Background: Prenatal anaemia causes serious consequences for both mother and foetus, and dietary factors are suggested to be associated with anaemia. However, research in pregnant women living in rural areas is limited. We aim to assess the contribution of dietary diversity to the magnitude of prenatal anaemia in rural China and identify the interactions between dietary diversity and several sociodemographic and maternal characteristics in relation to anaemia. Methods: A multi-stage random cluster sampling method was used to select pregnant women in rural western China. The Woman’s Dietary Diversity Score was created to measure dietary diversity, which was recoded into terciles. Multinomial logistic regression models were used to assess the associations between dietary diversity score terciles and the magnitude of prenatal anaemia. Multiplicative interactions were tested by adding the product term of dietary diversity and several sociodemographic and maternal characteristics into the regression models. Results: Out of 969 participants, 54.3% were measured as anaemic, with 28.6% mildly anaemic and 25.7% moderately to severely anaemic. There was an absence of agreement between self-reported and measured anaemia status (κ = 0.28, 95% CI [0.22–0.34]). Participants in the highest dietary diversity score tercile had lower odds of being moderately to severely anaemic after adjusting for potential confounders (RRR = 0.65, 95% CI [0.44, 0.98]). In participants with moderate to severe anaemia, significant interactions were found between dietary diversity score terciles, age, and parity (p for interaction < 0.05). Conclusions: The prevalence of prenatal anaemia in rural China remains high, and pregnant women living in these areas are insufficiently aware of their anaemia status. Improving dietary diversity is needed to manage prenatal anaemia in rural areas.

Infants born small for gestational age (SGA) remains a significant global public health concern, with potential interconnections among maternal diet, pre-pregnancy BMI, gestational weight gain (GWG), and SGA. This prospective study investigated the association between dietary diversity (DD) during pregnancy and the risk of SGA, as well as the synergistic effect of DD with pre-pregnancy BMI and GWG on SGA. Maternal dietary intake during pregnancy was assessed using 24 h dietary recalls, and dietary diversity scores (DDS) were calculated based on the FAO’s Minimum Dietary Diversity for Women index. Infant information was followed up. The Poisson regression model was employed to determine the association between maternal DD and SGA. Interactions between DD and pre-pregnancy BMI or GWG were evaluated under additive and multiplicative models. Among the 560 singleton live births, 62 (11.07%) were classified as SGA. After adjusting for potential confounders, the DDS exhibited a protective effect against SGA (aRR: 0.76; 95% CI: 0.62–0.95). DD modified the association between being underweight prior to pregnancy and SGA on the additive scale (interaction contrast ratio = 7.39; 95% CI: 5.84, 8.94). These findings suggest that improving dietary diversity during pregnancy, particularly among women with a low pre-pregnancy BMI, may be a feasible strategy to reduce the risk of SGA newborns.

A method of automatic recognition and extraction of echo image of underground pipeline is provided. In this method, the edges of hyperbolic features are extracted by adaptive thresholding method, and the least squares fitting method is applied to the lower edge feature points to obtain the hyperbolic curve fitting formula. Finally, by calculating hyperbolic wave velocity and fitting hyperbolic formulas, we get the information of the location and radius of underground pipeline. Based on gprMax software with finite difference time-domain method, the underground pipelines with different radii are simulated to obtain the results of forward image. The proposed method is used to obtain the location and radius information of underground pipeline. The results show that the proposed method has high precision for the horizontal position and depth measurement of underground pipelines with different radii. And the method has high accuracy and robustness.

Achieving minimum dietary diversity (MDD), a crucial indicator of infant and young child diet quality, remains a challenge in rural China, especially for infants aged 6-11 months. This study examined the rate of MDD attainment in rural China, identified its determinants using the Capability, Opportunity, Motivation, and Behavior (COM-B) model and Bayesian network analysis, and estimated the potential impact of improving each modifiable determinant. A multi-stage sampling design selected 1328 caregivers of infants aged 6-11 months across 77 rural townships in China. Data were collected through a cross-sectional survey via in-person household interviews. Bayesian network analysis identified key factors influencing MDD attainment and their interrelationships, while Bayesian inference estimated MDD attainment probabilities. Results showed that only 22.2 % of the sample infants attained MDD. Bayesian network analysis revealed that caregiver knowledge (a proxy of capability), self-efficacy and habits (proxies of motivation), and infant age directly influenced MDD attainment. Social support (a proxy of opportunity) indirectly promoted MDD attainment by boosting self-efficacy and habit. Notably, simultaneous improvements in knowledge, self-efficacy, and habit could increase MDD attainment by 17.6 %, underscoring the potential effectiveness of interventions focused on enhancing caregiver capability and motivation. The critically low MDD attainment rate among rural Chinese infants highlights the urgent need for targeted interventions. Strategies should prioritize enhancing caregiver feeding knowledge, self-efficacy, and habit formation to improve infant dietary diversity. Addressing these key factors could substantially boost MDD attainment in rural China.