Henry M. Blair
Does neighborhood deprivation predict urogynecologic access in Brooklyn? · Cross-sectional study · Maimonides Medical Center · Nov 2024 – Aug 2025
Cross-sectional study · Maimonides Medical Center · Nov 2024 – Aug 2025

Does neighborhood deprivation predict urogynecologic access in Brooklyn?

Colby Rondeau, MD · Luke Jenusaitis, MD · Elizabeth DeSellier · Rebecca Bergmann · Peace Deh · Michael Silver · Henry M. Blair · Allison Polland, MD

Deprivation indices are used as a proxy for the barriers a patient faces, and the barrier they most directly encode is travel: in most of the country, a poorer neighborhood means a longer, costlier trip to specialty care. New York has a subway. This study asks whether the index still tracks access where the transit system does not care how wealthy your block is.

120
women enrolled
30 vs 30 min
travel time, disadvantaged vs advantaged
$5.00 vs $2.95
travel cost
91.5% vs 77.2%
urge frequency (p = 0.033)
Status. Manuscript V3. Single-centre cross-sectional study, IRB approved, enrolling November 2024 – August 2025. I am co-corresponding author.

How the study was built

A survey study designed so that the null result would still say something — by measuring travel burden directly rather than inferring it from the index.

Who was included

English-speaking women aged 18–85 presenting for evaluation of overactive bladder — urgency, frequency, perceived incomplete emptying, hesitancy or incontinence. Women with known incomplete emptying, prior catheterization or a current catheter were excluded, as were women over 85, to preserve deidentification. Of 144 approached, 120 met criteria.

How deprivation was measured

Participants located their home on the Neighborhood Atlas — the University of Wisconsin's Area Deprivation Index map — and the ADI value for that block group was recorded, from 1 (least deprived) to 10 (most). Because the distribution in this population was so compressed, scores were dichotomized at advantaged (ADI ≤ 2) versus disadvantaged (ADI ≥ 3), following Gill et al. in a population with similarly limited variation.

What was measured instead of assumed

Travel time, travel cost, mode of transport (public versus private) and whether the patient was accompanied — asked directly, rather than modelled from address. Symptom severity was captured by presenting complaint and by self-reported inter-void interval as a proxy.

Powering and analysis

Sixty participants per group gives 80% power at α = 0.05 for an effect size of 0.65 on a two-sided Wilcoxon-Mann-Whitney test — that is, a 65% probability that a random observation from one group exceeds one from the other. Continuous variables were compared with Wilcoxon rank-sum and reported as medians with IQR; categorical variables with chi-square, or Fisher's exact where expected counts fell below five. Comparisons beyond the pre-specified travel-time and travel-cost outcomes were exploratory and not adjusted for multiple testing.

Why the powering matters for reading this study. The design was sized to detect a moderate difference. It did not find one — which is a more informative null than an underpowered study failing to reject.

Travel burden did not differ Table 2 · Figure 1

30 vs 30 min
median travel time
IQR 23.5–45 vs 15–40; p = 0.085
$5.00 vs $2.95
median travel cost
IQR 0.88–11.50 vs 0–7.00; p = 0.062
31.7% vs 20%
public transport use
p = 0.144
23.3% vs 31.7%
accompanied to the visit
p = 0.307

Neither primary outcome differed significantly. Both showed a trend in the expected direction — disadvantaged women travelling somewhat longer and paying somewhat more — and both distributions overlapped substantially, with only modest upward shifts.

<b>Figure 1.</b> Distributions of travel time and travel cost by ADI group. The overlap is the finding.
Figure 1. Distributions of travel time and travel cost by ADI group. The overlap is the finding.
A trend worth naming honestly. p = 0.062 on cost is not significance, and the paper does not claim it. But a recurring $2 difference each way, across a treatment course requiring repeated visits, can matter to a patient even when it does not reach α = 0.05.
<b>Figure 2.</b> Transport mode and visit accompaniment by ADI group.
Figure 2. Transport mode and visit accompaniment by ADI group.

One difference did appear Table 1

Disadvantaged women endorsed urinary urge frequency as a presenting complaint far more often — 91.5% versus 77.2% (p = 0.033). No other presenting complaint or comorbidity differed.

MeasureDisadvantagedAdvantagedp
Urinary urge frequency91.5%77.2%0.033
Urinary frequency (without urge)76.7%75.0%0.831
Inter-void interval, hours (median)2 (1–3)2 (1–3)0.753
Median age, years61.568.50.331
Travel time, minutes (median)30300.085
Travel cost, dollars (median)5.002.950.062
Public transportation31.7%20.0%0.144
Accompanied to visit23.3%31.7%0.307

Table 1–2, selected rows. Wilcoxon rank-sum for continuous variables, chi-square or Fisher's exact for categorical.

The distinction between the two frequency items is the interesting part: plain frequency was identical between groups, while urge frequency — the more bothersome symptom — was not. That points at a difference in burden or in delay before presenting, rather than a difference in how far anyone had to travel.

Everything else was null

Exploratory comparisons across the remaining survey items — leaking with urgency, leaking with Valsalva, urgency-related rushing, nocturia, prolapse symptoms, estimated and measured post-void residual, caffeine and non-caffeine intake, and telehealth experience and willingness — showed no significant differences by ADI group.

How to read a null result

Two explanations fit the data, and they have opposite implications. Either transit genuinely equalizes travel burden in New York, or the index failed to identify who is disadvantaged here.

The transit explanation

A subway system that costs the same from every neighborhood removes the mechanism through which deprivation usually produces travel burden. If that is what happened, the finding is encouraging: dense transit narrows one specialty-care disparity, and attention should move to the barriers it does not touch.

The instrument explanation

ADI weights home value heavily. In a city where a modest apartment costs what a comfortable house costs elsewhere, that weighting compresses and left-skews the deprivation distribution — which is exactly why scores had to be dichotomized at 2 rather than at the conventional midpoint. On this reading the null says the index is mis-calibrated for dense urban housing markets, and studies using it here may be measuring housing cost rather than deprivation.

The urge-frequency difference slightly favours the second reading: the groups did differ in something clinically real, so the dichotomization was not pure noise — it simply did not track travel.

Either way, telehealth looks acceptable

Willingness to use video visits was high in both groups (71.4% and 67.3%), which makes remote care a plausible way to reduce travel burden regardless of which explanation holds.

Limitations

The manuscript

Manuscript V3

With Colby Rondeau, MD and Allison Polland, MD. Behind a passphrase while the work is unpublished — open it here if I sent you one, or email me.

What comes next

A larger multisite study across cities with different transit density would separate the two explanations directly: if the null persists where transit is strong and disappears where it is not, the mechanism is transit rather than the instrument.

This page grows as the work does.