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Lung cancer mortality rates for U.S. men and women aged 25–64 years and those aged 65 years or older by the 1990 area socioeconomic status (SES) index, 1950–1998.
Source publication
Lung cancer and colorectal cancer are leading causes of U.S. cancer mortality. Because mortality rates for many cancers vary by socioeconomic characteristics, we used area socioeconomic indices to examine patterns in U.S. lung and colorectal cancer mortality between 1950 and 1998.
A factor-based area socioeconomic index was linked to 1950-1998 coun...
Contexts in source publication
Context 1
... socioeconomic patterns in lung cancer mortality among men aged 25-64 years have changed over the last five decades (Fig. 1, A). During 1950During -1964, there was a positive association between area socioeconomic position and lung cancer mortality. In 1950, lung cancer mortality was about two times greater in the highest area socioeconomic group than in the lowest. The posi- tive gradient narrowed with time, and by the late 1960s, there was little difference ...
Context 2
... mortality for men aged 25- 64 years was 56% (95% confidence interval [CI] 49% to 64%) greater in the lowest area socioeconomic group than in the highest. During 1950During -1980, lung cancer mortality among older men was generally high in high area socioeconomic groups, with socioeconomic differences narrowing consistently throughout this period (Fig. 1, B). Mortality was 148% (95% CI 111% to ...
Context 3
... cancer mortality trends also changed between 1950 and 1998 among women aged 25-64 years and those aged 65 years or older (Fig. 1, C and D, respectively). Mortality among women rose dramatically in the last four decades. Women aged 25-64 years in higher area socioeconomic groups had higher mortality rates than did those in lower area socioeconomic groups in the 1950s, 1960s, 1970s, and through the mid-1980s. However, by the early 1990s, socioeconomic patterns had ...
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Controversy surrounds the risk of colorectal cancer (CRC) in ulcerative colitis (UC). Many studies have investigated this risk and reported widely varying rates.
A literature search using Medline with the explosion of references identified 194 studies. Of these, 116 met our inclusion criteria from which the number of patients and cancers detected c...
Citations
... For our study, we used the ADI, which is perhaps the gold standard for summarizing the effects of living in a particular neighborhood (32), as it incorporates a host of regional factors including income, education, employment, and housing quality, to quantify the degree of deprivation at a census block-level (3, 14). Analyses using the ADI have shown that cancer patients living in areas of high deprivation have worse clinical outcomes (33)(34)(35). ...
Background
Prior work assessing disparities in cancer outcomes has relied on regional socioeconomic metrics. These metrics average data across many individuals, resulting in a loss of granularity and confounding with other regional factors.
Methods
Using subjects’ addresses at the time of diagnosis from the Ohio Cancer Incidence Surveillance System (OCISS), we retrieved individual home price estimates from an online real estate marketplace. This subject-level estimate was compared to the Area Deprivation Index (ADI) at the census block group level. Multivariable Cox proportional hazards models were used to determine the relationship between home price estimates and all-cause and cancer-specific mortality.
Results
667,277 subjects in OCISS were linked to individual home prices across 16 cancers. Increasing home prices, adjusted for age, stage at diagnosis, and ADI, were associated with a decrease in the hazard of all-cause and cancer-specific mortality, hazard ratio (HR) 0.92 (95% confidence interval [95% CI] 0.92-0.93) and HR 0.95 (95% CI 0.94-0.95), respectively. Following a cancer diagnosis, individuals with home prices two standard deviations above the mean had an estimated 10-year survival probability 7.8% (95% CI 7.2-8.3) higher than those with home prices two standard deviations below the mean. The association between home price and mortality was substantially more prominent for subjects living in less deprived census block groups (P-interaction <0.001) than for those living in more deprived census block groups.
Conclusion
Higher individual home price was associated with improved all-cause and cancer-specific mortality, even after accounting for regional measures of deprivation.
... County-level socioeconomic and demographic information was assessed using the five-year estimates provided by the American Community Survey [30]. SES was operationalized using a validated method that integrated the information into an index based on the county-level percent of residents with less than a high school diploma, the percent of adults with a four-year college degree, the unemployment rate, the median household income, and the percent living below the federal poverty line [31]. The index was determined to have good overall consistency (mean inter-item correlation = 0.46, Cronbach's a = 0.81). ...
Background:
In December 2020 the U.S. began a massive COVID-19 vaccination campaign, an action that researchers felt could catalyze inequalities in COVID-19 vaccination utilization. While vaccines have the potential to be accessible regardless of social status, the objective of this study was to examine how and when socioeconomic status (SES) and racial/ethnic inequalities would emerge in vaccination distribution.
Methods:
Population vaccination rates reported at the county level by the Centers for Disease Control and Prevention across 46 states on 3/30/2021. Correlates included SES, the share of the population who were Black, Hispanic, Female, or aged ≥65 years, and urbanicity (thousands of residents per square mile). Multivariable-adjusted analyses relied on zero-inflated negative binomial regression to estimate the odds of providing any vaccine, and vaccination rate ratios (aVRR) comparing the distribution rate for vaccinations across the U.S.
Results:
Across the U.S., 16.3 % of adults and 37.9 % of adults aged 65 and older were vaccinated in lower SES counties, while 20.45 % of all adults and 48.15 % of adults aged 65 and older were vaccinated in higher SES counties. Inequalities emerged after 41 days, when < 2 % of Americans were vaccinated. Multivariable-adjusted analyses revealed that higher SES was associated with improved vaccination distribution (aVRR = 1.127, [1.100-1.155], p < 1E-06), while increases in the percent reporting Black or Hispanic race/ethnicity was associated with lower vaccination distribution (aVRR = 0.998, [0.996-0.999], p = 1.03E-04).
Conclusions:
Social inequalities in COVID-19 vaccines reflect an inefficient and inequitable distribution of these technologies. Future efforts to improve health should recognize the central role of social factors in impacting vaccine delivery.
... Furthermore, according to the World Health Organization (WHO) predictions, in 2040 the number of new CRC cases will exceed 3 million and over 1.5 million deaths will be recorded worldwide. 2 CRC is related to age and lifestyle, with few occurrences caused by hereditary anomalies. ...
Cancer colon is the second most prevalent cancer for females and the third for males. Vitamin D's cellular impacts are achieved by 1,25 (OH) 2D binding to the Vitamin D receptor (VDR). This study aimed at assessing the relation between vitamin D receptor gene polymorphism and cancer colon. This case-control study included 50 colorectal cancer (CRC) cases which were candidates for colonoscopy and 50 controls with normal colonoscopy. The study was conducted in Suez Canal University hospitals. All cases were diagnosed with colonoscopy and confirmed with histopathology. Blood samples from study subjects were used for detection of vitamin D receptor FokI polymorphism. We found that more than two thirds of patients were males. Around half of the cases were over 60 years old. Most of the study participants were overweight (26%), obese (53%), non-alcoholics (99%), and non-smokers (72%). However, about one third of the patients were diabetic (31%). Noticeably, none of these factors was significantly variant among CRC group and normal colonoscopy group (p < 0.05). The most common presentation among cases with colon cancer was constipation (80%). Of the 100 studied cases, 74% had left-sided colon cancer, with a 66% of them were resectable. The odds ratio of VDR polymorphism between the cases and control groups was high (3 with 95% CI (0.3-31)), however it did not reach statistical significance (p= 0.3). Most of the cases with VDR polymorphism had colon cancer (75%). In conclusion, based on our findings, there was no correlation between colorectal cancer and vitamin D receptor gene polymorphism.
... As obesity, one of the best-known risk factors for CRC, became epidemic and the nutrition worsened, colorectal cancer incidence increased, especially in rural areas with low income. In comparison to the 1950s, CRC changed from a prosperity disease to a disease of higher prevalence in low-income social stratum [35,36]. This fact was clearly confirmed by our data indicating rural areas to have higher GISD and thus worse survival. ...
Background
Colorectal cancer (CRC) is one of the most common types of cancer in Western civilization and responsible for a high number of yearly deaths. Long-term outcome is influenced by many factors, potentially including socioeconomic aspects like income, education, and employment. Furthermore, annual surgical case volume plays a major role in achieving good oncological results. In our retrospective study, we evaluated the effect of socioeconomic deprivation and hospital volume on overall survival (OS) in the federal state of Saxony, Germany.
Methods
All patients with CRC who underwent surgery in Saxony, Germany between 2010 and 2020 and were living in Saxony at the time of diagnosis were included in our retrospective analysis. Uni- and multivariate analyses were conducted considering age, sex, tumor localization, UICC tumor stage, surgical approach (open/laparoscopic), number of resected lymph nodes, adjuvant chemotherapy, year of surgery, and hospital case volume. In addition, our model was adjusted for social disparity using the German Index of Socioeconomic Deprivation (GISD).
Results
A total of 24,085 patients were analyzed (15,883 with colon cancer and 8,202 with rectal cancer). Age, sex, UICC tumor stage and tumor localization were distributed as expected for CRC. Median overall survival time was 87.9 months for colon cancer and 110.0 months for rectal cancer. Univariate analysis revealed laparoscopic surgery (colon and rectum P < 0.001), high case volume (rectum: P = 0.002) and low levels of socioeconomic deprivation (colon and rectum P < 0.001) to be significantly associated with better survival. In multivariate analyses, the associations of laparoscopic surgery (colon: HR = 0.76, P < 0.001; rectum: HR = 0.87, P < 0.01), and mid-low to mid-high socioeconomic deprivation (colon: HR = 1.18–1.22, P < 0.001; rectum: HR = 1.18–1.36, P < 0.001–0.01) remained statistically significant. Higher hospital case volume was associated with better survival only in rectal cancer (HR = 0.89; P < 0.01).
Conclusion
In Saxony, Germany, better long-term survival after CRC surgery was associated with low socioeconomic deprivation, laparoscopic surgery and partly with high hospital case volume. Thus, there is a need to reduce social differences in access to high-quality treatment and prevention and increase hospital patient volume.
... Individual-level disadvantages, such as lower educational attainment, 8 unemployment or employment in lower-skilled jobs, [9][10][11] inadequate insurance coverage, 12 and lower income 9,11 are prevalent in childhood cancer survivors compared with peers. In addition, population-level measures of deprivation, such as the area deprivation index (ADI), which accounts for educational level, employment status, housing quality, and poverty measures at the Census block level (containing 600-3000 individuals per block), have been validated for a range of adverse health events in the general population [13][14][15][16][17] ; however, associations between prevalent social determinants of health, ADI, and late mortality in childhood cancer survivors remain unknown. In this longitudinal cohort study, we aimed to explore the association between potentially modifiable health conditions and cancer mortality within the context of social determinants of health. ...
... The last-reported residential addresses of on-campus participants were geocoded to determine neighborhood-level socioeconomic status (SES) by US Census blocks using the ADI, a composite measure derived from components of the American Community Survey reflective of 17 neighborhood-level SES measures (eg, household income, employment status, and educational level). [13][14][15][16][17] Each block was previously assigned a national percentile, ranking minimum SES disadvantage in the 1st percentile and maximum disadvantage in the 100th percentile. For 4.9% of on-campus participants, the ADI was designated as unassigned due to the unavailability of geocoding information (eg, post office boxes or international addresses). ...
Importance:
Associations between modifiable chronic health conditions (CHCs), social determinants of health, and late mortality (defined as death occurring ≥5 years after diagnosis) in childhood cancer survivors are unknown.
Objective:
To explore associations between modifiable CHCs and late mortality within the context of social determinants of health.
Design, setting, and participants:
This longitudinal cohort study used data from 9440 individuals who were eligible to participate in the St Jude Lifetime Cohort (SJLIFE), a retrospective cohort study with prospective clinical follow-up that was initiated in 2007 to characterize outcomes among childhood cancer survivors. Eligible individuals had survived 5 or more years after childhood cancer diagnosis, were diagnosed between 1962 and 2012, and received treatment at St Jude Children's Research Hospital were included in mortality estimates. A total of 3407 adult SJLIFE participants (aged ≥18 years) who completed an on-campus assessment were included in risk factor analyses. Vital status, date of death, and cause of death were obtained by linkage with the National Death Index (coverage from inception to December 31, 2016). Deaths occurring before inception of the National Death Index were obtained from the St Jude Children's Research Hospital Cancer Registry. Data were analyzed from June to December 2022.
Exposures:
Data on treatment exposures and causes of death were abstracted for individuals who were eligible to participate in the SJLIFE study. Information on modifiable CHCs (dyslipidemia, hypertension, diabetes, underweight or obesity, bone mineral deficiency, hypogonadism, hypothyroidism, and adrenal insufficiency, all graded by the modified Common Terminology Criteria for Adverse Events), healthy lifestyle index (smoking status, alcohol consumption, body mass index [calculated as weight in kilograms divided by height in meters squared], and physical activity), area deprivation index (ADI; which measures neighborhood-level socioeconomic disadvantage), and frailty (low lean muscle mass, exhaustion, low energy expenditure, slowness, and weakness) was obtained for participants.
Main outcomes and measures:
National Death Index causes of death were used to estimate late mortality using standardized mortality ratios (SMRs) and 95% CIs, which were calculated based on US mortality rates. For the risk factor analyses (among participants who completed on-campus assessment), multivariable piecewise exponential regression analysis was used to estimate rate ratios (RRs) and 95% CIs for all-cause and cause-specific late mortality.
Results:
Among 9440 childhood cancer survivors who were eligible to participate in the SJLIFE study, the median (range) age at assessment was 27.5 (5.3-71.9) years, and the median (range) duration of follow-up was 18.8 (5.0-58.0) years; 55.2% were male and 75.3% were non-Hispanic White. Survivors experienced increases in all-cause mortality (SMR, 7.6; 95% CI, 7.2-8.1) and health-related late mortality (SMR, 7.6; 95% CI, 7.0-8.2). Among 3407 adult SJLIFE participants who completed an on-campus assessment, the median (range) age at assessment was 35.4 (17.9-69.8) years, and the median (range) duration of follow-up was 27.3 (7.3-54.7) years; 52.5% were male and 81.7% were non-Hispanic White. Models adjusted for attained age, sex, race and ethnicity, age at diagnosis, treatment exposures, household income, employment status, and insurance status revealed that having 1 modifiable CHC of grade 2 or higher (RR, 2.2; 95% CI, 1.2-4.0; P = .01), 2 modifiable CHCs of grade 2 or higher (RR, 2.6; 95% CI, 1.4-4.9; P = .003), or 3 modifiable CHCs of grade 2 or higher (RR, 3.6; 95% CI, 1.8-7.1, P < .001); living in a US Census block with an ADI in the 51st to 80th percentile (RR, 5.5; 95% CI, 1.3-23.5; P = .02), an ADI in the 81st to 100th percentile (RR, 8.7; 95% CI, 2.0-37.6; P = .004), or an unassigned ADI (RR, 15.7; 95% CI, 3.5-70.3; P < .001); and having frailty (RR, 2.3; 95% CI, 1.3-3.9; P = .004) were associated with significant increases in the risk of late all-cause death. Similar associations were observed for the risk of late health-related death (1 modifiable CHC of grade ≥2: RR, 2.2 [95% CI, 1.1-4.4; P = .02]; 2 modifiable CHCs of grade ≥2: RR, 2.5 [95% CI, 1.2-5.2; P = .01]; 3 modifiable CHCs of grade ≥2: RR, 4.0 [95% CI, 1.9-8.4; P < .001]; ADI in 51st-80th percentile: RR, 9.2 [95% CI, 1.2-69.7; P = .03]; ADI in 81st-100th percentile: RR, 16.2 [95% CI, 2.1-123.7; P = .007], unassigned ADI: RR, 27.3 [95% CI, 3.5-213.6; P = .002]; and frailty: RR, 2.3 [95% CI, 1.2-4.1; P = .009]).
Conclusions and relevance:
In this cohort study of childhood cancer survivors, living in a Census block with a high ADI and having modifiable CHCs were independently associated with an increased risk of late death among survivors of childhood cancer. Future investigations seeking to mitigate these factors will be important to improving health outcomes and developing risk-stratification strategies to optimize care delivery to childhood cancer survivors.
... Socioeconomic disadvantage is an additional predictor variable for aim 2. We define socioeconomic disadvantage to be those patients living in the 20% most disadvantaged neighbourhoods according to the Area Deprivation Index. [57][58][59][60] We chose this measure of socioeconomic disadvantage based on the zipcode +4 as it allows early identification of disadvantaged patients by clinic staff without requiring a detailed patient assessment. This would allow early integration of any adjunct interventions targeting socioeconomically disadvantaged patients into clinical workflow. ...
Introduction
Socioeconomic disparities for breast cancer surgical care exist. Although the aetiology of the observed socioeconomic disparities is likely multifactorial, patient engagement during the surgical consult is critical. Shared decision-making may reduce health disparities by addressing barriers to patient engagement in decision-making that disproportionately impact socioeconomically disadvantaged patients. In this trial, we test the impact of a decision aid on increasing socioeconomically disadvantaged patients’ engagement in breast cancer surgery decision-making.
Methods and analysis
This multisite randomised trial is conducted through 10 surgical clinics within the National Cancer Institute Community Oncology Research Program (NCORP). We plan a stepped-wedge design with clinics randomised to the time of transition from usual care to the decision aid arm. Study participants are female patients, aged ≥18 years, with newly diagnosed stage 0–III breast cancer who are planning breast surgery. Data collection includes a baseline surgeon survey, baseline patient survey, audio-recording of the surgeon–patient consultation, a follow-up patient survey and medical record data review. Interviews and focus groups are conducted with a subset of patients, surgeons and clinic stakeholders. The effectiveness of the decision aid at increasing patient engagement (primary outcome) is evaluated using generalised linear mixed-effects models. The extent to which the effect of the decision aid intervention on patient engagement is mediated through the mitigation of barriers is tested in joint linear structural equation models. Qualitative interviews explore how barriers impact engagement, especially for socioeconomically disadvantaged women.
Ethics and dissemination
This protocol has been approved by the National Cancer Institute Central Institutional Review Board, and Certificate of Confidentiality has been obtained. We plan to disseminate the findings through journal publications and national meetings, including the NCORP network. Our findings will advance the science of medical decision-making with the potential to reduce socioeconomic health disparities.
Trial registration number
ClinicalTrials.gov Registry ( NCT03766009 ).
... The grouping of counties into SES categories was implemented following an approach initially developed and validated by Singh. [24][25][26] A principal component analysis was applied to 11 variables extracted from the most recent 5year American Community Survey (2015-2019) from the US Census Bureau 27 and calculated at the county level for all US counties. The 11 input variables we used reflect the population composition of each county in terms of the percentage of the population aged !25 years with <9 years of education; the percentage with !4 years of college education; the percentage of households below the federal poverty line; the median household income, including cash benefits such as social-security payments and adjusted to account for differences in local standards of living; the ratio of the average income of the wealthiest quintile of households to the poorest quintile within each county; the unemployment rate; the percentage of the labour force in white-collar occupations; the median housing price; the median gross rent; the percentage of households with no telephone; and the percentage of households with no or incomplete plumbing. ...
Background
Preliminary studies have suggested a link between socio-economic characteristics and COVID-19 mortality. Such studies have been carried out on particular geographies within the USA or selective data that do not represent the complete experience for 2020.
Methods
We estimated COVID-19 mortality rates, number of years of life lost to SARS-CoV-2 and reduction in life expectancy during each of the three pandemic waves in 2020 for 3144 US counties grouped into five socio-economic status categories, using daily death data from the Johns Hopkins University of Medicine and weekly mortality age structure from the Centers for Disease Control.
Results
During March–May 2020, COVID-19 mortality was highest in the most socio-economically advantaged quintile of counties and lowest in the two most-disadvantaged quintiles. The pattern reversed during June–August and widened by September–December, such that COVID-19 mortality rates were 2.58 times higher in the bottom than in the top quintile of counties. Differences in the number of years of life lost followed a similar pattern, ultimately resulting in 1.002 (1.000, 1.004) million years in the middle quintile to 1.381 (1.378, 1.384) million years of life lost in the first (most-disadvantaged) quintile during the whole year.
Conclusions
Diverging trajectories of COVID-19 mortality among the poor and affluent counties indicated a progressively higher rate of loss of life among socio-economically disadvantaged communities. Accounting for socio-economic disparities when allocating resources to control the spread of the infection and to reinforce local public health infrastructure would reduce inequities in the mortality burden of the disease.
... Yet, causes of lung cancer mortality are more complex 7 and are also linked with air pollution 8 , and socioeconomic conditions 3,9 . Some of these risk factors have not been previously included in the modeling of predicting the LBC mortality rate 7,8,[10][11][12][13] . ...
... Multivariate PD profile plots reveal a positive interaction between smoking and poverty rates since increasing both features leads to increased LBC mortality rates. The relationship between socioeconomic status and LBC mortality rates in the US is well established 13,82,84,85 . Access to health care is an economic issue, particularly in the US 7 . ...
Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners—generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.
... Similarly, challenges are faced by rural providers in delivering cancer care to their patients, with limited access to comprehensive care being the most important reason for outcome disparities [16]. Moreover, the growing divide in mortality between rural and urban dwellers [1,5,[17][18][19] highlights the need to evaluate multi-level interventions directed at providers and their patients to improve outcomes. ...
Background
Despite lower cancer incidence rates, cancer mortality is higher among rural compared to urban dwellers. Patient, provider, and institutional level factors contribute to these disparities. The overarching objective of this study is to leverage the multidisciplinary, multispecialty oncology team from an academic cancer center in order to provide comprehensive cancer care at both the patient and provider levels in rural healthcare centers. Our specific aims are to: 1) evaluate the clinical effectiveness of a multi-level telehealth-based intervention consisting of provider access to molecular tumor board expertise along with patient access to a supportive care intervention to improve cancer care delivery; and 2) identify the facilitators and barriers to future larger scale dissemination and implementation of the multi-level intervention.
Methods
Coordinated by a National Cancer Institute-designated comprehensive cancer center, this study will include providers and patients across several clinics in two large healthcare systems serving rural communities. Using a telehealth-based molecular tumor board, sequencing results are reviewed, predictive and prognostic markers are discussed, and treatment plans are formulated between expert oncologists and rural providers. Simultaneously, the rural patients will be randomized to receive an evidence-based 6-week self-management supportive care program, Cancer Thriving and Surviving, versus an education attention control. Primary outcomes will be provider uptake of the molecular tumor board recommendation and patient treatment adherence. A mixed methods approach guided by the Consolidated Framework for Implementation Research that combines qualitative key informant interviews and quantitative surveys will be collected from both the patient and provider in order to identify facilitators and barriers to implementing the multi-level intervention.
Discussion
The proposed study will leverage information technology-enabled, team-based care delivery models in order to deliver comprehensive, coordinated, and high-quality cancer care to rural and/or underserved populations. Simultaneous attention to institutional, provider, and patient level barriers to quality care will afford the opportunity for us to broadly share oncology expertise and develop dissemination and implementation strategies that will enhance the cancer care delivered to patients residing within underserved rural communities.
Trial registration
Clinicaltrials.gov , NCT04758338 . Registered 17 February 2021 – Retrospectively registered, http://www.clinicaltrials.gov/
... The revised ADI is a validated instrument that has been used by researchers and government agencies such as the Centers for Medicare and Medicaid Services [16]. Recently, a number of studies have been published using the ADI [17][18][19][20][21][22]. ...
Background
Individuals may use unhealthy coping mechanisms such as alcohol, tobacco, and unhealthy snack consumption. The purpose of this study was to assess how neighborhood disadvantage is associated with sales of alcohol, tobacco, and unhealthy snacks at stores of a discount variety store chain.
Methods
Alcohol, tobacco, and unhealthy snack sales were measured monthly for 20 months, 2017–2018, in 16 discount variety stores in the United States. Mixed effects linear regressions adjusted for population size, with store-specific random effects, to examine the relationship of weekly unit sales with three outcome variables and neighborhood disadvantage, measured using the Area Deprivation Index (ADI).
Results
The discount variety stores were located in neighborhoods where the median ADI percentile was 87 [interquartile range 83,89], compared to the median ADI percentile of 50 for all US communities, indicating that the stores were located in substantially disadvantaged neighborhoods. For every 1% increase in ADI, weekly unit sales of unhealthy snack food increased by 43 [95% confidence interval, CI 28–57], and weekly unit sales of tobacco products increased by 11.5 [95% CI 5–18] per store. No significant relationship between neighborhood disadvantage and the weekly unit sales of alcohol products was identified.
Conclusions
The positive relationship between neighborhood disadvantage and the sale of tobacco and snack foods may help explain the pathway between neighborhood disadvantage and poor health outcomes. It would be useful for future research to examine how neighborhood disadvantage influences resident health-related behaviors.
