Operational objectives- What do we expect from this Link NCA in terms of operational outcomes.
The objective of this study will be to identify drivers to malnutrition (wasting and stunting) as well as levers and barriers of resilience. The study specifically aims to:
Determine the prevalence of undernutrition.
Understand how the wasting and/stunting in this population and its causes changed
Over time due to historical trends,
seasonally due to cyclical trends,
due to recent shocks.
Have a better understanding of communities’ own perceptions and knowledge about under-nutrition and its drivers.
Identify under-nutrition pathways and associated risk factors: Description of local mechanisms which lead to under-nutrition and their interconnections.
Identify of vulnerable groups to major risks factor to undernutrition.
Define local community perceptions on risks, shocks and resilience’s capacities.
Identify barriers and levers/boosters associated with resilience capacities
Formulate an action plan to (re)design activities according to the study results
NCA data analysis
This is a short term assignment (Maximum 30days) which will be broken into two phases. Phase one will be conducted during the initial phase (Secondary data analysis) and the second phase will be done after field data collection is complete.
The Link NCA data analysis is more comprehensive with the use of a mixed method approach to identify statistically risk factors associated to malnutrition outcome using secondary datasets (SMART Surveys datasets or any survey including anthropometry variables). Quantitative datasets including anthropometric variables will be analyzed alongside qualitative data gathered from secondary data review (SMART report and published articles, DHS reports, data on rainfall, temperature, food security, livelihoods, epidemics etc.). The second analysis will entail a detailed analysis data from the risk factor survey to include triangulation from the qualitative data collected in the process.
The objectives of statistical analysis
To identify the risk factors associated with global acute malnutrition in the Link NCA studied area. (Annual GAM trends with rainfall, yield, fertility rates, temperature, epidemics or any indicators of shock).
To identify the risk factors that are associated with stunting in the Link NCA studied area. (Annual chronic trends with rainfall, yield, fertility rates, or any indicators of shock)
Seasonal GAM prevalence’s trends over a year (using SMARTs survey for the past 20 years)
GAM prevalence’s trends in regards to area differences.
Produce analysis report.
Key analysis Activities
Reporting to the NCA analyst, the statistician will conduct the following analysis among other responsibilities assigned:
Check for consistency in variables across surveys (if multiple available) and recode variables as necessary.
Re-run the ENA software to ensure that WHO Z-scores were calculated in a consistent way and outliers are excluded using the same criteria.
Ordering and merging data bases and data sets and creating new variables to define the year, season and geographical area where each survey was conducted.
Use descriptive statistics to summarize all variables in the survey which include: frequencies for categorical variables, calculations of the mean, standard deviation, and range for all continuous variables.
Classify all continuous variables into categorical variables to facilitate an easier interpretation of results.
Conduct logistic regression, linear analysis and trends test to identify risks factors associated with undernutrition.
Analyze secondary data (data on rainfall, temperature, food security and livelihoods variables, epidemics data, sex of the children, fertility rates, water access indicators, IYCF indicators, and gender qualitative data) to identify potential causal risks factors on annual, seasonal and regional characteristics associated with undernutrition.
Run separate bivariate regression models with each of the variables with bivariate regression models control for the region, year, and geographical area of the survey.
Estimate odds ratios for each level of the categorical variable and a test for trend to estimate a p value for the overall significance of the variable.
Report results in tabular form to allow for comparison of the change in association when other variables are controlled for.
Supporting NCA analyst in presentation and interpretation of NCA statistical results and
Conduct any other roles requested by the NCA analyst.
Required qualifications
An advanced University degree (Master’s degree ) in Statistics, Public health, or any relevant quantitative discipline;
At least 3 to 5 years’ Experience using data to support research efforts, strong levels of data literacy and solid ground in multivariate analysis, conducting association analysis among other statistical analysis.
Conversant in using statistical packages such as EPI info, STATA, R, and related programs;
Knowledge on a wide range of Health and Nutrition and Resilience programs and how other sectors affect nutrition outcome.
Excellent writing skills and good interpersonal and communication skills.
Excellent, proven skills in developing analytical, technical and informative materials.
Ability to work in a team.
Fluency in English both written and spoken.
