Data contain potentially identifying information and will only be made available upon request to the corresponding author.

Introduction {#sec004}
============

Maternal and child under nutrition is the underlying cause of 3·5 million deaths, 35% of the disease burden in children younger than 5 years and 11% of total global disability-adjusted life-years (DALYs). It is highly prevalent in low-income and middle-income countries, resulting in substantial increases in mortality and overall disease burden \[[@pone.0142301.ref001]\]. New evidence reinforces the importance of the maternal nutritional status, both for the health of the mother and for ensuring healthy fetal growth and development. It also strengthens the case for a continued focus on the crucial 1000 day window of life \[[@pone.0142301.ref002]\].

Women's employment increases household income, with consequent benefit to household nutrition in general and the woman's nutritional status in particular. Employment may increase women's status and power, and may bolster a woman's preference to spend her earnings on health and nutrition \[[@pone.0142301.ref003]\]. Its effect on child welfare is of enormous importance to development policy as healthy child development and improved economic opportunity for women are two general goals of development agencies worldwide \[[@pone.0142301.ref004]\]. A study from Malaysia showed that children of unemployed mothers were severely wasted compared to those who were employed \[[@pone.0142301.ref005]\].

Research indicates that there is a strong linkage between maternal education and children's health. Children born to less educated women suffer more from malnutrition which manifests as underweight, wasting and stunting in children. Maternal education has been associated with nutrition outcomes among children in studies in various settings including Bolivia \[[@pone.0142301.ref006]\] and Kenya \[[@pone.0142301.ref007]\]. Therefore the aim of this study was to assess the relationship between selected maternal characteristics, maternal nutritional status and children's nutritional status, in Ethiopia.

Materials and Methods {#sec005}
=====================

Study area and design {#sec006}
---------------------

This study uses the baseline survey of an intervention study conducted from September 2012 to March 2013, in Hula Woreda, Southern Nations Nationalities and Peoples' Region (SNNPR), Ethiopia \[[@pone.0142301.ref008]\]. This baseline survey with a cross sectional design was conducted between September and October 2012. Hula is mostly rural (94%), with 60% cultivable land. The study subjects were 197 mothers of children between the ages of 6 and 23 months living in two kebeles (Titicha and Debicha) of Hula Woreda. Only caregivers who had been residents of the study area for more than 6 months and who gave consent were eligible for the study. To select mother--child pairs, small areas (gots) were randomly chosen in each kebele.

Measurement {#sec007}
-----------

A baseline survey was performed with the use of a pretested, structured questionnaire to assess socio demographic characteristics of the family and maternal characteristics. The questionnaire was developed in English using process validation and then translated into the Sidamigna language. Wealth was estimated using a principal component analysis and which had 19 items with Cronbach's alpha of 0.7. Measurements of weight and height from both mother and children were taken by trained staff during the baseline survey.

Anthropometric measurements were performed using calibrated equipment and standardized techniques. Weight was measured to the nearest 0.1 kg with an electronic scale (Seca 770) with the children wearing a light shirt and without shoes. Recumbent length was measured to the nearest 0.1 cm with a length-measuring board. All anthropometric data were collected by the principal investigator.

Data analysis {#sec008}
-------------

The data were entered, checked for missing values and outliers, and analyzed with SPSS, version 16.1. The weights and heights of the children were converted to z-scores with WHO Anthro Software. Significant Pearson's correlation coefficients guided selection of education, demographic, socio-economic and anthropometric variables for linear regression models. To identify the predictors of child nutritional status only variables that were significantly associated at (p \< .2) in the bivariate analyses were entered into multivariable linear regression models. In all tests, P \< .05 was considered to indicate statistical significance.

Ethical clearance {#sec009}
-----------------

Before data collection, permission was obtained from the Hawassa University Ethical Review Committee. The local authorities were informed about the purpose of the study and its objectives. Prior to entry into the study, the mothers gave oral consent after the benefits of the study had been explained to them. Sick and severely malnourished children were referred to health facilities and advice was given to their parents.

Result {#sec010}
======

Socio economic and socio demographic factors {#sec011}
--------------------------------------------

Overall, the response rate of study participants was 98%. Almost all (99%) mothers in the study area were married and 98% were Sidama by ethnicity. As shown in [Table 1](#pone.0142301.t001){ref-type="table"} most mothers (81%) were followers of a Protestant religion and 61.9% had no formal education. The majority (81%) of families in the study area were reliant on farming for income and 32% of the mothers were farmers. They were living in houses made of a bamboo wall (75.1%), thatched grass roof (75.6%) and drinking water from a community tap water source (60.4%). Although 65.5% households were in the middle level for wealth status, only 14.2% indicated that children had priority for food at mealtime.

10.1371/journal.pone.0142301.t001

###### Socio demographic and household characteristics.

![](pone.0142301.t001){#pone.0142301.t001g}

  Variables (n = 197)       Frequency                Percent (%)   
  ------------------------- ------------------------ ------------- ------
  Religion                  Protestant               160           81.2
                            Orthodox                 4             2.0
                            Muslim                   8             4.1
                            Catholic                 25            12.7
  Educational Status        No formal education      122           61.9
                            Primary                  58            29.4
                            Secondary                11            5.6
                            Preparatory              4             2.0
                            Others                   2             1.0
  Occupation                Unemployed               128           64.9
                            Farmer                   62            31.5
                            Employed                 7             3.6
  Head of the household     Mother                   10            5.1
                            Father                   187           94.9
  Water source              Tap water                119           60.4
                            Non tap water            78            39.6
  Wall of the House         Wood and mud             49            24.9
                            Bamboo                   148           75.1
  Roof of the House         Corrugated iron sheet    25            12.7
                            Thatched grass           149           75.6
                            Bamboo                   23            11.7
  Main income source        Trade                    21            10.7
                            Farming                  160           81.2
                            Salary from employment   16            8.1
  Meal priority             Men                      149           75.6
                            Women                    5             2.5
                            Children                 28            14.2
                            Others                   15            7.6
  Wealth                    Low level                63            31.9
                            Medium level             129           65.5
                            High level               5             2.5
                            No                       6             3.1
  Mother listens to radio   Yes                      35            17.8
                            No                       162           82.2
  Mother watches TV         Yes                      7             3.6
                            No                       190           96.5

Ninety five percent of the households were male headed. Close to 18% of mothers listened to radio while 4% watched Television.

Mother-child nutritional status {#sec012}
-------------------------------

As [Table 2](#pone.0142301.t002){ref-type="table"} shows, most children (65%) were 12--24 months old. Seven percent of the children were below -2 weight for height Z score (WHZ), 11.5% were below -2 height for age Z score (HAZ) and 9.9% were below -2 weight for age Z score (WAZ). Almost half of the mothers (48%) were 25--29 years old, and 14.2% were below a body mass index of 18.5 kg/m^2^ (BMI).

10.1371/journal.pone.0142301.t002

###### Mothers\' and children\'s anthropometry.

![](pone.0142301.t002){#pone.0142301.t002g}

  Variables (n = 197)             Frequency               Percent   
  ------------------------------- ----------------------- --------- ------
  Children's sex                  Male                    95        48.2
                                  Female                  102       51.8
  Children's age                  6--8                    35        17.8
                                  9--11                   34        17.3
                                  12--24                  128       65
  Children's Nutritional status   Wasting (\<-2WHZ)       13        6.8
                                  Stunting (\<-2 HAZ)     22        11.5
                                  Underweight (\<-2WAZ)   19        9.9
  Mothers' age                    15--19 years            5         2.5
                                  20--24 years            38        19.3
                                  25--29 years            95        48.2
                                  30--34 years            45        22.8
                                  ≥35 years               14        7.1
  Mothers' height                 \<145 cm                32        16.2
                                  145--149.9 cm           28        14.2
                                  150--154.9 cm           48        24.4
                                  155--159.9 cm           61        31
                                  \> = 160 cm             28        14.2
  Mothers' BMI                    \<18.5 kg/m^2^          28        14.2
                                  18.5--24.9 kg/m^2^      166       84.3
                                  25--29.9 kg/m^2^        1         0.5
                                  30--34.9 kg/m^2^        2         1

WHZ: weight for height Z score; HAZ: height-for-age z-score; WAZ: weight-for-age z-score Mothers' BMI: Mothers' body mass index.

[Table 3](#pone.0142301.t003){ref-type="table"} shows correlations between maternal characteristics and child nutritional status. Both maternal BMI (*r* = 0.16 *P* = 0.02) and educational status (*r* = 0.25 *P* = 0.001) were correlated with WHZ of children [Table 3](#pone.0142301.t003){ref-type="table"}. Maternal height (*r* = 0.2 *P* = 0.007) was correlated with HAZ of children.

10.1371/journal.pone.0142301.t003

###### Pearson correlation coefficient for mother-child variables (n = 197).

![](pone.0142301.t003){#pone.0142301.t003g}

                       Family size                                   Edu-cational Status                           Main income source                            Sex                                           BMI                                           Maternal height                               Water source                                Maternal weight                             WHZ                                           HAZ
  -------------------- --------------------------------------------- --------------------------------------------- --------------------------------------------- --------------------------------------------- --------------------------------------------- --------------------------------------------- ------------------------------------------- ------------------------------------------- --------------------------------------------- ---------------------------------------------
  Family size          1                                                                                                                                                                                                                                                                                                                                                                                                                          
  Educational Status   .413[\*\*](#t003fn001){ref-type="table-fn"}                                                                                                                                                                                                                                                                                                                                                                                
  Main income source   0.071                                         .144[\*](#t003fn002){ref-type="table-fn"}                                                                                                                                                                                                                                                                                                                                    
  Sex                  0.107                                         0.133                                         0.061                                                                                                                                                                                                                                                                                                                          
  BMI                  -0.025                                        0.136                                         -0.045                                        -0.051                                                                                                                                                                                                                                                                           
  Maternal height      -0.064                                        0.008                                         0.1                                           0.099                                         -0.074                                                                                                                                                                                                                             
  Water source         -0.01                                         0.019                                         0                                             0.013                                         -0.022                                        0.063                                                                                                                                                                                
  Maternal weight      0.073                                         .216[\*\*](#t003fn001){ref-type="table-fn"}   0.082                                         0.029                                         .558[\*\*](#t003fn001){ref-type="table-fn"}   .470[\*\*](#t003fn001){ref-type="table-fn"}   0                                                                                                                                      
  WHZ                  .162[\*](#t003fn002){ref-type="table-fn"}     .236[\*\*](#t003fn001){ref-type="table-fn"}   0.126                                         .147[\*](#t003fn002){ref-type="table-fn"}     .165[\*](#t003fn002){ref-type="table-fn"}     -0.016                                        -0.055                                      0.125                                                                                      
  HAZ                  0.05                                          -0.002                                        0.135                                         .158[\*](#t003fn002){ref-type="table-fn"}     -0.006                                        .193[\*\*](#t003fn001){ref-type="table-fn"}   .147[\*](#t003fn002){ref-type="table-fn"}   0.101                                       -0.099                                         
  WAZ                  .144[\*](#t003fn002){ref-type="table-fn"}     .169[\*](#t003fn002){ref-type="table-fn"}     .189[\*\*](#t003fn001){ref-type="table-fn"}   .223[\*\*](#t003fn001){ref-type="table-fn"}   0.116                                         0.129                                         0.072                                       .156[\*](#t003fn002){ref-type="table-fn"}   .656[\*\*](#t003fn001){ref-type="table-fn"}   .679[\*\*](#t003fn001){ref-type="table-fn"}

\*\*. Correlation is significant at the 0.01 level (2-tailed).

\*. Correlation is significant at the 0.05 level (2-tailed). WHZ: weight for height Z score; HAZ: height-for-age z-score; WAZ: weight-for-age z-score Mothers' BMI: Mothers' body mass index

Predictors of child nutritional status {#sec013}
--------------------------------------

A stepwise regression analysis was used to identify the predictors of WHZ, HAZ and WAZ [Table 4](#pone.0142301.t004){ref-type="table"}. All socio demographic and maternal anthropometric variables that were significant at P≤0.2 were entered in to the models. Salary from an employment (either from government work or wage) and normal BMI (18.5 to 24.9kg/m^2^) significantly predicted WHZ score of children. Being an illiterate mother was negatively (P = 0.03) associated with WHZ score of children. A significant association was found between maternal height and HAZ. Even though, tap water source was associated with HAZ in the bivariate analysis, it did not become significant in the multivariate analysis. Moreover, salary from employment and WAZ were associated with each other.

10.1371/journal.pone.0142301.t004

###### Predictors of children's WHZ, HAZ and WAZ.

![](pone.0142301.t004){#pone.0142301.t004g}

                                               β coefficient   95% CI           P- value
  -------------------------------------------- --------------- ---------------- ----------
  **WHZ (R** ^**2**^ **= 14.1%)**                                               
  (Constant)                                   -0.621          -1.076; -0.166   0.008
  Salary from employment                       1.048           0.456; 1.641     0.001
  Mothers' BMI (18.5to24.9kg/m^2^)             0.627           0.198; 1.056     0.004
  Educational status (illiterate)              -0.365          -0.698; -0.032   0.032
  **HAZ (R** ^**2**^ **= 10.1%)**                                                
  (Constant)                                   -0.386          -0.776; 0.004    0.052
  Maternal height (3^rd^ tertile: ≥157.1 cm)   0.630           0.95; 1.165      0.021
  Salary from employment                       1.158           0.174; 2.143     0.021
  Maternal Age (≥35 years)                     -1.079          -2.037;-0.120    0.028
  **WAZ (R** ^**2**^ **= 16.1%)**                                               
  (Constant)                                   -0.299          -0.524; -0.075   0.009
  Salary from employment                       1.333           0.765; 1.9       \<0.001
  Sex (male)                                   -0.369          0.684; 0.055     0.022
  Maternal Age (≥35 years)                     -0.653          -1.26; -0.047    0.035

WHZ: weight for height Z score; HAZ: height-for-age z-score; WAZ: weight-for-age z-score Mothers' BMI: Mothers' body mass index, (Salary from employment (Yes = 1, No = 0), Sex (Male = 1, Female = 0)).

Discussion {#sec014}
==========

This baseline survey was part of an intervention study undertaken in the rural southern part of Ethiopia \[[@pone.0142301.ref008]\]. The results of this study show maternal anthropometry predicted children's nutritional status, i.e., normal maternal BMI (18.5 to 24.9kg/m^2^) predicted weight for height Z (WHZ) score. This effect could be explained as being due to mothers in the high wealth classification, who had higher BMI than those who were in middle and low wealth classification (P\<0.001). A study done in Nigeria showed that maternal BMI correlated with income and educational status \[[@pone.0142301.ref009]\]. Further, according to the 2011 Ethiopia demographic and health survey (EDHS), wasting (weight for height \<-2SD) was most common in the children of thin mothers (whose BMI is less than 18.5), in children whose mothers have no education, and in children in the lowest wealth quintiles \[[@pone.0142301.ref010]\]. In a study done in a daycare center in Brazil, both maternal weight and height had a significant impact on children's nutritional status \[[@pone.0142301.ref011]\].

Similarly in this study, maternal educational status was also a positive predictor for WHZ score of children. This may be explained as the more mothers obtain education, the more they will visit a health center and get nutritional advice from health professionals. Another explanation may be that as they are educated, they will have jobs that pay well and this will increase mothers' purchasing power, which is consistent with a study done in Bolivia \[[@pone.0142301.ref006]\].

Having a salary from employment (either from wage or government work) was a strong predictor for all of the following: weight for height Z (WHZ) score, height for age Z (HAZ) score and weight for age Z (WAZ) score of children. A cross-sectional study from Brazil \[[@pone.0142301.ref012]\] showed lower mean HAZ among children whose mothers presented a lower household income (p\<0.05). However, after adjustment for maternal age, educational level, household income and other factors, children whose mothers' height was \<145 cm had a 1.2 lower HAZ than those whose mothers were ≥160 cm tall (P for trend \<0.0001) \[[@pone.0142301.ref012]\]. In addition to the wealth of a household, it may also be important for mothers to have their own money so that they can buy a variety of food, thereby improving diet diversity. Thus empowering women financially could be an important step, to take in improving child nutritional status. Results consistent with this hypothesis were observed in a study in a low income neighborhood in Nicaragua where mothers' wealth correlated with children's WAZ and WHZ, but maternal employment particularly affected WHZ score of their children \[[@pone.0142301.ref004]\]. Moreover, a research conducted on Malaysian mothers, shows positive correlation between working hours and weight of their young children \[[@pone.0142301.ref005]\]. In a study conducted in Nigeria, maternal work status was associated with significant and positive impact on child growth in regions with high levels of malnutrition \[[@pone.0142301.ref013]\].

Maternal height (3^rd^ tertile: ≥157.1 cm) predicted height for age Z (HAZ) score of children in our study. This is probably due to maternal height influencing offspring linear growth over the growth period \[[@pone.0142301.ref014]\]. A study conducted in Brazilian children revealed that mothers in the highest tertile of height had children whose HAZ was significantly higher compared with those of children whose mothers were in the lowest height tertile, after adjusting for socioeconomic status \[[@pone.0142301.ref015]\]. Our finding agrees with the results observed in rural Kenya, in which HAZ score of children significantly increased with an increase in maternal height, wherein an increase in maternal height \[OR = 0.95 (CI: 0.91--0.99)\] was significantly associated with lower odds of stunting \[[@pone.0142301.ref016]\]. In our study, being male was negatively associated with height for age Z score. In a meta-analysis of 16 demographic and health survey of sub-Saharan countries, the mean Z-scores for males were consistently lower than for females with the differences statistically significant in 12 out of 16 studies \[[@pone.0142301.ref017]\].

In the present study, older maternal age (≥35 years) was negatively associated with HAZ and WAZ score of children. It could be that older mothers had less health seeking behaviors compared to younger mothers. However, it may not be a linear relationship, as an in-depth analysis of the Ethiopia demographic and health survey 2005 provides evidence that the youngest (15--19) and oldest (35--49) mothers are both less likely to have health seeking behaviors than mothers in the reference category (20--34) \[[@pone.0142301.ref018]\]. According to the World Bank, malnutrition is lowest among children born to mothers in their mid-late twenties. Mothers in their teens and their 30s, especially the late 30s, are significantly more likely to have children suffering from malnutrition \[[@pone.0142301.ref019]\].

Limitation {#sec015}
----------

The cross sectional nature of this study makes causal relationships between maternal and child nutritional status less probable. Moreover, since the study was from one area of Ethiopia, it could be difficult to make generalizations. However, since it was conducted in rural part of the country, it will give us an initial point for interventions and further studies in similar context.

Conclusion {#sec016}
==========

In conclusion, children born from mothers who were educated and have salary from employment had a better nutritional status. Normal maternal BMI was associated with WHZ score of children, while maternal height was associated with children's HAZ score. The interrelationship between maternal and child nutritional status stresses the value of improving maternal nutritional status to make better both maternal and child health outcomes.

The authors would like to thank the local health center (Teticha health center) employees for facilitating the study. We would also like to acknowledge the mothers in the study area for participating in the study.

[^1]: **Competing Interests:**The authors have declared that no competing interests exist.

[^2]: Conceived and designed the experiments: CN SJW TB. Performed the experiments: CN. Analyzed the data: CN TGH TB. Contributed reagents/materials/analysis tools: TGH SJW CJH. Wrote the paper: CN SJW TGH.
