How often would 983 births produce 453 girls? With a male ratio of 51.47%, I see it in 0.80% of 100,000 samples. Low, but not extraordinary.

children.py

#!/usr/bin/env python3

import numpy as np
import matplotlib.pyplot as plt
from numpy.random import default_rng
rng = default_rng()

n = int(100_000)
N = int(1_000_000_000)
pop = int(983)  # children born in Kiev
act = int(453)  # girls born in Kiev
ratio = 0.5147  # ref: <https://pubmed.ncbi.nlm.nih.gov/31492547/>

vals = rng.uniform(low=0.0, high=1.0, size=N)
kids = np.where(vals < ratio, 0, 1)  # zero if male, 1 if female

fem = np.zeros(n)

for i in range(n):
    idx = rng.integers(0, N, size=pop)
    fem[i] = int(np.sum(kids[idx]))

real = np.sum(np.where(fem == act, 1, 0))  # 1 if it matches

print("{} female births were observed in {} of {} samples ({:.2f}%)".format(act, real, n, float(100.0 * real) / n))

plt.figure(figsize=(4,4))
plt.title("Bootstrap of Gender Ratio per {} Births".format(pop))
plt.xlabel("number of female children")
plt.ylabel("count for {} samples".format(n))
plt.hist(fem, bins=51)
plt.savefig("fem.png", dpi=400, bbox_inches="tight")

Example of output:

453 female births were observed in 769 of 100000 samples (0.77%)