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Identifying how social determinants of health (SDH) influence the burden of disease in communities and populations is critically important to determine how to target public health interventions and move toward health equity. A holistic approach to disease prevention involves understanding the combined effects of individual, social, health system, and environmental determinants on geographic area-based disease burden. Using 2006–2008 gonorrhea surveillance data from the National Notifiable Sexually Transmitted Disease Surveillance and SDH variables from the American Community Survey, we calculated the diagnosis rate for each geographic area and analyzed the associations between those rates and the SDH and demographic variables. The estimated product moment correlation (PMC) between gonorrhea rate and SDH variables ranged from 0.11 to 0.83. Proportions of the population that were black, of minority race/ethnicity, and unmarried, were each strongly correlated with gonorrhea diagnosis rates. The population density, female proportion, and proportion below the poverty level were moderately correlated with gonorrhea diagnosis rate. To better understand relationships among SDH, demographic variables, and gonorrhea diagnosis rates, more geographic area-based estimates of additional variables are required. With the availability of more SDH variables and methods that distinguish linear from non-linear associations, geographic area-based analysis of disease incidence and SDH can add value to public health prevention and control programs.

Public health practitioners, researchers, and policy makers are examining the social determinants of health (SDH) to better understand the health of communities and to improve population health through targeted interventions. Knowing that a particular group bears excess burden of a disease is important, but knowing how the SDH influence that excess burden of disease is perhaps more important and useful in achieving health equity. The Centers for Disease Control and Prevention’s (CDC’s) National Center for HIV/AIDS, Viral Hepatitis, Sexually Transmitted Disease (STD), and Tuberculosis (TB) Prevention (NCHHSTP) uses a holistic, science-based approach that is modeled on the World Health Organization’s framework on SDH to promote health equity and improve the health of communities [

Gonorrhea is the second most common notifiable disease in the United States. In 2009, 301,174 cases of gonorrhea were reported—the lowest number ever recorded. Although gonorrhea rates had been declining since the mid 1970s, group-specific rates by sex, age, and race/ethnicity increased between 2009 and 2010. Reported gonorrhea diagnosis rate is highest among young women aged 15–24 years and race/ethnic disparities in gonorrhea diagnosis rates are among the highest for any disease with non-Hispanic black persons bearing a disproportionate burden of disease in the United States [

Understanding gonorrhea in the context of SDH also is important in the prevention of disease complications and antibiotic resistance. Among women, gonorrhea causes a spectrum of upper genital tract infections that can lead to infertility, ectopic pregnancy, and chronic pelvic pain—common consequences of pelvic inflammatory disease. Gonorrhea also increases the risk of HIV transmission. Growing cephalosporin antibiotic resistance and fewer alternative treatment options increase the importance of decreasing gonorrhea morbidity, perhaps by modifying those social, health system, and other environmental factors that are increasing population risk [

Song

In this article, which is focused on statistical methods in order to replicate the methods described by Song

To identify the relationship between SDH variables and AIDS diagnosis rates, Song

Gonorrhea diagnosis rates were computed from data collected in the National Notifiable STD Surveillance System. STD control programs and health departments in all 50 states, the District of Columbia, selected cities, Guam, Puerto Rico, and the Virgin Islands submit STD morbidity data in hard copy and electronically (via the National Electronic Telecommunications System for Surveillance) to the Division of STD Prevention, NCHHSTP, CDC. Diagnosis rate for each CPOA was calculated using data on people with a diagnosis of gonorrhea (age 15 years and above) reported to CDC between 2006 and 2008. If the number of cases reported for each CPOA within this period was less than 30, we included cases reported to CDC between 2003 and 2005. Based on this criterion, 30 of the 949 CPOAs contained data from the 2003–2005 period. Rates per 100,000 person-years were calculated for each CPOA.

We estimated Pearson product moment correlations (PMCs) between gonorrhea diagnosis rate and SDH and demographic variables. Because PMC measures linear relationships, we examined the relationships between gonorrhea diagnosis rate and other variables using scatter plots to detect non-linear patterns. To make these relationships more linear, we log-transformed the following variables: gonorrhea diagnosis rate (log_rate); population density (log_dens); proportions of foreign-born (q_foreign); Hispanic (q_hisp); non-Hispanic black (q_black); and all racial/ethnic minority groups (q_xwhite).

We also estimated the first order partial correlations between gonorrhea diagnosis rate and each SDH and demographic variable. The partial correlation between two variables, X and Y, given a set of n controlling variables _{1}, Z_{2},…, Zn), is the correlation between the residuals resulting from the multiple linear regression of X with

PMC captures only the strength of linear association and its usefulness is greatly reduced when associations are non-linear [^{2}) of the data relative to the regression function [

In a comparison of PMCs and MICs for noiseless functional relationships of cubic, exponential, sinusoidal (Fourier frequency), and parabolic forms, the PMCs ranged from −0.09 to 0.7, but the MICs were 1.0 for all the relationships [

We used the MINE application to estimate MICs for the gonorrhea diagnosis rate and SDH variables. In addition to MIC, we also calculated a measure of non-linearity, MIC-ρ^{2}, where ρ is the PMC [^{2} is near 0 for linear relationships and large for non-linear relationships with high values of MIC. Reshef

We also presented the strength of non-linear associations among SDH and demographic variables and gonorrhea diagnosis rate on a two-dimensional plane. Each variable is represented by a node in the graph and the relative strength of the association between two nodes is represented by the thickness of the line connecting the two nodes. We used the “qgraph” package in R software to create this graph [

Based on the 2000 U.S. census data in the ACS, Song

The partial correlation between gonorrhea diagnosis rate and the proportion of the CPOA population who are non-Hispanic black remains mostly strong (around 0.83) after controlling for all SDH variables individually. The exception is that the PMC is smaller (0.73) when controlling for proportion of CPOA population who are of any minority race/ethnicity or for proportion of CPOA population who are not currently married. This is to be expected since each of these variables is highly correlated with the gonorrhea diagnosis rate (around 0.63) and with the proportion of the CPOA population who are non-Hispanic black (>0.53) [

Correlations between the gonorrhea diagnosis rate and both SDH variables and demographic variables, and partial correlations between variable X and gonorrhea diagnosis rate adjusted with variable Y, based on data at the CPOA level: National Notifiable STD Surveillance and American Community Survey data, 2006–2008.

Y | ||||||||||||||

F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 | |||

X | log_rate | 0.33 | 0.32 | 0.25 | 0.11 | 0.83 | 0.65 | 0.61 | 0.32 | 0.26 | 0.29 | 0.26 | 0.11 | |

log_dens | (F1) | 0.33 | 0.25 | 0.33 | 0.32 | –0.10 | 0.21 | 0.18 | 0.50 | 0.36 | 0.32 | 0.35 | 0.32 | |

p_female | (F2) | 0.32 | 0.23 | 0.38 | 0.35 | 0.15 | 0.38 | 0.27 | 0.34 | 0.34 | 0.30 | 0.39 | 0.34 | |

p_young | (F3) | 0.25 | 0.25 | 0.32 | 0.22 | 0.22 | –0.03 | –0.05 | 0.17 | 0.14 | 0.24 | 0.12 | 0.22 | |

q_hisp | (F4) | 0.11 | 0.06 | 0.20 | 0.04 | 0.14 | –0.55 | 0.02 | 0.15 | –0.00 | 0.14 | 0.05 | 0.04 | |

q_black | (F5) | 0.83 | 0.81 | 0.81 | 0.83 | 0.83 | 0.72 | 0.75 | 0.82 | 0.82 | 0.82 | 0.84 | 0.83 | |

q_xwhite | (F6) | 0.65 | 0.62 | 0.67 | 0.62 | 0.77 | 0.31 | 0.44 | 0.63 | 0.62 | 0.64 | 0.63 | 0.74 | |

p_single | (F7) | 0.61 | 0.56 | 0.59 | 0.58 | 0.60 | 0.35 | 0.34 | 0.55 | 0.58 | 0.56 | 0.57 | 0.60 | |

p_pov | (F8) | 0.32 | 0.49 | 0.34 | 0.27 | 0.34 | 0.26 | 0.25 | 0.02 | 0.26 | 0.24 | 0.27 | 0.37 | |

p_hsch | (F9) | 0.26 | 0.30 | 0.28 | 0.17 | 0.24 | 0.21 | –0.09 | 0.12 | 0.17 | 0.24 | 0.25 | 0.25 | |

p_unemp | (F10) | 0.29 | 0.28 | 0.27 | 0.28 | 0.30 | 0.15 | 0.23 | 0.04 | 0.20 | 0.27 | 0.29 | 0.30 | |

p_moved | (F11) | 0.26 | 0.28 | 0.34 | 0.15 | 0.24 | 0.31 | 0.11 | 0.08 | 0.19 | 0.25 | 0.25 | 0.24 | |

q_foreign | (F12) | 0.11 | –0.06 | 0.16 | 0.05 | 0.03 | –0.01 | –0.48 | –0.06 | 0.22 | 0.05 | 0.14 | 0.05 |

log_rate = log(gonorrhea diagnosis rate); log_dens = log(population density) (F1); p_female = proportion female (F2); p_young = proportion aged ≤30 years (F3); q_hisp = log(proportion Hispanic) (F4); q_black = log(proportion non-Hispanic black); q_xwhite = log(proportion of minority race/ethnicity) (F6); p_single = proportion not currently married (F7); p_pov = proportion below the federal poverty level (F8); p_hsch = proportion with less than a high school education (F7); p_unemp = proportion unemployed (F10); p_moved = proportion moved in the past 12 months (F11); q_foreign = log(proportion foreign-born) (F12).

Similarly, the partial correlation between gonorrhea rate and the proportion of the CPOA population who are minority race/ ethnicity remains high (>0.62) except when controlling for proportion of the CPOA population who are non-Hispanic black or proportion of the CPOA population who are unmarried people (<0.44). There is a slight increase in the partial correlation when controlling for proportion of the CPOA population who are Hispanic or proportion of the CPOA population who are foreign-born, indicating that each of these two variables suppresses the correlation between gonorrhea rate and the proportion of the CPOA population who are minority race/ethnicity. The partial correlations between gonorrhea diagnosis rate and proportion of the CPOA population who are not currently married (around 0.6) only declined when controlled for proportion of the CPOA population who are non-Hispanic black or proportion of the CPOA population who are minority race/ethnicity (around 0.34).

The correlation between the gonorrhea diagnosis rate and the population density is 0.33, but the partial correlation when adjusted for the proportion of the CPOA population whose family incomes are below the federal poverty level, increases to 0.5. In other words, for the CPOAs with the same proportion of population below the federal poverty level, the correlation between the gonorrhea diagnosis rate and the population density is 0.5. On the other hand, when adjusted for proportion of the CPOA population who are non-Hispanic black, the partial correlation between the gonorrhea diagnosis rate and the population density declines to –0.10. Similarly, the correlation between gonorrhea diagnosis rate and proportion of the CPOA population whose family incomes are below the federal poverty level is 0.32, but when adjusted for population density, the partial correlation increases to 0.49. When this correlation is controlled for proportion of the CPOA population who are not currently married, the partial correlation is not significantly different from 0. Because the correlation of the proportion of CPOA population who are not currently married with proportion who live below the federal poverty level and the gonorrhea diagnosis rate are 0.51 and 0.61, respectively, the removal of the effects of proportion who are not currently married leads to the correlation between the gonorrhea diagnosis rate and proportion living below the federal poverty level collapsing to nearly zero. The correlation between the gonorrhea diagnosis rate and the proportion of the CPOA population who are foreign-born is only 0.11, but controlling for proportion of the CPOA population who are minority race/ethnicity, the partial correlation increases (absolutely) to –0.48.

PMCs and partial correlations assume linear relationships. Generally, this assumption is not valid, as linear relationships rarely exist in areas such as social sciences. In our calculation of PMCs, we used log transformations for some variables so that the transformed variables have linear relationships. To study the non-linearity of these relationships, we estimated the non-linear associations using MICs.

^{2} = 0.71) is the non-linearity measure (–0.07) that indicates the relationship between these two variables is almost linear. Because MIC measures the non-linear associations, the estimated MIC between two variables remains the same, regardless of whether the variables are transformed.

Histograms and scatter plots of the gonorrhea diagnosis rates and both SDH and demographic variables using the original variables (without log transformation): National Notifiable STD Surveillance and American Community Survey data, 2006–2008.

Maximal Information Coefficient (MIC) Strength, non-linearity, and correlation between the gonorrhea diagnosis rate (GRDR) and SDH variables and demographic variables: National Notifiable STD Surveillance and American Community Survey data, 2006–2008.

X | GRDR | Y | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 | |||

MIC (Strength) | ||||||||||||||

Non-linearity | ||||||||||||||

Correlation | ||||||||||||||

dens | (F1) | 0.21 | ||||||||||||

0.19 | ||||||||||||||

0.11 | ||||||||||||||

p_female | (F2) | 0.21 | 0.18 | |||||||||||

0.08 | 0.16 | |||||||||||||

0.37 | 0.14 | |||||||||||||

p_young | (F3) | 0.20 | 0.14 | 0.15 | ||||||||||

0.14 | 0.14 | 0.12 | ||||||||||||

0.23 | –0.03 | –0.17 | ||||||||||||

p_hisp | (F4) | 0.16 | 0.19 | 0.17 | 0.19 | |||||||||

0.16 | 0.17 | 0.14 | 0.07 | |||||||||||

–0.07 | 0.13 | –0.18 | 0.35 | |||||||||||

p_black | (F5) | 0.63 | 0.29 | 0.22 | 0.19 | 0.17 | ||||||||

–0.07 | 0.27 | 0.10 | 0.16 | 0.16 | ||||||||||

0.84 | 0.13 | 0.35 | 0.18 | –0.12 | ||||||||||

p_xwhite | (F6) | 0.43 | 0.24 | 0.18 | 0.25 | 0.38 | 0.52 | |||||||

0.14 | 0.18 | 0.17 | 0.08 | –0.04 | 0.17 | |||||||||

0.54 | 0.23 | 0.07 | 0.41 | 0.65 | 0.60 | |||||||||

p_single | (F7) | 0.37 | 0.17 | 0.17 | 0.28 | 0.18 | 0.31 | 0.39 | ||||||

–0.08 | 0.08 | 0.14 | 0.06 | 0.12 | –0.09 | 0.02 | ||||||||

0.67 | 0.29 | 0.18 | 0.46 | 0.23 | 0.64 | 0.61 | ||||||||

p_pov | (F8) | 0.21 | 0.27 | 0.19 | 0.17 | 0.19 | 0.20 | 0.17 | 0.25 | |||||

0.05 | 0.27 | 0.19 | 0.10 | 0.17 | 0.08 | 0.07 | –0.01 | |||||||

0.40 | –0.01 | –0.01 | 0.28 | 0.13 | 0.35 | 0.31 | 0.51 | |||||||

p_hsch | (F9) | 0.21 | 0.14 | 0.13 | 0.28 | 0.23 | 0.22 | 0.31 | 0.17 | 0.23 | ||||

0.16 | 0.14 | 0.13 | 0.02 | –0.12 | 0.17 | –0.03 | 0.09 | 0.11 | ||||||

0.23 | 0.02 | –0.02 | 0.51 | 0.59 | 0.23 | 0.59 | 0.28 | 0.34 | ||||||

p_unemp | (F10) | 0.18 | 0.15 | 0.14 | 0.14 | 0.13 | 0.18 | 0.17 | 0.21 | 0.19 | 0.14 | |||

0.04 | 0.15 | 0.13 | 0.14 | 0.13 | 0.06 | 0.11 | 0.02 | 0.06 | 0.13 | |||||

0.37 | 0.07 | 0.12 | 0.07 | –0.01 | 0.35 | 0.25 | 0.43 | 0.37 | 0.12 | |||||

p_moved | (F11) | 0.16 | 0.13 | 0.15 | 0.31 | 0.19 | 0.14 | 0.20 | 0.20 | 0.17 | 0.14 | 0.13 | ||

0.11 | 0.13 | 0.11 | –0.02 | 0.17 | 0.13 | 0.17 | 0.09 | 0.10 | 0.13 | 0.13 | ||||

0.24 | –0.06 | –0.18 | 0.60 | 0.16 | 0.07 | 0.19 | 0.33 | 0.27 | 0.07 | 0.05 | ||||

p_foreign | (F12) | 0.14 | 0.34 | 0.14 | 0.19 | 0.60 | 0.18 | 0.40 | 0.18 | 0.18 | 0.17 | 0.14 | 0.20 | |

0.14 | 0.18 | 0.14 | 0.15 | 0.11 | 0.18 | 0.02 | 0.10 | 0.15 | 0.07 | 0.14 | 0.20 | |||

–0.03 | 0.40 | –0.09 | 0.19 | 0.70 | 0.00 | 0.61 | 0.28 | –0.17 | 0.32 | –0.01 | 0.10 |

GRDR = gonorrhea diagnosis rate; dens = population density (F1); p_female = proportion female (F2); p_young = proportion aged ≤30 years (F3); p_hisp = proportion Hispanic (F4); p_black = proportion non-Hispanic black (F5); p_xwhite = proportion of minority race/ethnicity (F6); p_single = proportion not currently married (F7); p_pov = proportion below the federal poverty level (F8); p_hsch = proportion with less than a high school education (F7); p_unemp = proportion unemployed (F10); p_moved = proportion moved in the past 12 months (F11); p_foreign = proportion foreign-born (F12).

For example, the correlation between the gonorrhea diagnosis rate and the proportion of CPOA population who are minority race/ethnicity (p_xwhite), or PMC (rate, p_xwhite) = 0.54 and the MIC (rate, p_xwhite) = 0.43. The non-linearity measure for the two variables is 0.14, indicating a somewhat non-linear relationship. On the other hand, when both variables are log transformed, PMC (log_rate, q-xwhite) = 0.65 but MIC (log_rate, q_xwhite) remains the same—0.43. The non-linearity measure between the two log transformed variables has declined to almost zero (0.43–0.65^{2}), indicating that the relationship between the log transformed variables is almost linear.

Strength of non-linear associations among both demographic and SDH variables and the gonorrhea diagnosis rate. GRDR represents gonorrhea diagnosis rate and R/E represents the race/ ethnicity groups.

The largest measure for non-linearity is for the relationship between the proportion of the CPOA population who are non-Hispanic black (p_black) and population density (density). The MIC (p_black, density) = MIC (q_black, log-dens) = 0.29, PMC (p_black, density) = 0.13, and PMC (q_black, log_dens) = 0.46, leading to non-linear measures of 0.27 and 0.08 for the original variables and the log transformed variables, respectively. In this case, the log transformation successfully transformed the non-linear relationship to a linear relationship. The MIC and non-linear measures provide a method to verify the effectiveness of transformations used for PMCs. The strengths of the non-linear association between any two variables were presented in

The non-linear associations between race ethnicity (R/E) variables and gonorrhea diagnosis rate (GRDR) were stronger than non-linear associations between the SDH variables in the graph. Only the pairs of variables that have an MIC greater than 0.16 were connected by a line.

Our study explored the association between gonorrhea diagnosis rate and each of several SDH and demographic variables. The geographic unit of analysis was the CPOA, a mixed group of county and PUMA (CPOA), as defined in the ACS. Several authors have studied associations between health outcomes and area-based SDH and demographic measures at different geographic levels such as zip-code, census tract, census block, county, and state level. The rationale for studying area-based measures of SDH is that they provide information not captured by individual-level data. For example, community unemployment levels may affect all individuals living within a community, regardless of whether or not they are unemployed [

We measured the associations between gonorrhea rates and both SDH and demographic variables using correlations and partial correlations. We also calculated the non-linear associations among these variables using MICs and a measure of non-linearity between variables. MICs remained the same, regardless of whether the variables were log transformed. The partial correlation between two variables adjusted for a third variable does not depend on the value at which the third variable is held. However, for non-linear associations (MICs) this may not hold and further methodological research is required to extend the MIC to a measure similar to partial correlation for PMC [

The gonorrhea diagnosis rate is strongly correlated with the proportion of the CPOA population who are non-Hispanic black, proportion who are of minority race/ethnicity, and proportion not currently married. These demographic variables were also strongly correlated with AIDS diagnosis rate [

An analysis of neighborhood socio-cultural factors influencing the spatial pattern of gonorrhea in North Carolina using cases reported between 2005 and 2008, found that a high percentage of single mothers, more women than men, and low socioeconomic status (SES) appear to influence gonorrhea rates [

Another study of associations between the characteristics of counties and the rates of reported gonorrhea in the southeastern region of the United States from 1986 to 1995 found that when adjusted for variables measuring aspects of social structure, such as a race-based income distribution and residential segregation, the proportion of blacks no longer had an effect on rates of gonorrhea [

To conduct an extensive study of relationships of both the SDH and demographic variables to the gonorrhea rates, more geographic area-based estimates of additional variables are required. Whether considering incarceration rates and crime rates or other measures of SDH, expanding our understanding of SDH and integrating measures across the range of social, political, and economic institutions and structures will permit us to have a clearer understanding of how these factors work in our analysis of the geographic distribution of health and disease. The inclusion of data on both SDH and demographic variables into existing surveillance systems would enhance the analysis of these relationships because the diagnosis rates could be stratified by these variables.

Together with the collection of more SDH variables and methods to measure non-linear associations, geographic area-based analysis of disease incidence and SDH can add value to public health prevention and control programs. If a local or state health department or a community-based organization can pinpoint the local factors affecting gonorrhea rates, then more effective programs can be tailored to the populations they serve. By using science-based evidence to improve these programs, public health officials can hope for greater support of public programs among various stakeholders. Public health professionals may consider collecting such data or encouraging other partners to collect these data that can be used to improve intervention programs. In this way, coordinated, multisectoral efforts may help to address disparities in the distribution of gonorrhea diagnosis rates and help to control the spread of such diseases.

Histograms and scatter plots of the gonorrhea diagnosis rates and both SDH and demographic variables using the original variables (without log transformation): National Notifiable STD Surveillance and American Community Survey data, 2006–2008.

The authors wish to thank Rick Song, Hillard Weinstock, and Delicia Carey for providing ACS estimates for SDH variables and NCHHSTP STD Surveillance System data. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

The authors declare no conflict of interest.