Access to veterinary services and expenditure on pig health management: the case of smallholder pig farmers in Northern Uganda

Pig farming has gained momentum for most smallholder farmers in developing countries as a means of livelihood and household incomes. However, prospects of the pig enterprises are constrained by pig health management issues which affect both its productivity and profitability. Using a cross-sectional survey of 240 smallholder pig farmers, we assessed factors influencing farmers' access to veterinary services and expenditure on pig health management in northern Uganda. Data was analysed using the binary logit and censored tobit regression models. Access to veterinary services was significantly influenced by pig herd size (p < 0.05), Village Savings and Loan Association (VSLA) membership (p < 0.1), breed (p < 0.05), production system (p < 0.05) and number of health issues recorded on farm (p < 0.01). Education level (p < 0.01), farming household members (p < 0.05), pig herd size (p < 0.01), breed (p < 0.05), previous disease incidences (p < 0.05), household labour available (p < 0.1) and access to veterinary services (p < 0.01) significantly influenced pig health expenditure. Efforts to improve access to veterinary services and improve pig health management should focus on promoting more intensive production systems and improved breeds that are associated with better access to veterinary services and reduced cost of pig health management.

Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors

Background

Biological assays for the quantification of markers may suffer from a lack of sensitivity and thus from an analytical detection limit. This is the case of human immunodeficiency virus (HIV) viral load. Below this threshold the exact value is unknown and values are consequently left-censored. Statistical methods have been proposed to deal with left-censoring but few are adapted in the context of high-dimensional data.

Methods

We propose to reverse the Buckley-James least squares algorithm to handle left-censored data enhanced with a Lasso regularization to accommodate high-dimensional predictors. We present a Lasso-regularized Buckley-James least squares method with both non-parametric imputation using Kaplan-Meier and parametric imputation based on the Gaussian distribution, which is typically assumed for HIV viral load data after logarithmic transformation. Cross-validation for parameter-tuning is based on an appropriate loss function that takes into account the different contributions of censored and uncensored observations. We specify how these techniques can be easily implemented using available R packages. The Lasso-regularized Buckley-James least square method was compared to simple imputation strategies to predict the response to antiretroviral therapy measured by HIV viral load according to the HIV genotypic mutations. We used a dataset composed of several clinical trials and cohorts from the Forum for Collaborative HIV Research (HIV Med. 2008;7:27-40). The proposed methods were also assessed on simulated data mimicking the observed data.

Results

Approaches accounting for left-censoring outperformed simple imputation methods in a high-dimensional setting. The Gaussian Buckley-James method with cross-validation based on the appropriate loss function showed the lowest prediction error on simulated data and, using real data, the most valid results according to the current literature on HIV mutations.

Conclusions

The proposed approach deals with high-dimensional predictors and left-censored outcomes and has shown its interest for predicting HIV viral load according to HIV mutations.

Bayesian quantile regression analysis for continuous data with a discrete component at zero

In this work, we propose a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero, where these observations are believed to be left censored or true zeros. We combine the information provided by the quantile regression analysis to present a more complete description of the probability of being censored given that the observed value is equal to zero, while also studying the conditional quantiles of the continuous part. We build up a Markov Chain Monte Carlo method from related models in the literature to obtain samples from the posterior distribution. We demonstrate the suitability of the model to analyse this censoring probability with a simulated example and two applications with real data. The first is a well-known dataset from the econometrics literature about women labour in Britain, and the second considers the statistical analysis of expenditures with durable goods, considering information from Brazil.