Maternal & Child Health Analysis
Patricia Mugabe
June 2026
1. Introduction
This report presents a statistical analysis of maternal and child health data collected from 200 mothers who delivered at a regional referral hospital over a six-month period. The analysis addresses four research questions relating to demographic characteristics, antenatal care attendance, low birth weight, and neonatal ICU admission.
2. Methods
Data were collected from 200 mothers delivering at a regional referral hospital. Variables included maternal age, education level, number of antenatal care (ANC) visits, gestational age, birth weight, delivery mode, haemoglobin level, maternal outcome, and neonatal ICU admission status.
Data cleaning involved replacement of implausible values with NA rather than row deletion, to preserve sample size. Maternal ages below 15 or above 50 years and gestational ages exceeding 42 weeks were replaced with NA. Two derived variables were created: low birth weight (birth weight < 2.5kg) and maternal age group (adolescent <18 years, optimal 18-34 years, advanced >34 years).
Statistical analyses were performed in R. Descriptive statistics were reported as mean (SD) for continuous variables and frequency (percentage) for categorical variables. Spearman correlation was used to assess the association between ANC visits and birth weight following failure of normality assumption for ANC visits on Shapiro-Wilk testing. Fisher’s Exact Test was used to assess the association between maternal age group and low birth weight due to small cell counts. Logistic regression was used to identify predictors of NICU admission.
3. Results
3.1 Population Characteristics
| Characteristic | N = 2001 |
|---|---|
| age | 26.1 (4.5) |
| anc_visits | 4.22 (1.91) |
| birth_weight_kg | 3.02 (0.56) |
| gestational_age | 37.61 (2.38) |
| haemoglobin | 11.33 (1.46) |
| education | |
| None | 19 (9.5%) |
| Primary | 60 (30%) |
| Secondary | 88 (44%) |
| Tertiary | 33 (17%) |
| delivery_mode | |
| Assisted | 24 (12%) |
| C-Section | 51 (26%) |
| SVD | 125 (63%) |
| maternal_outcome | |
| Fair | 54 (27%) |
| Good | 122 (61%) |
| Poor | 24 (12%) |
| nicu_admission | 38 (19%) |
| low_birth_weight | 35 (18%) |
| age_group | |
| Adolescent(<18) | 5 (2.6%) |
| Optimal (18-34) | 185 (94%) |
| Advanced (>34) | 6 (3.1%) |
| 1 Mean (SD); n (%) | |
3.1b Data Visualizations
3.2 RQ2 — ANC Visits and Birth Weight
There was no statistically significant association between ANC visits and birth weight (Spearman rho = -0.04, p = 0.580). The scatterplot confirmed the absence of a meaningful trend, suggesting birth weight is influenced by multiple factors beyond ANC attendance alone.
3.3 RQ3 — Maternal Age Group and Low Birth Weight
Chi-square analysis could not be performed due to small cell counts in the adolescent (n=5) and advanced maternal age (n=6) subgroups. Following collapse of these groups into a single non-optimal category, Fisher’s Exact Test revealed no statistically significant association between maternal age group and low birth weight (OR = 2.10, 95% CI: 0.27–95.15, p = 0.692). The wide confidence interval reflects limited statistical power due to the small non-optimal subgroup (n=10).
3.4 RQ4 — Predictors of NICU Admission
Logistic regression identified ANC visits as the only statistically significant predictor of NICU admission (OR = 1.29, 95% CI: 1.04–1.60, p = 0.021). This finding is likely attributable to indication bias, whereby mothers with higher-risk pregnancies attended more ANC visits due to active clinical follow-up. Gestational age and birth weight did not reach statistical significance, potentially due to limited sample size and missing data.
4. Discussion
The study population had a mean age of 26.1 years with a mean of 4.2 ANC visits, borderline against the WHO minimum recommendation. The low birth weight rate of 18.2% exceeded Uganda’s national estimate of approximately 10-12%, consistent with the referral hospital context where higher-risk pregnancies are concentrated. Maternal anaemia was prevalent, with a mean haemoglobin of 11.3 g/dL.
No significant associations were identified between ANC visits and birth weight, or between maternal age group and low birth weight, likely reflecting the influence of confounding variables and limited sample size in subgroups. The logistic regression findings for NICU admission should be interpreted with caution given the indication bias identified.
5. Limitations
- Sample size of 200 limits statistical power, particularly for subgroup analyses
- Missing data in haemoglobin (n=15), ANC visits (n=10), and birth weight (n=8)
- Simulated dataset limits generalizability of findings
- ANC attendance was used as a proxy for antenatal care quality without capturing compliance or clinical content of visits
6. Conclusion
This analysis described the demographic and clinical characteristics of a referral hospital maternal cohort and examined factors associated with low birth weight and NICU admission. Findings highlight the importance of adequate sample sizes for subgroup analyses and the need to account for confounding in observational maternal health data.
7. Software
All analyses were performed using R Statistical Software (version 4.x). Packages used included tidyverse for data manipulation and visualization, gtsummary for table generation, and flextable for document export.
## R version 4.5.3 (2026-03-11)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Africa/Kampala
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] flextable_0.9.12 gtsummary_2.5.1 lubridate_1.9.5 forcats_1.0.1
## [5] stringr_1.6.0 dplyr_1.2.1 purrr_1.2.2 readr_2.2.0
## [9] tidyr_1.3.2 tibble_3.3.1 ggplot2_4.0.3 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.57 bslib_0.11.0
## [4] lattice_0.22-9 tzdb_0.5.0 vctrs_0.7.3
## [7] tools_4.5.3 generics_0.1.4 pkgconfig_2.0.3
## [10] Matrix_1.7-4 data.table_1.18.4 RColorBrewer_1.1-3
## [13] S7_0.2.2 uuid_1.2-2 gt_1.3.0
## [16] lifecycle_1.0.5 compiler_4.5.3 farver_2.1.2
## [19] textshaping_1.0.5 litedown_0.9 fontquiver_0.2.1
## [22] fontLiberation_0.1.0 htmltools_0.5.9 sass_0.4.10
## [25] yaml_2.3.12 pillar_1.11.1 jquerylib_0.1.4
## [28] openssl_2.4.1 cachem_1.1.0 nlme_3.1-168
## [31] fontBitstreamVera_0.1.1 commonmark_2.0.0 tidyselect_1.2.1
## [34] zip_2.3.3 digest_0.6.39 stringi_1.8.7
## [37] splines_4.5.3 labeling_0.4.3 fastmap_1.2.0
## [40] grid_4.5.3 cli_3.6.6 magrittr_2.0.5
## [43] cards_0.8.0 withr_3.0.2 gdtools_0.5.1
## [46] scales_1.4.0 timechange_0.4.0 rmarkdown_2.31
## [49] officer_0.7.5 otel_0.2.0 askpass_1.2.1
## [52] ragg_1.5.2 hms_1.1.4 evaluate_1.0.5
## [55] knitr_1.51 mgcv_1.9-4 markdown_2.0
## [58] rlang_1.2.0 Rcpp_1.1.1-1.1 glue_1.8.1
## [61] xml2_1.5.2 rstudioapi_0.18.0 jsonlite_2.0.0
## [64] R6_2.6.1 systemfonts_1.3.2 fs_2.1.0