Targeting the poor and smallholder farmers: empirical evidence from Malawi

This paper develops low cost, reasonably accurate, and simple models for improving the targeting efficiency of development policies in Malawi. Using a stepwise logistic regression (weighted) along with other techniques applied in credit scoring, the research identifies a set of easily observable and verifiable indicators for correctly predicting whether a household is poor or not, based on the 2004-05 Malawi Integrated Household Survey data. The predictive power of the models is assessed using out-of-sample validation tests and receiver operating characteristic curves, whereas the model’s robustness is evaluated by bootstrap simulation methods. Finally, sensitivity analyses are performed using the international and extreme poverty lines. The models developed have proven their validity in an independent sample derived from the same population. Findings suggest that the rural model calibrated to the national poverty line correctly predicts the status of about 69% of poor households when applied to an independent subset of surveyed households, whereas the urban model correctly identifies 64% of poor households. Increasing the poverty line improves the model’s targeting performances, while reducing the poverty line does the opposite. In terms of robustness, the rural model yields a more robust result with a prediction margin ±10% points compared to the urban model. While the best indicator sets can potentially yield a sizable impact on poverty if used in combination with a direct transfer program, some non-poor households would also be targeted as the result of model’s leakage. One major feature of the models is that household score can be easily and quickly computed in the field. Overall, the models developed can be potential policy tools for Malawi.Malawi, poverty targeting, proxy means tests, out-of-sample tests, bootstrap, Food Security and Poverty, Research Methods/ Statistical Methods, I32, C15,

No. 07: Household Food Security and Access to Medical Care in Maputo, Mozambique

The relationship between household access to medical care and food security is a potentially circuitous and challenging relationship to model. This discussion paper uses multiple modelling techniques to determine the quality of the relationships between these variables using household survey data collected by the Hungry Cities Partnership in 2014 in Maputo, Mozambique. The results of the investigation are framed according to the Sustainable Livelihood Framework and indicate a predictive relationship between household food security status and consistent household medical care access among the sampled households. The results also identify potential conditional independence in the relationship between other demographic variables and these two dependent variables among the surveyed households

Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures

Estimating HIV Medication Adherence and Persistence: Two Instruments for Clinical and Research Use

Antiretroviral therapy (ART) requires lifelong daily oral therapy. While patient characteristics associated with suboptimal ART adherence and persistence have been described in cohorts of HIV-infected persons, these factors are poor predictors of individual medication taking behaviors. We aimed to create and test instruments for the estimation of future ART adherence and persistence for clinical and research applications. Following formative work, a battery of 148 items broadly related to HIV infection and treatment was developed and administered to 181 HIV-infected patients. ART adherence and persistence were assessed using electronic monitoring for 3 months. Perceived confidence in medication taking and self-reported barriers to adherence were strongest in predicting non-adherence over time. Barriers to adherence (e.g., affordability, scheduling) were the strongest predictors of non-adherence, as well as 3- and 7-day non-persistence. A ten-item battery for prediction of these outcomes (www.med.unc.edu/ncaidstraining/adherence/for-providers) and a 30-item battery reflective of underlying psychological constructs can help identify and study individuals at risk for suboptimal ART adherence and persistence

Estimating the Area under a Receiver Operating Characteristic Curve For Repeated Measures Design

The receiver operating characteristic (ROC) curve is widely used for diagnosing as well as for judging the discrimination ability of different statistical models. Although theories about ROC curves have been established and computation methods and computer software are available for cross-sectional design, limited research for estimating ROC curves and their summary statistics has been done for repeated measure designs, which are useful in many applications, such as biological, medical and health services research. Furthermore, there is no published statistical software available that can generate ROC curves and calculate summary statistics of the area under a ROC curve for data from a repeated measures design. Using generalized linear mixed model (GLMM), we estimate the predicted probabilities of the positivity of a disease or condition, and the estimated probability is then used as a bio-marker for constructing the ROC curve and computing the area under the curve. The area under a ROC curve is calculated using the Wilcoxon non-parametric approach by comparing the predicted probability of all discordant pairs of observations. The ROC curve is constructed by plotting a series of pairs of true positive rate (sensitivity) and false positive rate (1- specificity) calculated from varying cuts of positivity escalated by increments of 0.005 in predicted probability. The computation software is written in SAS/IML/MACRO v8 and can be executed in any computer that has a working SAS v8 system with SAS/IML/MACRO.

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making