Introduction: Dietary diversity ensures that pregnant women consume the necessary range of nutrients from various food groups for the best possible outcomes for maternal health. Maternal health and fetal development are weakened by nutrient deficits caused by inadequate dietary diversity, typified by income constraints, and frequently restricted consumption of varied food categories. This study aimed to assess pregnant women's dietary diversity and household income status in Mbulu district, Tanzania. Methods: A cross-sectional study design was used among 384 pregnant women. Pregnant women were selected by using a systematic random sampling method. Eight health facilities were selected using a purposive sampling method. Face-to-face interviews were conducted by trained enumerators using a structured questionnaire that was divided into three sections to collect information on (i) socio-demographic characteristics, (ii) Dietary diversity, and (iii) Household income status. The Pearson Chi-square test and Logistic regression analysis were used to compare and test the association of dietary diversity and household income status to pregnant women's characteristics. Dietary diversity was evaluated using the Minimum Dietary Diversity Score (MDDS), while household income status was measured through income and expenditure data. Results: The findings revealed that about 43% of pregnant women had inadequate dietary diversity, while 57% had adequate dietary diversity. Dietary diversity was significantly associated with residential location areas (p = 0.016), education level (p = 0.045), and family size (p= 0.041). Results from logistic regression analysis showed that dietary diversity was associated with having residence in rural Mbulu district areas (OR = 0.357, p-value < 0.003), having a secondary education level (OR = 3.958, p-value < 0.007) and no formal education (OR = 1.122, p-value < 0.02), having average monthly household Income (OR = 4.934, p-value < 0.000) and husband (male partner) support (OR = 3.713, p-value < 0.001). Additionally, results from Chi-square test an average monthly household income (p = 0.001), food budget expenditure (p = 0.018), and food expenditure ability (p =0.000) were significantly associated with dietary diversity among pregnant women (p < 0.05). Conclusion: In this study, forty-three pregnant women had inadequate dietary diversity due to lower-income household level restricting access to diverse and nutritious food groups. Therefore, policymakers should encourage more dietary diversity and general maternal health requirements to raise sustainable household income-generating activities and improve nutrition education programs.
Published in | Journal of Food and Nutrition Sciences (Volume 13, Issue 2) |
DOI | 10.11648/j.jfns.20251302.11 |
Page(s) | 48-63 |
Creative Commons |
This is an Open Access article, 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 or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Dietary Diversity, Household Monthly Income, Pregnant Women, Maternal Health, Mbulu District Tanzania
Variable Category | Number of Respondents | Percent | |
---|---|---|---|
Location | Rural | 190 | 49.5 |
Urban | 194 | 50.5 | |
Age (years) | 18-24 | 158 | 41.1 |
25-40 | 219 | 57.0 | |
≥41 | 7 | 1.8 | |
Marital Status | Cohabiting | 23 | 6.0 |
Divorced | 2 | 0.5 | |
Married | 304 | 79.2 | |
Single | 53 | 13.8 | |
Widowed | 2 | 0.5 | |
Education Level | No formal education | 11 | 2.9 |
Primary school | 246 | 64.1 | |
Secondary school | 109 | 28.4 | |
College or University | 18 | 4.7 | |
Occupation | Employed for wage | 10 | 2.6 |
Farmer | 323 | 84.1 | |
Pastoralist | 2 | 0.5 | |
Self-employed | 42 | 10.9 | |
Unemployed | 7 | 1.8 | |
Family Size (members) | <2 | 57 | 14.8 |
3-5 | 186 | 48.4 | |
>5 | 141 | 36.7 | |
Monthly Household Income (TZS) | < 250,000/= | 152 | 39.6 |
250,000-500,000/= | 158 | 41.1 | |
>500,000/= | 74 | 19.3 | |
Religion | Christian | 374 | 97.4 |
Muslim | 10 | 2.6 | |
Husband (Partner) Supports | No | 50 | 13.0 |
Yes | 334 | 87.0 |
Variable Category | Dietary Diversity Status | Total (N= 384) | χ2 | Df | p-value | ||
---|---|---|---|---|---|---|---|
Adequate =n (%) | Inadequate =n (%) | ||||||
Location | Rural | 120 (63.2) | 70 (36.8) | 190 | 5.761 | 1 | 0.016 |
Urban | 99 (51) | 95 (49) | 194 | ||||
Age (years) | 18-24 | 76 (48.1) | 82 (51.9) | 158 | 3.355 | 2 | 0.19 |
25-40 | 123 (56.2) | 96 (43.8) | 219 | ||||
≥41 | 5 (71.4) | 2 (28.6) | 7 | ||||
Marital Status Education Level | Cohabiting | 12 (52.2) | 11 (47.8) | 23 | |||
Divorced | 1 (50) | 1 (50) | 2 | 0.081 | 4 | 0.90 | |
Married | 161 (53) | 143 (47) | 304 | ||||
Single | 29 (54.7) | 24 (45.3) | 53 | ||||
Widowed | 1 (50) | 1 (50) | 2 | ||||
No formal education | 4 (40) | 6 (60) | 10 | 7.738 | 4 | 0.045 | |
Primary school | 125 (49.4) | 128 (50.6) | 253 | ||||
Secondary school | 61 (59.2) | 42 (40.8) | 103 | ||||
College or University | 14 (77.8) | 4 (22.2) | 18 | ||||
Occupation | Employed | 7 (70) | 3 (30) | 10 | |||
Farmer | 163 (50.5) | 160 (49.5) | 323 | ||||
Pastoralist | 2 (100) | 0 (0.00) | 2 | 6.965 | 4 | 0.138 | |
Self-employed | 28 (66.7) | 14 (33.3) | 42 | ||||
Unemployed | 4 (57.1) | 3 (42.9) | 7 | ||||
Family Size | <2 | 39 (68.4) | 18 (31.6) | 57 | |||
3-5 | 95 (51.1) | 91 (48.9) | 186 | 6.355 | 2 | 0.041 | |
>5 | 70 (49.6) | 71 (50.4) | 141 | ||||
Religion | Christian | 198 (52.9) | 176 (47.1) | 374 | 0.195 | 1 | 0.658 |
Muslim | 6 (60) | 4 (40) | 10 | ||||
Husband (Partner) Support | No | 25 (50) | 25 (50) | 50 | 225 | 1 | 0.635 |
Yes | 179 (53.6) | 155 (46.4) | 334 |
Type of food group | Category | Frequency (N) | Percentage (%) | Location | p-value | |
---|---|---|---|---|---|---|
Rural =n (%) | Urban =n (%) | |||||
Dietary Diversity Status | Adequate | 219 | 57.0 | 120 (54.8) | 99 (45.2) | 0.016 |
Inadequate | 165 | 43.0 | 70 (42.4) | 95 (57.6) | ||
Pulses (beans, peas and lentils) | No | 153 | 39.8 | 80 (53.3) | 73 (47.7) | 0.370 |
Yes | 231 | 60.2 | 110 (47.6) | 121 (52.4) | ||
Nuts and seeds | No | 215 | 56 | 76 (35.3) | 139 (64.7) | 0.000 |
Yes | 169 | 44 | 114 (67.5) | 55 (32.5) | ||
Milk and milk products | No | 111 | 28.9 | 41 (36.9) | 70 (63.1) | 0.002 |
Yes | 273 | 71.1 | 149 (54.6) | 124 (45.4) | ||
Meat, poultry, and fish | No | 246 | 64.1 | 113 (45.9) | 133 (54.1) | 0.064 |
Yes | 138 | 35.9 | 77 (55.8) | 61 (44.2) | ||
Eggs | No | 297 | 77.3 | 131 (44.1) | 166 (55.9) | 0.000 |
Yes | 87 | 22.7 | 59 (67.8) | 28 (32.2) | ||
Dark green leafy vegetables | No | 82 | 21.4 | 39 (47.6) | 43 (52.4) | 0.695 |
Yes | 302 | 78.6 | 151 (50) | 151 (50) | ||
Other fruits | No | 134 | 34.9 | 75 (56) | 59 (44) | 0.063 |
Yes | 250 | 65.1 | 115 (46) | 135 (54) | ||
Other vegetables | No | 181 | 47.1 | 71 (39.2) | 110 (60.8) | 0.000 |
Yes | 203 | 52.9 | 119 (58.6) | 84 (41.4) | ||
Other vitamin A-rich fruits and vegetables | No | 205 | 53.4 | 124 (60.5) | 81 (39.5) | 0.000 |
Yes | 179 | 46.6 | 66 (36.9) | 113 (63.1) | ||
Grains, white roots and tubers, and plantain | No | 10 | 3 | 4 (40) | 6 (60) | 0.544 |
Yes | 374 | 97.4 | 186 (49.7) | 188 (50.3) |
Household Income Status | Category | Location (Residents) | p-value | Dietary Diversity Status | Total | p-value | ||
---|---|---|---|---|---|---|---|---|
Rural =n (%) | Urban = n (%) | Adequate =n (%) | Inadequate =n (%) | |||||
The main source of household income | Government employment | (20) | (80) | 0.022 | (70) | (30) | 10 | 0.138 |
Agriculture | 171 (52.6) | 154 (47.4) | 16 (50.8) | 16 (49.2) | 325 | |||
Business | 15 (35.7) | 2 (64.3) | 2 (66.7) | 1 (33.3) | 42 | |||
Others | 2 (28.6) | 5 (71.4) | (57.1) | (42.9) | 7 | |||
Average monthly household income (TZS) | 250,000-500,000/= | 67 (42.4) | 9 (57.6) | 0.039 | 7 (50.0) | 7 (50) | 158 | 0.001 |
<250,000/= | 79 (52) | 7 (48) | 10 (68.4) | 4 (31.6) | 152 | |||
>500,000/= | 4 (59.5) | 3 (40.5) | 3 (48.6) | 3 (51.4) | 74 | |||
Household Food expenditure per day | <5000 | 7 (38.9) | 11 (61.1) | 0.000 | 10 (56.8) | 8 (43.2) | 185 | 0.169 |
>5000 | 11 (59.3) | 8 (40.7) | 9 (49.7) | 10 (50.3) | 199 | |||
Monthly income percentage for food expenses | 10-25% | (14) | 3 (86) | 0.000 | 2 (62.8) | 1 (37.2) | 43 | 0.352 |
26-40% | (15) | 3 (85) | 2 (57.5) | 1 (42.5) | 40 | |||
unknown | (19.2) | 2 (80.8) | 1 (53.8) | 1 (46.2) | 26 | |||
< 10% | (20) | (80) | (20) | (80) | 5 | |||
> 50% | 17 (63.7) | 9 (36.3) | 13 (51.5) | 13 (48.5) | 270 | |||
Household Food budget expenses | No | 15 (55.1) | 124 (44.9) | 0.000 | 15 (56.9) | 11 (43.1) | 276 | 0.018 |
Yes | 3 (35.2) | 7 (64.8) | 4 (43.5) | 6 (56.5) | 108 | |||
Household Income Experience | No | 105 (44.7) | 130 (55.3) | 0.018 | 14 (59.6) | 9 (40.4) | 235 | 0.001 |
Yes | 85 (57) | 6 (43) | 6 (43) | 8 (57) | 149 | |||
Food budget adjustments | No | 12 (49.4) | 13 (50.6) | 0.974 | 15 (58.3) | 10 (41.7) | 259 | 0.003 |
Yes | 62 (49.6) | 63 (50.4) | 5 (42.4) | 7 (57.6) | 125 | |||
Food expenditure ability to purchase | No | 104 (46.6) | 119 (53.4) | 0.19 | 13 (62.3) | 8 (37.7) | 223 | 0.000 |
Yes | 86 (53.4) | 75 (46.6) | 6 (40.4) | 9 (59.6) | 161 | |||
The government assistance (IFAS) | No | 47 (85.5) | 8 (14.5) | 0.000 | 3 (67.3) | 1 (32.7) | 55 | 0.023 |
Yes | 143 (43.5) | 186 (56.5) | 16 (50.8) | 16 (49.2) | 329 |
Variable | Odds ratio | df | Sig. | 95% C.I.for Odds | |
---|---|---|---|---|---|
Lower | Upper | ||||
Location (District -Rural) | 0.357 | 1 | 0.003 | 0.182 | 0.701 |
Age group | 2 | 0.865 | |||
18-24 years | 0.775 | 1 | 0.812 | 0.094 | 6.377 |
25-40 years | 0.898 | 1 | 0.919 | 0.113 | 7.105 |
Marital status | 4 | 0.107 | |||
Cohabiting | 0.87 | 1 | 0.878 | 0.147 | 5.156 |
Divorced | 4.162 | 1 | 0.435 | 0.116 | 149.165 |
Married | 1.132 | 1 | 0.873 | 0.247 | 5.183 |
Single | 2.668 | 1 | 0.221 | 0.555 | 12.833 |
Education Level for Pregnant Women | 4 | 0.000 | |||
No formal education | 0.122 | 1 | 0.021 | 0.02 | 0.726 |
Primary school | 0.918 | 1 | 0.866 | 0.343 | 2.458 |
Secondary school | 3.958 | 1 | 0.007 | 1.457 | 10.752 |
High school/Certificate/Diploma | 0.75 | 1 | 0.529 | 0.307 | 1.835 |
Occupation | 4 | 0.000 | |||
Employed | 1.25 | 1 | 0.840 | 0.144 | 10.859 |
Farmer | 0.67 | 1 | 0.694 | 0.091 | 4.938 |
Pastoralist | 3.599 | 1 | 0.272 | 0.367 | 35.329 |
Self-employed | 3.823 | 1 | 0.202 | 0.487 | 29.997 |
Average monthly household income | 2 | 0.000 | |||
< 250,000/= | 4.934 | 1 | 0.000 | 2.283 | 10.662 |
250,000/= - 500,000/= | 1.009 | 1 | 0.978 | 0.521 | 1.957 |
Parity | 2 | 0.077 | |||
< 2 | 1.364 | 1 | 0.535 | 0.512 | 3.637 |
3-5 parities | 0.584 | 1 | 0.207 | 0.253 | 1.347 |
Family Size | 2 | 0.216 | |||
< 2 | 0.581 | 1 | 0.081 | 0.316 | 1.069 |
3-5 members | 0.674 | 1 | 0.373 | 0.283 | 1.606 |
Religion (Christian) | 0.637 | 1 | 0.246 | 0.298 | 1.364 |
Husband (Male Partner) Support (Yes) | 3.713 | 1 | 0.001 | 1.722 | 8.006 |
Constant | 0.534 | 1 | 0.709 |
LMICs | Low and Middle-Income Countries |
WHO | World Health Organization |
FAO | Food and Agriculture Organization |
MDD-W | Minimum Dietary Diversity for Women |
NBS | National Bureau Statistics |
MoHCDGEC | Ministry of Health, Community Development, Gender, Elderly and Children |
TDHS-MIS | Tanzania Demographic Health Survey and Malaria Indicator Survey |
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APA Style
Hudson, P., Muhimbula, H., Mosha, T. (2025). Dietary Diversity and Household Income Status Among Pregnant Women in Mbulu District, Tanzania. Journal of Food and Nutrition Sciences, 13(2), 48-63. https://doi.org/10.11648/j.jfns.20251302.11
ACS Style
Hudson, P.; Muhimbula, H.; Mosha, T. Dietary Diversity and Household Income Status Among Pregnant Women in Mbulu District, Tanzania. J. Food Nutr. Sci. 2025, 13(2), 48-63. doi: 10.11648/j.jfns.20251302.11
@article{10.11648/j.jfns.20251302.11, author = {Paul Hudson and Happiness Muhimbula and Theobald Mosha}, title = {Dietary Diversity and Household Income Status Among Pregnant Women in Mbulu District, Tanzania }, journal = {Journal of Food and Nutrition Sciences}, volume = {13}, number = {2}, pages = {48-63}, doi = {10.11648/j.jfns.20251302.11}, url = {https://doi.org/10.11648/j.jfns.20251302.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfns.20251302.11}, abstract = {Introduction: Dietary diversity ensures that pregnant women consume the necessary range of nutrients from various food groups for the best possible outcomes for maternal health. Maternal health and fetal development are weakened by nutrient deficits caused by inadequate dietary diversity, typified by income constraints, and frequently restricted consumption of varied food categories. This study aimed to assess pregnant women's dietary diversity and household income status in Mbulu district, Tanzania. Methods: A cross-sectional study design was used among 384 pregnant women. Pregnant women were selected by using a systematic random sampling method. Eight health facilities were selected using a purposive sampling method. Face-to-face interviews were conducted by trained enumerators using a structured questionnaire that was divided into three sections to collect information on (i) socio-demographic characteristics, (ii) Dietary diversity, and (iii) Household income status. The Pearson Chi-square test and Logistic regression analysis were used to compare and test the association of dietary diversity and household income status to pregnant women's characteristics. Dietary diversity was evaluated using the Minimum Dietary Diversity Score (MDDS), while household income status was measured through income and expenditure data. Results: The findings revealed that about 43% of pregnant women had inadequate dietary diversity, while 57% had adequate dietary diversity. Dietary diversity was significantly associated with residential location areas (p = 0.016), education level (p = 0.045), and family size (p= 0.041). Results from logistic regression analysis showed that dietary diversity was associated with having residence in rural Mbulu district areas (OR = 0.357, p-value p =0.000) were significantly associated with dietary diversity among pregnant women (p Conclusion: In this study, forty-three pregnant women had inadequate dietary diversity due to lower-income household level restricting access to diverse and nutritious food groups. Therefore, policymakers should encourage more dietary diversity and general maternal health requirements to raise sustainable household income-generating activities and improve nutrition education programs. }, year = {2025} }
TY - JOUR T1 - Dietary Diversity and Household Income Status Among Pregnant Women in Mbulu District, Tanzania AU - Paul Hudson AU - Happiness Muhimbula AU - Theobald Mosha Y1 - 2025/03/07 PY - 2025 N1 - https://doi.org/10.11648/j.jfns.20251302.11 DO - 10.11648/j.jfns.20251302.11 T2 - Journal of Food and Nutrition Sciences JF - Journal of Food and Nutrition Sciences JO - Journal of Food and Nutrition Sciences SP - 48 EP - 63 PB - Science Publishing Group SN - 2330-7293 UR - https://doi.org/10.11648/j.jfns.20251302.11 AB - Introduction: Dietary diversity ensures that pregnant women consume the necessary range of nutrients from various food groups for the best possible outcomes for maternal health. Maternal health and fetal development are weakened by nutrient deficits caused by inadequate dietary diversity, typified by income constraints, and frequently restricted consumption of varied food categories. This study aimed to assess pregnant women's dietary diversity and household income status in Mbulu district, Tanzania. Methods: A cross-sectional study design was used among 384 pregnant women. Pregnant women were selected by using a systematic random sampling method. Eight health facilities were selected using a purposive sampling method. Face-to-face interviews were conducted by trained enumerators using a structured questionnaire that was divided into three sections to collect information on (i) socio-demographic characteristics, (ii) Dietary diversity, and (iii) Household income status. The Pearson Chi-square test and Logistic regression analysis were used to compare and test the association of dietary diversity and household income status to pregnant women's characteristics. Dietary diversity was evaluated using the Minimum Dietary Diversity Score (MDDS), while household income status was measured through income and expenditure data. Results: The findings revealed that about 43% of pregnant women had inadequate dietary diversity, while 57% had adequate dietary diversity. Dietary diversity was significantly associated with residential location areas (p = 0.016), education level (p = 0.045), and family size (p= 0.041). Results from logistic regression analysis showed that dietary diversity was associated with having residence in rural Mbulu district areas (OR = 0.357, p-value p =0.000) were significantly associated with dietary diversity among pregnant women (p Conclusion: In this study, forty-three pregnant women had inadequate dietary diversity due to lower-income household level restricting access to diverse and nutritious food groups. Therefore, policymakers should encourage more dietary diversity and general maternal health requirements to raise sustainable household income-generating activities and improve nutrition education programs. VL - 13 IS - 2 ER -