Skip to content

Advertisement

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Low birth weight among term newborns in Wolaita Sodo town, South Ethiopia: a facility based cross-sectional study

BMC Pregnancy and Childbirth201818:160

https://doi.org/10.1186/s12884-018-1789-y

Received: 13 March 2017

Accepted: 26 April 2018

Published: 11 May 2018

Abstract

Background

In low income countries, many low birth weight newborns often miss the chance for survival sooner or later. Others who survive would also face increased risks in later life. Though not adequately documented in Ethiopia, maternal factors pose the main risk. This study was aimed to estimate the proportion of low birth weight among term singletons without congenital malformations and factors associated with it in Wolaita Sodo town in South Ethiopia.

Methods

We did a facility based survey involving 432 postpartum women with their term newborns. Data was collected through face to face interview from March to April in 2016. The outcome measure was newborn birth weight. Bivariate logistic regression was applied to look for crude associations. Multivariate logistic regression analysis was done to adjust for potential confounders to identify independent predictors. Adjusted Odds Ratio (AOR) and 95% confidence intervals (CI), and statistical significance at P < 0.05 were reported.

Results

The proportion of term low birth weight was 8.1% in the study area. Women who had less education (AOR = 6.23; 95% CI = 1.68, 23.1), house wives (AOR = 5.85; 95% CI = 1.40, 24.3) and not frequently consuming fruits during pregnancy (AOR 11.3; 95% CI = 1.98, 64.9) had a higher risk of having term low birth weight newborns. We documented a lesser odds of those from rural settings to have low birth weight newborns as compared to their counter urban equivalents (AOR = 0.06; 95% CI = 0.006, 0.6).

Conclusions

Dietary counselling to pregnant mothers specific diet and nutrition including fruit diets in particular might contribute to reduce the risk of term low birth weight. Better education might have enabled women to prefer diets and their job engagements might also have capacitated them to decide on dietary preferences.

Keywords

Low birth weightTerm singletonsWolaita SodoEthiopia

Background

If a newborn weighs less than 2500 g at birth, it is termed as low birth weight (LBW) [1]. A low birth weight carries an increased risk of death on the newborns early in life or exposes to multiple health and development challenges later. The burden of immediate health problems on low birth weight newborns has been relatively widely documented in many low income countries with national demographic surveys [2].

An estimated 13% all babies each year in Sub-Saharan Africa (SSA) are born LBW [36]. According to the Ethiopian Demographic and Health Surveys an estimated 13–11% of all babies in the country are born low birth weight [7, 8]. The proportions have been documented with a wide variation across the different settings in the country. The prevalence has so far documented as low as 6.3 to 28.3% across Ethiopia [713]. Thus the national prevalence of low birth weight has remained high in Ethiopia.

In Ethiopia, most commonly used data on birth weight of the newborns are often based on national survey indirect indicators. However, the national data is known for its limitations including sampling strategies which would not be assumed to be representative of the socio-economic disparities and cultural diversities across the country. Though the proportion of skilled childbirth attendance is increasing in Ethiopia, we lack adequate evidence on maternal factors which would potentially predict birth outcomes of the newborns. This is so limited particularly in South Ethiopia which is characterized by a diverse culture and dietary practices. Thus the proportion of low birth weight in health facilities has been least documented in South Ethiopia.

A densely populated Wolaita area in south Ethiopia [1419], many households often suffer from chronic food insecurity [20]. Thus even at times of crop harvest, many children in the area remain undernourished. However, we have very limited evidence on child health and nutritional status predictors particularly attached to maternal health during pregnancy and child births. Thus the main aim of this study was to estimate the proportion of low birth weight among term singletons that were born without congenital malformations in Wolaita Sodo town in South Ethiopia. It was also further aimed to identify socio-demographic and maternal factors associated with the outcome. The findings might help for local community based interventions to improve birth outcomes, and also expected to add on the existing knowledge.

Methods

Study setting

Wolaita Sodo town is an administrative capital for the Wolaita zonal administration in South Ethiopia. It is located at 380 km south from Addis Ababa. The town has 3 sub-cities; 11 lower administrative units. It had a census projected population of 110,659; 48% were female.

One government hospital and another private one, 3 health centres, 11 health posts and 21 other private health facilities deliver health services to the population in the town. Out of all facilities; only health centres and hospitals are staffed to attend skilled childbirth [21]. Based on the local health service administrators’ report for 2014/15, about 5000 childbirths were attended in government’s health facilities at Wolaita Sodo town.

Study design and period

We did a facility based cross-sectional study involving postpartum mothers and their newborns through birth records from March to April in 2016.

Population and sampling

All postpartum mothers and their newborns in government health facilities in Wolaita Sodo town were the source population. Consecutively recruited 432 mother-newborn-pairs were studied. Term newborns (born at 37 weeks or later of gestation) and singletons were included. Birth outcomes such as congenital malformations, multiple births, and stillbirths, and postpartum mothers with unknown last normal menstrual period were excluded.

A sample size of 451 was calculated with the following assumptions: 22.5% expected proportion of low birth outcome which was taken from similar Ethiopian study [13], 95% confidence level, 5% margin of error, and 10% none-response consideration. It was proportionally allocated to government health facilities in Wolaita Sodo town based on number of previous childbirth attendance. Finally all eligible postpartum mother- to-child pairs were consecutively recruited until subsamples for the facilities and the total sample for the study were achieved.

Variables and measurements

Outcome

Birth weight of a term singleton newborn was an outcome measure. It was considered low birth weight if the newborn weighed less than 2500 g. Birth weight measurement was taken within an hour after birth [1].

Exposure variables and covariates

Socio-demographic and socio-economic: maternal age, educational status, occupation, residence, family income and newborn’s sex [22].

Diets and supplements: consumption of any additional meals and supplements such as iron and folic acid during pregnancy [23].

Maternal morbidity and previous obstetric histories: history of hypertension, diabetes mellitus and infections during pregnancy [24], pregnancy intervals, number of childbirth, low birth weight, abortions, and stillbirths [24].

Maternal health service utilization: antenatal care (ANC), gestational age at first ANC visit, and dietary counselling in ANC [22, 23]. Whereas, a woman needs to make at least four ANC visits during pregnancy [25, 26].

Maternal nutritional status: Mid Upper Arm Circumference (MUAC) of mothers. Whereas, MUAC less than 23 cm centimetres defined the women as undernourished [27].

Data collection

We adopted a structured questionnaire from relevant articles and related literatures (Additional file 1) [7, 13]. The questionnaire was pretested in 23 respondents which were later not included to the main study. Four data collectors were trained on: the different modules of the questionnaire, participants’ selection, maternal MUAC and birth weight measurements, and ethics.

Anthropometric measures were standardised for Technical Errors against an expert measurer. Weights of newborns were measured within an hour after birth with a digital weight scale. The MUAC of mothers was measured by using a none-stretchable tape.

Statistical analysis

Data were entered, cleaned and analyzed by using SPSS version 20. Descriptive statistics were done for the main variables. Through binary logistic regression analysis we selected exposure variables with a crude association to the outcome. The multivariate candidate variables were prioritized by using a yardstick cut-off point for statistical significance on bivariate analysis. Thus exposures and covariates with p-value less than 0.25 were taken for multivariate analysis. Finally, a multivariate logistic regression analysis was done to control for potential confounders and identify independent predictors of the outcome. Accordingly, we reported AORs as effect measures with 95% CIs, and statistical significance declared at p-value < 0.05.

Results

Socio-demographic characteristics

Overall, 432 mother-newborn-pairs were involved in the study with a response rate of 95.8%. About nine in ten, 387 (89.6%) were aged 20–34 years. The majority 374 (86.6%) had some level of formal education and 187 (43.3%) were housewives. About two-third, 291(67.4%) were urban and a third were rural residents (Table 1).
Table 1

Socio-demographic characteristics of mothers and newborns involved in low birth weight study in health facilities at Wolaita Sodo town in South Ethiopia, March 2016

Variables (n = 432)

Frequency

Percent

Age

< 20

24

5.6

20–34

387

89.6

35+

21

4.9

Marital status

Married

408

94.4

Unmarried

24

5.6

Educational status

No formal education

58

13.4

Primary School (1–8)

143

33.1

Secondary School (9–12)

119

27.5

College and above

112

25.9

Occupation

Housewife

187

43.3

Government employee

89

20.6

Private employee

32

7.4

Merchant

92

21.3

Othersa

32

7.4

Residence

Rural

141

32.6

Urban

291

67.4

Average monthly income

≤810 ($ ≤ 37.5)

33

7.6

810–1296 ($37.5–60)

112

25.9

1297–2591 ($61–120)

121

28

≥2592 ($ ≥ 120)

166

38.4

Sex of the newborn

Male

222

51.4

 

Female

210

48.6

aStudents, daily labourers

Maternal obstetric and health service utilization

One hundred eighty (41.7%) of mothers were primiparous. About a third, 83 (32.9%) of the women gave birth to the current newborn within two years (≤24 months) after a previous child birth. Thirty four mothers (7.9%) had previous abortion and 18 (4.2%) had still birth. One hundred twenty-five (28.9%) mothers had some medical problems during recent pregnancy. The majority, 396 (91.7%) had at least a visit for antenatal during recent pregnancy; 376 (94.9%) of made their first visit during first trimester of gestation. A little higher than half, 210 (53%) had at least four ANC visits and 290 (73.2%) mothers affirmed for getting dietary advice during ANC follow-up (Table 2).
Table 2

Obstetric history, morbidity and health service utilization of mothers who visited health facilities for child birth at Wolaita Sodo town in South Ethiopia, March 2016

Variables (n = 432)

Frequency

Percentage

Parity

1

180

41.7

2–4

223

51.6

5+

29

6.7

Birth interval in months

≤24

83

32.9

> 24

169

67.1

History of abortion

Yes

34

7.9

No

398

92.1

History of stillbirth

Yes

18

4.2

No

414

95.8

History of having small baby

Yes

0

0

No

391

90.5

Not specified

41

9.5

Medical Illnesses during pregnancy

Yes

125

28.9

No

307

71.1

History of hypertension

Yes

8

1.9

No

424

98.1

History of diabetes mellitus

Yes

4

0.9

No

428

99.1

ANC visits for recent pregnancy

Yes

396

91.7

No

36

8.3

ANC visits

1–3

186

47.0

4+

210

53.0

Trimester for the first ANC visit

1st

376

94.9

3rd

20

5.1

Dietary counselling during ANC visit

Yes

290

73.2

 

No

106

26.8

Maternal nutritional status

Based on current findings, 166 (38.4%) mothers had MUAC less than 23 cm (undernourished). Three hundred fifty-five (92%) took iron and folic acid supplements at least once and 264 (61.1%) got less than 90 tablets during recent pregnancy. About two-third, 293 (67.8%) consumed at least one additional meal to their usual meals. Mothers involved in this study frequently consumed cereals 258 (59.7%) from food groups (Table 3).
Table 3

Dietary intake and supplements pregnancy, and nutritional status of mothers involved in the study at Wolaita Sodo town in South Ethiopia, March 2016

Variables (n = 432)

Frequency

Percent

Cereals

Yes

258

59.7

No

174

40.3

Starchy roots and tubers

Yes

47

10.9

No

385

89.1

Legumes

Yes

91

21.1

No

341

78.9

Dairy products

Yes

34

7.9

No

398

92.1

Meat

No

432

100

Fish and poultry

No

432

100

Vegetables

Yes

38

8.8

No

394

91.2

Fruits and nuts

Yes

43

10.0

No

389

90.0

Egg

Yes

2

0.5

No

430

99.5

Additional meal during pregnancy

Yes

293

67.8

No

139

32.2

Took iron supplements

Yes

355

92.0

No

31

8.0

Number of iron tablets taken

< 90

264

61.1

90–180

91

21.1

Unknown

77

17.8

MUAC of the mother

< 23 cm

166

38.4

23+ cm

266

61.6

Proportion and predictors of term low birth weight

The proportion of term low birth weight in this study was 8.1% (35/432). The mean weight of the newborns was 3532 g with standard deviation of 565. Mothers with less formal educational status and housewives had about six times at higher risk of giving birth to low birth weight newborns (AOR = 5.86; 95% CI: 1.64, 20.9) and (AOR = 5.41; 95% CI: 1.37,21.3). Mothers who consumed fruits rarely (less frequent than daily) had also a higher risk to give birth to term LBW (AOR 13.9; 95% CI: 2.29, 84.6). On the other hand, rural residents had a lower risk of giving birth to low birth weight newborns as compared to those from the nearby urban dwellers (AOR = 0.06; (95% CI = 0.006, 0.6) (Table 4).
Table 4

Factors associated with low birth weight among term newborns in health facilities at Wolaita Sodo town in South Ethiopia, March 2016

Variables (n = 432)

LBW

COR (95% CI)

AOR (95% CI)

Yes (%)

No (%)

Age

 < 25

11 (11.2)

87 (88.8)

1.63 (0.77,3.46)

1.53 (0.31,7.49)

 25+

24 (7.2)

310 (92.8)

1

1

Marital status

 Married

30 (7.4)

378 (92.6)

0.30 (0.10,0.86)

0.08 (0.006,1.18)

 Other

5 (20.8)

19 (79.2)

1

1

Educational status

 Primary and below

23 (11.4)

178 (88.6)

2.36 (1.14,4.87)

5.86(1.64,20.9)*

 Secondary and above

12 (5.2)

219 (94.8)

1

1

Occupation

 Housewife

20 (10.8)

166 (89.2)

2.08 (1.03,4.22)

5.41(1.37,21.3)*

 Others

15 (6.1)

231 (93.9)

1

1

Residence

 Rural

16 (11.3)

125 (88.7)

1.85 (0.92,3.73)

0.06 (0.006,0.6)*

 Urban

19 (6.5)

272 (93.5)

1

1

Average monthly income

 ≤ 810

6 (18.2)

27 (81.8)

3.88 (1.28,11.8)

2.55(0.19,34.09)

 810–1296

12 (10.7)

100 (89.3)

2.09 (0.85–5.15)

0.88(0.09,8.02)

 1297–2591

8 (6.6)

113 (93.4)

1.23 (0.46–3.30)

2.44(0.61,9.69)

 ≥ 2592

9 (5.4)

157 (94.6)

1

1

Parity

 1

22 (12.2)

158 (87.8)

2.56 (1.25,5.23)

1.95 (0.47,7.99)

 2+

13 (5.2)

239 (94.8)

1

1

Medical conditions

 Yes

6 (4.8)

119 (95.2)

1

1

 No

29 (9.4)

278 (90.6)

2.07 (0.84,5.11)

0.67 (0.18,2.52)

Dietary counselling

 Yes

17 (5.9)

273 (94.1)

1

1

 No

12 (11.3)

94 (88.7)

2.05 (0.94,4.45)

3.08 (0.64,14.77)

Number of additional meals

 1

2 (2.8)

70 (97.2)

1

1

 2+

21 (9.5)

200 (90.5)

3.67 (0.84,16.07)

3.5 (0.55,22.59)

Iron tablets consumed

 < 90

26 (9.8)

238 (90.2)

3.2 (0.94,10.85)

0.56 (0.13,2.46)

 ≥ 90

3 (3.3)

88 (96.7)

1

1

MUAC

 < 23 cm

15 (9)

151 (91)

1.22 (0.61,2.46)

3.15 (0.95,10.44)

 ≥ 23 cm

20 (7.5)

246 (92.5)

1

1

Cereals

 Daily

27 (10.5)

231(89.5)

1

1

 < Daily

8 (4.6)

166 (95.4)

2.42 (1.07,5.47)

2.89 (0.66,12.74)

Fruits

 Daily

9 (20.9)

34 (79.1)

1

1

 < Daily

26 (6.7)

363 (93.3)

3.69 (1.60,8.52)

13.9(2.29,84.6)*

*statistically significant at p < 0.05

Discussion

Evidence shows that birth weight is a good summary measure of multifaceted public health problems. Thus often used to indicate long-term maternal malnutrition, ill health, and poor health care during pregnancy [8]. This study documented that about 8.1% of term childbirths in government health facilities in Wolaita Sodo town ended up with low birth weight during the study period.

We observed a slightly lesser proportion of LBW in current study as compared to similar studies in Northern Ethiopia (10%) and elsewhere (10.6%) [28, 29]. It was also lower than similar study findings from Pakistan (9.9%) and Kenya (12.3%) [5, 30]. The main possible reason for the slight variation might be due to our exclusion of preterm newborns; whereas, the above studies included preterm babies. Conversely, we found a higher proportion of Term LBW compared to other studies in Ethiopia (6.3%), Iran (6.8%), and Egypt (7.3%) [10, 11, 31, 32].

In consistent with studies from India, Tanzania, and Sudan, lower level of maternal education was documented in this study as a positive predictor of low birth weight [4, 6, 33]. A better formal education might have improved mothers’ perceptions and dispositions in proper dietary habits during pregnancy and health service utilization. On the other hand, it might be assumed that the less educated would tend to be less informed on key dietary recommendations and health care during pregnancy [34].

Unlike many other study findings, this study documented that rural residents had a lower risk to have low birth weight babies as compared to those from nearby urban settings. This finding disagrees with similar study findings from Tigray (Northern Ethiopia) and Bale (South east Ethiopia) hospitals in Ethiopia [10, 11, 35] and also findings from Ghana [36].These might possibly be due to less representation of our sample for rural and urban groups which made our finding with a higher uncertainty (a very wide confidence interval for AORs). It might also be due to socioeconomic and other differences. The current finding agrees with a study in Jimma west Ethiopia which could be homogenous in many aspects to our settings [13].

Housewives had much higher odds of giving birth to low birth weight babies as compared to those who had some occupational engagements. This finding is in difference with similar studies South East in Ethiopia as well as Tanzania [6, 34, 35]. On the other hand, in favour of our finding, a study from Iran revealed an increased risk of low birth weight among housewives as compared to employees [37]. This might imply that housewives are mostly less educated, and often disadvantaged in accessing relevant health messages [7, 8, 11]. This might have affected their perceptions in dietary preferences and health service utilization. Furthermore, our study setting, Wolaita area is known for asset poverty; many households in the area are often food insecure [17, 20].

Mothers who did not consume fruits daily during pregnancy had much higher odds of giving birth to low birth weight as compared to those who did so. This was in agreement with a similar study in Nepal [38]. This might be due to a complementary nutritional benefits of fruits as main food sources for micronutrients; thus if particularly consumed in the first trimester, they could enhance organ development for the foetus. Some studies elsewhere in Ethiopia, Bangladesh, Indonesia and Nepal have documented a significant association between low MUAC measures of mothers and term low birth weight in their newborns [11, 39]; however our data did not imply such association. We recommend further studies to evaluate the effect of maternal undernutrition on newborn birth weight.

Study limitations

This study was limited to health facilities in scope and used a crossectional data. Thus mainly maternal side predictors of term low birth weight were assessed. Moreover, the time allotted for recruiting participants was limited though we assumed standard procedures of sample size calculation. The estimate might be better representative if a longitudinal follow-up data were used. Certain level of recall bias was expected with regard to menstrual dates and dietary habits; health workers conscious on cultural issues collected the data to reduce recall bias. Though all mothers attending delivery service with ANC follow up were ahead screened for HIV, we could not access this information due to limitations out of our scope. We have no data on other covariates such as food security and a reliable wealth data.

Conclusions

Though it would or would not be possible to bring all child bearing or potential mothers with less education to school; alternative opportunities of empowering women could be done. Occupational engagements of the women might also contribute to empower them on decision making capacities particularly on diet and health care.

Skilled counselling to diet during pregnancy including intake of fruits would benefit the women. Formal education when feasible and alternatively equivalent approaches targeting on diet and health care utilization would benefit. Moreover, we recommend community based data of birth weight through longitudinal studies for a better estimation of the prevalence of low birth weight.

Abbreviations

ANC: 

Antenatal care

AOR: 

Adjusted Odds Ratio

BMI: 

Body mass index

CI: 

Confidence interval

CIH: 

Centre for International Health

COR: 

Crude odds ratio

EDHS: 

Ethiopian Demographic and Health Survey

ETB: 

Ethiopian Birr

IFA: 

Iron and folic acid

LBW: 

Low birth weight

MUAC: 

Mid-Upper Arm Circumference

NORHED: 

Norwegian Programme for Capacity Development in Higher Education and Research for Development

SENUPH: 

South Ethiopia Network of Universities in Public Health

SPSS: 

Statistical Package for Social Sciences

SSA: 

Sub-Saharan Africa

UoB: 

University of Bergen

WSU: 

Wolaita Sodo University

Declarations

Acknowledgements

We would like to thank the SENUPH_NORHED project for granting the study. We are grateful to the School of Public Health at WSU for facilitating the research programme. We would like to thank Mr.Wondimagegn Paulos (a focal person for SENUPH project at WSU) for facilitating the research grant. We thank Wolaita Zone health department and the local authorities for official permissions and administrative supports. We acknowledge our responsible data collectors and supervisors. We are grateful to the consent and commitment of mothers involved in the study just after their childbirth without which this study would not be possible.

Funding

This study was financially supported by South Ethiopia Network Universities in Public Health SENUPH project from Norwegian Programme for Capacity Development in Higher Education and Research for Development (NORHED).Wolaita Sodo University facilitated the funding through a graduate level research programme. Neither of the parties had roles in design, conduct and decision to publish this research work.

Availability of data and materials

The datasets analyzed for this study is available with corresponding author which can be accessed on reasonable request.

Authors’ contributions

SK designed the protocol, coordinated data collection, made analysis and interpreted findings, and drafted the manuscript. TD contributed in design, analysis, interpretation of the findings, and reviewed the manuscript. BY contributed in design, analysis and interpretation of findings, reviewed progressive drafts, and proofread the manuscript. All authors read and approved the final version of the manuscript.

Authors’ information

SK is a human nutritionist by training. He studied MSc in Human nutrition and currently working as lecturer in School of Public Health at Wolaita Sodo University (WSU) in Ethiopia. TD is Public Health nutritionist by training. He is an associate Professor in the school of Public at WSU. BY is a public Health specialist. He has been assistant Professor in the School of public health at WSU. He is currently a joint PhD Fellow at the Centre for International Health (CIH) in the University of Bergen (UoB) in Norway and Hawassa University (Ethiopia).

Ethics approval and consent to participate

We obtained ethical clearance for the study from the ethical review committee at College of Health Sciences and Medicine in Wolaita Sodo University in Ethiopia. The committee justified for verbal informed consent in favour of many less educated mothers who would face difficulty in reading and comprehending the consent form. Moreover, those with less school grades could not be able to comprehend all issues in the document. Thus ethical clearance we obtained considered the least possible scenario and approved for verbal informed consent. The ethical clearance we obtained was written with a heading to Wolaita zone health department which is a higher government body in the area for health and health research conducts. Thus we got permission from this health department and its lower health administrative structures. Finally informed verbal consent was obtained from each participant. The data was made anonymous by identification numbers.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Public Health, Wolaita Sodo University, Wolaita Sodo, Ethiopia
(2)
School of Public Health, Hawassa University, Hawassa, Ethiopia
(3)
Centre for International Health, University of Bergen, Bergen, Norway

References

  1. WHO and UNICEF. In: E.a.P. section, editor. Low birth weight country, regional and global estimates. New York: WHO; 2004.Google Scholar
  2. Sutan R, et al. Determinants of low birth weight infants: a matched case control study. Open J Prev Med. 2014;4:91–9.View ArticleGoogle Scholar
  3. UNICEF. In: R.a.P. Data and Analytics Section; Division of Data, editor. Low birth weight: percentage of infants weighing less than 2,500 grams at birth; 2014.Google Scholar
  4. Saeed OAM, et al. Risk factors of low birth weight at three hospitals in Khartoum state, Sudan. Sudanese J Paediatr. 2014;14(2):22–8.Google Scholar
  5. Muchemi OM, Echoka E, Makokha A. Factors associated with low birth weight among neonates born at Olkalou District Hospital, Central Region, Kenya. Pan Afr Med J 2015;20(108).Google Scholar
  6. Siza JE. Risk factors associated with low birth weight of neonates among pregnant women attending a referral hospital in northern Tanzania. Tanzania J Health Res. 2008;10(1).Google Scholar
  7. CSA, Ethiopia demographic and health survey. 2011.Google Scholar
  8. [Ethiopia], C.S.A.C. and ICF. Ethiopia demographic and health survey 2016. In: Final report. Addis Ababa, Rockville: Central Statistical Authority; 2017.Google Scholar
  9. Zeleke BM, Zelalem M, Mohammed N. Incidence and correlates of low birth weight at a referral hospital in Northwest Ethiopia. Pan Afr Med J. 2012;12(4).Google Scholar
  10. Teklehaimanot N, Hailu T, Assefa H. Prevalence and factors associated with low birth weight in Axum and Laelay Maichew districts, North Ethiopia: a comparative cross sectional study. Int J Nutr Food Sci. 2014;3(6):560–6.View ArticleGoogle Scholar
  11. Gebremedhin M, et al. Maternal associated factors of low birth weight: a hospital based cross-sectional mixed study in Tigray, Northern Ethiopia. BMC Pregnancy Childbirth. 2015;15(222).Google Scholar
  12. Assefa N, Berhane Y, Worku A. Wealth status, Mid Upper Arm Circumference (MUAC) and Antenatal Care (ANC) are determinants for low birth weight in Kersa, Ethiopia. PLoS One. 2012;7(6).Google Scholar
  13. Tema T. Prevalence and determinants of low birth weight in Jimma zone, Southwest Ethiopia. East Afr Med J. 2006;83(7):366–71.View ArticlePubMedGoogle Scholar
  14. Leza T, Kuma B. Determinants of rural farm household food security in Boloso Sore District of Wolaita Zone in Ethiopia. Asian J Agric Ext Econ Sociol. 2015;5.Google Scholar
  15. Feyisso M, et al. Differentials of modern contraceptive methods use by food security status among married women of reproductive age in Wolaita zone, South Ethiopia. Arch Public Health. 2015;73:38.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Abo T, Kuma B. Determinants of food security status of female-headed households: the case of Wolaita Sodo town, South Nations, Nationalities and Peoples Region, Ethiopia. Int J Sci Foot Prints. 2015;3.Google Scholar
  17. Gecho Y, et al. Livelihood strategies and food security of rural households in Wolaita Zone, Southern Ethiopia. Dev Country Stud. 2014;4(14).Google Scholar
  18. Bogale Gebeyehu GR, Tebeje M. Rural households food security and livelihood strategies: the case of Offa woreda, in wolaita Sodo Zuria, Southern Nation, Nationalities and Peoples Regional State, Ethiopia. IJRESS. 2014;4.Google Scholar
  19. Eneyew A, Bekele W. Causes of household food insecurity in Wolaita: Southern Ethiopia. J Stored Prod Postharvest Res. 2012;3.Google Scholar
  20. Ayele T. Livelihood adaptation, risks and vulnerability in rural Wolaita, Ethiopia. In: Department of International Environment and Development Studies. Norway: Noragric Norwegian University of Life Sciences, UMB; 2008. p. 164.Google Scholar
  21. Ethiopian Minstery of Health. In: R. Health, editor. Community based new born care implementation plan. Addis Ababa, Ethiopia. Addis Ababa: Federal Ministry of Health in Ethiopia; 2013.Google Scholar
  22. Wardlaw GM, Hampl JS. Perspectives in Nutrition. 7th ed. New York: McGraw_Hill; 2007.Google Scholar
  23. Ethiopia, M.o.H.o., National Nutriton Programme. 2016.Google Scholar
  24. Plessis LMd, Naude CE. Community nutrition textbook for developing countries. 2008.Google Scholar
  25. Yaya S, et al. Timing and adequate attendance of antenatal care visits among women in Ethiopia. PLoS One. 2017;12(9):e0184934.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Lindtjørn B, et al. Reducing maternal deaths in Ethiopia: results of an intervention programme in Southwest Ethiopia. PLoS One. 2017;12(1):e0169304.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Tang AM, et al. Determining a global mid-upper arm circumference cutoff to assess malnutrition in pregnant women. Washington, DC: FHI 360/Food and Nutrition Technical Assistance III Project (FANTA); 2016.Google Scholar
  28. Gebregzabiherher Y, et al. The Prevalence and risk Factors for Low Birth Weight among Term Newborns in Adwa General Hospital, Northern Ethiopia. Obstet Gynecol Int. 2017;2017.Google Scholar
  29. Khan A, Nasrullah FD, Jaleel R. Frequency and risk factors of low birth weight in term pregnancy. Pak J Med Sci. 2016;32(1):138–42.PubMedPubMed CentralGoogle Scholar
  30. Badshah S, et al. Risk factors for low birth weight in the public hospitals at Peshawar, Pakistan. BMC Public Health. 2008;4(8):197.View ArticleGoogle Scholar
  31. Jafari F, et al. Socio-economic and medical determinants of low birth weight in Iran: 20 years after establishment of a primary healthcare network. Public Health. 2010;124(3):153–8.View ArticlePubMedGoogle Scholar
  32. Hong R, Ruiz Beltran M. Low birth weight as a risk factor for infant mortality in Egypt. East Mediterr Health J. 2008;14(5):992–1002.PubMedGoogle Scholar
  33. Metgud CS, Naik VA, Mallapur MD. Factors Affecting Birth Weight of a newborn - A community Based Study in Rural Karnataka, India. PLoS One. 2012;7(7).Google Scholar
  34. Ezugwu EC, et al. Singleton low birth weight babies at a Tertiary hospital in Enugu, South East Nigeria. Int J Gynaecol Obstet. 2009;14(1).Google Scholar
  35. Demelash H, et al. Risk factors for low birth weight in Bale zone hospitals, South-East Ethiopia: a case-control study. BMC Pregnancy Childbirth. 2015;15(264).Google Scholar
  36. Kayode GA, et al. Contextual risk factors for low birth weight: a multilevel analysis. PLoS One. 2014;9(1).Google Scholar
  37. Khojasteh F, et al. The relationship between maternal employement status and pregnancy outcomes. Global J Health Sci. 2015;8(9).Google Scholar
  38. Osrin D, et al. Effects of antenatal multiple micronutrient supplementation on birthweight and gestational duration in Nepal: double blind, randomised controlled trial. Lancet. 2005;365:955–62.View ArticlePubMedGoogle Scholar
  39. Islam M, et al. Effect of maternal status and breastfeeding practices on infant nutritional status - a cross sectional study in the south-west region of Bangladesh. Pan Afr Med J. 2013;16:139.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement