Racial and Ethnic Diversity Among Students, Graduates, and Faculty in Biostatistics and Epidemiology, 2010-2020.

OBJECTIVE: This study is a follow-up to a study in 2020 that reviewed changes in the racial and ethnic composition of public health students, graduates, and faculty among Association of Schools and Programs of Public Health (ASPPH)-member institutions. In the current study, we evaluated how the racial and ethnic composition among biostatistics and epidemiology students, graduates, and faculty changed from 2010 to 2020. METHODS: We analyzed data on race and ethnicity of enrolled graduate students, graduates (master's and doctoral), and faculty at ASPPH-member institutions by using institutionally reported data from the ASPPH Data Center. We tabulated frequencies, percentages, and percentage-point changes by race and ethnicity. We measured differences between groups by using a test for difference in 2 proportions. RESULTS: The number of enrolled students, graduates, and faculty in all departments increased during the study period, while the number of tenure-track faculty in biostatistics decreased. The percentage of enrolled Hispanic/Latino biostatistics graduate students increased from 5.6% in 2010 to 10.2% in 2020 (P = .007), and the percentage of epidemiology graduates increased from 8.8% to 13.8% (P = .008). We found no differences among other underrepresented racial and ethnic groups. Most biostatistics and epidemiology professors at all ranks were non-Hispanic White, despite substantial decreases. The percentage of underrepresented racial and ethnic minority biostatistics and epidemiology professors was constant across all ranks. CONCLUSION: Although more Hispanic/Latino students are enrolled in and graduating from biostatistics and epidemiology departments at ASPPH-member institutions, we found no change among faculty. More work is needed to recruit and retain other (American Indian/Alaska Native, Black or African American, Native Hawaiian/Other Pacific Islander) underrepresented students and faculty.

Innovative approach to improve information accuracy in a two-district cross-sectional study in Bihar, India.

OBJECTIVE: Combine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a measure of HMIS data error. SETTING: This study is set in Bihar, the fifth poorest state in India, where half the population lives below the poverty line. An important source of data, used by health professionals for programme decision making, is routine health facility or HMIS data. Its quality is sometimes poor or unknown, and has no measure of its uncertainty. Using AT, we combine district-level HMIS and probability survey data (n=475) for the first time for 10 indicators assessing antenatal care, institutional delivery and neonatal care from 11 blocks of Aurangabad and 14 blocks of Gopalganj districts (N=6 253 965) in Bihar state, India. PARTICIPANTS: Both districts are rural. Bihar is 82.7% Hindu and 16.9% Islamic. PRIMARY OUTCOME MEASURES: Survey prevalence measures for 10 indicators, corresponding prevalences using HMIS data, combined prevalences calculated with AT and SEs for each type of data. RESULTS: The combined and survey estimates differ by <0.10. The combined and HMIS estimates differ by up to 84.2%, with the HMIS having 1.4-32.3 times larger error. Of 20 HMIS versus survey coverage estimate comparisons across the two districts only five differed by <0.10. Of 250 subdistrict-level comparisons of HMIS versus combined estimates, only 36.4% of the HMIS estimates are within the 95% CI of the combined estimate. CONCLUSIONS: Our statistical innovation increases the accuracy of information available for local health system decision making, allows evaluation of indicator accuracy and increases the accuracy of HMIS estimates. The combined estimates with a measure of error better informs health system professionals about their risks when using HMIS estimates, so they can reduce waste by making better decisions. Our results show that AT is an effective method ready for additional international assessment while also being used to provide affordable information to improve health services.

How to estimate health service coverage in 58 districts of Benin with no survey data: Using hybrid estimation to fill the gaps.

The global movement to use routine information for managing health systems to achieve the Sustainable Development Goals, relies on administrative data which have inherent biases when used to estimate coverage with health services. Health policies and interventions planned with incorrect information can have detrimental impacts on communities. Statistical inferences using administrative data can be improved when they are combined with random probability survey data. Sometimes, survey data are only available for some districts. We present new methods for extending combined estimation techniques to all districts by combining additional data sources. Our study uses data from a probability survey (n = 1786) conducted during 2015 in 19 of Benin's 77 communes and administrative count data from all of them for a national immunization day (n = 2,792,803). Communes are equivalent to districts. We extend combined-data estimation from 19 to 77 communes by estimating denominators using the survey data and then building a statistical model using population estimates from different sources to estimate denominators in adjacent districts. By dividing administrative numerators by the model-estimated denominators we obtain extrapolated hybrid prevalence estimates. Framing the problem in the Bayesian paradigm guarantees estimated prevalence rates fall within the appropriate ranges and conveniently incorporates a sensitivity analysis. Our new methodology, estimated Benin's polio vaccination rates for 77 communes. We leveraged probability survey data from 19 communes to formulate estimates for the 58 communes with administrative data alone; polio vaccination coverage estimates in the 58 communes decreased to ranges consistent with those from the probability surveys (87%, standard deviation = 0.09) and more credible than the administrative estimates. Combining probability survey and administrative data can be extended beyond the districts in which both are collected to estimate coverage in an entire catchment area. These more accurate results will better inform health policy-making and intervention planning to reduce waste and improve health in communities.

Identifying treatment effects using trimmed means when data are missing not at random.

Patients often discontinue from a clinical trial because their health condition is not improving or they cannot tolerate the assigned treatment. Consequently, the observed clinical outcomes in the trial are likely better on average than if every patient had completed the trial. If these differences between trial completers and non-completers cannot be explained by the observed data, then the study outcomes are missing not at random (MNAR). One way to overcome this problem-the trimmed means approach for missing data due to study discontinuation-sets missing values as the worst observed outcome and then trims away a fraction of the distribution from each treatment arm before calculating differences in treatment efficacy (Permutt T, Li F. Trimmed means for symptom trials with dropouts. Pharm Stat. 2017;16(1):20-28). In this paper, we derive sufficient and necessary conditions for when this approach can identify the average population treatment effect. Simulation studies show the trimmed means approach's ability to effectively estimate treatment efficacy when data are MNAR and missingness due to study discontinuation is strongly associated with an unfavorable outcome, but trimmed means fail when data are missing at random. If the reasons for study discontinuation in a clinical trial are known, analysts can improve estimates with a combination of multiple imputation and the trimmed means approach when the assumptions of each hold. We compare the methodology to existing approaches using data from a clinical trial for chronic pain. An R package trim implements the method. When the assumptions are justifiable, using trimmed means can help identify treatment effects notwithstanding MNAR data.

Statistical Estimation of the Reproductive Number From Case Notification Data.

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

Hybrid prevalence estimation: Method to improve intervention coverage estimations.

Delivering excellent health services requires accurate health information systems (HIS) data. Poor-quality data can lead to poor judgments and outcomes. Unlike probability surveys, which are representative of the population and carry accuracy estimates, HIS do not, but in many countries the HIS is the primary source of data used for administrative estimates. However, HIS are not structured to detect gaps in service coverage and leave communities exposed to unnecessary health risks. Here we propose a method to improve informatics by combining HIS and probability survey data to construct a hybrid estimator. This technique provides a more accurate estimator than either data source alone and facilitates informed decision-making. We use data from vitamin A and polio vaccination campaigns in children from Madagascar and Benin to demonstrate the effect. The hybrid estimator is a weighted average of two measurements and produces SEs and 95% confidence intervals (CIs) for the hybrid and HIS estimators. The estimates of coverage proportions using the combined data and the survey estimates differ by no more than 3%, while decreasing the SE by 1-6%; the administrative estimates from the HIS and combined data estimates are very different, with 3-25 times larger CI, questioning the value of administrative estimates. Estimators of unknown accuracy may lead to poorly formulated policies and wasted resources. The hybrid estimator technique can be applied to disease prevention services for which population coverages are measured. This methodology creates more accurate estimators, alongside measured HIS errors, to improve tracking the public's health.

Role of survey response rates on valid inference: an application to HIV prevalence estimates.

BACKGROUND: Nationally-representative surveys suggest that females have a higher prevalence of HIV than males in most African countries. Unfortunately, these results are made on the basis of surveys with non-ignorable missing data. This study evaluates the impact that differential survey nonresponse rates between males and females can have on the point estimate of the HIV prevalence ratio of these two classifiers. METHODS: We study 29 Demographic and Health Surveys (DHS) from 2001 to 2010. Instead of employing often used multiple imputation models with a Missing at Random assumption that may not hold in this setting, we assess the effect of ignoring the information contained in the missing HIV information for males and females through three proposed statistical measures. These measures can be used in settings where the interest is comparing the prevalence of a disease between two groups. The proposed measures do not utilize parametric models and can be implemented by researchers of any level. They are: (1) an upper bound on the potential bias of the usual practise of using reported HIV prevalence estimates that ignore subjects who have missing HIV outcomes. (2) Plausible range intervals to account for nonresponses, without any additional parametric modeling assumptions. (3) Prevalence ratio inflation factors to correct the point estimate of the HIV prevalence ratio, if estimates of nonresponders' HIV prevalences were known. RESULTS: In 86% of countries, males have higher upper bounds of HIV prevalence than females, this is consonant with males possibly having higher infection rates than females. Additionally, 74% of surveys have a plausible range that crosses 1.0, suggesting a plausible equivalence between male and female HIV prevalences. CONCLUSIONS: It is quite reasonable to conclude that there is so much DHS nonresponse in evaluating the HIV status question, that existing data is plausibly generated by the situation where the virus is equally distributed between the sexes.

Multidrug-resistant tuberculosis treatment failure detection depends on monitoring interval and microbiological method.

Debate persists about monitoring method (culture or smear) and interval (monthly or less frequently) during treatment for multidrug-resistant tuberculosis (MDR-TB). We analysed existing data and estimated the effect of monitoring strategies on timing of failure detection.We identified studies reporting microbiological response to MDR-TB treatment and solicited individual patient data from authors. Frailty survival models were used to estimate pooled relative risk of failure detection in the last 12 months of treatment; hazard of failure using monthly culture was the reference.Data were obtained for 5410 patients across 12 observational studies. During the last 12 months of treatment, failure detection occurred in a median of 3 months by monthly culture; failure detection was delayed by 2, 7, and 9 months relying on bimonthly culture, monthly smear and bimonthly smear, respectively. Risk (95% CI) of failure detection delay resulting from monthly smear relative to culture is 0.38 (0.34-0.42) for all patients and 0.33 (0.25-0.42) for HIV-co-infected patients.Failure detection is delayed by reducing the sensitivity and frequency of the monitoring method. Monthly monitoring of sputum cultures from patients receiving MDR-TB treatment is recommended. Expanded laboratory capacity is needed for high-quality culture, and for smear microscopy and rapid molecular tests.

Evaluation of high-dose rifampin in patients with new, smear-positive tuberculosis (HIRIF): study protocol for a randomized controlled trial.

BACKGROUND: Evidence has existed for decades that higher doses of rifampin may be more effective, but potentially more toxic, than standard doses used in tuberculosis treatment. Whether increased doses of rifampin could safely shorten treatment remains an open question. METHODS/DESIGN: The HIRIF study is a phase II randomized trial comparing rifampin doses of 20 and 15 mg/kg/day to the standard 10 mg/kg/day for the first 2 months of tuberculosis treatment. All participants receive standard doses of companion drugs and a standard continuation-phase treatment (4 months, 2 drugs). They are followed for 6 months post treatment. Study participants are adults with newly diagnosed, previously untreated, smear positive (≥2+) pulmonary tuberculosis. The primary outcome is rifampin area under the plasma concentration-time curve (AUC0-24) after at least 14 days of study treatment/minimum inhibitory concentration. 180 randomized participants affords 90 % statistical power to detect a difference of at least 14 mcg/mL*hr between the 20 mg/kg group and the 10 mg/kg group, assuming a loss to follow-up of up to 17 %. DISCUSSION: Extant evidence suggests the potential for increased doses of rifampin to shorten tuberculosis treatment duration. Early studies that explored this potential using intermittent, higher dosing were derailed by toxicity. Given the continued large, global burden of tuberculosis with nearly 10 million new cases annually, shortened regimens with existing drugs would offer an important advantage to patients and health systems. TRIAL REGISTRATION: This trial was registered with clinicaltrials.gov (registration number: NCT01408914 ) on 2 August 2011.

Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project.

MOTIVATION: Population stratification is one of the major sources of confounding in genetic association studies, potentially causing false-positive and false-negative results. Here, we present a novel approach for the identification of population substructure in high-density genotyping data/next generation sequencing data. The approach exploits the co-appearances of rare genetic variants in individuals. The method can be applied to all available genetic loci and is computationally fast. Using sequencing data from the 1000 Genomes Project, the features of the approach are illustrated and compared to existing methodology (i.e. EIGENSTRAT). We examine the effects of different cutoffs for the minor allele frequency on the performance of the approach. We find that our approach works particularly well for genetic loci with very small minor allele frequencies. The results suggest that the inclusion of rare-variant data/sequencing data in our approach provides a much higher resolution picture of population substructure than it can be obtained with existing methodology. Furthermore, in simulation studies, we find scenarios where our method was able to control the type 1 error more precisely and showed higher power. CONTACT: dmitry.prokopenko@uni-bonn.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimating the prevalence of transmitted HIV drug resistance using pooled samples.

In many resource-poor countries, hiv-infected patients receive a standardized antiretroviral cocktail. In these settings, population-level surveillance of drug resistance is needed to characterize the prevalence of resistance mutations and to enable antiretroviral therapy programs to select the optimal regimen for their local population. The surveillance strategy currently recommended by the World Health Organization is prohibitively expensive in some settings and may not provide a sufficiently precise rendering of the emergence of drug resistance. By using a novel assay on pooled sera samples, we decrease surveillance costs while simultaneously increasing the accuracy of drug resistance prevalence estimates for an important mutation that impacts first-line antiretroviral therapy. We present a Bayesian model for pooled-testing data that garners more information from each resistance assay conducted, compared with individual testing. We expand on previous pooling methods to account for uncertainty about the population distribution of within-subject resistance levels. In addition, our model accounts for measurement error of the resistance assay, and this added uncertainty naturally propagates through the Bayesian model to our inference on the prevalence parameter. We conduct a simulation study that informs our pool size recommendations and that shows that this model renders the prevalence parameter identifiable in instances when an existing non-model-based estimator fails.

A Generalized Entropy Measure of Within-Host Viral Diversity for Identifying Recent HIV-1 Infections.

There is a need for incidence assays that accurately estimate HIV incidence based on cross-sectional specimens. Viral diversity-based assays have shown promises but are not particularly accurate. We hypothesize that certain viral genetic regions are more predictive of recent infection than others and aim to improve assay accuracy by using classification algorithms that focus on highly informative regions (HIRs).We analyzed HIV gag sequences from a cohort in Botswana. Forty-two subjects newly infected by HIV-1 Subtype C were followed through 500 days post-seroconversion. Using sliding window analysis, we screened for genetic regions within gag that best differentiate recent versus chronic infections. We used both nonparametric and parametric approaches to evaluate the discriminatory abilities of sequence regions. Segmented Shannon Entropy measures of HIRs were aggregated to develop generalized entropy measures to improve prediction of recency. Using logistic regression as the basis for our classification algorithm, we evaluated the predictive power of these novel biomarkers and compared them with recently reported viral diversity measures using area under the curve (AUC) analysis.Change of diversity over time varied across different sequence regions within gag. We identified the top 50% of the most informative regions by both nonparametric and parametric approaches. In both cases, HIRs were in more variable regions of gag and less likely in the p24 coding region. Entropy measures based on HIRs outperformed previously reported viral-diversity-based biomarkers. These methods are better suited for population-level estimation of HIV recency.The patterns of diversification of certain regions within the gag gene are more predictive of recency of infection than others. We expect this result to apply in other HIV genetic regions as well. Focusing on these informative regions, our generalized entropy measure of viral diversity demonstrates the potential for improving accuracy when identifying recent HIV-1 infections.

Comparing two survey methods of measuring health-related indicators: Lot Quality Assurance Sampling and Demographic Health Surveys.

OBJECTIVES: Two common methods used to measure indicators for health programme monitoring and evaluation are the demographic and health surveys (DHS) and lot quality assurance sampling (LQAS); each one has different strengths. We report on both methods when utilised in comparable situations. METHODS: We compared 24 indicators in south-west Uganda, where data for prevalence estimations were collected independently for the two methods in 2011 (LQAS: n = 8876; DHS: n = 1200). Data were stratified (e.g. gender and age) resulting in 37 comparisons. We used a two-sample two-sided Z-test of proportions to compare both methods. RESULTS: The average difference between LQAS and DHS for 37 estimates was 0.062 (SD = 0.093; median = 0.039). The average difference among the 21 failures to reject equality of proportions was 0.010 (SD = 0.041; median = 0.009); among the 16 rejections, it was 0.130 (SD = 0.010, median = 0.118). Seven of the 16 rejections exhibited absolute differences of <0.10, which are clinically (or managerially) not significant; 5 had differences >0.10 and <0.20 (mean = 0.137, SD = 0.031) and four differences were >0.20 (mean = 0.261, SD = 0.083). CONCLUSION: There is 75.7% agreement across the two surveys. Both methods yield regional results, but only LQAS provides information at less granular levels (e.g. the district level) where managerial action is taken. The cost advantage and localisation make LQAS feasible to conduct more frequently, and provides the possibility for real-time health outcomes monitoring.

Assessing Biases in the Evaluation of Classification Assays for HIV Infection Recency.

Identifying recent HIV infection cases has important public health and clinical implications. It is essential for estimating incidence rates to monitor epidemic trends and evaluate the effectiveness of interventions. Detecting recent cases is also important for HIV prevention given the crucial role that recently infected individuals play in disease transmission, and because early treatment onset can improve the clinical outlook of patients while reducing transmission risk. Critical to this enterprise is the development and proper assessment of accurate classification assays that, based on cross-sectional samples of viral sequences, help determine infection recency status. In this work we assess some of the biases present in the evaluation of HIV recency classification algorithms that rely on measures of within-host viral diversity. Particularly, we examine how the time since infection (TSI) distribution of the infected subjects from which viral samples are drawn affect performance metrics (e.g., area under the ROC curve, sensitivity, specificity, accuracy and precision), potentially leading to misguided conclusions about the efficacy of classification assays. By comparing the performance of a given HIV recency assay using six different TSI distributions (four simulated TSI distributions representing different epidemic scenarios, and two empirical TSI distributions), we show that conclusions about the overall efficacy of the assay depend critically on properties of the TSI distribution. Moreover, we demonstrate that an assay with high overall classification accuracy, mainly due to properly sorting members of the well-represented groups in the validation dataset, can still perform notoriously poorly when sorting members of the less represented groups. This is an inherent issue of classification and diagnostics procedures that is often underappreciated. Thus, this work underscores the importance of acknowledging and properly addressing evaluation biases when proposing new HIV recency assays.

Race, law, and health: Examination of 'Stand Your Ground' and defendant convictions in Florida.

Previous analyses of Stand Your Ground (SYG) cases have been primarily descriptive. We examine the relationship between race of the victim and conviction of the defendant in SYG cases in Florida from 2005 to 2013. Using a regression analytic approach, we allow for simultaneous examination of multiple factors to better understand existing interrelationships. Data was obtained from the Tampa Bay Times SYG database (237 cases) which was supplemented with available online court documents and/or news reports. After excluding cases which were, still pending as of January 2015; had multiple outcomes (because of multiple suspects); and missing information on race of victim and weapon of victim, our final analytic sample has 204 cases. We chose whether the case resulted in a conviction as the outcome. We develop logistic regression models using significant bivariate predictors as candidates. These include race of the victim (White, non-White), whether the defendant could have retreated from the situation, whether the defendant pursued the victim, if the victim was unarmed, and who was the initiator of the confrontation. We find race of the victim to be a significant predictor of case outcome in this data set. After controlling for other variables, the defendant is two times (OR = 2.1, 95% CI [1.07, 4.10]) more likely to be convicted in a case that involves White victims compared to those involving non-White victims. Our results depict a disturbing message: SYG legislation in Florida has a quantifiable racial bias that reveals a leniency in convictions if the victim is non-White, which provides evidence towards unequal treatment under the law. Rather than attempting to hide the outcomes of these laws, as was done in Florida, other states with SYG laws should carry out similar analyses to see if their manifestations are the same as those in Florida, and all should remediate any injustices found.

Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys.

Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring and evaluation in resource-limited settings. Recently several methods have been proposed to combine LQAS with cluster sampling for more timely and cost-effective data collection. For some of these methods, the standard binomial model can be used for constructing decision rules as the clustering can be ignored. For other designs, considered here, clustering is accommodated in the design phase. In this paper, we compare these latter cluster LQAS methodologies and provide recommendations for choosing a cluster LQAS design. We compare technical differences in the three methods and determine situations in which the choice of method results in a substantively different design. We consider two different aspects of the methods: the distributional assumptions and the clustering parameterization. Further, we provide software tools for implementing each method and clarify misconceptions about these designs in the literature. We illustrate the differences in these methods using vaccination and nutrition cluster LQAS surveys as example designs. The cluster methods are not sensitive to the distributional assumptions but can result in substantially different designs (sample sizes) depending on the clustering parameterization. However, none of the clustering parameterizations used in the existing methods appears to be consistent with the observed data, and, consequently, choice between the cluster LQAS methods is not straightforward. Further research should attempt to characterize clustering patterns in specific applications and provide suggestions for best-practice cluster LQAS designs on a setting-specific basis.

Institutional Tuberculosis Transmission. Controlled Trial of Upper Room Ultraviolet Air Disinfection: A Basis for New Dosing Guidelines.

RATIONALE: Transmission is driving the global tuberculosis epidemic, especially in congregate settings. Worldwide, natural ventilation is the most common means of air disinfection, but it is inherently unreliable and of limited use in cold climates. Upper room germicidal ultraviolet (UV) air disinfection with air mixing has been shown to be highly effective, but improved evidence-based dosing guidelines are needed. OBJECTIVES: To test the efficacy of upper room germicidal air disinfection with air mixing to reduce tuberculosis transmission under real hospital conditions, and to define the application parameters responsible as a basis for proposed new dosing guidelines. METHODS: Over an exposure period of 7 months, 90 guinea pigs breathed only untreated exhaust ward air, and another 90 guinea pigs breathed only air from the same six-bed tuberculosis ward on alternate days when upper room germicidal air disinfection was turned on throughout the ward. MEASUREMENTS AND MAIN RESULTS: The tuberculin skin test conversion rates (>6 mm) of the two chambers were compared. The hazard ratio for guinea pigs in the control chamber converting their skin test to positive was 4.9 (95% confidence interval, 2.8-8.6), with an efficacy of approximately 80%. CONCLUSIONS: Upper room germicidal UV air disinfection with air mixing was highly effective in reducing tuberculosis transmission under hospital conditions. These data support using either a total fixture output (rather than electrical or UV lamp wattage) of 15-20 mW/m(3) total room volume, or an average whole-room UV irradiance (fluence rate) of 5-7 µW/cm(2), calculated by a lighting computer-assisted design program modified for UV use.

Extending cluster lot quality assurance sampling designs for surveillance programs.

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate.