR, 50 patients were randomly selected in order to measure the variability of the first observer 4 weeks after the initial analysis, and the variability of the second independent observer, who was blinded to the initial results.Statistical analyses were performed using IBM SPSS Statistics for Windows, version 20.0 . Normally distributed continuous data were expressed as mean and standard deviation (SD). Non-normally distributed continuous data were expressed as median and interquartile range. Categorical data were expressed as number and percentages. Normally distributed continuous data of multiple (> 2) groups were compared using one-way analysis of variance (ANOVA). Non-normally distributed continuous data of multiple (> 2) groups were compared using Kruskal-Wallis test. Categorical data were compared by Chi-squared or Fisher’s exact test.Cox regression was used for unadjusted and adjusted hazard ratios. Kaplan-Meier event curves for the 4 groups of patients were constructed for the combined cardiovascular outcomes of all-cause mortality, ACS, de novo or decompensated heart failure, coronary revascularization, and stroke.p-value < 0.1 on univariate analysis were entered into a multivariate analysis. Multivariable analysis was performed to determine independent predictors. The intra- and inter-observer reliabilities of PWV measurements were analyzed using the intraclass correlation coefficient.To analyze the predictive value of PWV and myocardial ischemia for the combined cardiovascular outcomes, a Cox-regression analysis was performed for the assessment of univariate predictors from baseline characteristics, medications, and CMR parameters. Variables with p-value < 0.05 was considered as statistically significant.The hazard ratios (HRs) and 95% confidence intervals (CIs) for the various outcomes were calculated; a Comprised of 259 males and 261 females, a total of 520 patients were consecutively enrolled. Their mean age was 68.9 ± 10.6 years, and their baseline characteristics and CMR parameters are summarized in Table The cumulative event rates for the composite outcomes of all-cause mortality, ACS, de novo or decompensated heart failure, coronary revascularization, and stroke in each group are presented in Table p <  0.001). The group 2 (lower PWV-positive ischemia) and the group 3 (higher PWV-negative ischemia) also showed significantly increased the combined endpoints . Remarkably, there were no significant differences between the group 2 and 3 in terms of the event rates . Additionally, patients with myocardial ischemia (groups 2 and 4) who had higher PWV (group 4) demonstrated significantly higher event rates than those who had lower PWV (group 2) .Using the group 1 (lower PWV-negative ischemia) as the reference group, the group 4 (higher PWV-positive ischemia) had the significantly worst outcomes for the combined endpoints , followed by infection (28.6%) and cancer (21.4%). The groups 2 and 4 with positive ischemia by adenosine stress CMR had the highest mortality rate , and ST-segment elevation myocardial infarction (STEMI). There were 41 cases of ACS , 30 (73%) of which were in the higher PWV groups (group 3 and 4). The Kaplan–Meier event curves for the ACS cases in each group are illustrated in Fig. p = 0.002). The Kaplan–Meier curves for the heart failure events are presented in Fig. The leading cause of cardiovascular death was de novo or decompensated heart failure; of the 54 events recorded, 43 (80%) required hospital admission. The higher PWV groups (group 3 and 4) were associated with an increased rate of de novo or decompensated heart failure than the groups with a lower PWV (group 1 and 2) .There were 14 strokes during the follow-up period. Most stroke events (11 from 14) was in the higher PWV group (3 and 4). All strokes were ischemic in origin, and most patients recorded a good recovery without major morbidity.p <  0.001 [per 10% decrement]), myocardial ischemia , and PWV  as independently associated with combined cardiovascular events.The univariate and multivariable analyses to predict the combined cardiovascular outcomes are shown in Table p <  0.001) for the first observer in the initial analysis and 4 weeks later, respectively, and 10.55 ± 5.12 m/sec  for the second observer in the initial analysis  aortic stiffness measured by CMR independently predicted composite cardiovascular events in patients with known or suspected CAD underwent adenosine stress test; 2) the presence of inducible myocardial ischemia was a powerful predictor for cardiovascular events; and 3) the combination of aortic stiffness and myocardial ischemia provided significant improvement of prognostic predictions.Arterial stiffness refers to alterations of medial properties leading to reduced distensibility of the arterial wall. Many factors and diseases influence arterial stiffness, including aging –4, hyperSeveral functional and structural changes contribute to arterial stiffness, for instance, high BP, impaired smooth muscle function, impaired endothelium-dependent dilation, increased collagen content expression, and decreased elastin content. Furthermore, numerous potentials signaling events contribute to age- and disease-related arterial stiffness, such as oxidative stress, inflammation, and decreased expression of endothelial nitric oxide synthase activity. Increased aortic stiffness has been established in various cardiovascular diseases and metabolic abnormalities.Carotid-femoral PWV using a tonometer is generally accepted as a simple, noninvasive, and inexpensive method to measure arterial stiffness. This technique is the measure used in most clinical studies and is a strong predictor of cardiovascular events , 15, 16.PWV measurement using CMR is one of the preferred methods for evaluation of arterial stiffness, giving high spatial resolution without ionizing radiation. This technique can assess PWV accurately across any segment of aorta, but the level of the mid-ascending and mid-descending aorta was chosen due to the corresponding location of the heart in CMR examination. Moreover, CMR can measure the distance of the aorta without geometrical assumptions, unlike carotid-femoral PWV using a tonometry. The PWV measured by CMR in our study demonstrated excellent images with significantly high reliability comparable to the previous studies .The pathophysiology pathway of increased arterial stiffness and myocardial ischemia is a complex issue. One of the explanations could be the translation of the arterial wave reflection progressively from diastole into late systole .Arterial stiffness causes an early arrival of wave reflections in systole instead of diastole and, thus, increases the systolic afterload and reduces the diastolic coronary perfusion pressure. As a result of this pathophysiological changes, arterial stiffness may cause an ischemic heart injury through a reduction in the subendocardial oxygen supply and increase in oxygen demand. However, a recent meta-analysis demonstrated that the arterial reflection times of most subjects were well within systole rather than diastole . This meWith regard to CAD, aortic stiffness is associated with a broad range of patients, from apparently healthy subjects to those with cardiovascular risks and stable CAD, to post-coronary intervention –28. One Previous studies have shown that myocardial ischemia is one of the most important predictors of future hard cardiac events , 30. ThiThe utility of CMR in the evaluation of CAD is increasingly being recognized. In particular, vasodilator stress perfusion CMR and viability assessment by delayed enhancement technique. The accuracy of the adenosine stress CMR for diagnosis of CAD is substantially high with sensitivity and specificity of 89 and 87%, respectively .Aortic stiffness using PWV can be performed as a part of a comprehensive stress perfusion CMR protocol. In our study, PWV was measured during the waiting period between the stress and viability studies; the non-breath-hold technique proved convenient for patients. The PWV images was acquired after adenosine injection approximately 10 min. Even if adenosine may affect the arterial compliance, given its very rapid half-life (< 10 s), this effect did not alter PWV measurement in our study.According to the primary study objective, aortic stiffness assessed by CMR was determined as a potential risk marker for cardiovascular disease with high accuracy and reproducibility.There were some study limitations. Firstly, this study was retrospective in its methodology. Some confounding factors cannot be totally eliminated due to the nature of retrospective study. In addition, no standard, cut-off level was currently used for the PWV quantification. Finally, variations in heart rates may have resulted in slightly different velocity waveforms between cardiac cycles, resulting in errors in the PWV measurements. However, a prior validation study of PWV assessed by CMR determined agreement between invasive intra-aortic pressure measurements .The addition of aortic stiffness to myocardial ischemia results improves the prognostic prediction of cardiovascular events. The combination of aortic stiffness and adenosine stress test results might become an integral part of clinical risk stratification and the monitoring of therapy.Aortic stiffness can be detected long before the clinical appearance of cardiovascular diseases. From our results, measurement of aortic stiffness can be performed along with an assessment of myocardial ischemia, thereby improving cardiovascular risk stratification.There has been some evidence that has suggested that antihypertensive drugs are beneficial in reducing aortic stiffness and thus might modify cardiovascular events beyond a decrease in BP . This asThis retrospective study demonstrated that the addition of aortic stiffness to myocardial ischemia could improve the prognostic prediction of CV events. The combination of aortic stiffness and adenosine stress test might become an integral component for risk stratification in CAD.


Mid-upper arm circumference (MUAC) and weight-for-height (WHZ) are commonly used indicators to identify acute malnutrition. However, MUAC and WHZ diagnose different children, and produce prevalence estimates that are meaningfully different. Previous research in Somalia has suggested improved concordance using MUAC-for-age (MUACZ) rather than MUAC. We further evaluate the relationship between MUACZ, MUAC, and WHZ using surveys conducted globally.We analyzed 882 population representative surveys from 41 countries. Children ages 6–59 months were classified as acutely malnourished using three independent criteria: WHZ < − 2 (WHZ2), MUAC< 125 mm (MUAC125), MUACZ < − 2 (MUACZ2). Population prevalence using each of the three criteria are presented by country and region. Correlations of survey prevalence for each indicator pair were assessed. Multivariable regression models of MUACZ and MUAC125 adjusted for WHZ2, stunting prevalence, age, and sex. To evaluate individual level diagnostic concordance, we compared the proportion of children identified by each of the three criteria.2 = 0.50) relative to the adjusted model for MUAC125 (R2 = 0.43). The proportion of children identified by both MUAC125 and WHZ2 was 25.5%, smaller than the proportion identified by both MUACZ2 and WHZ2 (30.6%).Median prevalence of acute malnutrition overall was highest for MUACZ2 (14.0%) followed by WHZ2 (10.6%), and lowest for MUAC125 (7.3%). The absolute difference in prevalence between MUACZ2 and WHZ2 was smaller than the difference between MUAC125 and WHZ2 for 51.3% of surveys. The correlations of WHZ2 with both MUACZ2 as well as with MUAC125 were weak, positive associations , but MUAC125 and MUACZ2 had a strong, linear relationship (Pearson’s r = 0.9265). The adjusted regression model for MUACZ2 had greater fit (RMUACZ identified more children as malnourished than MUAC, resulting in a higher prevalence of acute malnutrition in nearly all settings. Prevalence by MUACZ was not consistently more similar to WHZ than that estimated by MUAC, and correlations with WHZ were only slightly improved relative to MUAC. Consequently, programmatic use of MUACZ cannot be justified based on improved concordance with WHZ. Further research on morbidity and mortality of children with low MUACZ only are needed before recommending MUACZ for wider use. Presence of acute malnutrition in children is ascertained through anthropometric measurement. Two independent anthropometric indicators have been endorsed by the Standing Committee on Nutrition (SCN) and are widely used in determining admission into selective nutrition treatment programs as well as assessing population prevalence —weight-for-height Z-score (WHZ) and mid-upper arm circumference (MUAC) –3.While these indicators intend to measure the same condition of acute malnutrition, numerous studies have found that the children identified for admission to treatment programs by the two indicators are not the same. A 2009 report by the World Health Organization (WHO)  noted th2 = 0.46). The research suggested that prevalence of WHZ was higher than MUAC in most surveys, but the reverse could be identified in select populations.Subsequent research has demonstrated that these diagnostic differences translated into meaningful variations in the population-prevalence of acute malnutrition determined by the two-indicators; a contrast to the initial intention of researchers to derive MUAC prevalence cut-offs for programmatic action and severity classification that corresponded with those produced by WHZ < -2 . Based oResearchers have hypothesized that MUAC-for-age (MUACZ) may have more diagnostic consistency with WHZ than MUAC due to the fact that MUACZ is adjusted by age and sex. As such, the calculation of the MUACZ indicator is similar to WHZ, both of which compare a child’s anthropometric measurements to an international reference population, whereas a single threshold is used for MUAC irrespective of age (125 mm) to classify acute malnutrition in children aged 6–59 months. Preliminary evidence from Somalia supports this hypothesis. Custodio et al.  found thFindings of improved diagnostic similarity of MUACZ and WHZ, relative to MUAC, may have broad practical implications. First, current global protocols for therapeutic feeding centers that recommend measuring mid-upper arm circumference nearly all include admission on low MUAC not low MUACZ. Second, measurement of MUAC using color-banded strips is widely used in humanitarian settings in large part given its simplicity, demonstrated to be feasible for low-literacy community members with limited training . ScreeniThe aim of our study is therefore to further investigate the relationship between both indicator pairs (WHZ and MUAC v. WHZ and MUACZ) to assess differences in population prevalence and individual child diagnosis when using MUACZ vs. MUAC. Building on the work of Custodio et al. , using aData reviewed for analysis were single stratum, cross-sectional, population representative surveys  provided by the United Nations High Commissioner for Refugees (UNHCR) and by Action Contre le Faim (ACF). Surveys were reviewed if they were conducted between 2013 and 2016 (UNHCR) and 2001–2017 (ACF) and measured age, sex, weight, height and MUAC in children aged 6–59 months. Survey datasets were examined for duplication prior to inclusion in the study.Surveys with sample size smaller than 196 persons and cluster surveys with fewer than 25 clusters were excluded from all analyses as they did not meet minimum standards for small scale surveys . SurveysSurvey countries were categorized into seven regions: Latin America and the Caribbean; Eastern and Southern Africa; Democratic Republic of Congo (DRC); West and Central Africa; Southeast Asia and Pacific; Sudan; and Middle East and North Africa . Sudan aWeight-for-height (WHZ), MUAC-for-age (MUACZ), and Height-for-age (HAZ) z-scores were calculated for all children using the WHO SAS macro, which applies the WHO 2006 growth standards . ChildreTo examine the relationship between calculated prevalences for the three indicators, three analyses were performed. First, for each survey the difference in prevalence was calculated for each of the three indicator pairs . Second, we plotted the correlations of survey level prevalences for each of the three indicator pairs. Both Spearman and Pearson’s correlations were calculated. Additionally, multivariable linear regression was modeled with MUACZ2 and MUAC125 prevalence as outcome. For each of the multivariable models, we included the following predictor variables, previously found to be associated with MUAC: WHZ2, stunting prevalence (HAZ < − 2), age ratio, and sex ratio of the survey sample. Age ratio was calculated as the proportion of children 6–23 months v. 24–59 months in the sample. All predictor variables were retained in the multivariable model regardless of significance in univariate models . Stunting was highly significant in both models , with small, positive coefficients (βHAZ2 = 0.23 for MUACZ2 and βHAZ2 = 0.14 for MUAC125). In the adjusted models, the age ratio of survey sample (6–23 months: 24–59 months) was not significant  in MUACZ2 model but highly significant  in the MUAC125 model. In the adjusted model for MUAC125, the age ratio variable had a strong, positive association with MUAC125 (βageratio = 7.15). The sex ratio was insignificant in both models.Table Analysis of diagnostic convergence of indicators for diagnosing acute malnutrition in individual children, presented in Table The overall median proportion of children identified by both MUACZ2 and WHZ2 was slightly higher 30.6%) than by both MUAC125 and WHZ2. By region, the median proportion of children in a survey identified by both MUACZ2 and WHZ2 was highest in Sudan (38.8%), and lowest in Latin America and Caribbean (22.9%). The proportion of acutely malnourished children diagnosed by both MUACZ2 and WHZ2 also varied considerably by survey (range: 0 to 64.2%) , presented in Table Consequently, the pattern of diagnostic overlap of the indicators was different for younger and older children Table . For MUAThe primary aim of our analysis was to evaluate the finding by Custudio et al.  that acuIn addition to population level associations, we examined child level diagnostic implication of using MUACZ, MUAC and WHZ. Custudio et al.  previousThe major factor that distinguishes absolute MUAC and MUACZ is the age-standardization. Consistent with expectations and previous research , 11, in 2 ≈ 0.85). Consequently, it may be possible to devise a relatively reliable formula for conversion of MUAC125 into MUACZ2 prevalence, as was done previously for converting estimates from National Center for Health Statistics (NCHS) growth reference to estimates using the WHO growth standards where a high degree of fit (R2 > 0.9) was observed [Since the development and publication of the MUAC-for-age reference data in 1997 , there hobserved .Interestingly, while the association between MUACZ2 and MUAC125 was strong, only 40% of children were identified by both indicators. The proportion of children identified by both is very age dependent. Both MUAC125 and MUACZ2 identified similar children in the younger age group (70% overlap). However, in the older age group, only 20% of malnourished children were identified by both indicators. The finding that MUAC125 identified no children aged 24 months or older not otherwise identified by MUACZ2, conforms with expectations but has not been described previously in the research. The programmatic implications of this require more research. Data on the prognostic ability of MUACZ2 to predict mortality, especially in older age group, is limited. A study in Guinea-Bissau found no significant difference comparing MUAC125 and MUACZ2 in ability to predict mortality at 30 and at 90 days among children aged 6–35 months .This study is subject to several limitations. First, analysis is limited to sub-national surveys conducted primarily in emergency settings that were made available for review. These subnational surveys are conducted more frequently in countries with poor nutritional status and ongoing nutritional programming. Consequently, few surveys from Latin American countries and no European countries were available for analysis. In addition, large multi-indicator national surveys, such as Demographic and Health Surveys, measure WHZ but not MUAC or MUACZ. While the final dataset included surveys from 41 countries, the geographic distribution may impact generalizability of findings. Additionally, the surveys included in our study were designed to achieve reasonable precision for global acute malnutrition. The number of children with severe acute malnutrition within these samples was consequently quite small. Therefore, future analysis looking at the association of severe rather than total acute malnutrition by WHZ, MUAC and MUACZ, would require surveys with larger samples or a pooled analysis design. Finally, analysis of age differences requires accurate age estimation. Settings with high acute malnutrition also tend to have poor vital registration. Age is consequently estimated to the nearest month for many of the children (where exact date of birth is not available). Imperfect age estimation may impact the associations described.The presented analysis demonstrates some limited improvement in convergence with WHZ when using MUACZ instead of MUAC. Wider use of MUACZ for programming, however, cannot be justified based on these limited improvements. In terms of field logistics, MUAC is more convenient and quick to measure, as MUACZ additionally requires age estimation and use of field-reference tables. Additionally, limited research exists on whether children with low MUACZ have an increased risk of mortality, as has been previously documented for low MUAC and low WHZ. Finally, use of MUACZ for admission to treatment should be informed by research on responsiveness of children with low MUACZ, but not low MUAC or low WHZ, to nutrition treatment.


When considering malaria disease severity, estimation of parasitemia in erythrocytes is important, but sometimes misleading, since the infected erythrocytes may be sequestered in peripheral capillaries. In African children and Asian adults with falciparum malaria, parasitemia as assessed by quantitative PCR (qPCR) in plasma seems to be a valuable indicator of disease severity, but data on African adults as well as the impact of co-infection with HIV is lacking.In 131 patients with falciparum malaria in a public tertiary teaching hospital in Mozambique, plasma malaria parasitemia as assessed by qPCR, compared to qualitative malaria PCR in blood cell fraction, was related to malaria disease severity and HIV co-infection.p = 0.037) in univariate analysis, but this difference was attenuated after adjusting for age, sex and HIV co-infection (p = 0.055). A quarter of the patients with severe malaria had negative qPCR in plasma.Of the 131 patients with falciparum malaria, based on positive qualitative PCR in the blood cell fraction, 93 patients (72%) had positive malaria qPCR in plasma. Patients with severe malaria as defined by the WHO criteria had higher malaria quantitative plasma parasitemia (median 143 genomes/µL) compared to those with uncomplicated malaria (median 55 genomes/µL, This study of adult African in-patients with falciparum malaria with and without HIV co-infection, neither confirms nor rejects previous studies of malaria qPCR in plasma as an indicator of disease severity in patients with falciparum malaria. There is a need for further and larger studies to clarify if parasitemia as assessed malaria qPCR in plasma could be a surrogate marker of disease severity in falciparum malaria. In spite of decreasing incidence, falciparum malaria still causes about half a million fatalities every year, whereof 93% occur in sub-Saharan Africa and about two thirds are children below 5 years of age . When coP. falciparum [In African children in a moderate-to-high transmission area, histidine-rich-protein 2 (HRP 2)-concentration in plasma distinguished severe malaria from coincidental parasitemia due to carrier state . Similarlciparum . Most oflciparum , 7, refllciparum , TARE-2 lciparum . This melciparum . Therefolciparum . Howeverlciparum –13. We hlciparum , 14. Hern = 212) patients consecutively admitted during regular work hours during weekdays during two malaria seasons from January to March 2011 and from November 2011 to March 2012 in the Central Hospital of Maputo, Mozambique [The study design has previously been described . BrieflyPlasmodium, HIV-1 and HIV-2.The blood samples were collected from a pre-alcohol-cleaned peripheral vein into pyrogenic-free tubes with EDTA (Ethylene-diamine-tetra-acetic acid). The EDTA vacutainer tubes were turned gently, placed immediately on melting ice, and centrifuged within 30 min at 2000 rpm for 20 min. Plasma was aliquoted and stored first at − 20 °C for 24 h; then at − 80 °C until further analyses. The remaining blood cell fraction was also stored at − 80 °C. From this, we later purified the total nucleic acids using a MagNA Pure (Roche) robot and analysed with a qualitative PCR analysis for P. falciparum, Premium Medical Corporation Ltd., Daman, India and in 2011–2012 ICT Malaria P.f.®, using ICT Diagnostics Cape Town, South Africa, thick smears (Giemsa 20% for 5 min) and HIV testing  and other routine blood tests .According to the hospital’s routine and consistent with the standard procedures in the hospital’s laboratory, we performed malaria antigen test (HRP-2 Rapid Diagnostic Test (RDT)) for malaria in 2010–2011 using First Response® Malaria antigen Severe malaria was defined according to WHO  criteria  and adjuCytochrome b gene on the mitochondrial genome, was performed on the extracted DNA from EDTA blood cell fractions. For positive samples the Plasmodium species was determined by conventional species-specific 18S PCR or divergent results by sequencing [A conventional genus-specific malaria PCR, targeting the quencing .P. falciparum DNA in plasma was measured by quantitative real-time PCR (qPCR) as described elsewhere [P. falciparum (US 03 F Benin I), containing exclusively ring stage parasites in a concentration of 2000 p/μL, was used for dilution series, fivefold, to prepare standard curve for estimating efficiency of the PCR and for quantification. DNA quantity for samples with P. falciparum DNA less than the Limit of Quantification (LOQ) was set to be equal to or less than the LOQ (estimated to ≤ 6.4 parasites/µL). Samples with a P. falciparum DNA concentration higher than the most concentrated standard were diluted and qPCR repeated. The qPCR was also repeated for samples with weak positive or uncertain results. The efficiency (using the formula E = 10–1/slope – 1) was 91.2%, and Error value (E) of the assay 0.012. A unidirectional workflow pre- to post-PCR was enforced, and preparation of PCR reaction mixture, DNA preparations and PCR were carried out in facilities physically separated from each other.The concentration of lsewhere  using Prp values from Wald tests. For the regression analysis, malaria qPCR values were log10 transformed and the assumption of linearity of the effect of log qPCR was tested with flexible modelling  in Stata version 16.0, the rest of the statistical analyses were performed in IBM SPSS v. 24.Descriptive statistics are presented as mean values, ranges (min–max), medians, interquartile ranges , and as counts and percentages. Histograms and Box plots were used for illustration. Comparison of patients with severe malaria as assessed by WHO criteria with and without positive qPCR in plasma was done with Chi-squared tests for dichotomous variables and Mann Whitney test for continuous variables. Association between patients with severe malaria and plasma qPCR, age, sex and HIV co-infection was evaluated with binary logistic regression analysis with 95% confidence intervals (CI) and P. falciparum malaria, defined as positive qualitative malaria PCR in whole blood (n = 129) or positive thick slide and rapid test when qualitative PCR was not performed (n = 2). Only two patients had co-infection with P. vivax and P. malariae, respectively. Of the 131 falciparum malaria patients, there were 10 fatalities (8%), whereof nine were HIV positive. Owing to the low number of fatalities, mortality was not evaluated in further analyses. Seventy patients (53%) were co-infected with HIV, and there was a higher proportion of severe malaria cases among these (81%) than among the HIV negative patients (46%) (p < 0.001) [The patient population is described earlier . In summlood n = 9 or posiP. falciparum PCR in blood cells, there was no significant difference between the 93 patients with positive qPCR in plasma and the 38 patients without, in relation to HIV status  nor to malaria severity . All the ten fatalities had severe malaria and the eight of them that had qPCR done, were all positive. Clinical characteristics are shown in Table Malaria qPCR in plasma was positive for 93 of the 131 patients (71%). Of the 131 patients with positive qualitative The quantitative plasma parasitemia distribution is seen in Fig. n = 63) and uncomplicated malaria (n = 30) among the 93 patients with positive plasma qPCR (p = 0.030) . The severe malaria patients with positive plasma qPCR had significant more frequent severe liver failure and hyperparasitemia as assessed by thick blood smears, and more pronounced thrombocytopenia (Table There was a significant difference in the qPCR between patients with severe (30) Fig. , with meia Table .Fig. 2Thp < 0.001 and an OR of 5.36, indicating that a HIV co-infected malaria patient is about five times more likely to develop severe malaria than a HIV negative patient, adjusting for age, sex and qPCR (Table p = 0.037), but this association was attenuated when adjusting for age, sex and HIV co-infection (p = 0.055), even if the OR was about the same before and after these adjustments . The difference in plasma qPCR between HIV positive (n = 49) and HIV negative patients (n = 44) was not significant .In binary logistic regression analyses the strongest predictor for severe malaria was HIV co-infection with p = 0.97) or malaria antigen test (p = 0.062).No significant differences according to disease severity was found in the 131 malaria patients by the use of positive thick blood smears  had parasitemia below the limit of detection for the quantitative plasma PCR. As the parasite has erythrocytes as its major target cells in whole blood, it is not surprising that PCR analyses from whole blood are in general more sensitive than PCR analyses from plasma. A former analysis by RT-PCR for the 18S ribosomal gene for falciparum malaria in small children also found 3.5-fold higher parasitemia in whole blood compared to plasma . The malThis study cannot confirm nor oppose the results of the Aquamat sub-study. On the other hand, this study underlines the importance of considering HIV co-infection in malaria. With the great geographic overlap of HIV and malaria together with the increased risk of severe malaria, HIV status should always be included. There is need for further studies to clarify if qPCR may be a useful method for quantification of parasite load in patients with falciparum malaria as an indication of very severe malaria. Provided that the sensitivity of method is improved, it is possible that qPCR will have a future potential for evaluating disease severity in falciparum malaria, and there is a need for further and larger studies to clarify whether quantitative qPCR may be an indicator of clinical disease severity in falciparum malaria.


P. falciparum infection prevalence by microscopy, the pre-specified interim safety outcome, in relation to iron status, nutritional indicators and menarche assessed at recruitment. The interim survey was attended by 1223 (82%) of 1486 eligible participants, 1084 (89%) of whom were <20 years at baseline and 242 (22%) were pre-menarcheal. At baseline, prevalence of low body iron stores was 10%. At follow-up, 38% of adolescents had predominantly asymptomatic malaria parasitaemias, with no difference by menarcheal status. Higher body iron stores at baseline predicted an increased malaria risk in the following rainy season  after adjusting for bed net use, age, menarche, and body mass index. We conclude that routine iron supplementation should not be recommended without prior effective malaria control.High levels of storage iron may increase malaria susceptibility. This risk has not been investigated in semi-immune adolescents. We investigated whether baseline iron status of non-pregnant adolescent girls living in a high malaria transmission area in Burkina Faso affected malaria risk during the following rainy season. For this prospective study, we analysed data from an interim safety survey, conducted six months into a randomised iron supplementation trial. We used logistic regression to model the risk of  Iron deficiency (ID) is a lack of mobilisable iron stores with a compromised supply of iron to tissues . Iron dep < 0.001) /0.1207 /0.1207 . Unadjusted ferritin <15 μg/L , allowinA non-pregnant internal regression slope log (ferritin) against log (CRP) estimate was used for ferritin correction, adjusting ferritin levels where CRP exceeded a reference level of the tenth centile to that reference level .sTfR concentration of >8.3 µg/mL .10 adjusted ferritin (μg/L) >5.6, which assesses both stored and functional iron and is possibly less affected by inflammation. The ratio derives from the cut-offs sTfR > 8.3 μg/mL and ferritin < 30 μg/L. RAMCO was the assay used for sTfR, as its cut-off (>5.6) best predicted iron deficient bone marrow stores , in another area of high malaria transmission [Ratio of sTfR (mg/L) to logsmission .The median hepcidin reference level of serum/plasma based on a healthy non-malarious Dutch population . The hepBIS was calculated using the regression-adjusted ferritin estimate. Low body iron was defined as zero iron stores <0 mg/kg.Iron deficiency and iron status were estimated using specific biomarker definitions with adjusted ferritin estimates allowing for inflammation. These were:10 if < 400 μL), a third independent reading was made. Parasite density was calculated by counting the number of asexual parasites per 200 white blood cells in the thick blood film by light microscopy at 100× magnification. The parasite density per μL was calculated assuming a white cell count of 8000 mm. Clinical malaria was defined as a positive blood slide with fever (≥37.5 °C) or history of fever.Whole blood for malaria films were Giemsa stained and read by two qualified microscopists. For discrepant findings . For the remaining interim assessments, data from both trial arms were therefore combined.There was no difference in malaria prevalence risk by trial arm at the interim survey  was considered, but not presented as there was no statistical evidence for different slopes, nor was there evidence of non-linear effects. Significance values are presented for the age (slope) and menarche effects. Skewed biomarker values were log-transformed. Mean values and 95% confidence intervals (CI) for each integer age are presented. The relationships between malaria prevalence and age and BMI (Z-score) were similarly analysed using analogous logistic regression models.Z-score) as linear terms, and bed net use and menarcheal status at the time of assessment as categorical variables. More complex models were considered, but did not improve the fit to the data. Each biomarker was then tested in turn as an addition to this 4-variable base model. Biomarkers were standardised by the mean and standard deviation of the observed distribution in the sample in order to obtain comparable odds-ratio estimates between biomarkers. All analyses were conducted in the R statistical environment (v3.6).Similar logistic regression models were used to quantify the relationships between malaria prevalence at the interim survey and baseline iron biomarkers. A linear, iron effect was fitted for the menarcheal and non-menarcheal groups as there was no statistical evidence for an interaction effect or for any non-linear behaviour. Fitted lines are presented with rug plots to indicate actual data values. Multivariable logistic regression was used to assess the relationship between iron biomarkers and malaria prevalence, after adjusting for age and BMI . The number re-screened included 1084 adolescents (<20 years) and 139 young adults (20–24 years) . The reasons for non-attendance are shown in the participant flow diagram .Of the adolescents, 22% (242) were non-menarcheal at baseline . Mean BM10 ferritin >5.6. (p < 0.001), hepcidin (p < 0.001), and adjusted BIS (p < 0.001) decreased and the sTfR/log10 ferritin ratio (p = 0.004) increased with age. The same trends were seen in non-menarcheal girls with very little difference between menarcheal and non-menarcheal girls after adjusting for age.Iron deficiency prevalence ranged from 10% with low adjusted BIS (<0 mg/kg) to 26% using the sTfR/login >5.6.  In Figur3 (IQR 110–656). For all women both RDT (p = 0.004) and parasite prevalence p < 0.001) decreased with age (p = 0.21) nor RDT positivity (p = 0.35) decreased significantly with increasing BMI when age-adjusted using Z-scores. Field workers visited participants for a minimum of 12 weeks . On the night previous to the survey, 76% reported sleeping under a bed net. Malaria blood slides were positive for 38% of adolescents; 53% were RDT positive, with similar proportions for all women . Only 4%with age . Regressp = 0.007), BIS (p = 0.002) or hepcidin , indicating poorer iron status. A similar pattern of associations was observed for non-menarcheal girls , whereas increasing baseline sTfR/log10 ferritin ratios were associated with lower risk . Bed net use the night previous was associated with RDT positivity, but not microscopy positivity. Menarche and BMI were not independently associated with malaria. A multivariable analysis was conducted that adjusted for bed net use, age, menarche, and BMI, testing for independent associations with malaria . MalariaThe interim safety study was conducted in the rainy season to ensure consistently high exposure to malaria. At baseline, during the dry season, between 74–90% of rural, largely illiterate, adolescents had adequate iron stores. In the rainy season survey, more than half had evidence of malaria infections. At a population level, iron deficiency predicted a lower malaria risk six months later, a problematic concept when weighing the risks and benefits of an iron intervention . This was the first study to assess the predictive level of iron stores for subsequent malaria risk in a large population of adolescent girls living under hyper-endemic malaria conditions. IFA guidelines for menstruating adolescents were largely based on limited evidence from five (GRADE low) studies (one providing unpublished data) from malaria endemic regions in sub-Saharan Africa ,19,20,21Currently, routine adolescent IFA programmes are considered safe if delivered alongside malaria surveillance and control strategies . In our The major strengths of this analysis are the size of the population who attended the interim survey, and the fact that malaria exposure was consistently high, being assessed during a short period in the rainy season. In total, 18% of participants did not attend the interim survey, largely due to temporary absence, as it was not uncommon for young unmarried girls to be sent away to assist other households domestically , and theP. falciparum infections were concurrently gametocytaemic (51–89%) with a higher prevalence in individuals ≤17 years [Age was an independent predictor of increasing malaria immunity , neverth17 years . In Burk17 years . An area17 years .National surveys to measure ID and IDA prevalence are required for program planners to estimate the impact and cost-effectiveness of improving iron status ,46, with


A mass drug administration trial was carried out in Southern Province, Zambia, between 2014 and 2016, in conjunction with a standard of care package that included improved surveillance, increased access to malaria case management, and sustained high levels of vector control coverage. This was preceded by mass test and treatment in the same area from 2011 to 2013. Concordant decreases in malaria prevalence in Southern Province and deaths attributed to malaria in Zambia over this time suggest that these strategies successfully reduced the malaria burden. Genetic epidemiological studies were used to assess the consequences of these interventions on parasite population structure. Analysis of parasite material derived from 1,620 rapid diagnostic test (RDT)-positive individuals obtained from studies to evaluate trial outcomes revealed a reduction in the average complexity of infection and consequential increase in the proportion of infections that harbored a single parasite genome (monogenomic infections). Highly related parasites, consistent with inbreeding, were detected after interventions were deployed. Geographical analysis indicated that the highly related infections were both clustered focally and dispersed across the study area. These findings provide genetic evidence for a reduced parasite population, with indications of inbreeding following the application of comprehensive interventions, including drug-based campaigns, that reduced the malaria burden in Southern Province. Genetic data additionally revealed the relationship between individual infections in the context of these population-level patterns, which has the potential to provide useful data for stratification and targeting of interventions to reduce the malaria burden. A common MDA strategy uses dihydroartemisinin plus piperaquine (DHAp) to clear human infections and reduce the parasite reservoir, while providing longer chemoprophylactic protection against new infections.Population-level drug-based interventions including mass drug administration (MDA) and mass test and treatment (MTAT) have been evaluated with a goal to reduce the malaria burden in a number of settings.5 whereas overall deaths in Zambia attributed to malaria decreased 62% from 4,834 reported in 2010 to 1,827 in 2016.6 Much of this success is credited to a combination of sustained, high-level vector-based interventions, better surveillance, and improved access to malaria diagnosis and treatment through the scale-up of case management at health facilities and in villages through an increased number of community health workers. Clinical trials of population-level drug-based interventions were conducted in Southern Province, Zambia, including MTAT from 2011 to 2013, followed by MDA from 2014 to 2016. Outcomes from these trials have been reported.9 The MTAT study found that children in the intervention group had lower odds of a malaria infection than that of individuals in the control group  and that outpatient case incidence for malaria showed a modest decrease of 17% in the intervention group relative to that in the control group .8 In addition to the high coverage of vector control and improved access to diagnosis and case management in the community, the MDA trial found that two rounds of MDA with DHAp rapidly reduced infection prevalence, infection incidence, and confirmed-case incidence rates, especially in low-transmission areas, with parasite prevalence decreasing from 7.7% at baseline to 0.5% after MDA in lower transmission areas, resulting in an 87% reduction compared with the control .7 These declines were sustained after four rounds of MDA interventions, with a 72% (95% CI: 12–91%) reduction in malaria parasite prevalence as compared with those with the standard of care without any mass treatment.9 Thus, use of population-level, drug-based interventions, in the context of high coverage of vector-based interventions and improved treatment access, contributed to the decreased malaria burden observed between 2012 and 2016 in Southern Province, Zambia.Collectively, efforts have decreased malaria prevalence in Southern Province, Zambia, from 8% in 2012 to 0.6% in 2015,10 Declining transmission reduces parasite population genetic diversity as a consequence of reduced outcrossing and increased inbreeding.11 Genetic signatures that reflect reductions in effective parasite population size, population bottlenecks, and reduced transmission include a decrease in the complexity of infection (COI), the appearance of highly related and even genetically identical  parasites, and evidence of persisting parasite lineages across years.10 To assess the consequences of decreased malaria burden in Southern Province from 2012 to 2016 on parasite population structure, we compared the genotypes of parasites before and after application of drug-based interventions and performed a population genetic analysis. We expected that significant declines in malaria prevalence and incidence would correspond to a restricted parasite population that would exhibit decreased genetic diversity. Indeed, we observed strong evidence of a reduced parasite population with decreased COI including an increase in the proportion of infections that are monogenomic, highly related and clonal parasites after multiple rounds of MDA, and evidence for both focal and dispersed malaria transmission using genetic information from individual infections within and between households.Rapid reduction in malaria prevalence and incidence through intervention use has been shown to dramatically change the parasite population structure.Informed consent was obtained from all study participants. The study protocol was authorized by the Ministry of Health in Zambia and approved by the research ethics committees of the University of Zambia, PATH, and Tulane University , and reviewed by the Human Research Protection Program of the Harvard T.H. Chan School of Public Health (IRB protocol 22688).12 with an estimated overall prevalence of 14.9% and 20.3% among children younger than 5 years in malaria indicator surveys conducted in 201213 and 2015,14 respectively. Transmission is highly seasonal, with the peak occurring from March to May. Rapid diagnostic test–positive samples were obtained from household-level data collection during two community-randomized trials conducted in the same geographical region of Southern Province between 2012 and 2016. Methods for the randomly selected household surveys and individual cohort are described in detail elsewhere.15 Dried blood spots on Whatman FTA filter paper (Whatman WB120205) were collected for all individuals in these two data collection activities. In brief, the first sample set (n = 836 children younger than 6 years) was collected during the peak malaria transmission season (April–May) in both 2012 and 2013 (baseline) as part of a community-randomized controlled trial designed to assess the impact of three rounds of an MTAT intervention that used RDTs for testing and artemether–lumefantrine for treatment.8 The surveys were standard 2-stage cluster designs with first-stage selection proportional to cluster sizes. The second sample set  was obtained from an 18-month longitudinal cohort study, with monthly follow-up visits, conducted from December 2014 to May 2016 (cohort). The cohort was designed to evaluate a cluster-randomized controlled trial for assessing the impact of four rounds of community-wide MDA and household-level  MDA (fMDA) with DHAp compared with that of no mass treatment (control).9 The analysis included comparisons of the differences between batches of survey (2012–2013) and cohort (2014–2016) samples.Malaria transmission in Southern Province is moderate, albeit heterogeneous, ranging from < 1% to > 50% in some areas,12 Clusters randomized to receive MDA and fMDA arms were administered the assigned intervention in four rounds .For the cohort, two sample batches were created for genotyping and analysis, with the first batch representing samples collected between December 2014 and May 2015 and the second batch including samples collected between June 2015 and May 2016. Analysis was performed on these two batches or on sample sets based on the seasonality or intervention arm. Samples were grouped into sets corresponding to the first rainy season (season 1: December 2014–May 2015), dry season , and second rainy season (season 2: December 2015–May 2016). The cohort was recruited equally from the three different treatment arms: MDA, fMDA, and control.n = 1,620) detected during the two cross-sectional surveys and the longitudinal cohort. The genetic material was subjected to genotyping16 and the resulting data used for genetic analysis . Briefly, the dried blood spots were extracted for genomic DNA from 2 to 3, 6-mm punches using the manufacturer protocol from the Promega Maxwell DNA IQ Casework Sample kit .17 The samples were then subjected to pre-amplification18 and molecular barcode analysis.16 The genotypes were called by their base designation  with missing alleles identified by “X” and working alleles where both the minor and major allele were designated by “N.”Nucleic acid material was extracted from dried blood spots on filter papers collected from all RDT-positive individual observations  positions and identified these samples as having “failed” genotyping . We determined that samples with more than one site showing both fluorescent signals in genotyping  were “polygenomic” infections with more than one genome present in the patient sample. The remaining samples were referred to as “monogenomic” infections that contained only one parasite genome. We included all samples that “passed” genotyping for COI analysis and for calculating the proportion of infections that were monogenomic. Only monogenomic infections were included in the identity by descent (IBD) analysis and geographical comparisons. Household distances were calculated as the Euclidean distance between samples, expressed in kilometers (km), based on the recorded household latitude and longitude. Temporal distance was expressed as the difference in days between sample collection dates.A summary of decision-making for sample inclusion in the analysis is provided in the 19 accessed through the Broad.io/coil website. Significance was tested by comparing, as a test statistic, the square of the average difference in COI with an empirical distribution of the test statistic obtained by random subsampling (with replacement) pairs of samples, with a total of 551 and 354 samples representing the monogenomic infections from the surveys and cohort study, respectively, from the entire set of 905 pooled samples.Complexity of infection analysis was carried out using THE REAL McCOIL method 16 from all monogenomic infections were analyzed using a clustering tool (https://github.com/ndaniels/cluster-barcodes). Barcode groups are shown as nodes, with node size representing the number of samples with an identical genotype. Black and gray nodes represent cohort samples, and dark blue and light blue nodes represent baseline samples. Black and dark blue nodes indicate complete genotypes , whereas nodes with highly related ambiguous barcodes  are represented with lighter shading. Edges are drawn between nodes whose barcodes have a Hamming distance of exactly one (“N” and “X” entries do not count toward Hamming distance).Genotyping data representing the molecular barcode20 Default parameters were used, except that the maximum number of fit iterations was doubled to 10 because computation time was not an issue. The IBD estimate used was “fract_sites_IBD.” Pairwise analysis was carried out and the proportion IBD calculated .Identity by descent was estimated using the software package hmmIBD.Supplemental Methods. Statistical analysis was performed to rule out any potential bias between sample sets due to genotyping failures that would impact conclusions about parasite population changes . Baseline and cohort sample sets (and designated subdivisions of these sample sets) were assessed for significant differences using a Fisher exact test for small sample sizes, with a significance level of 0.05. A resampling analysis was performed to evaluate whether differences in average COI and distribution of COI were significant between baseline and cohort samples. Significance of the difference in average COI among the baseline and cohort sample sets was tested using the test statistic (Mean[baseline samples] – Mean[cohort samples])2 and comparing this with the empirical distribution of the test statistic obtained by repeatedly sampling with replacement pairs of samples of the same size as in the baseline and cohort sample sets. Sampling was from the pooled distribution of COIs in the baseline and cohort sample sets under the null hypothesis of no difference between baseline and cohort. Resampling was carried out in Mathematica 10.4.0 using the random choice algorithm.We estimated the failure/passage rates and monogenomic/polygenomic proportions based on the rules specified in the P < 0.00001) from 2.38 (baseline) to 1.67 (cohort), coinciding with the scale-up of the scaled intervention package and the mass treatment interventions in the study area between 2014 and 2016 . This decrease between baseline and cohort samples in the average COI was evident after accounting for potential sources of error, including increased genotyping failure rate among the cohort samples, and biases as a consequence of missing assays . Comparison of infections from the baseline and cohort studies revealed a significant increase in the proportion of those infections that were monogenomic, from 14% (baseline) to 45% (cohort) (P < 0.00001) . The decrease in parasite population genetic diversity between the baseline and cohort samples implies a reduction in the potential for outcrossing (greater inbreeding) after application of population-level interventions.A dramatic decrease in parasite genetic diversity, measured by COI and proportion of monogenomic infections, was detected between baseline (2012–2013) and cohort (2014–2016) sample sets. The average COI decreased significantly . Initial division of the cohort into batches (December 2014–May 2015 versus June 2015–May 2016) showed a significant increase in the proportion of monogenomic infections ; but removal of 13 samples that lacked a collection date revealed no significant differences between these two sample sets . Comparison of samples from one malaria season to the next (December 2014–May 2015 versus December 2015–May 2016) showed no significant difference in the COI or proportion of monogenomic infections (P = 0.7), even though there was an approximately 48-fold decrease in the number of RDT-positive samples from the first (n = 484 RDT-positive samples) to the second (n = 10 RDT-positive samples) malaria season. Stratification of samples by high or low prevalence, with a break point of approximately 10% prevalence,12 also revealed no significant differences between sample subsets . Comparison of cohort samples based on the arm of intervention  showed no significant differences in the proportion of monogenomic infections. Thus, changes in COI and proportion of monogenomic infections, consistent with reduction in parasite population diversity and parasite population constraint, were only detected when comparing baseline with cohort samples. These genetic trends mirror the epidemiological findings of a significant decline in parasite prevalence across a similar time frame and study population, where the overall parasite prevalence declined from 31.3% at baseline to 4.0% in 2016 at the end of the trial.7Although a significant decrease in genetic diversity among parasites was observed between baseline and cohort samples, similar effects were not detected when the longitudinal cohort samples were interrogated based on seasonality (wet versus dry season), prevalence stratification (high versus low strata), or arm of intervention (MDA versus fMDA versus control), owing at least in part to the reduced statistical power of the comparisons due to a limited number of positive samples across time periods. For example, the observation that only 1.4% (5/354) of genotypes sampled from the longitudinal cohort were detected in the second rainy season (compared with 60.5% [214/354] from the first rainy season) precluded all comparisons of parasite population diversity between sample subsets  and tests for IBD analysis 20 between infections were performed to test for the relatedness of parasite genotypes detected across the study. These analyses found no evidence of highly related parasites among baseline samples, consistent with these parasites representing a genetically diverse population based on the high COI value and relatively low proportion of monogenomic infections (14%) . By contrast, analysis of the cohort samples revealed highly related infections as well as sets of clonal  parasites . Furthermore, we observed highly related parasites shared between the baseline and cohort samples. Cluster analysis of all monogenomic baseline and cohort samples (n = 248) identified 19 different highly related barcode groups with a Hamming distance of less than or equal to one  . These barcode groups were supported by IBD and identity-by-state analysis . For this analysis, highly related parasites were defined as having a Hamming distance of no more than one. Within these barcode groups were two sets of clonal parasites (Hamming distance of 0) identified among individuals after two rounds of mass treatment had been administered (detected between June and August 2015) . Four of the highly related barcode groups contained genotypes of parasites from the baseline study, with parasites sampled across a 3-year time span that crossed multiple transmission seasons . Detection of highly related parasites along with the identification of genetically identical parasites in Southern Province, Zambia, coinciding with the large decline in malaria transmission from 2012 to 2016 indicated that the overall parasite population was reduced and provided evidence of inbreeding, similar to patterns previously detected in malaria transmission settings that have decreased transmission to low prevalence levels.17Highly related parasites, including genetically identical  samples, were evident in the cohort coinciding with the scale-up of the scaled intervention package and the mass treatment interventions in the study area between 2014 and 2016. Cluster analysis , with samples of barcode group F7 found among three different households in very close physical proximity (within 17 m) of each other. Further support of focal patterns of transmission include the identification of individuals repeatedly found with infections of the same highly related parasite group in half (4/8) of these households. This pattern may result from focal transmission of the same parasite genotype or failure to clear a parasite infection despite antimalarial drug treatment of the infected individual. Focal patterns of infection were mainly found on the same date within a household (6/8 = 75%) but did occur as far apart as 124 days .Given the observation of highly related or genetically identical parasites in the cohort study, we analyzed the household and individual sources of the infections for possible patterns of transmission. Overall, two general patterns were detected, consistent with focal and dispersed transmission . Barcode-related infections that were dispersed and found in multiple households were identified within as few as 5 days of each other (D1) or as many as 314 days apart (F + D_2). Two of the eight barcode groups identified within households (F + D_1 and F + D_2) were also detected in another household at some distance  was separately infected with two parasite genotypes. The first infection was of a focal barcode group shared with another household member (F3); the second infection that occurred 146 days later was part of a dispersed barcode group found in three households that were up to 40 km away.Evidence of dispersed transmission was provided by the detection of highly related parasite groups (9/16 = 56%) at geographic distances ranging from within 3 to 227 km apart  to after (cohort) the MDA intervention in Southern Province.Challenges to this analysis include an increase in genotyping failures among the cohort compared with baseline samples and a lack of infection-positive samples in the second season of the longitudinal cohort collection due to the dramatic reductions in malaria in the study area.9 Indeed, among the 354 successfully genotyped samples in the longitudinal cohort, only five (1.4%) of these were detected in the second malaria transmission season . Heterogeneity in transmission levels across the cohort may also have impacted the analysis because the majority of infections detected (303/354 = 86%) were from the high prevalence regions and no positive RDT samples were genotyped from the low transmission regions during the second malaria season. Moreover, previous reports suggest the lack of sensitivity of RDTs in areas with low transmission due to infections with low parasite density.22 Nevertheless, dramatic differences in parasite prevalence across the study period (2012–2016) significantly impacted the parasite population structure by decreasing genetic diversity across this time in Southern Province.The inability to detect differences between sample sets from the longitudinal cohort based on seasonality, level of transmission, or arm of intervention may be accounted for by dramatic reductions (up to 50-fold) in the number of RDT-positive infections available for analysis from one malaria season to the next across all intervention arms of the longitudinal cohort.23 We have previously observed a similar pattern in Senegal, where reduced transmission has been accompanied by both an increase in clonal parasite populations and the persistence of clonal lineages.17 Detection of genetically related infections in Zambia is consistent with a decrease in malaria transmission coincident with the application of the scaled intervention package plus mass treatment, which suggests that patterns originally detected in Senegal may be broadly applicable and that genetic signatures are relevant across a range of transmission levels. These genetic signatures were found in Senegal with prevalence values as low as 1%24 and in Southern Province, Zambia, with prevalence values as high as 15%.13Reduction of parasite population diversity  was accompanied by the appearance of highly related and even clonal parasites after the scaled intervention package and mass treatment campaigns were applied in the study area. These highly related parasites indicate a reduction in the potential for outcrossing or greater inbreeding among the cohort study samples. Although we did not detect these highly related parasites in the baseline samples alone, when we analyzed all samples together, we detected four genotypes from among the baseline samples that share barcode genotypes with the cohort infections, suggesting that some of these highly related parasite lineages have been retained for more than 3 years. The survival of specific lineages may indicate these parasite types have a fitness advantage, such as is observed in Southeast Asia, where drug-resistant parasites have emerged and dominated the surviving parasite population.10 Thus, genetic information provides more granular information that reveals potential patterns of transmission that can inform how interventions are working, what interventions should be used, and where best to target these interventions for greatest impact. For example, if there is evidence that most infections in a very low transmission area are due to importation, then this might support the use of case investigation. Alternatively, if there is evidence for local transmission, then the use of vector-based interventions would be warranted.Genetic “fingerprinting” of specific parasite infections, coupled with information about the households, the individual, and the date of infection, allows for the mapping of relationships between infections during the scale-up of aggressive intervention in the Zambia study area. Genetic data provide evidence for both focal and dispersed patterns of infection in this area. Focal infections accounted for about half of the highly related and clonal parasite groups, which were localized to individual or very geographically proximal households. Dispersed infection patterns accounted for the remaining half of the highly related barcode groups and were distributed across as much as 227 km and as long as 314 days between infections (excluding baseline samples). Several of these dispersed infections occurred along Lake Kariba and might have resulted from movement of infected individuals over longer distances via fishing boats. Interesting patterns included repeated sampling of highly related and barcode-identical parasites in specific individuals, either because of failure to clear the original infection or as a consequence of repeatedly being infected with the same parasite barcode group. Finally, some barcode groups had hybrid patterns, with both focal (within a household) and dispersed (shared across both time and space). Coupled with data about location and time, genetic information can be used to describe the relatedness between infections and can begin to map transmission linkages. Genetic data can also be used to identify the origin or source of infections, address questions of whether an infection is local or imported, or detect whether specific dominant parasite types  are evident in a population. With this information, the basic reproductive number across geographic areas over time can be estimated.11 but as transmission becomes very low, precise estimates of prevalence and incidence become more difficult. Genetic methods not only offer an opportunity to validate traditional metrics but also provide more fine-grained information about the relationship between infections, thereby helping to describe patterns of transmission when levels are low. Previous studies have detected dramatic changes in parasite population genetic signatures over time in Senegal following the implementation of major interventions.17 In this context, evidence for both transmission decline and rebound was identified using epidemiological modeling.10 The use of genetic measures can both detect population-level changes and track individual parasite patterns in low or very low malaria transmission settings. At the population level, genetics can detect changes due to population bottlenecks and identify persistence of parasite lineages. At the individual level, genetic “fingerprinting” can track individual parasite types with the potential for genetic relatedness to help map transmission networks. Thus, the use of genetic signatures, along with clinical and epidemiological data, can help guide decision-making related to the reduction of malaria burden.Efforts to reduce malaria burden across the transmission spectrum require ways to measure the impact of interventions on transmission. Traditionally, changes in prevalence or incidence have been used to assess intervention impact,Supplemental materials


Plasmodium falciparum histidine-rich protein 2 (PfHRP2)-based RDTs (Paracheck Pf® Test (Paracheck) and Malaria Pf™ ICT (ICT)) to polymerase chain reaction (PCR) in a community survey.Microscopy and rapid diagnostic tests (RDTs) are the main techniques used to diagnose malaria. While microscopy is considered the gold standard, RDTs have established popularity as they allow for rapid diagnosis with minimal technical skills. This study aimed to compare the diagnostic performance of two Plasmodium cytochrome b mitochondrial gene. Data analysis was performed using the Cohen’s Kappa test to determine the interrater agreement and the sensitivity and specificity of the diagnostic test were reported.A cross-sectional study was conducted between October 2012 and December 2014 in Mutasa District, Manicaland Province, eastern Zimbabwe. Households were randomly selected using satellite imagery, and 224 households were visited. Residents present in the household on the date of the visit were recruited for the study. Participants of all age groups from the selected households were screened with Paracheck and ICT RDTs in parallel. Dried blood spots (DBS) and thin and thick smears were collected. Parasite DNA extracted from the DBS was subjected to nested PCR targeting the p < 0.001). The participant was febrile on the day of the visit, but relying on PfHRP2-based RDT would miss this case. Among the diagnostic methods evaluated, with reference to PCR, the sensitivity was higher with the RDT (52.4%) while specificity was higher with the microscopy (99.9%). The positive predictive value (PPV) was higher with the microscopy (87.5%), while the negative predictive values were similar for both microscopy and RDTs (98%). Overall, a strong correlated agreement with PCR was observed for the microscopy (97.9%) and the RDTs (95.2%).Results from a total of 702 participants were analysed. Most were females, 397 (57%), and the median age of participants was 21 years with an interquartile range of 9–39 years. Of those who were screened, 8 (1.1%), 35 (5.0%), and 21 (2.9%) were malaria parasite positive by microscopy, RDT and PCR, respectively. Paracheck and ICT RDTs had a 100% agreement. Comparing RDT and PCR results, 34 participants (4.8%) had discordant results. Most of the discordant cases were RDT positive but PCR negative (n = 24). Half of those RDT positive, but PCR negative individuals reported anti-malarials to use in the past month, which is significantly higher than reported anti-malarial drug use in the population (P. falciparum infection can be critical.Paracheck and ICT RDTs showed 100% agreement and can be used interchangeably. As malaria transmission declines and Zimbabwe aims to reach malaria elimination, management of infected individuals with low parasitaemia as well as non- Malaria presents a major health threat in sub-Saharan Africa, and Zimbabwe is not an exception. Transmission intensity of malaria in Zimbabwe is largely affected by the annual rainfall, and Manicaland Province, where the highest annual rainfall is reported, is the province most affected by malaria . ThroughPlasmodium falciparum histidine-rich protein 2 (PfHRP-2)-based RDTs are used for parasitological confirmation in sub-Saharan Africa, including Zimbabwe. The ICT Malaria-Pf® Cassette Test and Paracheck-Pf® Test are available in the Zimbabwe. At the time of the study, the Paracheck-Pf was registered in Zimbabwe, while ICT Malaria Pf Cassette was pending registration subject to field testing.Despite the increased effort of national and international organizations, malaria remains a public health burden in high burden provinces in Zimbabwe, such as Manicaland . In an aOne of the WHO’s recommended strategies is to screen the community members even if an individual does not have malaria signs or symptoms. Malaria screening on the community can give us important epidemiological data to understand the prevalence of the infection in the community and how best to plan the future malaria control or elimination strategy. The present study compared the community-based diagnostic performance of two PfHRP-2 based RDTs and microscopy against PCR as a reference.Honde Valley part of the district, creating breeding sites for malaria vectors, mosquitoes. Malaria outbreaks have been a common occurrence in the district over the past few years despite the district meeting set targets for prevention interventions like indoor residual spraying (IRS) with pyrethroids and long-lasting insecticidal nets distribution. It is interesting, however, to note that National Malaria Control Program statistics reveal that Manicaland Province, mainly in Mutasa District, has become and remains a problem zone for the country, with a malaria incidence of 181/1000 in 2010, contributing a third to national malaria cases and deaths annually. This is despite the province meeting set targets of preventative and control interventions [Anopheles funestus [Mutasa District has a hot, temperate climate and high rainfall pattern ideal for vector breeding and malaria transmission. Perennial streams and gravity-fed irrigation channels span the low-lying ventions . Mutasa funestus . Honde V2 geographical grids were selected. Households within each grid were enumerated, and each household was visited once.The study was a part of a cross-sectional community survey conducted by the Southern and Central Africa International Centre of Excellence for Malaria Research and the study design and procedures were described previously . BrieflyA total of 702 participants were enrolled in the study from 224 households, enabling detection of a minimum 5% difference in sensitivity between diagnostic tests with 95% confidence and 80% study power. All age groups were enrolled following requisite informed consent.All consenting residents from selected households were enrolled in the study from October 2012 to July 2014. Signed informed consent was sought from all study participants, with parental/guardian permission for all children and child assent for children younger than 16 years of age. The socio-demographic characteristics and clinical data of the participants were collected using a structured questionnaire.The temperature was taken with an ear thermometer , and a tympanic temperature above 38 °C was considered to be a fever. Finger-prick blood samples were collected for thick and thin smears, preparation of dried blood spots (DBS) and two RDTs  and ICT Malaria P.f. . Individuals positive by RDTs were treated for malaria according to the Zimbabwean Ministry of Health guidelines . The thiThe technicians were blinded to the results of the RDTs as well as the results by the other microscopist. In cases where the results were discordant, a third expert reader was used, and the results of the third expert reader were considered final. The DBS were transported to the National Institute of Health Research molecular laboratory in Harare for storage and PCR analysis.cytb) conserved in the major human Plasmodium species, using the primer sequences and cycling conditions previously reported [DNA was extracted from the DBS using the Chelex method as previously described . Extractreported . The corDescriptive statistics were reported as frequencies and percentages for categorical variables and median and interquartile range (IQR) for continuous variable. To compare differences in the prevalence of malaria parasitaemia between the diagnostic methods and covariates, the independent Chi-square was applied. The Cohen’s Kappa test was used to determine the interrater agreement between diagnostic tests. We estimated the area under the ROC curve of the RDTs and microscopy. The level of significance was set at 5%. The analysis was conducted using Stata 15.1.A total of 702 participants from 224 households agreed to participate and were screened using two PfHRP2-based RDTs between October 2012 and July 2014 (Table Plasmodium parasites detected by microscopy was 1.14% (n = 8) of which seven were Plasmodium falciparum mono-infections and one Plasmodium ovale mono-infection. The two PfHRP2-based RDTs, Paracheck-Pf® and ICT, detected the same individuals as PfHRP2-positive giving an equal malaria prevalence of 4.99% (n = 35). The amount of agreement of the results between the Paracheck-Pf® and ICT was a 100% amount which indicated that the results were not determined randomly (p < 0.001). The prevalence of Plasmodium infection based on PCR was 2.99% (n = 21). Multiple comparison of the malaria prevalence estimates based on RDT (4.99%), PCR (2.99%) and microscopy (1.14%) showed that there was no significant difference between RDT vs PCR estimates (p = 0.4705); marginal difference between RDT vs microscopy estimates (p = 0.0973) and no significant difference between PCR and microscopy estimates (p = 0.3124).The prevalence of P. ovale mono-infection and seven were P. falciparum mono-infection with very low parasitaemia (12 parasites/μl) by thick smear. The other 24 (70.6%) were PCR malaria negative and RDT malaria positive, and all were microscopy negative. Half of these individuals (n = 12) reported anti-malarial use within the past month, which was significantly higher than anti-malarial use among PCR and RDT negative participants (10% (68/654), p < 0.001).A contingency table displaying the numbers of individuals by RDT test result and PCR result showed that there were 34 individuals with discordant malaria results by the two tests. Of these 10 (29.4%) were PCR malaria positive and RDT malaria negative of which two were microscopy positive, one was p = 0.707) or age groups (p = 0.353); there was a significant association between RDT malaria infection and gender (p = 0.006) or age groups (p = 0.025); and, there was a significant association between PCR malaria infection and year of enrolment (p = 0.028).Parasite prevalence varied across age groups Fig. . Those aP. ovale mono-infection by microscopy, and the presence of parasite DNA was confirmed by PCR.Malaria-positive participants bore infections with parasite densities ranging from 8–7256 and 16–7032 asexual parasites/µl of peripheral blood by first and second microscopist, respectively. In this community, the prevalence of fever was low. Of the 702 participants, 699 had tympanic temperature recorded on the day of the visit, and only 6 (0.9%) were febrile. None of the six febrile individuals was positive by RDTs, but one had The performance of the diagnostic tests with reference with each other are summarized in Table P. falciparum parasite carriers in Mutasa District in Zimbabwe was low in an area of seasonal P. falciparum transmission. The current standard malaria diagnostics is based on microscopy and rapid diagnostic tools (RDT) that have the lower detection limit of approximately 50–100 parasites per µL of blood, much higher than the WHO-recommended limit of ≤ 200 IE/100 µL [The prevalence of afebrile E/100 µL , 12. UsiPlasmodium DNA in the capillary blood. It has been reported that the RDTs can remain positive for more than five weeks after successful treatment because of the prolonged persistence of PfHRP2 antigen in the circulation after parasite clearance [Plasmodium infection identified was due to P. ovale, which was missed by both RDTs tested. Transmission of P. ovale in this area has been previously reported, albeit at very low incidence [. Supplementing pLDH-based RDT at clinical settings may improve the detection of non-falciparum infections.In recent years, the use of RDT expanded from medical facilities to community testing. Integrated community case management, a strategy to extend care for common causes of childhood mortality and morbidity , is provided by the community by health workers , and in learance . Half ofncidence . SupplemPfhrp2/3 gene has been reported in sub-Saharan African countries [pfhrp2/3 deletions in the study area; therefore, the possibility of false-negative RDTs due to target gene deletions could not be ruled out. For the RDT negative but PCR positive samples, microscopy data were examined and nine out of ten were confirmed as microscopy negative and the positive sample had low parasitaemia, below the typical detection limit of RDTs (200 parasite/μL) [PfHRP2-based RDT sensitivity could be affected by the genetic diversity of the target antigen in some settings , and delountries . There wsite/μL) .pfhrp2/3 deletions. The specificity and sensitivity of the two RDTs (Paracheck P.f®. and ICT RDT kits) and microscopy were estimated relative to Cytochrome b PCR. PCR is more sensitive and specific than an examination of thick or thin blood smears, particularly in cases with low parasite rates or mixed infections [. [. [. [Microscopists classified a slide as negative after 1,000 white blood cells, providing a theoretical detection limit of 8 parasites/μL. A well-trained microscopist with long years of experience has superior detection limit compared to currently available RDT ; therefofections –27. PCR fections , and it tions [.  reportedtions [. –32. Barkns [. [.  carefull[. [. [.  determinpfhrp2/3 deletions in the study area. Therefore, the possibility of false-negative RDTs due to target gene deletions could not be ruled out. Further studies are recommended to examine pfhrp2/3 deletions in the area.The current study did not collect information about P. falciparum infection, two RDT brands, Paracheck- Pf and ICT, detected the same number of infections with 100% agreement and could be used interchangeably. However, a high proportion of false-positive results was observed, presumably due to persisting antigenaemia. Based on microscopy results, RDT-negative, PCR-positive individuals had low-level parasitaemia, but further studies are needed to rule out the possibility of pfhrp2/3 deletions. This study also highlights the fact that there is a risk of missing non-falciparum infections when relying on PfHRP2-based RDTs.In screening community-based individuals for