The main objective of this study was to evaluate the effects of the economic factors, education and empowerment of women in the utilization of maternal health services (Descriptive Analysis).The analysis also determines the effect of improvements on these variables on the key outcome of interest(Predicative Analysis). It was observed that education and empowerment had major influence on maternal health service utilization.
Education and economic status have an effect on women’s empowerment or dis-empowerment and hence their ability to effectively affect the use maternal health care services. It has also been concluded that maternal health care services is one of the major determinants of maternal mortality, which one of the major problems in Sub Saharan Africa- Jose et al, (2009). This study focuses on the effect of the above factors in maternal health care service utilization. The data used is drawn from a maternal health survey carried out in South Western of Uganda. Multinomial Logistic Regression Analysis is used to determine the significance of education attainment, economic status and empowerment of women in maternal health service utilization. Maternal health utilization will focus on Ante-natal Care (ANC), Post-natal Care (PNC) and Normal delivery services.
Co developed the ODK tool used for by the Ministry of Health and Capacity Kenya project for health workers mapping. IntraHealth having partnered with the Government of Kenya and health sector leaders with the aim of strengthening health workforce policy and planning, build the knowledge and skills of health workers, and improve health worker productivity and retention, they decided to use Open Data Kit for the health workers education mapping country wide.
The project targeted over mapping 33,787+ health workers in Kenya. ODK collect was used for data collection and an ODK aggregate server hosted on a private cloud was used to host the data.Over 100+ devices were used for data collection and over 15,000 health workers were mapped in level 5,4 and 3 facilities. R and Stata was used for data management and cleaning.
During my stay at Population council, I co-developed and co-monitored mobile data collection questionnaire for Kibera Health Survey 2011. The main aim of the ODK tool was to map the areas the Alcohol health survey was carried out. Google maps and KMZ files were generated from the ODK app data; this helped a great way in monitoring and evaluation.
To add on that we developed an ODK tool for Uganda RH Vouchers Survey 2011. The process happens over largely rural areas with vast distances between households and health service providers. ODK was a superb tool for data collection and submission of data to ODK aggregate server daily. Read More>>>
Good data is always the goal ; open data kit is a tool to help in achieving the goal, proper training is always the key to the goal. On the importance of properly training the enumerators to get good data using ODK, and on anticipating the many many things that can go wrong.
I always have a plan which has been successful with NASCOP, Amref and Intrahealth projects. Ensure as a project manager you hire people with attention to detail and with research experience and can speak the language used on the tool.
I usually plan for a 20hrs training, spread over two days for initial ODK usage training. The 15hrs training comes after the research assistants have been informed about the project; sampling methodology, survey methodology , ethics e.t.c.
First we familiarize the RA's on using hand-held devices, (Android phones), then I give an introduction on ODK vs paper or other research tools.
Secondly I introduce the RA's to the tool (ODK COllect) and start using it Until everyone has familiarity with the survey questions and with the methodology of sampling and interviewing.It's not about the gear, it's about the data. You get good data by asking good questions, so first, get to that point. Ensure each RA sign for each hand-held device they pick and when they return them, remember we are handing out dozens of pocket "simus" that are worth as much as people make in a year. Thirdly; let each RA do a self interview, then pair them and each to carry out the interview and also do a field test. Field test involves visiting an identified area (not part of the study area) and carry out the survey.Let the RA's follow the sampling selection process, go out into the neighbourhood on their own, choose people to interview, and complete several interviews.
Research is often presented in very abridged packages: slideshows, journal articles, books, or maybe even websites. These presentation documents announce a project’s findings and try to convince us that the results are correct (Mesirov, 2010). It’s important to remember that these documents are not the research. Especially in the computational and statistical sciences, these documents are the “advertising”. The research is the “full software environment, code, and data that produced the results” (Buckheit and Donoho, 1995; Donoho, 2010, 385). When we separate the research from its advertisement we are making it difficult for others to verify the findings by reproducing them. - Christopher Gandrud
Building an ODK platform for Nextel in conjunction with MOH Botswana was the first major project to handle virtualy. The aim of the project was to do data collection for facilities and capture the tools and equipments in those facilities. The building and data management was done from Nairobi Kenya, the project deployment was in Botswana.
This is what the client had to say ODK is a cost effective tool but for this project it ended up costing us more that we had anticipated simply because we had to employ additional resources to automate the data cleaning after submission. Nonetheless we have also implemented ODK on our local server and this has allowed us to manipulate the database as needed. ODK is a powerful tool and we will continue to use it. Thanks to Mega Six solutions"
The client wanted to use ODK to collect data and integrate their fingerprint scanner with the ODK as a Sensor. This was to allow when a person goes in the field for a survey, along with other data, they will also capture a fingerprint image of the person and save it in the Collect. Once the connectivity with the Server is available, the fingerprint image should be saved in the Aggregate as well. This also was another major project for an USA based company and it was a success and the app is hosted on Google play
Worked in a team that set up the Introduction to Statistical Methods using R and RStudio course for Pwani University Kenya . The course mainly covered materials beneficial to students pursuing public health related courses. The course materials were adapted from the STATA® workshops that KEMRI-WTRP and LSHTM have been running in the program for over five years. All the data sets were converted from .dta to .csv formats using foreign package in R. The codes used in the presentations were converted from the initial .do files(STATA®) to .r files.
The course is reproducible since the presentations were prepared using literate programming (code + content). The course content was hosted on GitHub and we ran the course using the RStudio Server platform. I presented a poster on the course during the useR! conference 2015. The poster title was Reproducible Statistics course for the future, from Stata to R