Seasonality of Acute Malnutrition in African Drylands: Evidence From 15 Years of SMART Surveys.

Reduction of wasting, or low weight-for-height, is a critical target for the Zero Hunger Sustainable Development Goal, yet robust evidence establishing continuous seasonal patterns of wasting is presently lacking. The current consensus of greatest hunger during the preharvest period is based on survey designs and analytical methods, which discretize time frame into preharvest/postharvest, dry/wet, or lean/plenty seasons. We present a spatiotemporally nuanced study of acute malnutrition seasonality in African drylands using a 15-year data set of Standardized Monitoring and Assessment of Relief and Transition surveys (n = 412,370). Climatological similarity was ensured by selecting subnational survey regions with 1 rainy season and by spatially matching each survey to aridity and livelihood zones. Harmonic logit regression models indicate 2 peaks of wasting during the calendar year. Greatest wasting prevalence is estimated in April to May, coincident with the primary peak of temperature. A secondary peak of wasting is observed in August to October, coinciding with the primary peak of rainfall and secondary peak of temperature. This pattern is retained across aridity and livelihood zones and is sensitive to temperature, precipitation, and vegetation. Improved subnational estimation of acute malnutrition seasonality can thus assist decision makers and practitioners in data-sparse settings and facilitate global progress toward Zero Hunger.

Plain language titleFifteen Years of Rapid Assessment Surveys Indicate Seasonal Variability in Prevalence of Acute Malnutrition Among Children Younger Than 5 Years in African DrylandsPlain language summaryWasting or low weight-for-height is a key indicator of short-term or acute malnutrition. The timing of highest wasting prevalence, particularly among children younger than 5 years, is of interest for humanitarian efforts to reduce hunger. Current knowledge about this timing derives from survey designs, which discretize continuous time into preharvest/postharvest, dry/wet, or lean/plenty seasons. Instead of this categorical approach, we utilize harmonic regressions that allow for modeling of continuous time in our analysis of 15 years of Standardized Monitoring and Assessment of Relief and Transition surveys. Surveys conducted in parts of North Africa with 1 rainy season (unimodal regions) were selected for similar climate, and survey locations were further subdivided by aridity and livelihood zones. The seasonal pattern of extreme wasting prevalence in each group was modeled using survey data for a total of 412,370 children. We identified 2 periods of highest wasting prevalence in April to May and August to October. The April to May peak occurs during highest temperatures, and the August to October peak occurs during periods of highest rainfall and warmer temperatures in the study area. These findings can inform the timing of nutrition programs in unimodal dryland regions and guide future quantitative models of acute malnutrition seasonality.

How Seasonality of Malnutrition Is Measured and Analyzed.

Seasonality is a critical source of vulnerability across most human activities and natural processes, including the underlying and immediate drivers of acute malnutrition. However, while there is general agreement that acute malnutrition is highly variable within and across years, the evidence base is limited, resulting in an overreliance on assumptions of seasonal peaks. We review the design and analysis of 24 studies exploring the seasonality of nutrition outcomes in Africa's drylands, providing a summary of approaches and their advantages and disadvantages. Over half of the studies rely on two to four time points within the year and/or the inclusion of time as a categorical variable in the analysis. While such approaches simplify interpretation, they do not correspond to the climatic variability characteristic of drylands or the relationship between climatic variability and human activities. To better ground our understanding of the seasonality of acute malnutrition in a robust evidence base, we offer recommendations for study design and analysis, including drawing on participatory methods to identify community perceptions of seasonality, use of longitudinal data and panel analysis with approaches borrowed from the field of infectious diseases, and linking oscillations in nutrition data with climatic data.

Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.

Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient's place of residence (PoR) can be utilized to better understand a hospital's case load and strengthen surveillance among mobile populations. This study examined geographic patterns of patients treated for cholera at a major hospital in south India. We abstracted 1401 laboratory-confirmed cases of cholera between 2000-2014 from logbooks and electronic health records (EHRs) maintained by the Christian Medical College (CMC) in Vellore, Tamil Nadu, India. We constructed spatial trend models and identified two distinct clusters of patient residence-one around Vellore (836 records (61.2%)) and one in Bengal (294 records (21.5%)). We further characterized differences in peak timing and disease trend among these clusters to identify differences in cholera exposure among local and visiting populations. We found that the two clusters differ by their patient profiles, with patients in the Bengal cluster being most likely older males traveling to Vellore. Both clusters show well-aligned seasonal peaks in mid-July, only one week apart, with similar downward trend and proportion of predominant O1 serotype. Large hospitals can thus harness EHRs for surveillance by utilizing patients' PoRs to study disease patterns among resident and visitor populations.

Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin.

Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5-24, 25-44, 45-64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5-24 years (RR = 0.31; 95% CI 0.22-0.41 vs. RR = 0.14; 95% CI 0.09-0.22, respectively). A significant increase in tests was observed during Spring Break in 45-64 years old adults (RR = 2.12; 95% CI 1.14-3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.

From hospitalization records to surveillance: The use of local patient profiles to characterize cholera in Vellore, India.

Despite availability of high quality medical records, health care systems often do not have the resources or tools to utilize these data efficiently. Yet, hospital-based, laboratory-confirmed records may pave the way for building reliable surveillance systems capable of monitoring temporal trends of emerging infections. In this communication, we present a new tool to compress and visualize medical records with a local population profile (LPP) approach, which transforms information into statistically comparable patterns. We provide a step-by-step tutorial on how to build, interpret, and expand the use of LPP using hospitalization records of laboratory-confirmed cholera. We abstracted case information from the databases maintained by the Department of Clinical Microbiology at Christian Medical College in Vellore, India. We used a single-year age distribution to construct LPPs for O1, O139, and non O1/O139 serotypes of Vibrio cholerae. Disease counts and hospitalization rates were converted into fitted kernel-based probability densities. We formally compared LPPs with the Kolmogorov-Smirnov test, and created multi-panel visuals to depict temporal trend, age distribution, and hospitalization rates simultaneously. Our first implementation of LPPs revealed information that is typically gathered from surveillance systems such as: i) estimates of the demographic distribution of diseases and identification of a population at risk, ii) changes in the dominant pathogen presence; and iii) trends in disease occurrence. The LPP demonstrated the benefit of increased resolution in pattern detection of disease for different Vibrio cholerae serotypes and two demographic categories by showing patterns and anomalies that would be obscured by traditional methods of analysis and visualization. LPP can be used effectively to compile basic patient information such as age, sex, diagnosis, location, and time into compact visuals. Future development of the proposed approach will allow public health researchers and practitioners to broadly utilize and efficiently compress large volumes of medical records without loss of information.