On January 21, 2020, the first case of Coronavirus disease 2019 in the 2 United States was reported in Washington State [1] . The first case was not reported in 3 New York state until March 1, 2020 [2] . By the time the World Health Organization 4 (WHO) declared a global pandemic on March 11, 2020 , there were 345 cases in New 5 York City (NYC), and this number skyrocketed to nearly 18,000 cases just two weeks 6 later [2, 3] . NYC rapidly became the epicenter of the pandemic in the United States, 7 with a transmission rate five times higher than the rest of the country, and over a third 8 of all confirmed national cases by early April [4] . 9 During a pandemic, there is likely to be large variation in both disease transmission 10 and disease testing between regions [5] . These two factors cause large variation in 11 disease reporting between different areas [6] . This is particularly true in the early stages 12 of the outbreak, before disease testing has become widespread and standardized. 13 Contemporary and historical studies on previous pandemics, including H1N1 14 pandemics in 1918 and 2009, suggest that socioeconomic factors on a national level can 15 affect detection rates and medical outcomes [7] [8] [9] . Thus, socioeconomic factors such as 16 young or old populations, race, affluence, inequality, poverty, unemployment, insurance, 17 or access to healthcare may account for differences in reported cases of COVID-19 18 between neighborhoods in NYC. 19 The aim of this ecological study was to identify potential neighborhood-level 20 socioeconomic determinants of the number of detected COVID-19 cases and explain 21 between-neighborhood variation during the early, exponential growth stage of the 22 pandemic in NYC: from the first detected case in March 1 until April 5, 2020. 23 Materials and methods 24 Data collection 25 Data on positive COVID-19 cases were collected from NYC Department of Health and 26 Mental Hygiene (DOHMH) Incident Command System for COVID-19 Response 27 (Surveillance and Epidemiology Branch in collaboration with Public Information Office 28 Branch) [2] . Since the NYC DOHMH was discouraging people with mild to moderate 29 symptoms from being tested during the time period covered, the data primarily 30 represents people with more severe illness. Since at the time of writing the pandemic is 31 still ongoing, data were taken at a snapshot on April 5, 2020. This date was chosen to 32 cover the first month of the pandemic in NYC, since understanding early etiology of the 33 pandemic and local influences is important in helping to inform future management [10] . 34 Data were a cumulative count up to and including April 5, 2020. On this date, NYC 35 had a cumulative total of 64,955 cases [11] , including deaths and hospitalizations. 36 The available dataset included 64,512 cases (99.3% of total cases), with each case 37 representing a positive diagnosis of COVID-19 along with the patient's Zip Code 38 Tabulation Area (ZCTA). ZCTAs are generalized areal representations of United States 39 Postal Service (USPS) Zip Code service areas. ZCTAs were the areas in which patients 40 reported their home address, as opposed to either where they became symptomatic or 41 where they reported for testing/treatment. The area of interest covered 177 ZCTAs Queens, and Staten Island); Fig 1B displays Bureau American Community Survey (ACS). ACS is a continuous sample survey of 52 3.5 million households every year including questions beyond the decadal census on 53 subjects such as education, employment, internet access, and transportation. Data were 54 collected at ZCTA level from the ACS 2014-2018 5-year estimate [12] , which is the most 55 recent publicly available. The 5-year estimate was chosen instead of the most recent 56 1-year estimate because the latter was not available in an aggregated form at ZCTA 57 level. Whilst the 5-year estimate is less current, it has a smaller margin of error than 58 the 1-year estimates and greater statistical reliability for small geographic areas. i.e., populations typically economically inactive. Aged population was the percentage of 65 the total population 65+. The increased severity of COVID-19 with increasing age has 66 been well documented [13] , and there has been recent evidence of asymptomatic carrier 67 transmission particularly amongst young people [14, 15] . Males per 100 females was 68 chosen to capture the balance of sex in the population. We were interested in whether 69 sex differences lead to significant variation in detected cases. Finally, some reports 70 suggest a racial disparity in case detection rates across the USA. A report from NYU 71 Furman Center for housing, neighborhoods, and urban policy suggests mortality rates 72 are higher amongst the city's "Hispanic, Black, and non-Hispanic/Latino: Other" 73 populations [16] . For the present study, we chose to include the percentage of the against the adverse effects of unequal income [17] , an association has been demonstrated 85 between inequality and population health [18] . We also included household income, 86 which was a significant predictor for hospitalizations in the 2009 influenza 87 pandemic [19] . Specifically, in the present study we use median household income as a 88 ZCTA-level predictor. Finally, unemployment and poverty both have documented 89 association with health outcomes, including in pandemic scenarios [20, 21] . Whilst there 90 is some level of collinearity between these two variables, we include both as one relates 91 to the economically active labor force whereas the other to the total population. The 
Two parameters related to healthcare access were included in the study: percentage of 95 population uninsured, Uninsured; and total number of hospital bed per 1000 people 96 within 5 km, Beds. It has been documented that lack of insurance can delay access to 97 timely healthcare, particularly during pandemics [22] . We hypothesized that this could 98 affect virus transmission and/or access to testing, therefore affecting detection rates.
Finally, we chose Beds as a parameter related to proximity to healthcare, which has 100 been shown to be inversely associated with adverse outcomes in other geospatial public 101 health studies [23] . For a city containing multiple hospitals such as NYC, we defined a 102 proximity metric in this study as population normalized number of hospital beds within Prior to analysis of potential predictors, we considered multiple base regression models. 110 Given the significant spatial correlation in the present case data as evidenced by the 111 Moran Index, I (176) = 0.481, p < 0.0005 [24] , we explored potential regression models 112 both with and without spatial effects. We compared four base models (no predictors): 113 1) a Poisson model with random intercept; 2) a Poisson Besag-York-Mollié (BYM) by Eq 1:
where υ i has an intrinsic conditional autoregressive (ICAR) structure [27] . We used the 119 reparameterization of the BYM model proposed by Riebler et al. [28] , known as the 120 BYM2 model and shown in Eq 2:
where τ γ is the overall precision hyperparameter, ϕ ∈ [0, 1] is the mixing hyperparameter 122 representing the proportional division of variance between the spatial and nonspatial 123 effects, υ * is the spatial (ICAR) effect with a scaling factor such that Var (υ * ) ≈ 1, and 124 ν * is the nonspatial random-effect with ν * ∼ N (0, 1). Penalized complexity (PC) priors 125 are applied to hyperparameters τ γ and ϕ (compared to log-gamma priors in the random 126 intercept model) [29] . All four models used ZCTA population as the exposure and a 127 log-link function. We selected the model with the lowest Deviance Information Criterion 128 (DIC) [30] , representing the best trade-off between model fit and complexity.
Characteristics for the four base models examined, including hyperparameters, are 130 shown in 
i , scaled nonspatial random-effect; υ * i , scaled spatial random-effect with intrinsic conditional autoregressive structure; τ ν , precision for nonspatial random effect, log-Gamma prior; τ γ , overall precision, penalized complexity (PC) prior; ϕ, mixing parameter, PC prior; n, overdispersion parameter, PC Gamma prior.
Multiple regression models were built using a method adjusted from Nikolopoulos et 136 al. [31] . In the univariable models, we considered each predictor variable separately (i.e., 137 one model per variable). In the multivariable model we considered all predictor 138 variables together. We further built a partial multivariable model using only those 139 predictors that were significant in the univariable models. Finally we built a model 140 using stepwise backwards elimination procedure, starting with the fully saturated model 141 April 16, 2020 5/16
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The copyright holder for this preprint this version posted April 22, 2020. . https://doi.org/10.1101/2020.04.17.20069823 doi: medRxiv preprint and removing the least significant predictor until we were left with a model containing 142 only significant predictors [31] . In all cases, the expected number of detected COVID-19 143 cases in ZCTA i, λ i , was represented by Eq 3:
where E i is the exposure (i.e., population) for ZCTA i, β 0 is the intercept, β p is 145 coefficient of the fixed effect for predictor p ∈ {1...P }, x ip is the value of predictor p in 146 ZCTA i, and the spatial and nonspatial random effects for ZCTA i are described by the 147 BYM2 model detailed above. Vague Gaussian priors are assumed on all β. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. between income, poverty, and unemployment. There was only one bivariate correlation 178 above 0.7 (median household income and poverty), and none above 0.8. It was decided 179 to leave all predictors in the analysis and to build multiple regression models in order to 180 consider the effects of collinearity. Fig 6 shows (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Males per 100 females. 4 Percentage of population that identify as white (alone or in combination with another race). 5 Gini index. 6 Median household income in $1,000s. 7 Percentage of working age population unemployed. 8 Percentage of population living below the poverty threshold. 9 Percentage of population uninsured. April 16, 2020 8/16
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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. p < 0.0005) in the full multivariable model, an increase of 1.6% (95% CI: 0.6% to 2.6%, 191 p = 0.001) in the partial multivariable model, and an increase of 2.2% (95% CI: 1.3% to 192 3.2%, p < 0.0005) in the stepwise backwards elimination model. Concerning income, a 193 $10,000 decrease in median household income leads to an increase in COVID-19 cases of 194 3.3% (95% CI: 1.7% to 5.0%, p < 0.0005) in the univariable model, an increase of 2.9% 195 (95% CI: 0.5% to 5.5%, p = 0.020) in the full multivariable model , and an increase of Table 3 shows a summary of 215 the regression estimates from this model. Fig 7A shows the area specific relative risk ζ i 216 for this model, whilst Fig 7B shows the posterior probability that the relative risk is 217 greater than 1, p (ζ i > 1|y). In this model, a 1% increase in the dependent population 218 leads to a 2.5% (95% CI: 1.6% to 3.4%, p < 0.0005) increase in detected COVID-19 219 cases. An increase of 1 male per 100 females leads to a 1.0% (95% CI: 0.6% to 1.5%, 220 p < 0.0005) increase in detected cases. Finally, a $10,000 decrease in median household 221 income leads to a 2.5% (95% CI: 1.0% to 4.1%, p = 0.002) increases in detected cases. 222 proportion of dependents in the population and male to female ratio. We also see a 236 significant inverse relationship between detected cases and median household income. 237 We do not see a consistently significant relationship between detected cases and the 238 other potential predictors; even those such as poverty, unemployment, and proximity to 239 healthcare that were significant in a univariable model.
Our findings indicated statistically significant associations between two of the four 241 demographic predictors included in the study. We find percentage of dependents in the 242 population to be a statistically significant predictor in all of the models in which it 243 appears as a factor. Conversely, we find that the aged percentage of the population 244 (65+) is never a significant predictor of detected COVID-19 cases. We believe that 245 taken together, this indicates that the youthful end of the dependent population is 246 associated with higher case numbers. This is congruent with evidence from Chan et 247 al. [14] and Bai et al. [15] , both of whom suggest significant transmission by young 248 asymptomatic carriers. We further hypothesize that attitudes and behavioral patterns 249 could play a significant role in this effect. As an example, increasing mortality of 250 COVID-19 with age has been well publicized, and we suggest this may incline older 251 communities to adhere to preventative public-health measures more. Conversely the 252 same information may be interpreted by younger populations that they are not at 253 significant risk, potentially encouraging riskier behaviors.
The balance of males and females as a significant factor indicates that areas with Studies have posited sex differences in immunological function [36] or smoking prevalence/pattern [37] as potential causes of differing medical outcomes. We found no 261 studies to date examining sex specific behavior trends in relation to COVID-19 262 transmission and incidence. Looking back further, we found conflicting evidence from 263 studies on the 2009 H1N1 pandemic. Some studies suggested that females were more 264 willing to engage in public health precautions [38] , whilst others suggested no significant 265 sex effects [39] . We suggest that further studies be undertaken to consider whether sex 266 specific behavioral, employment, or other trends are mechanisms that could explain sex 267 effects on detected cases. Race was marginally insignificant in our statistical analysis.
Thus, we caution against any association beyond the confounding sociological 269 relationship between race and economic affluence [40] . Further, our study used 270 percentage of population identifying as white, splitting the demographic into two 271 groups, white and non-white. A study exploring multiple racial categories at an 272 aggregate and/or individual level, may draw additional insight [19] .
Regarding the economic predictors, we note that our findings are in agreement with 274 a previous, non-pandemic study [41] , which found that affluence (in our case household 275 income) was a significant predictor on self-rated health whilst poverty and income 276 inequality (the Gini index) were not significant factors. Wen et al. suggest that the 277 presence of affluence sustains neighborhood social organizations, which in turn positively 278 affect health. If we extend this argument to the current pandemic, we could hypothesize 279 that these social organizations further act to pass on information and promote 280 community adoption of transmission-reduction policies such as social distancing [42] .
Furthermore, we note that those in low affluence neighborhoods are more likely to live 282 in higher density residence arrangements, for example community housing and shared 283 family dwellings. The increased urban density likely contributes to transmission of the 284 virus among the neighborhood [43] . Whilst previous studies [44] have found influence of 285 unemployment on disease transmission, we note that the unprecedented shut-down of 286 national infrastructure and the economy has meant that many previously employed 287 people suddenly found themselves either unemployed, furloughed, or working from home. 288 In a short period of time this drastic measure has completely altered the employment 289 landscape of NYC such that it is unsurprising the predictor from 2018 is not significant. 290 We found that neither of our healthcare-related predictors was consistently 291 significant. Lack of insurance has previously been a barrier to both diagnosis and 292 treatment [45, 46] . However, in the COVID-19 pandemic, significant state resources were 293 directed such that testing was freely available to all eligible New York residents. 294 We note four key limitations of the ecological study. First, our dependent variable is 295 the number of detected COVID-19 cases, which may be significantly different from the 296 number of true cases [47] . We believe, however that this does not detract from the 297 validity of the study, since characterization of the detection and prevalance is important 298 for pandemic management [48] . Studies on HIV rates amongst at risk populations 299 suggest that the relationship between predictors and the number of detected cases is 300 likely a complex interaction via at least three pathways: the true number of cases, access 301 to testing (means) [49] , and population attitudes to testing (motivation) [50, 51] . Thus, 302 we can still develop valid inferences, even if we cannot elicit with certainty which one (or 303 ones) of these pathways the significant predictors act through. Second, any associations 304 made must be interpreted with caution since, as with any observational study, spurious 305 correlations produced by unstudied confounding factors may be present. Caution is also 306 advised due to the ecological fallacy of making individual inferences from aggregate data. 307 Further verification is required to determine true causative links between predictors and 308 detected cases even when associations are significant. Third, the significant predictors 309 found are likely not the only explanations for different detection rates between different 310 neighborhoods. However this study does provide useful insight into explaining city limits at the borough level, there may be borough-level biases relating to 313 COVID-19 testing. However, if these biases exist, they likely inhibit testing access in 314 low-income neighborhoods [52, 53] such that the inverse association found between 315 income and positive cases is more pronounced than what the model suggests.
Within the constraints imposed by the limitations of an ecological analysis, we conclude 318 that there exist consistent, significant associations between COVID-19 detected cases 319 and the percentage of dependents in the population and male to female ratio. Further, 320 there is also a significant association between detected COVID-19 cases and low income 321 neighborhoods. The significance of dependents likely comes from the youth rather than 322 the aged population. We suggest further studies be undertaken to determine any 323 underlying causative mechanisms to these associations, paying particular attention to 324 willingness to engage in public health behaviors and to asymptomatic carrier 325 transmission. We finally highlight that whilst predictors may change with increased 326 time and access to testing, this study provides important insights into public health 327 behavior in the early stages of the current, and future, pandemic. The authors declare that the research was conducted in the absence of any commercial 333 or financial relationships that could be construed as a potential conflict of interest. The datasets analyzed for this study are publicly available, a repository can be found on 341 GitHub: https://github.com/rswhittle/NYC-COVID19-socioeconomic. 