Complementary Food Feeding Hygiene Practice and Associated Factors among Mothers with Children Aged 6–24 Months in Tegedie District, Northwest Ethiopia: Community-Based Cross-Sectional Study

Round 1

Reviewer 1 Report

This is an important study that highlights the importance of good hygiene practices regarding the preparation food, in an Ethiopian district. The study is well conducted and I only have a few comments.

1. explicitly describe the aim and the variables of interest (and why is that important)
2. Add an analytical plan
3. Add more info on the methods to favor reproducibility of the findings

In general, please describe how these findings can be used in other context (African and non Africa contexts)

Please also make sure to read and check for grammar and typo the manuscript

Author Response

Answers to reviewer 1

1. After reviewing the reviewer comments  all authors  describe the aim and some important variable that we want to check in  the study since in different study these factors suggested as predictors.
2. we added the analytical plan 
3. we added the the how describe the finding  in the method section and in addition this ,we look the severity  in world and Africa context 

Reviewer 2 Report

This is an interesting study that reports the factors associated with good hygiene practices regarding preparation of complementary food, in an Ethiopian district.

I think the data collection and study is well conducted, but few improvements should be done, to increase the scientific soundness and validity of the manuscript.

1. I suggest reading and referring to recent literature about child health issues and distal causes (e.g. Bizzego, A., Gabrieli, G., Bornstein, M. H., Deater-Deckard, K., Lansford, J. E., Bradley, R. H., ... & Esposito, G. (2021). Predictors of contemporary under-5 child mortality in low-and middle-income countries: a machine learning approach. International journal of environmental research and public health, 18(3), 1315.)
2. The statistical analysis is not well described, starting from the study's aims and hypotheses. Please clarify:
   1. What are the aims of the study (L126-128) in terms of research questions with objective/quantifiable variables.
   2. There should be a section in Methods, where the specific study hypotheses are described. For each hypothesis, a brief description of the statistical modeling used to test the hypothesis should be reported
   3. It is not clear how the multivariate logistic regression models have been used: is it only one model with all the variables? Is there a variable selection step? How did you adjust for confounding variables (L 260)? More details are needed to be able to reproduce the results of this study
3. Variables could be better described, possibly with a table summarizing the key question and operational steps to obtain the final groups/categories
4. I think there is something strange with maternal education: how do you explain that mothers with no education have AOR 4 of having good practices with respect to mother with diploma (reference)?
5. Please check through the manuscript that the text is correct and understandable.

 

Author Response

Answer to reviewer 2

1. we to Added the recent reference like you suggested
2. the statistical analysis plan well stated which is In Bivariate logistic regression, p-value < 0.25 was considered to retain variables for multivariable logistic regression model. A backward stepwise analysis method was used to selection variable in multivariable logistic regression to control the confounding effect. 
3. In multivariable logistic regression mode some variables removed in the regression table since we used backward stepwise analysis method 
4. we tried to correct it.
5. from the understandable problems  we try edit  after review all authors  

Round 2

Reviewer 2 Report

I think the Authors improperly used the term Bivariate logistic regression.

Bivariate logistic regression is when there are 2 independent variables (IVs) that should be explained with one Dependent Variable (IV). The study only lists one IV. I assume the Authors meant "binary" logistic regression, instead of "bivariate" logistic regression.. is that correct? Please verify and correct the manuscript, or provide more details about the two IVs that are modeled.

Some more details on the statistical methods used would be required. In particular the Authors do not explain how they controlled for the confounding variables (and what the confounding variables are).

Author Response

response to reviewer one:

1. Bivariate analysis looks at two paired data sets, studying whether a relationship exists between them and can also measure the correlations between the two variables. Although, we can use the bivariate logistic regression model if we have two binary dependent variables (Y1, Y2).

• We assumed bivariable logistic regression and make a correction from the whole document.

2. Confounding variables or confounders are often defined as the variables that correlate (positively or negatively) with both the dependent variable and the independent variable (1). A Confounder is an extraneous variable whose presence affects the variables being studied so that the results do not reflect the actual relationship between the variables under study. There are various ways to modify a study design to actively exclude or control confounding variables including Randomization, RestrictionMultivariate Models, and Matching.

• In randomization the random assignment of study subjects to exposure categories to break any links between exposure and confounders.
• In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
• Multivariate Models: Stratified analysis works best in the way that
  there are not a lot of strata and if only 1 or 2confounders have to be controlled. If the number of potential confounders or the level of their grouping is large, the multivariate analysis offers the only solution.
• Matching is a technique used to avoid confounding in a study design. In a cohort study, this is done by ensuring an equal distribution among exposed and unexposed of the variables believed to be confounding.

3. Epi-data version 4.6 was used to enter data, which was then exported to SPSS 23 for further analysis. The proportion, median, and demographic characteristics of study participants and the proportion for complementary feeding use were calculated using descriptive statistics. For the final report, tables, figures, and texts were used. The connection between the dependent variable and the explanatory variables was measured using binary logistic regression. To determine the crude odds ratio, bivariable analysis was employed, and bulk variables with p-values less than 0.2 were screened for inclusion in the multivariable analysis model. After controlling for confounding variables, multivariable analysis was performed to investigate the association between several predictors and a single outcome in order to obtain the adjusted odds ratio. Furthermore, a p-value of 0.05 was used as a cut-off point to declare that the association was statistically significant, with a 95 percent confidence interval to declare that statistically significant. Hosmer and Lemeshow's goodness of fit test was used to assess model fitness.
4. Dega means in Ethiopia contexts: Coldish and less than the temperate zones with attitudes ranking between2600 and 3200 meters, weynadege is warm, wet and lies below2600m and kola means the calamite of hot lowland with an attitude large annual temperature larges between20⁰ c and 30⁰c

Thanks for your valued comments!!

Author Response File: Author Response.docx