Depression, Alcohol Use, and Sexual Behaviors by HIV Infection Stage and Diagnosis Timing Among STI Clinic Patients in Lilongwe, Malawi.

Understanding depression, alcohol use, and sexual behaviors according to HIV infection stage and diagnosis timing is important for HIV prevention efforts. We enrolled persons with recent infection and diagnosis (i.e., acute HIV infection (AHI) (n = 92) persons newly diagnosed seropositive (n = 360)) and persons previously diagnosed with HIV (n = 190) into a randomized controlled trial in Lilongwe, Malawi (N = 641) and estimated the prevalence of probable depression (Patient Health Questionnaire-9 ≥ 5), hazardous alcohol use (Alcohol Use Disorder Identification Test-C: men ≥ 4; women ≥ 3), and sexual behaviors (transactional sex, condomless sex). Compared with previously diagnosed participants, participants newly seropositive and those with AHI reported a higher proportion of probable depression (7%, 27%, 38%; AHI/Previous: Table Probability: 0.02, p < 0.01; AHI/New: Table Probability: <0.01, p < 0.01), hazardous alcohol use (8%, 18%, 29%; AHI/Previous and AHI/New: Table Probability: <0.01, p < 0.01), and transactional sex (5%, 14%, 20%; AHI/Previous: Table Probability: <0.01, p < 0.01; AHI/New: Table Probability: 0.06, p = 0.24), respectively. HIV prevention services addressing mental health and alcohol misuse may be particularly beneficial for persons with recent HIV infection and or diagnosis.

Variations in Tobacco Retailer Type Across Community Characteristics: Place Matters.

INTRODUCTION: The density of tobacco retailers varies by community characteristics such as poverty levels or racial and ethnic composition. However, few studies have investigated how specific types of tobacco retailers vary by community characteristics. Our objective was to assess how the types of tobacco retailers in Ohio varied by the characteristics of the communities in which they were located. RESULTS: For all US Census tracts, convenience stores were the most common type of retailer selling tobacco. Yet, the prevalence of convenience stores was higher in high-poverty urban tracts than in low-poverty urban tracts. Discount stores were the second-most common type of tobacco retailer and were most prevalent in rural tracts and high-racial and ethnic minority urban tracts. Grocery stores, pharmacies, and vape or hookah shops typically had the highest prevalence in more advantaged tracts. CONCLUSION: Our findings demonstrate that the distribution of specific retailer types varies by community characteristics. The distribution of these retailer types has implications for product availability and price, which may subsequently affect tobacco use and cessation. To create equitable outcomes, policies should focus on retailers such as convenience and discount stores, which are heavily located in communities experiencing tobacco-related health disparities.

Associations between disparities in tobacco retailer density and disparities in tobacco use.

Research has separately established that there are disparities in tobacco use, that greater tobacco retailer density (TRD) is positively associated with tobacco use, and that TRD is greater in high poverty and high racial/ethnic minority neighborhoods. Connecting these topics, this study examined the association between disparities in TRD and disparities in the prevalence of tobacco use among adults and youth. We obtained Ohio data on tobacco use from two statewide adult surveys and two sub-state regional youth surveys (2017-2019). Licensed tobacco retailers in Ohio were geocoded within census tracts. Disparity in TRD within regions across the state was defined as the ratio of TRD in high vs. low poverty (and in high vs. low racial/ethnic minority) census tracts per region. Disparity in cigarette smoking (adults) and any tobacco use (youth) was defined as the ratio of use prevalence among socioeconomically disadvantaged vs. non-disadvantaged (and racial/ethnic minority vs. non-minority) individuals. We estimated Pearson correlation coefficients to assess the linear relationship between the TRD disparity ratios and tobacco use disparity ratios. Poverty-based and race/ethnicity-based TRD disparities were positively associated with smoking disparities among adults. Negative associations between TRD disparities and tobacco use disparities were found among youth. To our knowledge, this is the first analysis directly linking TRD disparities and tobacco use disparities. Different adult and youth findings may be due to trends by age and product preferences. For adults in particular, this analysis suggests a detrimental effect of the tobacco retail environment on disadvantaged populations.

The legacy of redlining: Associations between historical neighborhood mapping and contemporary tobacco retailer density in Ohio.

In the 1930s United States, urban neighborhoods were graded on their desirability for investment (often based on race), a process known as "redlining." This study examined how historical redlining relates to current disparities in an important health determinant: tobacco retailer density. Analyses were conducted for thirteen Ohio cities using negative binomial models that accounted for retailer spatial dependence and controlled for present-day sociodemographic characteristics. Findings indicated that as grades increased from "Best" to "Still Desirable" to "Definitely Declining" and "Hazardous," retailer density increased monotonically. These results highlight the persisting impacts of redlining and how disparities, once intentionally created, can be perpetuated over time.

Evaluating how licensing-law strategies will impact disparities in tobacco retailer density: a simulation in Ohio.

OBJECTIVES: To assess tobacco licensing-law strategies (eg, restricting the sale of tobacco near schools, banning the sale of tobacco in pharmacies) in terms of the equity of their impact and ability to correct existing disparities in tobacco retailer density. METHODS: We geocoded all 11 392 tobacco retailers in Ohio, categorised neighbourhoods based on their demographic characteristics and calculated current disparities in tobacco retailer density. We next simulated the four main types of licensing-law strategies (capping-based, declustering-based, school-based and pharmacy-based), as well as strategy combinations. Finally, using statistical methods that account for residual spatial dependence, we evaluated how each strategy would impact density disparities. FINDINGS: The most impactful licensing-law strategy depended on the type of community. School-based reductions were equitable for low-income, African-American and urban neighbourhoods (eg, eliminating retailers from 1000 feet of all schools produced a 9.2% reduction in the log retailer rate for neighbourhoods with a low prevalence of African-Americans and a 17.7% reduction for neighbourhoods with a high prevalence of African-Americans). Conversely, capping-based reductions were equitable for rural neighbourhoods. Pharmacy-based reductions demonstrated inequitable impacts. CONCLUSION: Licensing-law strategies could be a central tobacco control effort that benefits both the overall population and vulnerable communities. Policymakers will need to consider their community's characteristics when selecting licensing-law strategies to correct (rather than inadvertently widen) density disparities. But when matched with the appropriate strategy, high-risk communities could remove over 20% of their tobacco retailers.

The Relationship between Tobacco Retailer Density and Neighborhood Demographics in Ohio.

INTRODUCTION: Studies from various parts of the country suggest that tobacco-related health disparities are exacerbated by disparities in the distribution of tobacco retailers (convenience stores, tobacco shops, etc.). The purpose of the present study was to use advanced spatial modeling techniques for count data to estimate current disparities in tobacco retailer density in Ohio. METHODS: We identified and geocoded 11,392 tobacco retailers in Ohio. Next, we obtained census tract-level information on race/ethnicity, poverty, and age and obtained county-level information on whether an area was Urban, Suburban, or Rural. Finally, we used negative binomial generalized linear models, adapted for residual spatial dependence, to determine the association between per capita tobacco retailer density and demographic characteristics-summarized by adjusted rate ratios. RESULTS: There were more (from 1.4-1.9 times as many) retailers per capita in high-poverty, vs. low-poverty tracts. Poverty also interacted with age: the association between high poverty and high retailer density was stronger for tracts with a low youth population. Density was also greater in tracts with a high (vs. low) prevalence of African Americans (1.1 times as many) and Hispanics (1.2 times as many). Finally, density was generally greater in rural (vs. suburban or urban) tracts, although the effect was modified by a three-way interaction: density was particularly high for rural tracts that also had both a high prevalence of poverty and a low youth population. DISCUSSION: Overall, our findings indicate that Ohio's vulnerable populations are exposed to a greater per capita density of tobacco retailers. PUBLIC HEALTH IMPLICATIONS: There is a need for state and local-level tobacco control policies that will improve equity and reduce health disparities.