An Overview of the Relationship Between Environmental and Social Factors on select Diseases at CSVS

A Report on CSVS select diseases Utilizing CalEnviroScreen

Author

Oguchi Nkwocha & Dayanne Herrera

Published

December 19, 2024

Goal

  • Assess CSVS Chronic disease rates and obstetric outcomes within the context of CA environmental factors
  • Provide CSVS and other stakeholders with actionable insights.
  • Provide groundwork for future research.

Introduction

Chronic diseases and obstetric outcomes are intertwined with social and environmental factors that impact health disparities. Clinica de Salud del Valle de Salinas (CSVS) delivers essential healthcare services to underserved, farmworker families and other demographic groups in the state of California.

The CalEnviroScreen (https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) is a screening methodology that can be used to help identify California communities that are disproportionately burdened by multiple sources of pollution and social determinants of health.

By applying the same methodology to select clinical conditions (chronic diseases and OB outcomes) at CSVS, we hope to identify similarly impacted communities and detect the relationship between the diseases and pollutants.

CA Environmental Screen Dictionary

These are the State conditions or parameters.

Components of the Environmental Screen include

  • Pollution Burden

  • Exposures

  • Environmental effects

  • Population characteristics

  • Sensitive Populations

  • Socioeconomic Factors

We can summarize them by some categories:

Pollutants

Clinical Problems

Demographics

SDOH

Race

Hazards

Ozone
PM2.5
Diesel PM
Drinking Water
Lead
Pesticides
Tox. Release
Traffic
Cleanup Sites
Groundwater Threats
Haz. Waste
Imp. Water Bodies
Solid Waste
Pollution Burden
Asthma
Low Birth Weight
Cardiovascular Disease
Education
Linguistic Isolation
Poverty
Unemployment
Housing Burden
Pop. Char.
Census Tract
California County
Total Population
Children < 10 years (%)
Pop 10-64 years (%)
Elderly > 64 years (%)
Hispanic (%)
White (%)
African American (%)
Native American (%)
Asian American (%)
Other/Multiple (%)

Methodology

This analysis was conducted to explore the relationship between chronic diseases, obstetric outcomes, and environmental factors in the service area of CSVS. The methodology employed is detailed below:

Study Design

This report utilized a cross-sectional design to analyze data collected during the period from 2022-2024.

The analysis focused on identifying correlations between health outcomes (chronic diseases and obstetric outcomes) and environmental and social determinants of health, as captured by the CalEnviroScreen tool.

Data Sources

  • Health Outcome Data:
    • Chronic disease rates were obtained from CSVS records in the EHR (Next-Gen) and then, standardized for population differences and adjusted for age.
      1. Health outcomes data included rates of conditions such as:
        • Hypertension (HTN),
        • Asthma
        • Obesity
        • Lipid Disorders
        • Thyroid Diseases
        • Gastroesophageal Reflux Disease (GERD)
        • Type 2 Diabetes Mellitus (DM)
        • Cardiovascular Disease (CVD)
        • Chronic Liver Disease (CLD)
        • Chronic Obstructive Pulmonary Disease (COPD)
        • Psoriasis
        • Chronic Kidney Disease (CKD)
        • Parkinson’s Disease
        • Chronic Diseases (ChrDs)
      2. The OB data was obtained from Natividad Hospital’s OB delivery records for CSVS patients analyzed subsequently at CSVS:
        • Low Birth Weight (LBW)
        • High-Risk Pregnancy (HghRskOB).
  • Environmental Data:
    • Environmental and socioeconomic data were sourced from the CalEnviroScreen tool (https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40), which provides region-specific scores, rates and percentiles across ZIP codes in the study area.

Data Preparation

  • Health outcome data from CSVS were analysed using standard population adjustment methods based on California Census 2020 population report.

  • Age adjustments were performed using standard age adjustment based on a California Census 2020 population report.

  • CA Environmental data were harmonized with CSVS health outcome data by matching ZIP codes and geographic boundaries.

Variables and Measures

  • Independent Variables: Environmental factors such as PM2.5, ozone levels, pesticide exposure, and socioeconomic indicators (e.g., poverty, unemployment); low birth weight, Hypertension, etc

  • Dependent Variables: Population-standardized and age-adjusted rates of chronic diseases and obstetric outcomes and percentiles.

    • For population standardization LBW and High Risk Pregnancy, female populations were used as denominator

  • Covariates: Socioeconomic factors (e.g., education level, linguistic isolation) were considered as potential confounders.

Statistical Analysis

  • Descriptive Statistics: Summary statistics were generated to describe the distribution of health outcomes and environmental factors across the study area.
  • Correlation Analysis: Pearson and Spearman correlation coefficients were calculated to examine relationships between environmental factors and health outcomes. Statistical significance was assessed at p < 0.05.
  • Spatial Analysis:
    1. Moran’s I and Geary’s C were calculated to assess the spatial autocorrelation of health outcomes and environmental factors.
    2. Local Indicators of Spatial Association (LISA) were used to identify hotspots and clusters.
  • Visualization: Correlation matrices and spatial maps were generated to illustrate relationships and geographic patterns.

Software and Tools

Data cleaning and processing, statistical analyses, and visualization were performed using R (version 4.4.0) with the following packages:

  • Data manipulation: dplyr, tidyr, janitor and others

  • Statistical analysis: corrplot, sf, epitools

  • Spatial analysis: spdep, sf, lisa and others

  • Visualization: ggplot2, leaflet, mapview

  • Interactive graphics: Shiny

  • Report (this document): quarto

Limitations

  • The analysis was limited to data collected within 2 years, which may not capture long-term trends.

  • Some variables may be underrepresented in the available datasets. For instance, the pesticide indicator covers 132 active pesticide ingredients used in production agriculture, but lacks data on non-production and non-agricultural pesticide use, which is available only at the county level.

  • The active patients in the CSVS do not represent the entire county; they are a small subset from their respective zip codes.

  • The states only report disease counts greater than 12 for statistical purposes. Since our numbers are lower, we used all counts, including those below 12.

  • To gain a better understanding by comparing individual-level data with community-level data, we need to run a multi-level fixed effects model. This model will include a within-person effect for individuals and a separate effect for areas (zip codes). Additionally, we may consider using a count-based model, such as Poisson, and potentially a zero-inflated model if our outcomes exhibit a skewed distribution with a low skew.

RESULTS

Example MAPS

Mapping by Zipcode the rates of some select chronic diseases. Mapping is also available by percentile distribution by zipcode for each condition

<Link to Shiny App URL, app.R>

1. Correlations

We compared State Conditions with CSVS conditions using statistical Correlation methods. The table to the right shows Significant correlations among CSVS and State Conditions Rates.

First,we picked statistically significant correlations, therefore, all correlations for paired conditions in the tale are significant.

Next, we highlight correlations of PRACTICAL significance, which is correlation that are + 0.5 or -0.5 or larger.

The analysis reveals:

  • significant correlations between Chronic Kidney Disease (CKD) rates and various other health conditions as environmental factors

  • CKD exhibits

    • both statistically and practically significant positive correlations with Chronic Obstructive Pulmonary Disease (COPD), Cardiovascular Disease (CVD), High-Risk Pregnancy (HghRskOB), and Low Birth Weight (LBW) rates

    • statistically significant positive correlations with Lipid, Obesity, and Psoriasis.

  • In contrast, CKD shows negative correlations with PM2.5 levels and Pollution scores

  • Chronic Liver Disease (CLD) has a very strong (statistically and practically significant) positive correlation with Parkinson’s Disease

  • COPD has

    • both statistically and practically significant positive correlations with CVD, HghRskOB, and LBW
    • statistically significant positive correlations with Lipid and Psoriasis.

  • On the other hand, COPD is negatively correlated with Ozone, PM2.5, and Pollution, highlighting an inverse association between respiratory conditions and these environmental factors.

  • CVD exhibits
    • strong positive correlations with HghRskOB and LBW rates
    • positive correlations with Lipid, Obesity, and Psoriasis
    • negative correlations with Ozone, PM2 and Pollution.
  • Other notable findings include:
    • positive correlation between Chronic Disease (ChrDs), CLD, Hypertension (HTN), and Lipid
    • positive correlation between Diabetes Mellitus (DM), HTN, Lipid and Thyroid Disease
  • High-Risk Pregnancy (HghRskOB) shows
    • a statistically and practically significant positive correlation with LBW rates
    • statistically significant positive correlations with Lipid, Obesity, and Psoriasis.
    • negative correlations with PM2.5, and Pollution
Significant correlations among CSVS and State Conditions Rates
CSVS & State Condition1 CSVS & State Condition2 Correlation P_Value Correlation Direction Statistically Significant Practically Significant
CKD_rate COPD_rate 0.7015814 0.0000000 ↑ Yes Yes
CKD_rate CVD_rate 0.6857228 0.0000000 ↑ Yes Yes
CKD_rate HghRskOB_rate 0.9197652 0.0000000 ↑ Yes Yes
CKD_rate LBW_rate 0.8027499 0.0000000 ↑ Yes Yes
CKD_rate LIPID_rate 0.1380761 0.0429640 ↑ Yes No
CKD_rate OBESITY_rate 0.1270219 0.0489740 ↑ Yes No
CKD_rate PM2_5 -0.1712646 0.0017917 ↓ Yes No
CKD_rate PSORIASIS_rate 0.1598222 0.0050678 ↑ Yes No
CKD_rate Pesticides 0.1982067 0.0004168 ↑ Yes No
CKD_rate Pollution -0.0456261 0.0251356 ↓ Yes No
CKD_rate PollutionScore -0.0456261 0.0251356 ↓ Yes No
CLD_rate Parkinson_rate 0.9432960 0.0000000 ↑ Yes Yes
COPD_rate CVD_rate 0.9341342 0.0000000 ↑ Yes Yes
COPD_rate HghRskOB_rate 0.6531102 0.0000000 ↑ Yes Yes
COPD_rate LBW_rate 0.5595983 0.0000000 ↑ Yes Yes
COPD_rate LIPID_rate 0.1348358 0.0450249 ↑ Yes No
COPD_rate Ozone -0.1074270 0.0303209 ↓ Yes No
COPD_rate PM2_5 -0.1825432 0.0007374 ↓ Yes No
COPD_rate PSORIASIS_rate 0.1988266 0.0018043 ↑ Yes No
COPD_rate Pesticides 0.2311310 0.0001675 ↑ Yes No
COPD_rate Pollution -0.0315621 0.0182577 ↓ Yes No
COPD_rate PollutionScore -0.0315621 0.0182577 ↓ Yes No
CVD_rate HghRskOB_rate 0.6437518 0.0000000 ↑ Yes Yes
CVD_rate LBW_rate 0.5143918 0.0000000 ↑ Yes Yes
CVD_rate LIPID_rate 0.1445545 0.0381668 ↑ Yes No
CVD_rate OBESITY_rate 0.1259491 0.0482050 ↑ Yes No
CVD_rate Ozone -0.1132039 0.0297410 ↓ Yes No
CVD_rate PM2_5 -0.1927883 0.0007421 ↓ Yes No
CVD_rate PSORIASIS_rate 0.2042724 0.0016682 ↑ Yes No
CVD_rate Pesticides 0.2553823 0.0000980 ↑ Yes No
CVD_rate Pollution -0.0326592 0.0196896 ↓ Yes No
CVD_rate PollutionScore -0.0326592 0.0196896 ↓ Yes No
ChrDs_rate CLD_rate 0.2085732 0.0143378 ↑ Yes No
ChrDs_rate HTN_rate 0.3783198 0.0000105 ↑ Yes No
ChrDs_rate LIPID_rate 0.3406696 0.0000190 ↑ Yes No
ChrDs_rate OBESITY_rate 0.1965699 0.0033693 ↑ Yes No
ChrDs_rate Parkinson_rate 0.2183232 0.0146606 ↑ Yes No
Cleanups HTN_rate -0.0361357 0.0471897 ↓ Yes No
DM_rate HTN_rate 0.3170321 0.0000158 ↑ Yes No
DM_rate LIPID_rate 0.3314622 0.0002440 ↑ Yes No
DM_rate Pollution -0.0839124 0.0308810 ↓ Yes No
DM_rate PollutionScore -0.0839124 0.0308810 ↓ Yes No
DM_rate ThyroidDs_rate 0.5380157 0.0000000 ↑ Yes Yes
HTN_rate LIPID_rate 0.2435055 0.0001208 ↑ Yes No
HTN_rate OBESITY_rate 0.2157563 0.0039644 ↑ Yes No
HTN_rate ThyroidDs_rate 0.3261153 0.0000269 ↑ Yes No
HghRskOB_rate LBW_rate 0.9158799 0.0000000 ↑ Yes Yes
HghRskOB_rate LIPID_rate 0.1473643 0.0397802 ↑ Yes No
HghRskOB_rate OBESITY_rate 0.1333590 0.0463762 ↑ Yes No
HghRskOB_rate PM2_5 -0.1781355 0.0017170 ↓ Yes No
HghRskOB_rate PSORIASIS_rate 0.1639050 0.0055791 ↑ Yes No
HghRskOB_rate Pesticides 0.2200658 0.0003210 ↑ Yes No
HghRskOB_rate Pollution -0.0417125 0.0256512 ↓ Yes No
HghRskOB_rate PollutionScore -0.0417125 0.0256512 ↓ Yes No
LBW_rate LIPID_rate 0.1313685 0.0461943 ↑ Yes No
LBW_rate OBESITY_rate 0.1259201 0.0492684 ↑ Yes No
LBW_rate PM2_5 -0.1590149 0.0017496 ↓ Yes No
LBW_rate PSORIASIS_rate 0.1303952 0.0090871 ↑ Yes No
LBW_rate Pesticides 0.2020137 0.0004672 ↑ Yes No
LBW_rate Pollution -0.0376904 0.0240200 ↓ Yes No
LBW_rate PollutionScore -0.0376904 0.0240200 ↓ Yes No
LIPID_rate OBESITY_rate 0.2510721 0.0002955 ↑ Yes No
LIPID_rate PM2_5 -0.0941291 0.0284901 ↓ Yes No
LIPID_rate Pollution -0.0303585 0.0378697 ↓ Yes No
LIPID_rate PollutionScore -0.0303585 0.0378697 ↓ Yes No
OBESITY_rate Pollution -0.0399506 0.0423155 ↓ Yes No
OBESITY_rate PollutionScore -0.0399506 0.0423155 ↓ Yes No
PM2_5 PSORIASIS_rate -0.0770409 0.0274381 ↓ Yes No
Pollution ThyroidDs_rate -0.0590682 0.0453837 ↓ Yes No
PollutionScore ThyroidDs_rate -0.0590682 0.0453837 ↓ Yes No

2. Spatial Analysis

Here we analyze a different type of relationship – the spatial distribution and spatial profile of each condition or variable using zip codes as spatial units. We aim to answer the question:

  • does the condition or variable form clustering patterns among the zip codes or is it dispersed?

Moran’s I for Each Variable

Assess clustering tendency of conditions

Moran’s I measures Local spatial autocorrelation, revealing how strongly variables cluster across regions.

  • Environmental factors like ozone and PM2.5 have the highest Moran’s I values, suggesting strong global clustering, likely driven by shared geographic or industrial factors.

  • In contrast, variables like psoriasis and Native American population distribution exhibit low Moran’s I values, indicating more spatial randomness.

  • These findings underscore the need for targeted spatial interventions for environmental risks.

  • For example, high Moran’s I values for air quality variables suggest a need for regional air pollution control strategies, while conditions like psoriasis may require non-geographic approaches focusing on broader population health management.

Geary’s C for Each Variable

Assess dispersal tendency of conditions

Geary’s C, on the other hand, measures negative spatial autocorrelation, which means “dispersal” tendency, with higher values indicating stronger dispersal.

  • variables such as Psoriasis, Native American population and Pacific Islander exhibit higher Geary’s C values in their distribution, indicating more spatial randomness (dispersal) and less localized clustering.

  • variables like ozone levels and PM2.5 and High Risk OB exhibit have the lowest Geary’s C value, indicting weak dispersal tendency, equivalent to strong local clustering distribution

  • These findings emphasize the importance of spatial targeting in public health and environmental interventions.

  • For instance, policies to mitigate air pollution should focus on identified hotspots, while addressing conditions like psoriasis may require more distributed, population-wide approaches.

Combined Moran’s I and Geary’s C

This scatterplot combines Moran’s I positive local spatial autocorrelation (Moran’s I) with (Geary’s C) negative local spatial autocorrelation. This is a supportive graph showing that high Moran’s I coincides with low Geary’s C, and vice-versa. Thus:

  • variables in the upper-left quadrant reflect high dispersal but low clustering, indicating broad spatial trends without isolated hotspots.
  • variables in the bottom-right quadrant show strong local clustering without significant dispersal trends.
  • This dual perspective is critical for tailoring interventions.
  • For example, addressing variables with high local clustering / low dispersal (RLQ) (e.g., PM2.5 or ozone, asthma) may require localized efforts.
  • On the other hand, variables with minimal clustering and high dispersal, such as psoriasis and native American, may benefit more from broader, non-spatially specific health initiatives.

Comparing Spatial Distribution

While the above analyzes spatial correlation, this section, which features a Network plot, with connecting lines indicating a closely matching relationship, will compare the spatial appearance of the conditions across zipcodes among themselves, thus to answer the question:

  • when mapped, do the spatial patterns match (resemblance), where, for example, a complete overlap when overlaid would be a perfect score?

From the plot, we may make the following broad conclusions:

  • spatial pattern matching is more likely mostly among the state variables as a group or among CSVS variables as a group, without much cross-matching
  • matching variables in either major group contain mostly 2 elements; just one has 3 elements.
  • the exception is one match with 7 CSVS variables and 4 State variables
  • the implication with this 11-element cluster is that State variables like African American, Native American, PM2 and Ozone spatial distribution may coincide with CSVS Obesity, DM, CVD, CLD, High Risk OB, Thyroid Disease, and HTN distribution.

We will pick out significant spatial matches using p-value =0.1 (instead of p-value = 0.05). The 5 condition-pairs in the table stand out. Each pair’s distribution maps are quite similar statistically. Some of this can be seen in the maps that follow.

  • Pairs:

    • CLD-CVD
    • Elderly_65-Hispanic
    • Low_Birth_Weight - Cardiovascular
    • CLD_rate - HghRskOB_rate
    • CVD_rate - HghRskOB_rate
Unique Statistically Significant Variable Pairs with Jaccard Index > 0.3, p-value = 0.1
Pair Jaccard_Index p_value
CLD_rate - CVD_rate 1.0000000 0.000
Elderly_65 - Hispanic 1.0000000 0.000
Low_Birth_Weight - Cardiovascular 0.9096147 0.091
CLD_rate - HghRskOB_rate 0.9049089 0.096
CVD_rate - HghRskOB_rate 0.9049089 0.096

What does the map look like for these significantly closely matched conditions?

Comparing CSVS Vs State conditions

In the next section, we will compare the rates and distribution of the only clinical conditions included by the State with CSVS data: Low Birth Weight, Asthma, and Cardiovascular Disease

Low birth weight

  • Greenfield (93927): CSVS reports a 10.32% LBW rate, nearly double the state average (5.49%).
  • Salinas (93905, 93906): These ZIP codes report 6.19% and 10.80% LBW rates, respectively.
ZIP City Ca LBW Rate CSVS LBW Rate comparison
93901 Salinas 4.80 5.64 ↑
93925 Unincorporated Monterey County area 4.72 0.00 ↓
93927 Greenfield 5.49 10.32 ↑
93923 Carmel-by-the-Sea 0.00 0.00 =
93955 Seaside 0.00 1.38 ↑
93905 Salinas 3.38 6.19 ↑
93933 Marina 4.61 0.00 ↓
93940 Monterey 2.54 0.00 ↓
93930 Unincorporated Monterey County area 3.95 4.30 ↑
93960 Unincorporated Monterey County area 5.65 1.99 ↓
93924 Unincorporated Monterey County area 5.95 0.00 ↓
93906 Salinas 5.24 10.80 ↑
93907 Salinas 4.86 0.00 ↓
93908 Unincorporated Monterey County area 3.38 9.40 ↑
93926 Gonzales 4.01 1.94 ↓
93950 Pacific Grove 5.06 0.00 ↓
93944 Monterey 0.00 0.00 =
93953 Del Monte Forest 5.69 0.00 ↓
95076 Prunedale 3.06 0.00 ↓

Asthma rates

  • CSVS has lower asthma rates compared to California averages due to a small patient subset.
  • The State reports higher rates in the south of Salinas: 93901 and 93905.
  • The CSVS reports higher rates in Marina (93933) at 0.35% compared to California’s 66.12%, and in Salinas (93907, Prundale area) at 0.31% compared to 50.95%.
  • Despite low rates, the high exposure to pesticides and poor air quality can trigger respiratory issues.
  • Targeted asthma screenings and education programs in high-risk areas are essential for addressing undiagnosed cases and protecting vulnerable groups.
ZIP City Ca Asthma Rate CSVS Asthma Rate comparison
93901 Salinas 91.48 0.06 ↓
93925 Unincorporated Monterey County area 49.29 0.00 ↓
93927 Greenfield 51.40 0.07 ↓
93923 Carmel-by-the-Sea 28.78 0.00 ↓
93955 Seaside 45.66 0.03 ↓
93905 Salinas 74.24 0.12 ↓
93933 Marina 66.12 0.35 ↓
93940 Monterey 34.61 0.21 ↓
93930 Unincorporated Monterey County area 37.29 0.04 ↓
93960 Unincorporated Monterey County area 47.40 0.19 ↓
93924 Unincorporated Monterey County area 38.04 0.00 ↓
93906 Salinas 51.14 0.13 ↓
93907 Salinas 50.95 0.31 ↓
93908 Unincorporated Monterey County area 62.91 0.00 ↓
93926 Gonzales 49.82 0.08 ↓
93950 Pacific Grove 35.05 0.00 ↓
93944 Monterey 20.14 0.00 ↓
93953 Del Monte Forest 16.18 0.00 ↓
95076 Prunedale 28.21 0.00 ↓

Cardiovascular Disease rates

CVD rates are also lower in CSVS patients compared to statewide trends, reflecting the clinic’s younger patient base. However, within CSVS, concerns remain:

  • Marina (93933) and Salinas (93905, 93901): CVD rates are higher within the CSVS population (up to 0.31%), suggesting early signs of cardiovascular burden.
  • Unincorporated Areas of Monterey County: While state-level CVD rates can reach 17.85%, CSVS data remains low due to either some areas having few patients accessing CSVS services or a lack of access altogether.
ZIP City Ca CVD Rate CSVS CVD Rate comparison
93901 Salinas 12.35 0.31 ↓
93925 Unincorporated Monterey County area 14.39 0.08 ↓
93927 Greenfield 19.45 0.05 ↓
93923 Carmel-by-the-Sea 6.57 0.00 ↓
93955 Seaside 8.66 0.18 ↓
93905 Salinas 13.05 0.15 ↓
93933 Marina 11.73 0.31 ↓
93940 Monterey 7.53 0.09 ↓
93930 Unincorporated Monterey County area 17.17 0.12 ↓
93960 Unincorporated Monterey County area 17.85 0.08 ↓
93924 Unincorporated Monterey County area 13.14 0.00 ↓
93906 Salinas 10.04 0.14 ↓
93907 Salinas 8.19 0.30 ↓
93908 Unincorporated Monterey County area 12.13 0.00 ↓
93926 Gonzales 17.35 0.08 ↓
93950 Pacific Grove 9.17 0.00 ↓
93944 Monterey 4.71 0.00 ↓
93953 Del Monte Forest 7.19 0.00 ↓
95076 Prunedale 6.56 0.14 ↓

5. Summary of Results

  1. Younger Population Health: The prevalence of asthma, obesity, and high-risk pregnancies reflects the clinic’s younger Latino farmworker population.

  2. ZIP Code Priorities:

    • Greenfield (93927): High LBW rates and environmental risks require urgent maternal health and environmental interventions. CITED FROM WHERE WE GOT THE RESULT

    • Salinas (93905, 93906): Elevated rates of LBW, CVD, and asthma highlight overlapping health risks. CITED FROM WHERE WE GOT THE RESULT AND LARGEST NUMBERS, DENSE POPULATION

  3. Environmental Risks: Air pollution, pesticide exposure, and poor living conditions exacerbate respiratory and cardiovascular burdens in farmworking communities. For our population we couldnt find it menaninful, even when among the state they are.

  4. Health Co-Occurrences: Obesity, diabetes, and high-risk pregnancy are interconnected issues requiring integrated prevention programs

  5. Consistency between the Correlations, Co-occurrence, and Spatial Patterns that Ozone and PM 2.5 are the environmental factors more related to Chronic disease among CSVS Patients.

6. Recommendations

  1. Health Screening and Management Programs:
    • Prioritize cardiovascular, asthma, and chronic disease screenings in ZIP codes with elevated rates, particularly 93933 (Marina) and 93905/93906 (Salinas).
  2. Maternal and Child Health Services:
    • Enhance prenatal care and education programs in Greenfield and Salinas to reduce LBW rates and improve maternal outcomes. Explain that this conditation may play a role even when there was not statistical significance.
  3. Environmental Health Initiatives:
    • Mitigate air pollution and pesticide exposure in Greenfield and surrounding areas to reduce respiratory and cardiovascular risks. I can mention that the main source of pollution is traffic(use of personal cars).
  4. Community Outreach Programs:
    • Tailor health initiatives to serve aging populations, addressing cultural and demographic needs.
  5. Geographic Resource Allocation:
    • Use spatial clustering insights to prioritize healthcare resources in high-burden areas, focusing on ZIP codes with overlapping health and environmental challenges.

These targeted efforts will allow CSVS to address systemic health disparities, improve population health outcomes, and create sustainable changes in the community.

7. Conclusion

This analysis and presentation

  • show our capability to deploy State Data tools and Statistical methods to generate disease rates and percentiles in CSVS geographic area of operation
  • allow us to compare rates between and among different variables, State Vs CSVS, or within each camp
  • demonstrate our ability to determine correlations in rates and percentiles across and within camps
  • facilitate spatial correlations enabling overlap comparisons among all variables, State or CSVS