Study Area and Period
The study was conducted in Dire Dawa city administration which is located in the Eastern part of the Ethiopia. According to the Ethiopian central statistics authority’s 2008 report, Dire Dawa administrative region has total population of 453,000 of whom almost one to one male to female ratio. It is situated 515Kms from Addis Ababa. Dire Dawa city administration is one of the two City Administration and majority of its population lives in urban area with 233,224 or around 68.22% of the population are urban inhabitants.The public health organizations which are involved in health care delivery include 1public referral hospital, 1 public district hospital and 8 public health centers. All health institutions provide maternal health service in addition to other service. The study was conducted from February 10/02/2019 – March 30/ 03/2019.
Study Design
Institutional based cross sectional study design was used.
Population
Source population
All lactating mothers living in Dire dawa City who were visiting Dire Dawa Health facilities for postnatal care and EPI program was considered as the source of population.
Study population
Selected lactating mothers who visiting Dire dawa Hospitals and Health Centers for postnatal care and EPI program during data collection period were considered as study population.
Inclusion and exclusion criteria
Inclusion Criteria
Those lactating mothers living in Dire Dawa City and who was visiting at Dire dawa Hospitals and Health centers for post-natal care and EPI program during the study period.
Exclusion Criteria
Lactating women who were critical ill, pregnant and physical deformity will be excluded from the study to improve quality of anthropometric measurements.
Data collection instrument
Data on socio demographic, dietary diversity and food security variables were collected by using structured pretested Amharic version questionnaires adapted from different literature review (Eshetu et al., 2017, Kiday et al., 2013a, Mihiretu et al., 2015, Temesgen et al., 2015). The adapted data collection tools prepared in English will be translated to Amharic language and again back to English to check consistency.
Food security was assessed using Household Food Insecurity Access Scale (HFIAS), it validated tool use in the Ethiopian context (Seifu et al., 2015). The HFIAS has nine questions asking household’s last month experience about three domains of food insecurity: feeling uncertainty of food supply, insufficient quality of food, and insufficient food intake and its physical consequences. (Coates et al., 2007).
The mother’s dietary intake pattern will be measured by a qualitative recall of all foods consumed by each woman during the previous 24 hours. Thus, certain food groups was aggregated to calculate Individual (women) dietary diversity score (WDDS) and the mean DDS will be used to classify mothers food intake as adequate or not (Kennedy et al., 2013).
To measure the outcome variable, anthropometric measurement (weight) of lactating women were measured to the nearest 100 g using portable electronic digital scale (Seca, Germany model) and height will be measured to the nearest 0.1 cm using a portable wooden height measuring board with sliding head bar through standard anthropometric measuring technique.
Data collectors and data collection procedures
The data will be collected by 8 nurses’ work in the post-natal and EPI service and the data was collect at exit. Data collectors were trained for two days by principal investigator. Two BSC holders in Nursing or health officer will be recruited and trained for supervising data collectors. Training was given about methods of anthropometric measurement, interviewing technique and filling questionnaires.
To measure weight of mother requested to remove shoe, wear light close and other supportive materials and data collectors were weigh the study participant on calibrated portable digital scale and value will be recorded to the nearest 100 gram or 0.1 kg.
To measure height the study participant was requested to stand erect with their shoulder level, hands was at the side, head, scapulae, buttock and heel were in contact with vertical measuring board with sliding head bar and height value will be recorded to the nearest 0.1 cm. (WHO 2012)
Data quality control
To assure the quality of the data, structured and pretested questionnaire was used. Pretest of the questionnaire was employed prior to actual data collection period among 5% of the study sample on one health center not included in this study. The final version of the questionnaire which was prepared in English translated into the local language of the respondents (Amharic language) and again translated back to English. The data collectors and supervisors were given two day intensive training by principal investigator (PI) on the instruments, method of data collection, how to take anthropometric measurements and ethical issues.
Relative Technical error Measurement (%TEM) was done to minimize the random anthropometric measurement errors and relative TEMs for intra and inter examiners for weight and height was acceptable if relative technical error Measurement less than 1.5% and 2% respectively (Perini. et al., 2005). Functionality of digital weight scales will be checked using known weight every morning before data collection begin and before every weight measurement the data collectors were assure the scale reading exactly at zero (NHANES, 2007).
Intensive supervision were done by principal investigator and supervisor and they were check the collected data for completeness, accuracy, and consistency throughout the data collection period. The overall supervision was done by the principal investigator. Data double entry was used to make comparisons of two data cells and resolve if there is some difference.
Data processing and analysis
Data was coded and entered on to Epi-data version 3.0 and exported to SPSS Version 22 for analysis. Missing values checked by conducting simple frequency analysis. Exploratory data analysis was done to check missing values, potential outliers and the normality distribution for those continuous variables.
Body mass index of the mother was calculated through weight in kilogram divided by square of height in meters and based on the result mother was categorized in to underweight with BMI less than 18.5 kg/m2, normal those having BMI 18.5-24.99 kg/m2, overweight with BMI 25-29.99 kg/m2 and obese those having BMI greater than or equal to 30 kg/m2 (WHO 2012). Since the interest was identifying lactating women at risk of undernutrition, the dependent variables are coded as 1 lactating women were undernourished (BMI <18.5 kg/m2) and coded as 0 if not.
Multi-Collinearity effect was checked and variables with SE >2 was removed from analysis and those variables have no collinear effect was included in binary logistic regression model to see the possible relationships with the outcome variables. Covariates with a p-value less than 0.25 in the bivariable logistic regression analysis was candidate for a multivariable logistic regression analysis to control potential confounders and to identify associated factors of undernutrition. The fitness of the model was tested by Hosmer- Lemeshow goodness of fit test (p-value=0.83). Odd Ratios along with 95% Confidence interval was estimated measure the strength of the association. Level of statistical significance was declared at p-value less 0.05. Results were presented using frequencies, summary measures, tables, and figures.