How to Research Unexplained Fetal Deaths and Their Possible Links to Medical Practice During COVID
If you are trying to investigate unexplained fetal deaths and any possible connections to medical practices during the COVID-19 pandemic, you need to know where the data lives and what its gaps are. This is not a simple lookup. The records are scattered across multiple government databases, state health departments, and peer-reviewed journals that use different definitions and reporting timelines. I spent about eight months pulling together a dataset like this for a project, and the hardest part was not finding information. It was dealing with incomplete records and inconsistent coding. Here is how the process actually works.
USA In Crisis Mode Unraveling The Mystery Of Mysterious Fetal Deaths Medical Corruption And Covid19 Pandemic
The Data Sources You Need
Start with the CDC Wonder database. It has pregnancy outcomes data going back decades, broken down by state, year, and gestational age. The fetal death tables are the most relevant section. They include gestational age at death, maternal age, race, and whether the mother had any reported conditions. This is public and free. The VAERS database tracks adverse events after vaccination. During COVID, vaccination rates among pregnant people increased significantly, and VAERS received a large volume of reports. But VAERS has a major limitation that most people miss. It is a passive reporting system. Anyone can submit a report, and a report does not mean the vaccine caused the outcome. The signal-to-noise ratio is extremely low. I have seen people treat VAERS data as proof of causation. It is not. It is a starting point for hypothesis generation. CDC Pregnancy Mortality Surveillance System data tracks maternal deaths, which sometimes overlap with fetal loss cases. The case definitions here are stricter. A death is confirmed through medical records and autopsy reports when available. The granularity is better but the timeliness is worse. Reports often come out two to three years after the events they describe.
State-level vital records are another source. Some states release detailed fetal death certificates that include underlying causes, chronic conditions, and prenatal care information. Others redact significant detail. You will need to request these through state health departments, and processing times vary wildly. In my experience, some states respond within two weeks. Others take six months or do not respond at all.
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Building a Comparable Dataset
The core challenge is establishing a baseline. You need to compare fetal death rates before, during, and after the peak COVID period, while controlling for confounding variables like maternal age, pre-existing conditions, and access to prenatal care. I downloaded CDC Wonder data from 2018 through 2023. I pulled fetal death counts by trimester, maternal age group, and state. Then I cross-referenced with COVID case and hospitalization rates at the county level from CDC NEDSS. The goal was to see whether counties with higher COVID severity showed disproportionate fetal death increases after accounting for demographics. Here is the practical problem I ran into. County-level fetal death data is unreliable because many states do not report fetal deaths at the county level with enough precision. Gestational age is sometimes recorded imprecisely, and some states group early pregnancy losses differently than others. I ended up working at the state level instead, which reduced resolution but improved data quality. If you can get county-level data from a specific state bureau of vital statistics directly, that is worth pursuing. The online portals are often better than the CDC aggregate tables.
Common Pitfalls
The biggest mistake I see is correlation treated as causation. Fetal death rates fluctuate naturally. They were trending upward in some categories before COVID. Any analysis needs to account for secular trends. A simple before-and-after comparison is almost always misleading. Another issue is survivorship bias in the literature. Studies that find no association get published less often. When you search the academic literature, you will find more papers reporting links between COVID and adverse pregnancy outcomes than papers reporting no link. That does not mean the link is real. It means the publication ecosystem favors novel findings. Vaccine safety studies in pregnancy are also tricky. Pregnant people were largely excluded from initial COVID vaccine trials. Post-licensure observational studies exist, but they vary in methodology. Some adjust well for confounders. Some do not. I recommend looking at the CDC's v-safe pregnancy registry and the March of Dimes data, but treat them as supplementary, not definitive.
What the Evidence Actually Shows
The largest and most rigorous studies to date, including work from the CDC and organizations like the American College of Obstetricians and Gynecologists, show that pregnant people who contract COVID-19 face higher risks of preterm birth, preeclampsia, and stillbirth compared to uninfected pregnant people. The absolute risk increase is real but modest for most individuals. The data on unvaccinated pregnant people with COVID is clearer on harm than the data on vaccines and fetal loss, which remains inconclusive due to study design limitations. There is no evidence of a coordinated cover-up. There is evidence of messy, imperfect systems. Hospitals were overwhelmed. Prenatal care was disrupted. Autopsy rates for fetal deaths dropped in some areas because of infection control restrictions. These factors make it harder to determine causes of death during that period, which creates uncertainty that gets interpreted in different ways depending on what people already believe.
A Practical Approach
If you want to do this work yourself, start with CDC Wonder. Download the fetal mortality tables for your state of interest. Use Excel or a free tool like R to calculate rates per 1,000 live births plus fetal deaths, stratified by gestational age. Then overlay COVID hospitalization rates from CDC data. Look for deviations from the pre-2020 trend line. If you find something, check whether it holds up when you control for maternal age and race. If it does, search PubMed for peer-reviewed studies on that specific association. If it does not, note that and move on. This process takes time. A careful analysis of one state over a five-year window took me roughly forty hours spread across three weeks. Most of that time was spent cleaning data, not analyzing it. But the effort produces something you can actually stand behind. The uncomfortable truth is that we do not have a complete answer yet. The data is there. It is just fragmented, inconsistent, and slow to update. Anyone who tells you otherwise is selling something.
