County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018

Fanghui Shi; Jiajia Zhang; Chengbo Zeng; Xiaowen Sun; Zhenlong Li; Xueying Yang; Sharon Weissman; Bankole Olatosi; Xiaoming Li

doi:10.1371/journal.pone.0286497

. 2023 May 31;18(5):e0286497. doi: 10.1371/journal.pone.0286497

County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018

Fanghui Shi ^1,^2,^3,^*, Jiajia Zhang ^1,^3,⁴, Chengbo Zeng ^1,^2,³, Xiaowen Sun ^1,^3,⁴, Zhenlong Li ^3,⁵, Xueying Yang ^1,^2,³, Sharon Weissman ^3,⁶, Bankole Olatosi ^1,^3,⁷, Xiaoming Li ^1,^2,³

Editor: Csaba Varga⁸

PMCID: PMC10231826 PMID: 37256896

Abstract

Background

Timely linkage to care (LTC) is key in the HIV care continuum, as it enables people newly diagnosed with HIV (PNWH) to benefit from HIV treatment at the earliest stage. Previous studies have found LTC disparities by individual factors, but data are limited beyond the individual level, especially at the county level. This study examined the temporal and geographic variations of county-level LTC status across 46 counties in South Carolina (SC) from 2010 to 2018 and the association of county-level characteristics with LTC status.

Methods

All adults newly diagnosed with HIV from 2010 to 2018 in SC were included in this study. County-level LTC status was defined as 1 = “high LTC (≥ yearly national LTC percentage)” and 0 = “low LTC (< yearly national LTC percentage)”. A generalized estimating equation model with stepwise selection was employed to examine the relationship between 29 county-level characteristics and LTC status.

Results

The number of counties with high LTC in SC decreased from 34 to 21 from 2010 to 2018. In the generalized estimating equation model, six out of 29 factors were significantly associated with LTC status. Counties with a higher percentage of males (OR = 0.07, 95%CI: 0.02~0.29) and persons with at least four years of college (OR = 0.07, 95%CI: 0.02~0.34) were less likely to have high LTC. However, counties with more mental health centers per PNWH (OR = 45.09, 95%CI: 6.81~298.55) were more likely to have high LTC.

Conclusions

Factors associated with demographic characteristics and healthcare resources contributed to the variations of LTC status at the county level. Interventions targeting increasing the accessibility to mental health facilities could help improve LTC.

Introduction

Timely linkage to care (LTC) is a crucial early step for treatment success in HIV control, but it remains a significant challenge for people newly diagnosed with HIV (PNWH) in South Carolina (SC) [1–3]. According to the latest established federal benchmark, timely LTC refers to the completion of a visit with an HIV healthcare provider (at least one documentation of CD4 or viral load test) within the first month (30 days) after HIV diagnosis [4]. According to the state surveillance data, there were around 748 PNWH annually in SC from 2009 to 2020 [5]. Among them, men, African Americans, people aged 20–29, and men who have sex with men were disproportionately affected by HIV, making up low percentages of SC’s total population but comprising high percentages of PNWH [5]. For example, men comprise 48% of SC’s total population but makeup 80% of 1,556 PNWH in SC during the two-year period 2018–2019 [5]. In 2019, only 76% of 797 PNWH were linked to care within one month in SC, which was lower than the national goal (85%) launched by the White House in 2020 [1]. In addition, it was much lower than the goal launched by the SC Department of Health and Environmental Control (SC DHEC), which aims to achieve that 90% of newly diagnosed individuals should be linked to care by December 31, 2024 [5]. More investigations on factors associated with delayed LTC are needed to provide empirical evidence for future potential interventions.

Previous studies have explored factors associated with HIV outcomes, but most have focused on the individual level. For instance, consistent findings show disparities in LTC based on race/ethnicity, gender, and age [6–10]. However, these studies do not account for important social and structural factors that may impact LTC. Understanding these factors is essential, as this could provide evidence for future efforts in policymaking and structural-level strategies to improve LTC [11–13]. Also, findings in the current literature on LTC are mixed, especially regarding structural/social factors. For instance, there are inconsistent findings regarding the impact of distance to care and transportation accessibility on LTC. Some studies show these factors do not impact LTC, but others find these as significant LTC barriers [14–17].

Previous studies have only considered a limited number of structural/social factors, which may underestimate the association of structural factors with LTC. The structural factors associated with LTC can be summarized into four dimensions based on the sociological framework: (1) demographic characteristics (e.g., racial heterogeneity, percent of poverty, and educational attainment), (2) physical characteristics (e.g., the number of mental health centers or Ryan White HIV centers, the primary care provider rate), (3) social characteristics (e.g., violent crime, religious adherence, and social capital), and (4) health behaviors (e.g., smoking and excessive drinking) [15–20]. To our best knowledge, a dearth of studies on LTC incorporates all these four dimensions of structural predictors. The current study aimed to investigate the relationship between county-level factors and LTC among PNWH in SC when considering multi-dimensional structural factors.

Methods

Data sources and linkage

The study population included all people (aged ≥18) newly diagnosed with HIV from January 2010 to December 2018 across 46 counties in SC. Individual de-identified laboratory reports of CD4 counts and viral load were extracted from the enhanced HIV/AIDS reporting system (eHARS) in the SC DHEC [21]. They were used to calculate the county-level timely LTC percentage based on the Centers for Disease Control and Prevention (CDC) definition [4].

First, county-level variables were extracted from multiple public database sources with the Federal Information Processing Standards (FIPS) as the identification of each county, including the American Community Survey (ACS), County Health Rankings & Roadmaps, and the US Congress Joint Economic Committee. According to the census data user guide, the ACS 5-year estimates data were used since multi-year estimates could increase statistical reliability for small population groups [22]. Then, county-level LTC data and all county-level factors were linked by FIPS code and calendar year. The Institutional Review Boards at the University of South Carolina and SC DHEC approved the study protocol (#Pro00068124). The IRB approved this study as a non-human subject study, and no participant consent is needed.

County-level LTC status

According to CDC, timely LTC was measured by records of ≥ 1 CD4 (count or percentage) or viral load tests performed within one month after HIV diagnosis, including tests performed on the same date as the date of diagnosis [4]. Based on this definition, we classified the individual-level LTC status as “timely LTC” and “delayed LTC.” The county-level timely LTC percentage was calculated as the number of “timely LTC” divided by the number of newly diagnosed HIV cases for each county in the specified calendar year. By comparing the yearly county-level LTC percentage to the yearly national LTC percentage in the US from 2010 to 2018 (70.2%, 70.4%, 71.4%, 72.6%, 74.5%, 75%, 75.9%, 78.3%, and 80.2%) [23], we defined county-level LTC status as 1 = “high LTC (≥ national LTC percentage)” or 0 = “low LTC (< national LTC percentage)” (reference group). According to the technical notes from CDC NCHHSTP AtlasPlus, national LTC is presented for persons aged ≥ 13 years and only for states with complete laboratory data (at least 95% of laboratory results are reported to the surveillance programs and transmitted to the CDC). From 2010 to 2018, the calculation of national LTC percentage ranges from 14 to 43 jurisdictions [24]. The list of jurisdictions for which data are presented by year is presented in S1 Table.

County-level variables

We included county-level variables that are publicly available from multiple datasets or aggregated from individual-level EHR data, and these factors were organized into four dimensions: demographic, physical, social characteristics, and health behaviors [15]. A total of 29 county-level factors were included in this study, and detailed information (e.g., definition, data source, and years of data used) for each variable is provided in Table 1. All missing data from 2010 to 2018 were imputed using the information from the neighboring year.

Table 1. The detailed definition, data source, and years of data extracted for each county-level variable.

Variables	Definitions	Year
Sociodemographic characteristics
Population size^a	Total weighted population	2010–2018
Blacks among persons newly diagnosed with HIV (%)^b	Percent of Black persons among people newly diagnosed with HIV each year	2010–2018
Males among persons newly diagnosed with HIV (%)^b	Percent of male persons among people newly diagnosed with HIV each year	2010–2018
Male (%)^a	Percent of male persons	2010–2018
Age (≥18, %)^a	Percent of persons aged > = 18 years old	2010–2018
Black (%)^a	Percent of Black persons	2010–2018
High education (%)^a	Percent of 25 years and older persons with at least four years of college	2010–2018
Low education (%)^a	Percent of 25 years and older persons with less than a high school education	2010–2018
Vacant houses (%)^a	Percent of vacant houses in high SES neighborhoods in addition to abandoned housing	2010–2018
Poverty (%)^a	Percent of 18–64 years old persons living below the federally defined poverty line	2010–2018
Median income ($)^a	Annual median household income	2010–2018
No insurance (%)^a	Percent of persons with no health insurance coverage	2010–2018
Public assistance (%)^a	Percent of households with public assistance	2010–2018
Unemployed (%)^a	Percent of 16 years and older persons who are unemployed	2010–2018
No transportation (%)^a	Percent of occupied housing units without access to a vehicle	2010–2018
White/non-White residential segregation index^c	The percentage of either White or non-White residents that would have to move to different geographic areas to produce a distribution that matches that of the larger area	2016–2018
Black/White residential segregation index^c	The percentage of either Black or White residents that would have to move to different geographic areas to produce a distribution that matches that of the larger area	2016–2018
Physical characteristics
primary care providers^a	Number of primary care providers per 100,000 population	2010–2018
Ryan White HIV centers^d	Number of Ryan White HIV centers per newly diagnosed HIV case each year within 25 miles radius	2010–2018
Mental health centers^d	Number of mental health centers per newly diagnosed HIV case each year within 25 miles radius	2010–2018
Social characteristics
Gini index ^a	Income inequality represented by statistical measure of income dispersion	2010–2018
Religious adherence (%) ^a	Percent of persons with religious adherence	2010
Family unity ^e	The share of births that are to unwed mothers, children living in single-parent families, and women aged 35–44 who are married	2018
Community health^e	Non-religious non-profits per capita, congregations per capita, and the informal civil society subindex	2018
Institution health^e	Presidential voting rate, census response rate, and confidence subindex	2018
Collective efficiency^e	Violent crimes per 100,000 people	2018
health behaviors
Smoking (%)^c	Percent of adults who are current smokers	2011–2018
Drinking (%)^c	Percent of adults reporting binge or heavy drinking	2011–2018

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^a Extracted from American Community Survey 5-year Estimate

^b Aggregated from individual-level enhanced HIV/AIDS reporting system (eHARS) in SC DHEC

^c Extracted from County Health Rankings & Roadmaps

^d Extracted from US Department of Health and Human Services (DHHS) Data Warehouse and health department websites in SC and its neighboring states

^e Extracted from US Congress Joint Economic Committee

Sociodemographic characteristics

County-level sociodemographic information refers to the population’s demographic composition and broad socioeconomic characteristics in a local area [25]. For demographic composition, eight variables were considered. Four variables were extracted from ACS 5-year estimates, including population size, male (%), age (≥18 years, %), and Black (%). For each calendar year from 2010 to 2018, the 5-year estimates refer to data collected over the past five years. For example, in 2018, the 5-year estimation refers to data collected from 2014 to 2018. Two demographic compositions of PNWH, including the percent of Black persons among PNWH and the percent of Male persons among PNWH each year, were aggregated and calculated based on the individual level race and gender data from eHARS. Two segregation indices, including the White/non-White residential segregation index and the Black/White residential segregation index, were extracted from County Health Rankings & Roadmaps. The residential segregation index, ranging from 0 to 100, can be interpreted as the percentage of one racial group that have to move to a different geographic area (census tract) to produce a distribution that of the larger area (county). The higher the residential segregation index score, the greater the residential segregation between two racial groups [26].

For socioeconomic characteristics, nine variables were extracted from ACS (5-year estimates), including the percentage of persons aged over 25 years old with less than high school education (lower education), the percentage of persons aged over 25 years old with at least four years of college (higher education), the proportion of people aged 18–64 years living in below the federally defined poverty line, the proportion of household with public assistance income, median household annual income, percentage of no health insurance coverage, unemployment rate, percentage of vacant homes in neighborhoods with high socioeconomic status (SES) in addition to abandoned housing, and transportation accessibility (proportion of occupied housing units without access to a vehicle) [27–29].

Physical characteristics

Physical characteristics represent the accessibility of social settings in the built environment and relevant social resources. Three factors, including the number of primary care providers per 100,000 people based on US Health data, the number of Ryan White HIV centers per PNWH, and the number of mental health centers per PNWH within 25 miles radius of each county in SC, were used to reflect local people’s access to health care access opportunity [30, 31].

Social characteristics

Social characteristics refer to social networks and social culture-related characteristics that are related to inequities or social disorganization [32, 33]. We included one factor about income inequalities (GINI index), one factor about the religious environment, and four factors about social capital. GINI index—a measure of income inequality between 0 and 1, with 0 being complete equality and 1 being complete inequality—was extracted from ACS, and the religious environment was measured by the proportion of religious adherents based on US Religious Data. Social capital factors included four variables, namely community health, institutional health, family unity, and collective efficacy, extracted from the 2018 US Congress Joint Economic Committee [34]. The detailed procedure for creating the former three indices (community health, institutional health, and family unity) is described elsewhere, and these factors were coded that higher scores corresponded with higher social capital levels [34, 35]. Collective efficacy was measured by the number of reported violent crimes per 100,000 population.

Health behaviors

Health behaviors refer to actions individuals take that may affect their health. County-level health behaviors, including excessive drinking and adult smoking, were extracted from CHRR. Excessive drinking was measured by the percentage of adults reporting binge or heavy drinking in the past 30 days. Adult smoking was calculated by the percentage of adults who are current smokers.

Statistical analysis

First, spatial-temporal distribution and variation of yearly LTC status were described by nine geographic maps of LTC percentage differences between the county and national levels from 2010 to 2018. The 46 counties in the nine maps were further grouped based on four Public Health Regions in SC, including Upstate, Midlands, Pee Dee, and Lowcountry [36]. Second, LTC percentages across 46 counties from 2010 to 2018 were illustrated using a heat map. Third, descriptive statistics were reported for all the county-level variables, including the 25^th percentile, median, 75^th percentile, and Interquartile Range (IQR). Fourth, we used longitudinal data from 2010 to 2018 to fit a Generalized Estimating Equation (GEE) model with stepwise selection to explore the relationship between county-level characteristics and LTC status. The stepwise selection is a procedure where we fit our regression model from a set of candidate variables by entering and removing variables based on the cut point of the p-value being 0.2 [37]. The exchangeable correlation structure within counties was used for the GEE approach to account for the repeated measure of county-level information. All analyses were conducted using R version 4.0.3, except for the geographic map created using GeoPandas. The significant level of statistical results was set at a P-value of 0.05.

Results

Descriptive statistics

The yearly number of adult PNWH was 746, 739, 687, 709, 760, 699, 775, 760, and 749 in SC from 2010 to 2018, respectively. (S1 Fig) The number of counties with a high LTC decreased from 34 (73.91%) in 2010 to 21 (45.65%) in 2018. (Table 2) However, the state average timely LTC percentage in SC is relatively stable, with the timely LTC percentage being 78.55% in 2010 and 80.99% in 2018. Additionally, the percentage of linkage to care within 60 days was 88.07% in 2010 and 88.49% in 2018. 90.75% and 90.63% of all PNWH were linked to care within 90 days after diagnosis in 2010 and 2018, respectively. (S2 Fig).

Table 2. Linkage to care status across 46 counties in South Carolina, n (%).

Linkage to care status	Below the jurisdictive national level	Above the jurisdictive national level
2010	12 (26.09%)	34 (73.91%)
2011	10 (21.74%)	36 (78.26%)
2012	14 (30.43%)	32 (69.57%)
2013	14 (30.43%)	32 (69.57%)
2014	19 (41.30%)	27 (58.70%)
2015	21 (45.65%)	25 (54.35%)
2016	16 (34.78%)	30 (65.22%)
2017	17 (36.96%)	29 (63.04%)
2018	25 (54.35%)	21 (45.65%)

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Table 3 describes the distribution of county-level characteristics across 46 counties in SC, and only data in the years 2010, 2014, and 2018 were described due to limited table space. In over 25% of the counties, 100% of PNWH were Black in 2010, but this percentage decreased to 88.89% in 2018. Half of the counties consistently have more than 48% males, more than 32% Blacks, and more than 76% aged above 18 years. Across the counties, there were relatively large variations in the proportion of Black persons (25^th percentile: 24%, 75^th percentile: 7%) and religious adherence (25^th percentile: 41.2%, 75^th percentile: 59.4%), with the IQRs over 15% in 2010, 2014, and 2018. In contrast, there were relatively more minor variations across the counties for the percent of lower/higher education attainment, poverty, vacant homes, transportation accessibility, no insurance coverage, and smoking/drinking behaviors, with the IQRs ranging from 2% to 10%.

Table 3. Descriptive statistics of county-level variables.

Predictors	25^th percentile	Median	75^th percentile	Interquartile Range (IQR)
Demographic characteristics
Population size
2010	27282	57499	133577	106295
2014	27003	58048	141594	114590
2018	27259	59158	151246	123987
Blacks among persons newly diagnosed with HIV (%)
2010	66.67	82.58	100.00	0.33
2014	50.54	66.67	88.89	0.38
2018	50.61	75.00	85.58	0.35
Male among persons newly diagnosed with HIV (%)
2010	60.00	76.51	87.96	0.28
2014	75.00	83.33	100.00	0.25
2018	71.43	80.63	97.06	0.26
Male (%)
2010	48.25	48.58	49.58	1.33
2014	47.85	48.57	49.26	1.41
2018	47.85	48.54	49.26	1.41
Black (%)
2010	24.99	33.51	47.42	22.43
2014	24.76	33.23	46.66	21.90
2018	23.84	32.2	47.02	23.18
Age (≥18, %)
2010	75.12	76.22	76.95	1.83
2014	76.08	77.28	78.81	2.73
2018	76.69	78.04	79.78	3.09
Low education (%)
2010	17.39	21.74	24.68	7.29
2014	15.32	19.33	21.84	6.52
2018	13.35	16.92	19.21	5.86
High education (%)
2010	12.88	16.52	21.81	8.93
2014	13.03	18.06	22.41	9.38
2018	14.43	18.59	24.49	10.06
Poverty (%)
2010	14.21	17.31	19.55	5.34
2014	16.60	20.38	22.90	6.30
2018	15.43	18.35	20.84	5.41
Median income ($)
2010	33066	38588	42871	9805
2014	33615	38610	43203	9588
2018	36276	42514	49392	13116
Public assistance (%)
2010	1.36	1.58	2.04	0.68
2014	1.27	1.65	2.06	0.79
2018	1.22	1.38	1.76	0.54
Vacant house (%)
2010	12.79	16.67	20.69	7.90
2014	13.30	16.45	20.78	7.48
2018	13.30	17.74	22.78	9.48
Transportation accessibility (%)
2010	6.52	8.52	10.32	3.80
2014	6.30	8.04	10.01	3.71
2018	6.04	7.29	9.87	3.83
No insurance coverage (%)
2010	15.50	16.95	18.70	3.20
2014	15.17	16.34	18.26	3.09
2018	10.09	11.33	12.18	2.09
White/non-White residential segregation index^a	24.67	30.00	35.33	10.66
Black/White residential Segregation index^a	26.67	30.83	38.67	12.00
Physical characteristics
Primary care providers
2010	61.70	78.76	105.93	44.23
2014	35.78	48.12	58.93	23.15
2018	37.44	47.56	68.25	30.81
Ryan White HIV centers
2010	0.03	0.15	0.33	0.31
2014	0.03	0.11	0.39	0.36
2018	0.03	0.15	0.47	0.44
Mental health centers
2010	0.15	0.41	1.00	0.85
2014	0.16	0.33	0.79	0.63
2018	0.16	0.38	1.00	0.84
Social characteristics
Gini index
2010	0.44	0.45	0.47	0.03
2014	0.45	0.46	0.48	0.03
2018	0.45	0.47	0.49	0.03
Religious adherence (%)^b	41.2	53.6	59.4	18.2
Family unity ^b	-1.81	-1.16	-0.51	1.30
Community health ^b	-0.79	-0.55	-0.31	0.48
Institution health ^b	-0.05	0.25	0.38	0.43
Collective efficiency ^b	406.70	503.00	629.20	222.50
Health behaviors
Smoking (%)^c
2010	0.21	0.23	0.26	0.05
2014	0.19	0.21	0.23	0.01
2018	0.17	0.19	0.20	0.03
Drinking (%)^c
2010	0.11	0.13	0.15	0.04
2014	0.11	0.12	0.15	0.04
2018	0.15	0.16	0.17	0.03

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Notes:

^a Variables were only available since 2016 and data from 2010 to 2015 were imputed using data from 2016 throughout the analysis

^b Variables were only available in one year and were used as constant variables throughout the analysis

^c Variables were only available since 2011, and data in 2010 were imputed using data from 2011 throughout the analysis

LTC status across counties in SC

Figs 1 and 2 illustrate the spatiotemporal variations of LTC across 46 counties, and county-level disparities in LTC were identified. The timely LTC percentage in some Upstate counties, including Greenville and Anderson, was consistently higher than the national level. In contrast, some Midlands (e.g., Edgefield, Saluda, Chester, and Lexington) and Lowcountry (e.g., Allendale and Bamberg) counties had low LTC in at least six years from 2010 to 2018.

Generalized estimating equation model with stepwise selection

Table 4 shows that after stepwise selection, 9 out of 29 county-level variables were retained in the final GEE model. Six variables were significantly associated with LTC status after including nine variables in the adjusted model. Counties with high LTC in SC decreased from 2010 to 2018 (OR: 0.87, 95%CI: 0.80~0.95). For demographic characteristics-related factors, the proportion of male persons (OR = 0.07, 95%CI: 0.02~0.29) and the proportion of high education (OR = 0.07, 95%CI:0.02~0.34) was negatively associated with high LTC. In addition, living in a county with a larger ratio of mental health centers per PNWH was related to a higher likelihood of high LTC (OR = 45.09, 95%CI:6.81~298.55). Among social characteristics-related factors, the number of violent crimes per 100,000 people was positively associated with high LTC (OR = 4.86, 95% CI: 1.43~16.59).

Table 4. The association of county-level factors with linkage to care status across the counties in South Carolina from 2010 to 2018: Stepwise based Generalized Estimating Equations (GEE) model.

Factors	Crude OR (95%CI)	Adjusted OR (95%CI)
Year	0.88 (0.81~0.95)	0.87 (0.80~0.95)
Blacks among persons newly diagnosed with HIV (%)	0.79 (0.35~1.77)	-
Male among persons newly diagnosed with HIV (%)	0.62 (0.24~1.58)	-
Sociodemographic characteristics
Population size	2.22 (0.92~5.35)	5.17 (1.40~19.16)
Male (%)	0.14 (0.05~0.41)	0.07 (0.02~0.29)
Age (≥18, %)	0.23 (0.06~0.91)	-
Black (%)	0.71 (0.32~1.67)	-
Low education (%)	1.51 (0.52~4.44)	-
High education (%)	0.62 (0.24~1.57)	0.07 (0.02~0.34)
Median income ($)	0.47 (0.17~1.33)	-
Public assistance (%)	0.94 (0.28~3.21)	-
Unemployment (%)	3.38 (0.81,14.16)	-
Transportation accessibility (%)	1.24 (0.36~4.25)	0.19 (0.04~1.01)
Poverty (%)	0.93 (0.25~3.42)	-
Vacant house (%)	1.33 (0.47~3.70)	-
No insurance coverage (%)	3.76 (1.23~11.50)	-
White/non-White residential segregation index	2.53 (1.08~5.96)	0.15 (0.00~8.85)
Black/White residential Segregation index	2.76 (1.25~6.95)	29.07 (0.57~1478.21)
Physical characteristics
Ryan White HIV centers	0.63 (0.19~2.00)	-
Mental health centers	2.32 (0.71~7.58)	45.09 (6.81~298.55)
Primary care providers	5.49 (1.04~28.96)	-
Social characteristics
Gini index	0.92 (0.22~3.84)	-
Religious adherence (%)	3.36 (1.27~8.90)	-
Family unity	0.98 (0.43~2.24)	-
Community health	0.27 (0.65~1.10)	-
Institution health	1.04 (0.38~2.84)	-
Collective efficacy	3.90 (1.32~11.50)	4.86 (1.43~16.59)
Health behaviors
Smoking (%)	1.98(0.58~6.75)	-
Drinking (%)	0.37(0.13~1.01)	-

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Notes:

OR: Odds Ratio. CI: Confidence Interval.

-: Variables were not selected by the stepwise selection. All OR in bold means statistically significant

Discussion

This study described both the temporal and spatial variations of LTC status across 46 counties in SC from 2010 to 2018 and investigated the relationship between county-level characteristics and these variations. Twenty-nine county-level variables across demographic, physical, social characteristics and health behaviors domains were selected, and six of them were detected to be significantly associated with LTC status.

There were apparent spatial disparities in LTC percentage in SC, with some counties constantly having lower or higher LTC percentages than the national level. Generally, compared to the counties in the Lowcountry and Midlands region, the Upstate area tended to have high LTC. According to the epidemic profile 2020 of HIV and AIDS in Upstate, as of December 31, 2019, the Upstate has the highest number and proportion (33%) of people living with HIV in SC [36]. Despite the large prevalence, LTC efforts have improved in Upstate, with various programs and outlets for LTC [36]. In the Lowcountry region, Bamberg had the second-highest number of newly diagnosed HIV cases [38]. However, it has reported low LTC compared to the national level for at least six years from 2010 to 2018. This highlights the urgency and significance of interventions to improve LTC status in these counties [39].

Among demographic characteristics, we found a significant and negative association between the percent of the Male population and LTC status in SC, and this association persisted after controlling the percentage of PNWH who are male in the model. This finding was consistent with previous individual-level research, in which male persons were less likely to be linked to care timely [40–42]. One possible explanation is that a high proportion of male persons may be related to masculinity norms in the local area, especially in the Deep South States [42]. Traditional masculinity ideology deters males’ perception of the risks of HIV to their health and ultimately deters their health-seeking behaviors [11]. Men were disproportionately affected by HIV/AIDS, and they were likely to be influenced by the atmosphere of masculinity culture [11]. These results warrant intensified intervention efforts among male PNWH and in counties with a high proportion of male persons when promoting LTC.

The number of mental healthcare centers within a 25-mile radius of each county per PNWH was found to be positively associated with LTC status. Previous studies have found various HIV-associated mental health problems (e.g., stigma, depression, anxiety, and fear) were significant barriers to timely LTC [43–45]. In a US sample of PNWH, depression was a statistically significant predictor of failed linkage to care within three months after initial HIV diagnosis [44]. This emphasized the potential need for integration of mental health services alongside interventions at the early stage of the HIV continuum of care, such as immediately after HIV diagnosis and when initiating contact with treatment services. As one significant aspect of accessible healthcare facilities, accessible mental health centers provide PNWH with psychological counselling and services for mental health treatment. Our findings implied that interventions aimed at counties with limited mental healthcare resources might promote county-level LTC.

This study is innovative in leveraging multiple public datasets, incorporating many county-level predictors, and applying longitudinal models to investigate the county-level variations of LTC status in SC. However, there are still some limitations that need to be acknowledged. First, some counties only have a few new HIV cases, limiting the statistical power of detecting potential predictors. Second, more potential structural predictors (e.g., HIV-related discrimination and structural racism in incarceration) should be included in future studies, which are not included in the current study due to the unavailability of data. Third, individual-level factors were not included in this study. More investigations on the accumulative impacts of individual and structural factors can provide more insights into the barriers and facilitators of LTC. Fourth, there may be the modifiable areal unit problem (MAUP) since county-level data were used in the analysis. We need to be cautious when generating findings of the current study to other administrative units, such as the census tract.

Conclusion

Considering the unsatisfactory results of LTC status in SC when compared to the national level and the concentration of low LTC percentages in counties with large HIV cases, more efforts on promoting LTC are still needed to curb the HIV epidemic. Counties with a large proportion of male persons require intensive attention, and actions that focus on improving accessible mental healthcare centers tend to be effective interventions. To get a more thorough understanding of the structural/social determinants of the LTC percentage, the effectiveness of interventions based on these factors should be evaluated. More significant country-level factors that are unavailable at present should be measured and incorporated into future studies.

Supporting information

S1 Fig. Linkage to care within 30 days, 60 days, or 90 days of HIV diagnosis in South Carolina, 2010 through 2018.

(TIF)

Click here for additional data file.^{(1.4MB, tif)}

S2 Fig. The number of newly diagnosed HIV cases in South Carolina, 2010 through 2018.

(TIF)

Click here for additional data file.^{(1.8MB, tif)}

S3 Fig. Bivariate map on Log odds of linkage to care status and the number of mental health centers per person newly diagnosed with HIV (PNWH) across South Carolina in 2018.

(TIF)

Click here for additional data file.^{(1.4MB, tif)}

S1 Table. Jurisdiction(s) meeting National HIV surveillance system laboratory reporting requirements, 2010–2018.

(DOCX)

Click here for additional data file.^{(14.2KB, docx)}

Acknowledgments

The authors thank the SC Department of Health and Environmental Control (DHEC), the Office of Revenue and Fiscal Affairs (RFA), and other SC agencies for contributing the data in South Carolina.

Data Availability

Data is not publicly available due to provisions in our data use agreements with state agencies/data providers, institutional policy, and ethical requirements. We make access to such data available via approved data access requests from the IRB of the University of South Carolina (contact Lisa M. Johnson at lisaj@mailbox.sc.edu).

Funding Statement

The research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI127203 (PI: XL) and R01AI164947 (PI: JZ,BO). This work was also partially supported by a SPARC Graduate Research Grant from the office of the Vice President for Research at the University of South Carolina (grant #: 115400-22-59203) (PI: FS). Dr. Xueying Yang’s effort is supported by ASPIRE -I, TRACK-2 from the office of the Vice President for Research at the University of South Carolina (grant #: 115400-22-60028). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0286497.r001

Decision Letter 0

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30 Jan 2023

PONE-D-22-15791County-level determinants of linkage to care among people living with HIV in South Carolina: A longitudinal analysis from 2010 to 2018PLOS ONE

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Reviewer #3: Yes

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5. Review Comments to the Author

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Reviewer #1: This is a well-written manuscript describing a county-level analysis of predictors of linkage to care among people living with HIV in South Carolina. Overall, the paper is clearly presented. I only have a few minor comments and questions.

The outcome of interest was a viral load or CD4 measurement within one month of HIV diagnosis. i recognize that this is based on federal recommendations, but at the very least a descriptive presentation of time to viral load or CD4 count would be useful. For example, how many occurred >30 days but <60 days or <90 days?

Methods, line 99: should this be: "Successful linkage to care" divided by the number of newly diagnosed HIV cases? I.e., addition of 'newly' to this sentence?

Methods, line 126: It seems as though the analysis is conducted at the county level, but this line refers to data at the census tract level. More information about how these data are aggregated up to the county level would be useful.

Results, lines 188-195: This paragraph is difficult to follow and would benefit from editing for clarity.

Table 3: Consider adding one to two decimal places to the count of Ryan White HIV centers and mental health centers per 100,000 people. Also, there is a minor typo here ("Pyan White" instead of "Ryan White").

Results, lines 201-206: Lancaster and Sumter counties are repeated in this list.

The authors have used statistical methods to identify predictors of linkage to care; however, they have not conducted a causal analysis. Thus, use of words like 'determinants' is unwarranted. In most of the Discussion, 'predictors' is used. However, in the title and in the first paragraph of the Discussion, the word 'determinants' is used. The authors are encouraged to reconsider this word usage so as not to overstate the findings.

Reviewer #2: Overall, the authors are bringing to light the social and environment factors that might be contributing to patterns in linkage to care in the state of South Carolina. This is helpful information to learn about, however, there are several items that have me hesitant to accept for publication. Please see attached document for comments.

Reviewer #3: This manuscript examines factors that influence linkage to HIV care in South Carolina at the county level between 2010 and 2018. The paper is very well written and clearly and concisely summarized. Overall, the study is well done and don't have any major concerns, but numerous minor issues should be addressed before publication consideration.

Minor comments:

-Please provide the IRB IDs approved bu SC and SC DHEC in text (line 93).

-Please improve the resolution of figures 1 and 2.

-Figure 1: Please add a white halo around the labels in the 2010 map. Overall, I think the maps need some work. Please choose different colors for the LTC rate above and below symbology since you are using blue and red for the choropleth map.

-Lines 177-178: I don't think a a 0.86% increase between 2010-2018 is steady, nor notable. It's clearly stable. Suggest combining sentences 177-180 to show # of counties with high LTC decreased (significant difference?), but the overall state rate was stable.

-Please add "%" in the parentheses in Table 2 and a space between the n and (%).

-Could be worthwhile to produce a few bivariate maps of the LTC outcome and the covariates with the highest predictive power.

-Can you show a time series of new cases per year?

-Please discuss uncertainty of using county-level data, which does not accurately depict the spatial heterogeneity of care at smaller administrative units (think of the MAUP problem).

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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PLoS One. 2023 May 31;18(5):e0286497. doi: 10.1371/journal.pone.0286497.r002

Author response to Decision Letter 0

16 Apr 2023

Reviewer #1

This is a well-written manuscript describing a county-level analysis of predictors of linkage to care among people living with HIV in South Carolina. Overall, the paper is clearly presented. I only have a few minor comments and questions.

1. The outcome of interest was a viral load or CD4 measurement within one month of HIV diagnosis. I recognize that this is based on federal recommendations, but at the very least a descriptive presentation of time to viral load or CD4 count would be useful. For example, how many occurred >30 days but <60 days or <90 days?

Response: Thank you for the suggestion. In the revision, we added a text description of the percentage of linkage to care within 30 days, 60 days and 90 days (Page 11; lines 175-179) with a bar chart illustrated in supplemental figure 2.

S2 Fig. Linkage to care within 30 days, 60 days, or 90 days of HIV diagnosis in South Carolina, 2010 through 2018

2. Methods, line 99: should this be: "Successful linkage to care" divided by the number of newly diagnosed HIV cases? I.e., addition of 'newly' to this sentence?

Response: We agree and have added ‘newly’ to this sentence.

3. Results, lines 188-195: This paragraph is difficult to follow and would benefit from editing for clarity.

Response: We have rewritten this paragraph . Please refer to lines 181 to 191 in the manuscript.

4. Table 3: Consider adding one to two decimal places to the count of Ryan White HIV centers and mental health centers per 100,000 people. Also, there is a minor typo here ("Pyan White" instead of "Ryan White").

Response: We added two decimal places as suggested, and we changed the typo of “Pyan White” to “Ryan White”.

5. Results, lines 201-206: Lancaster and Sumter counties are repeated in this list.

Response: We deleted the repeated counties in the list.

6. The authors have used statistical methods to identify predictors of linkage to care; however, they have not conducted a causal analysis. Thus, use of words like 'determinants' is unwarranted. In most of the Discussion, 'predictors' is used. However, in the title and in the first paragraph of the Discussion, the word 'determinants' is used. The authors are encouraged to reconsider this word usage so as not to overstate the findings.

Response: We agreed and rephrased ‘determinants’ and ‘predictors’ into ‘Associations’ and ‘relationships’ throughout the manuscript.

Reviewer #2

Overall, the authors are bringing to light the social and environment factors that might be contributing to patterns in linkage to care in the state of South Carolina. This is helpful information to learn about, however, there are several items that have me hesitant to accept for publication. Please see below:

1. Background

a. Lines 48 & 52: you state that PLWH and their linkage to care. Linkage to care is not based on persons living with HIV. It is based on persons newly diagnosed with HIV. Throughout the paper, you state PLWH and it should be newly diagnosed. PLWH are based on prevalence. That is, persons who are currently living with diagnosed HIV but could have been diagnosed at any time. The care outcomes for PLWH are receipt of care, retention in care, and viral suppression. https://www.cdc.gov/hiv/library/reports/hiv-surveillance/vol-27-no-3/index.html

Response: Thank you for pointing this out. We agreed and changed the description of people living with HIV (PLWH) into people newly diagnosed with HIV (PNWH) when talking about linkage to care throughout the manuscript.

a. There are no mention of what HIV looks like in SC. How many cases on average annually? What is the distribution of cases by age, sex, race/ethnicity, transmission category? This information would be helpful to the reader to understand SC.

Response: We agreed and added the related description of HIV epidemiology in SC in the introduction part (page 2; lines 28 to 33).

“According to the state surveillance data, there were around 748 PNWH annually in SC from 2009 to 2020. [5] Among them, men, African Americans, people aged 20-29, and men who have sex with men were disproportionately affected by HIV, making up low percentages of SC’s total population but comprising high percentages of PNWH. [5] For example, men comprise 48% of SC’s total population but makeup 80% of 1,556 PNWH in SC during the two-year period 2018-2019. [5]”

2. Methods

a. Line 83: eHARS is enhanced not electronic

Response: We have changed ‘electronic’ into ‘enhanced’.

b. Lines 57 & 84: How do you calculate LTC rates? If LTC is a percentage? Also, you reference using CDC defn for LTC, you want to cite it here. If it’s the same as reference 4, then it is not clear

Response: We agreed that “LTC rate” is not the right term for our calculation, and we changed it to “LTC percentage” throughout the manuscript. According to CDC, timely LTC was measured by records of � 1 CD4 (count or percentage) or viral load tests performed within one month after HIV diagnosis, including tests performed on the same date as the date of diagnosis. [4] Based on this definition, we classified the individual-level LTC status as “timely LTC” and “delayed LTC.” The county-level timely LTC percentage was calculated as the number of “timely LTC” divided by the number of newly diagnosed HIV cases for each county in the specified calendar year. In addition, we cited the website of CDC’s definition of LTC as suggested.

c. Line 86: you state them as “risk factors” however you are obtaining information such as gender, age, race, etc. I recommend not using the word “risk”. Some risk-related terms can be stigmatizing and may imply that the condition is inherent to a person or group rather than the actual causal factors. Could use “variables” or “factors”

Response: We agreed and replaced ‘risk factors’ with ‘variables”.

d. What 5-year estimates did you use from the American Community Survey? That is not clear.

Response: In ACS, it reported the 5-year estimates, which refer to data collected over the past five years for each calendar year from 2010 to 2018. For example, in 2018, the 5-year estimation refers to data collected from 2014 to 2018. We use different ACS dataset for different years. We clarified this issue in lines 110-112 and 193-198.

e. For all the data sources, ACS, County Health Rankings & Roadmaps, and US Congress joint Economic Committee, please include what year(s) of data you used.

Response: We added one column in table 1 to describe the year used for each variable in analyses to make it clear.

f. Why were the variables “Male” and “Black” picked for predictors?

Response: The percentage of Black residents, alternatively referred to as Black racial composition, has been suggested as a proxy measure of racial residential segregation among the Black population at the county level. A higher value of the Black racial composition aligns with more Black and non-Black residential segregation. The percentage of male persons may be related to masculinity norms in the local area, especially in the Deep South States. [42] Thus, we assumed counties with higher percentages of male or black residents were less likely to have high LTC percentages. That’s why we incorporate “Male” and “Black” into the model.

g. Under the header County-level LTC status (lines 94-103): It appears that percentages, not rates, are calculated. So that readers are clear that your calculation aligns with CDC’s, I highly recommend using the same language as CDC when reporting on the outcome. Linkage to HIV medical care within 1 month after HIV diagnosis was measured by documentation of ≥ 1 CD4 (count or percentage) or viral load tests performed ≤ 1 month after HIV diagnosis, including tests performed on the same date as the date of diagnosis https://www.cdc.gov/hiv/library/reports/hiv-surveillance/vol-27-no-3/content/technical-notes.html. Also, linkage to care for national-level linkage to care data from CDC include different states for different years (ranging from 14 jurisdictions in 2010 to 43 jurisdictions in 2018 – see Technical Notes from NCHSSTP AtlasPlus here: https://gis.cdc.gov/grasp/nchhstpatlas/main.html). Therefore, it may not be accurate to call them national comparisons for the different years (and may need to state which jurisdictions are included in the “national level” for each year you are using).

Response: As suggested, we modified and used the same language as CDC in the outcome definition part. Please refer to lines 77 to 79 in the un-tracked version manuscript. In addition, we agreed that it’s needed to state which jurisdictions were included. We described the jurisdictions in the methods part when mentioning the nation-level LTC (lines 86 to 91) and listed the detailed jurisdiction for each year in supplemental table 1.

h. For Table 1: it would be helpful to include what year(s) for each variable. Also, table 1’s label states, “The detailed definition and cut-point of each county-level predictor”. However, I do not see any cutpoints in the table.

Response: Thank you for the suggestion. We added one column in table 1 to describe the year used for each variable in analyses to make it clear. We did not use the cutoff in defining the variables and deleted it in the revision.

i. For the ACS data, how did the writer compute data for the 3 years: 2010, 2014, and 2018? Since the 5-year estimate is a period estimate, was the same ACS dataset used for the different years? If so, then that needs to be stated in the methods.

Response: Different ACS data set is used for different year. In ACS, it reported the 5-year estimates refer to data collected over the past five years for each calendar year from 2010 to 2018. For example, in 2018, the 5-year estimation refers to data collected from 2014 to 2018. The 5-year estimated reported at the diagnosis year were used. Due to the limit of space, we only described the summary of 5-year estimates at year 2010, 2014, and 2018 in table 3. We clarified this issue in the methods part as suggested. Please refer to page 8, lines 110 -112 in the clean version.

j. Similar to the previous statement, it needs to be clear which years of each of the 25 variables was used.

Response: We added one column in table 1 to describe the year used for each variable.

k. Line 164: The statistical analysis section needs to provide more information on exactly how the analysis was computed for each for the 25 variables. Also, it is not clear how the data years (2010, 2014, and 2018) fit into the GEE model. More information is needed for clarity for the reader.

Response: We used longitudinal data from 2010 to 2018 to fit a Generalized Estimating Equation (GEE) model with the stepwise selection to explore the relationship between 25 county-level variables and LTC status. In the stepwise selection, we choose a p-value of 0.2 to select the variables. We modified the statistical analysis section to make the description clearer. Please refer to page 10, lines 162-168.

3. Results

a. Lines 177-180: “The state average LTC rate of SC steadily increased from 60.41% in 2010 to 61.27% in 178 2018, but still in a smaller magnitude than the increasing temporal trend at the national level. 179 And this explained why the number of counties with a high LTC decreased from 16 (34.8%) in 180 2010 to 8 (17.4%) in 2018 among 46 counties.” Again, this isn’t an apples-to-apples comparison. There are different numbers of jurisdictions in the national data for each year.

Response: Agreed. We added the information of the jurisdictions in each year for the “national level” calculation in detail in the methods part. Please see below.

“According to the technical notes from CDC NCHHSTP AtlasPlus, national LTC is presented for persons aged � 13 years and only for states with complete laboratory data (at least 95% of laboratory results are reported to the surveillance programs and transmitted to the CDC). From 2010 to 2018, the calculation of national LTC percentage ranges from 14 to 43 jurisdictions. [24] The list of jurisdictions for which data are presented by year are presented in S1 Table.”

b. Throughout the results section, please determine if you will use one decimal point or 2. It is not consistent throughout.

Response: We decided to use two decimals. All results were updated throughout the paper for consistency.

c. Table 3: again, it is not clear if you used the same dataset for the predictor variables for the different years. Also, Ryan White is misspelled in the table (it says Pyan White).

Response: Except for adding one column in Table 1 describing the years for each variable, additional notes were made under Table 3 to make the variables and corresponding years of data used clear. In addition, we edited the typo of “Pyan White”.

d. Lines 200-206: There is discussion of counties in different regions (Upstate, Midlands, Pee Dee). This is the first time this information is mentioned. This type of information should be included in the Methods section.

Response: We agreed and added the information in the description of Figure 1 in the statistical analysis. The information added is as below:

“The 46 counties in the nine maps were further grouped based on four Public Health Regions in SC, including Upstate, Midlands, Pee Dee, and Lowcountry. [36]”

e. Line 217: The CI should include ~ and not –

Response: Thank you for pointing this out. We changed ~ into – as suggested.

f. Table 4: Some of the confidence intervals have ~ and others have , . Also in Table 4, please include a footnote/note at the bottom of the table stating that the results in bold are statistically significant. Also, what does “N/A” mean? It’s not clearly stated.

Response: Thank you for pointing this out. We changed all “,” into “~ “ to make it look consistent throughout the table. We added footnote at the bottom of table to indicate that the results in bold are statistically significant and “-“ were variables not selected by stepwise selection in Table 4.

g. There is a concern with some counties having small numbers. The rule of thumb that CDC uses for HIV surveillance data is that results are based on stable numbers (i.e., based on 12 or more diagnoses). If they are less than 12, then they are not considered reliable. It appears there are some counties in your study that run into this issue

Response: We examined the new diagnosis over time using the GEE model which does not have any convergence issue in our study. Thus, we have less concern about the modelling approach. We acknowledge that there are some counties that run into the issue of having less 12 newly diagnosed cases in some years, and we pay caution in the interpretation.

h. Also, there may be some counties with small population sizes. What that taken into account?

Response: We agreed that the population size might influence the results and incorporated population size as one variable in the regression model.

i. For the results, did the writers take into account the number of HIV diagnosis by specific demographics? You results show that males and Black persons were statistically significant findings, but your model didn’t take into account the number of HIV diagnoses that are Black and that are males. From NCHHSTP AtlasPlus, it shows that in just in 2018 alone, here are the numbers and rates for the top 3 racial/ethnic groups:

Race Cases Rate per 100,000 Population

Black/African American 448 40.0 1,119,585

White 172 6.1 2,815,183

Hispanic/Latino 56 25.9 216,019

Additionally, there are more males being diagnosed in SC:

Sex Cases Rate per 100,000 Population

Male 564 27.3 2,063,390

Female 161 7.2 2,232,959

Wouldn’t this have some impact on the results? Wouldn’t this introduce some type of bias into your analysis? Also, of those cases diagnosed, what percentage were linked to care for the demographic information? This information would be needed to tell the full picture.

Response: We agreed that this might impact the results and we added the percentage of male persons and Black persons among people newly diagnosed with HIV in each year from 2010 to 2018 into the analysis. We described the percentage of male and Black persons among people newly diagnosed in Table 3 and added them as county-level variables in the GEE model.

4. Discussion

a. Lines 243-246: For instance, Allendale is a county located in the Lowcountry area with a consistently low LTC rate. This may be because it has the largest proportion of the Black population, the highest percentage of unemployment/poverty, and the lowest median income.” Language that implicitly contains a negative judgement about the character of a person or a group of people (especially the statement about “the Black population”). It also may blame people for circumstances beyond their control. Such language often contributes to disapproving views of, or discrimination against, a group of people. I recommend making sure the statements are written as to not place blame, stigmatize, or offend readers.

Response: We agreed and re-wrote the whole paragraph to avoid placing blame. Please refer to lines 238 to 247 in the clean version manuscript.

b. For the discussion on counties, there needs to be more understanding of these regions and counties. Readers outside of SC (and even those inside SC), may not know the distribution of population characteristics for the state. Therefore, further explanation is needed. The geospatial work could actually be a paper in and of itself.

Response: Thank you for pointing this out. We agreed and added some brief discussions on those regions and counties. Please refer to lines 241 to 247 in the clean version manuscript.

c. Lines 250-256: You found a negative association between % Black and high LTC. Again, couldn’t some of this be explained because most newly diagnosed cases are among Black persons? The study you mention from Florida is a good suggestion for your analysis. Using a multi-level (or HLM) modeling to that take into account both individual and group level factors.

Response: We acknowledge that it’s very valuable to conduct the multilevel analysis. However, the scope of this paper is to investigate the percentage of linkage to care at the county level and identify the county that could be the target of intervention in the future. In the multilevel analysis, the individual linkage to care status should be used as the outcome, which cannot address our focus on the percentage of linkage to care at the county level. In the revision, we incorporate more information aggregated from the individual level (e.g., percentage of Black persons among people newly diagnosed with HIV) into our analysis to address the issue that most newly diagnosed cases are among Black persons.

d. Lines 257-265: Similar to above, your study found a negative association between males and high LTC. Again, couldn’t some of this be explained by most cases being among males?

Response: Same as above, we added the percentage of male persons among people newly diagnosed with HIV in each year from 2010 to 2018 into analysis to address the issue that most newly diagnosed cases are among male persons.

e. Lines 286 and 294: the fact that the numbers are so small, these results may be very unstable. There should be caution when presenting results from such case count. Therefore, there should be no discussion on these results.

Response: Thank you for this suggestion. We agreed and deleted related discussions if there were a small number issue.

5. Overall Recommendations

a. Background needs to be reworked based on recommendations above

Response: Thank you for the recommendation. We have reworked the background as suggested.

b. Methods needs to include pertinent information that is recommended above

Response: Thank you for the recommendation. We have included the information suggested in the methods part.

c. The analysis needs to be reconsidered. Suggestion is to run a multilevel analysis that looks at individual and group level data

Response: Thank you for the recommendation. We acknowledge that it’s very valuable to conduct the multilevel analysis. However, the scope of this paper is to investigate the rate of linkage to care at the county level and identify the county that could be the target of intervention in the future. In the multilevel analysis, the individual linkage to care status should be used as the outcome, which cannot address our focus on the percentage of linkage to care at the county level. In the revision, we incorporate more information generated from the individual level (e.g., percent of Black persons among people newly diagnosed with HIV) into our analysis.

d. Proceed with caution when discussing numbers less than 12

Response: Thank you for the recommendation. We agreed and deleted discussion regarding numbers less than 12.

e. Overall, several concerns with the analysis and results (and interpretation of some of the results)

Response: Thank you for the recommendation. We modified the analysis, results, and interpretation as suggested.

f. Be aware of language that is stigmatizing

Response: Thank you for the recommendation. We modified the language that is stigmatizing.

Reviewer #3

This manuscript examines factors that influence linkage to HIV care in South Carolina at the county level between 2010 and 2018. The paper is very well written and clearly and concisely summarized. Overall, the study is well done and don't have any major concerns, but numerous minor issues should be addressed before publication consideration.

Minor comments:

1. Please provide the IRB IDs approved bu SC and SC DHEC in text (line 93).

Response: This study was approved by the USC and SC DHEC IRB (#Pro00068124) in 2017. We added the IRB IDs as suggested.

2. Please improve the resolution of figures 1 and 2.

Response: Thank you for pointing this out. We have improved the resolution of figures 1 and 2 as suggested.

3. Figure 1: Please add a white halo around the labels in the 2010 map. Overall, I think the maps need some work. Please choose different colors for the LTC rate above and below symbology since you are using blue and red for the choropleth map.

Response: We added the white halo around the labels, and chose different colors for the LTC percentage above and below symbology as suggested.

4. Lines 177-178: I don't think a a 0.86% increase between 2010-2018 is steady, nor notable. It's clearly stable. Suggest combining sentences 177-180 to show # of counties with high LTC decreased (significant difference?), but the overall state rate was stable.

Response: We edited this part to “The number of counties with a high LTC decreased from 34 (73.91%) in 2010 to 21 (45.65%) in 2018 among 46 counties. However, the state average timely LTC percentage in SC is relatively stable, with the LTC percentage being 78.55% in 2010 and 80.99% in 2018.”

5. Please add "%" in the parentheses in Table 2 and a space between the n and (%).

Response: We added % and the space as suggested.

6. Could be worthwhile to produce a few bivariate maps of the LTC outcome and the covariates with the highest predictive power.

Response: We agreed and added a bivariate choropleth map illustrating the spatial distribution of the LTC outcome and the covariates with the highest predictive power, which is the number of mental health centers per people newly diagnosed with HIV in the current study. Please refer to S3 Fig.

7. Can you show a time series of new cases per year?

Response: We added a bar and line chart to illustrate the time series of new cases per year as suggested. Please check S1 Fig.

Supplemental Figure 1 The number of newly diagnosed HIV cases in South Carolina, 2010 through 2018

8. Please discuss uncertainty of using county-level data, which does not accurately depict the spatial heterogeneity of care at smaller administrative units (think of the MAUP problem).

Response: Thanks for the comment. We discuss the uncertainty of using county-level data in the limitation part as suggested. Please check below:

Fourth, there may be a modifiable areal unit problem (MAUP) since county-level data were used in the analysis. We need to be cautious when generating findings of the current study to other administrative units, such as census tract.

Editor’s comments and additional requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: Thank you for the comment. We formatted the style of this manuscript and renamed the files based on PLOS one’s submission guideline as required.

Response: The IRB approved this study as a non-human subject study, and no participant consent is needed. Additional information was added to the methods part as suggested.

3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“The research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI127203 (PI: XL) and R01AI164947 (PI: JZ,BO). This work was also partially supported by a SPARC Graduate Research Grant from the office of the Vice President for Research at the University of South Carolina (grant #: 115400-22-59203) (PI: FS). Dr. Xueying Yang’s effort is supported by ASPIRE -I, TRACK-2 from the office of the Vice President for Research at the University of South Carolina (grant #: 115400-22-60028). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

Response: Thank you for pointing this point. We revised the acknowledgement part into “The authors thank the SC Department of Health and Environmental Control (DHEC), the office of Revenue and Fiscal Affairs (RFA), and other SC agencies for contributing the data in South Carolina”.

The amended funding statements are “The research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI127203 (PI: XL) and R01AI164947 (PI: JZ,BO). This work was also partially supported by a SPARC Graduate Research Grant from the office of the Vice President for Research at the University of South Carolina (grant #: 115400-22-59203) (PI: FS). Dr. Xueying Yang’s effort is supported by ASPIRE -I, TRACK-2 from the office of the Vice President for Research at the University of South Carolina (grant #: 115400-22-60028). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” We added the amended statement in the cover letter.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Response: Data is not publicly available due to provisions in our data use agreements with state agencies/data providers, institutional policy, and ethical requirements. We make access to such data available via approved data access requests from the IRB of the University of South Carolina (contact Lisa M. Johnson at lisaj@mailbox.sc.edu).

We added the data availability statement in the cover letter as required. Please check it out. Thanks!

In your revised cover letter, please address the following prompts:

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: We added the data availability statement in the cover letter as required. Please check it out. Thanks!

Response: The Institutional Review Boards at the University of South Carolina and the SC Department of Health and Environmental Control approved the study protocol; the IRB number is #Pro00068124. The IRB approved this study as a non-human subject study, and no participant consent is needed. We added this information io the methods part. Please refer to lines 72 to 75 in the clean version manuscript. Thanks!

Response: Thank you for the comments. We have double-checked the copyright issue, and we are confident that no copyright materials were used in the maps.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0286497.r003

Decision Letter 1

Csaba Varga

18 May 2023

County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018

PONE-D-22-15791R1

Dear Dr. Fanghui Shi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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PLoS One. doi: 10.1371/journal.pone.0286497.r004

Acceptance letter

Csaba Varga

22 May 2023

PONE-D-22-15791R1

County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Linkage to care within 30 days, 60 days, or 90 days of HIV diagnosis in South Carolina, 2010 through 2018.

(TIF)

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S2 Fig. The number of newly diagnosed HIV cases in South Carolina, 2010 through 2018.

(TIF)

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S3 Fig. Bivariate map on Log odds of linkage to care status and the number of mental health centers per person newly diagnosed with HIV (PNWH) across South Carolina in 2018.

(TIF)

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S1 Table. Jurisdiction(s) meeting National HIV surveillance system laboratory reporting requirements, 2010–2018.

(DOCX)

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Attachment

Submitted filename: Manuscript Review_County-level determinants of linkage to care among people living with HIV in South Carolina.docx

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Submitted filename: Response to Reviewers.docx

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Data Availability Statement

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PERMALINK

County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018

Fanghui Shi

Jiajia Zhang

Chengbo Zeng

Xiaowen Sun

Zhenlong Li

Xueying Yang

Sharon Weissman

Bankole Olatosi

Xiaoming Li

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Data sources and linkage

County-level LTC status

County-level variables

Table 1. The detailed definition, data source, and years of data extracted for each county-level variable.

Sociodemographic characteristics

Physical characteristics

Social characteristics

Health behaviors

Statistical analysis

Results

Descriptive statistics

Table 2. Linkage to care status across 46 counties in South Carolina, n (%).

Table 3. Descriptive statistics of county-level variables.

LTC status across counties in SC

Fig 1. Linkage to care percentage differences between county level and jurisdictive national level among people living with HIV across 46 counties in South Carolina from 2010 to 2018.

Fig 2. Heatmap of linkage to care percentage among people living with HIV across 46 counties in South Carolina from the year 2010 to 2018.

Generalized estimating equation model with stepwise selection

Table 4. The association of county-level factors with linkage to care status across the counties in South Carolina from 2010 to 2018: Stepwise based Generalized Estimating Equations (GEE) model.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Csaba Varga

Roles

Author response to Decision Letter 0

Decision Letter 1

Csaba Varga

Roles

Acceptance letter

Csaba Varga

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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