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[Note: Text that references rankings, charts, and data will be updated as work progresses on the 2020 Family Prosperity Index.]
The health of each person within a family has a direct effect on its economic circumstances through higher medical costs and loss of income due to reduced productivity or death. The Family Health major index measures the combined impact of physical and mental health factors on economic prosperity in each state.
The worst outcome for family health is death. As such, it is important to measure how each state prevents all forms of premature death. The common measurement of premature death is called Years of Potential Life Lost (YPLL). YPLL measures mortality after birth but before the age of 75. Put simply, a person who dies at 25 would have lost 50 years of productive life (75 – 25 = 50).
While not all forms of premature death can be prevented, such as those from cancer or other diseases, many forms of premature death arise from risk behavior—such as drinking, smoking, and using illicit drugs—which are within the realm of personal and societal choice as well as government policy. 
The Surgeon General estimates in a recent study that the total economic costs of smoking in 2009 were $289 billion. That amount included $132.5 billion for direct medical care, $151 billion for lost productivity, and $5.6 billion for lost productivity due to secondhand smoke.
The study also estimated that direct medical care costs would grow to $175.9 billion in 2012.
The total economic costs of excessive alcohol consumption in 2006 were $223.5 billion—including $161.3 billion for lost productivity and $24.6 billion for direct medical care. Most of the economic costs are due to binge drinking ($170.7 billion). Almost one-third of the total economic costs, or $73.3 billion, involved the criminal justice system in some way. That number included the costs borne by victims, the cost of trials and the expenses of incarcerating individuals whose criminal activity involved alcohol.
The obesity epidemic is relatively new, so its economic costs are still being compiled. One study that performed a thorough review of the existing literature estimates that they exceed $215 billion per year. A more recent study suggests obesity’s direct medical costs alone are $190 billion per year.
In any case, the costs of obesity have a significant impact on the economy and are climbing rapidly.
Obesity also is becoming a national security issue. It is now the leading reason why the armed forces reject recruits, with 23 percent being refused for that condition. In addition, obesity among active duty service members increased 61 percent between 2002 and 2011 and was responsible for $1.1 billion in medical costs for the military, plus $106 million in lost productivity and 684,000 lost workdays per year.
Illicit drug use is increasing in America and so are concerns about it. A 2016 Gallup poll found that 44 percent of Americans worry a great deal about drug use—an increase of 10 percentage points in only two years.
The economic burden on society caused by illicit drug use is substantial. A recent study by the National Drug Intelligence Center found that the total costs of illicit drug use in 2007 were $193 billion—crime ($113 billion), health ($11 billion), and productivity ($68 billion).
Illicit drug use (and excessive alcohol consumption) is unique among health problems in that the most expensive costs it imposes stem from the costs of crime, including prosecution, incarceration, and the burden borne by its victims. Drug-related incarceration harms family structure by increasing the number of single-parent households, setting in motion a vicious cycle that must be broken, especially for the children involved. (As noted previously, the incidence of crime among children of single-parent families is higher than that for children of married, intact families.)
There is an epidemic of sexually transmitted diseases (STDs), which is growing with every passing year. Consider these facts from the Centers for Disease Control and Prevention:
-- There are an estimated 20 million new infections every year. They disproportionately affect young people (between the ages of 15 and 24), who account for half of all new infections.
-- Roughly 110 million Americans are affected by an STD in any given year—
1 out of every 3.
-- STDs account for 50 percent of all preventable infertility. In particular, chlamydia and gonorrhea cause pelvic inflammatory disease, which can lead to infertility.
Conservative estimates say that the direct healthcare costs of treating the eight most common STDs total $16 billion every year. This number does not include indirect costs such as lost productivity or infertility, which would dramatically increase the total costs.
More troubling is the rise in drug-resistant gonorrhea, whose threat level, according to the CDC, has reached “urgent”—the highest threat level possible:
If cephalosporin-resistant N. gonorrhoeae becomes widespread, the public health impact during a 10-year period is estimated to be 75,000 additional cases of pelvic inflammatory disease (a major cause of infertility), 15,000 cases of epididymitis, and 222 additional HIV infections because HIV is transmitted more readily when someone is co-infected with gonorrhea. In addition, the estimated direct medical costs would total $235 million. Additional costs are anticipated to be incurred as a result of increased susceptibility monitoring, provider education, case management, and the need for additional course of antibiotics and follow-up.
The first year used for estimating the costs associated with abortion is 1973, as that was the year of the Roe v. Wade decision, which made abortion legal in all 50 states. Estimates suggest that between 1973 and 2012, approximately 54 million abortions were performed.
Abortion affects both America’s social and economic fabric. In purely economic terms, abortion eliminates a child’s future contributions to society in the form of work, as someone who is never born cannot work and thus provide goods or services to anyone else. A thorough analysis by the Marriage & Religion Research Institute found that abortion costs the economy between $70 billion and $135 billion every year, leading to a loss of $10 to $33 billion in tax revenue.
Yet abortion does not just destroy a single person; it also destroys that person’s future lineage. Many writers refer to “ghost abortions” when accounting for the lives lost in this way. There are two forms of ghost abortions.
First, aborted females—roughly 14.25 million between 1973 and 1992 —never had the chance to have a baby of their own.  Females born prior to 1992 would be at least 26 years old or older—the average age of a women has her first child. That represents, conservatively, another 14.25 million children who were not born in the next generation.
Second, abortion has been linked to a substantial rise in STDs. One study found that since abortion reduces the personal risk associated with sex, it contributes to an increase in sexual activity, and in turn, a 25 percent increase in gonorrhea and syphilis rates. As noted in the STD section, gonorrhea is a prime cause of preventable infertility. As such, every baby not born because the would-be mother was made infertile by the rising incidence of STDs is a member of the ghost abortion population.
An increase in the marriage rate would likely mean fewer abortions. According to the Centers for Disease Control and Prevention, in 2013, only 14.8 percent of all abortions were to married women. Not only do married women make up a small proportion of women who obtain an abortion, but the abortion ratio is also significantly lower among married women (46 abortions per 1,000 live births) than for unmarried women (387 per 1,000 live births).
Abortion also has negative impacts on the mother, with implications for the economy and public health. A synthesis of published studies concluded:
“Women who had undergone an abortion experienced an 81% increased risk of mental health problems, and nearly 10% of the incidence of mental health problems was shown to be attributable to abortion.”
On the social side, John D. Mueller summarizes abortion’s negative impact:
“The main serious threats to American family stability are relatively recent and most traceable to the legalization of abortion. The data clearly suggests that returning abortion law to its status quo ante would raise the net marriage rate, reduce the rate of illegitimacy, and increase the birth rate, just as quickly as those indicators moved in the other direction once abortion was legalized.”
Infant mortality, incidences of which are a fraction of the number of abortions performed, generally doesn’t carry the moral stigma of abortion. There are, however, the possible exceptions of infant mortality due to illicit drug use, smoking, alcohol, and other detrimental activities that are harmful to the baby in utero and post-neonatal.
Alarmingly, there are signs that previous reductions in infant mortality may be reversing (see the Maine state highlight for more information).
The economic costs of suicide in 2010 totaled $44.7 billion, with the vast majority due to lost productivity ($44.5 billion). The economic burden of prescription opioid overdose, abuse, and dependence in 2013 was estimated at $78.5 billion. With the arrival of high-purity heroin, fentanyl, and carfentanil since then, the economic burden of opioids has surely soared.
Alarmingly, the total numbers of deaths from drugs, alcohol, and suicide are rapidly increasing. A recent analysis finds that between 2016 and 2025, these deaths will increase 60 percent from the previous decade (from 1 million to 1.6 million). On an annualized basis, they will increase from 127,524 in 2015 to 194,023 in 2025. This will most certainly have a negative impact on other variables in the Family Prosperity Index, such as YPLL and Risk Behavior.
As shown in Chart 64 and Table 7:
Years of Potential Life Lost (YPLL)
The Years of Potential Life Lost (per 100,000 population) decreased nationally by 1 percent from 7,334 in 2000 to 7,288 in 2016 (Chart 65). In 2016, West Virginia had the highest YPLL at 11,313, while California had the lowest at 5,512—or 49 percent of the West Virginia rate.
California had the top score for the YPLL sub-index (7.52), followed by Minnesota (7.37), Washington (7.33), New York (7.01), and Connecticut (6.95). West Virginia had the lowest score (0.82). Other low-scoring states included Mississippi (1.53), Alabama (1.71), Kentucky (1.83), and Louisiana (2.57).
Charts 66 through 70 show the variance in common health measures—including obesity, tobacco use, alcohol use, marijuana use, and use of illicit drugs other than marijuana—for the nation and in the 50 states. The data is for the years 2002 to 2015, except for the obesity rate, which is 2000 to 2015.
The obesity rate (as a percent of the population) increased nationally by 49 percent from 20 percent in 2000 to 29.8 percent in 2015 (Chart 66). In 2015, Louisiana had the highest obesity rate at 36.2 percent, while Colorado had the lowest rate at 20.2—or 56 percent of the Louisiana rate.
The tobacco use rate (as a percent of population) decreased nationally by 16 percent from 24.9 percent in 2002 to 20.4 percent in 2015 (Chart 67). In 2015, West Virginia had the highest tobacco use rate at 31.9 percent, while Utah had the lowest rate at 12—or 38 percent of the West Virginia rate.
The alcohol use rate (as a percent of population) increased nationally by 4 percent from 41.6 percent in 2002 to 43.3 percent in 2015. (Chart 68) In 2015, New Hampshire had the highest alcohol use rate at 54.8 percent, while Utah had the lowest rate at 24.3—or 44 percent of the New Hampshire rate.
The marijuana use rate (as a percent of population) increased nationally by 37 percent from 5.1 percent in 2002 to 6.9 percent in 2015 (Chart 69). In 2015, Colorado had the highest marijuana use rate at 13.6 percent, while Utah had the lowest rate at 3.9—or 29 percent of the Colorado rate.
The illicit drug use other than marijuana rate (as a percent of population) decreased nationally by 11 percent from 3.1 percent in 2002 to 2.7 percent in 2014 (Chart 70). In 2014, Colorado had the highest rate at 3.4 percent, while Wyoming had the lowest rate at 1.7—or 50 percent of the Colorado rate.
Utah had the top score for the risk behavior sub-index (8.00), followed by Idaho (7.28), Hawaii (7.22), New Jersey (6.31), and Montana (6.27). Vermont had the lowest score (3.71). Other low-scoring states included Louisiana (3.73), Arkansas (3.77), Maine (3.84), and New Hampshire (3.93).
Note: The obesity rate, tobacco use rate, alcohol use rate, marijuana use rate, and illicit drug use other than marijuana rate were all weighted equally in the risk behavior sub-index.
Sexually Transmitted Disease
Charts 71 through 77 show the variance in sexually transmitted diseases—including gonorrhea, chlamydia, syphilis, and HIV diagnoses—for the nation and in the 50 states. The data is from 2000 to 2016 for gonorrhea, chlamydia, and syphilis, and from 2008 to 2015 for HIV diagnoses.
The gonorrhea rate (as a percent of the population) increased nationally by 13 percent from 0.13 percent in 2000 to 0.14 percent in 2016 (Chart 71). In 2016, Mississippi had the highest gonorrhea rate at 0.24 percent, while Vermont had the lowest rate at 0.02—or 8 percent of the Mississippi rate.
Vermont had the lowest gonorrhea rate in 2016 of 0.02 percent, which is lower than the national rate at any point during the 1941-to-2016 time period (Chart 72). Hawaii’s gonorrhea rate (0.1 percent, ranked 15th) is equivalent to the 2008 U.S. average, and Virginia’s (0.13 percent, ranked 25th) is equivalent to the 1996 U.S. average.
However, there are 22 states with gonorrhea rates above the 2016 U.S. average. Mississippi’s gonorrhea rate (0.24 percent)—the highest in the country—is 66 percent above the U.S. average (0.14 percent) and is equivalent to the U.S. average in 1991.
The chlamydia rate (as a percent of the population) increased nationally by 97 percent from 0.25 percent in 2000 to 0.49 percent in 2016 (Chart 73). In 2016, Alaska had the highest chlamydia rate at 0.77 percent, while New Hampshire had the lowest rate at 0.26—or 34 percent of the Alaska rate.
Also, Chart 74 illustrates how the chlamydia rate has increased 15,261 percent between 1984 (0.003 percent) and 2016 (0.495 percent). It also show how the rates for the states in 2016 compare to the U.S. average as it moves through time.
For example, New Hampshire had the lowest rate in 2016 of 0.26 percent, which is equivalent to the 2001 U.S. average. Idaho’s rate (0.35 percent, ranked 6th) is equivalent to the 2006 U.S. average, and Pennsylvania’s (0.45 percent, ranked 19th) is equivalent to the 2011 U.S. average.
However, there are 21 states with rates above the 2016 U.S. average. Alaska’s rate (0.77 percent)—the highest in the country—is 56 percent above the U.S. average (0.49 percent) and exceeds the national rate at any point during the 1984-to-2016 time period.
The syphilis rate (as a percent of the population) increased nationally by 145 percent from 0.011 percent in 2000 to 0.0271 percent in 2016 (Chart 75). In 2016, Louisiana had the highest syphilis rate at 0.0555 percent, while Montana had the lowest rate at 0.0023—or 4 percent of the Louisiana rate.
Also, Chart 76 illustrates how the syphilis rate has decreased 93 percent between 1941 (0.364 percent) and 2016 (0.027 percent). It also compares how the rates for the states in 2016 compare to the U.S. average as it moves through time.
For example, Montana had the lowest rate in 2016, at 0.002 percent, which is lower than the rate at any point during the 1941-to-2016 time period. New York’s rate (0.048 percent, ranked 49th) is equivalent to the 1991 U.S. average, and Indiana’s (0.0117 percent, ranked 18th) is equivalent to the 2000 U.S. average.
However, there are 11 states with rates above the 2016 U.S. average. Louisiana’s rate (0.0555)—the highest in the country—is 105 percent above the U.S. average (0.027 percent) and is equivalent to the U.S. average in 1966.
Note: The gonorrhea rate, chlamydia rate, syphilis rate, and HIV diagnoses rate were all weighted equally in the sexually transmitted diseases sub-index.
The HIV diagnoses rate (as a percent of the population) decreased nationally by 22 percent from 0.0157 percent in 2008 to 0.0122 percent in 2016 (Chart 77). In 2016, Georgia had the highest HIV diagnoses rate at 0.0263 percent, while Vermont had the lowest rate at 0.0013—or 5 percent of the Georgia rate.
Vermont had the top score for the sexually transmitted diseases sub-index (7.65), followed by West Virginia (7.01), New Hampshire (6.95), Maine (6.95), and Wyoming (6.77). Louisiana had the lowest score (1.58). Other low-scoring states included Georgia (2.37), Mississippi (3.14), Nevada (3.30), and Florida (3.54).
Charts 78 through 80 show the variance in infant survival—including abortion and infant mortality—for the nation and in the 50 states. The data is from 2000 to 2014 for abortions and from 2000 to 2015 for infant mortality.-
The abortion rate (as a percent of births) decreased nationally by 29 percent from 32.7 percent in 2000 to 23.3 percent in 2014 (Chart 78). In 2014, New York had the highest abortion rate at 50.6 percent, while Wyoming had the lowest rate at 1.6—or 3 percent of the New York rate.
Also, Chart 79 illustrates how the abortion rate increased between 1973 (22.3 percent) and 1984 (42.3 percent), but has since been on the decline through 2014 (23.3 percent). It also shows how the abortion rates for the states in 2014 compare to the U.S. average as it moves through time.
For example, Wyoming had the lowest abortion rate in 2014, at 1.6 percent, which is lower than the U.S. abortion rate at any point during the 1973-to-2014 time period. Maryland’s abortion rate (38.7 percent, ranked 48th) is equivalent to the 1989 U.S. average, and Illinois’ (26.8 percent, ranked 40th) is equivalent to the 2011 U.S. average.
However, there are 14 states with abortion rates above the 2014 U.S. average. New York’s abortion rate (50.6 percent)—the highest in the country—is 117 percent above the U.S. average (23.3 percent) and is higher than the national abortion rate at any point during the 1973-to-2014 time period.
The infant mortality rate (as a percent of births) decreased nationally by 16 percent from 0.7 percent in 2000 to 0.59 percent in 2015 (Chart 80). In 2015, Mississippi had the highest infant mortality rate at 0.93 percent, while Iowa had the lowest rate at 0.42—or 45 percent of the Mississippi rate.
Idaho had the top score for the infant survival sub-index (6.86), followed by South Dakota (6.84), Utah (6.67), Wyoming (6.59), and Iowa (6.46). New York had the lowest score (1.38). Other low-scoring states included New Jersey (2.12), Maryland (2.59), Connecticut (2.86), and Florida (3.00).
Note: The abortion rate was weighted 90 percent and the infant mortality rate was weighted 10 percent in the infant survival sub-index.
The time-series abortion data from the Guttmacher Institute was provided sporadically from 2000 to 2014. Missing years (2001, 2002, 2003, 2006, 2009, and 2012) were linearly interpolated.
Charts 81 and 82 show the variance in self-mortality—including suicide and drug-induced deaths—for the nation and in the 50 states from 2000 to 2016.
The suicide rate (as a percent of the population) increased nationally by 34 percent from 0.0104 percent in 2000 to 0.0139 percent in 2016 (Chart 81). In 2016, Alaska had the highest suicide rate at 0.026 percent, while New Jersey had the lowest rate at 0.0077—or 29 percent of the Alaska rate.
The drug-induced death rate (as a percent of the population) increased nationally by 197 percent from 0.007 percent in 2000 to 0.0208 percent in 2016 (Chart 82). In 2016, West Virginia had the highest drug-induced death rate at 0.0499 percent, while Nebraska had the lowest rate at 0.0077—or 15 percent of the West Virginia rate.
Nebraska had the top score for the self-mortality sub-index (7.01), followed by California (6.85), New York (6.82), Hawaii (6.76), and Mississippi (6.75). West Virginia had the lowest score (2.08). Other low-scoring states included New Hampshire (2.82), Alaska (3.28), New Mexico (3.63), and Pennsylvania (3.65).
Note: The suicide rate and drug overdose rate were weighted equally in the self-mortality sub-index.
 The Centers for Disease Control and Prevention, the federal agency that collects statistics on health in America, uses the term “risk behavior” rather than “risky behavior.” We follow the CDC’s usage in this report.
 “The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General,” U.S. Department of Health and Human Services: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014. http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf
 Bouchery, Ellen E.; Brewer, Robert D.; Harwood, Henrick J.; Sacks, Jeffrey J.; and Simon, Carol J., “Economic Costs of Excessive Alcohol Consumption in the U.S., 2006,” American Journal of Preventive Medicine, Vol. 41, No. 5, 2011. http://www.ajpmonline.org/article/S0749-3797(11)00538-1/pdf
 Hammond, Ross A. and Levine, Ruth, “The Economic Impact of Obesity in the United States,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 2010:3, pp. 285-295. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047996/pdf/dmso-3-285.pdf
 Cawley, John and Meyerhoefer, Chad, “The Medical Care Costs of Obesity: An Instrumental Variables Approach,” Journal of Health Economics, Vol. 31, No. 1, January 2012, pp. 219-230. https://www.ncbi.nlm.nih.gov/pubmed/22094013
 Beck, Stacy E.; Rayburn, Jack; and Segal, Laura M., “The State of Obesity: 2017,” Trust for America’s Health, August 2017. http://healthyamericans.org/assets/files/TFAH-2017-ObesityReport-FINAL.pdf
 Davis, Alyssa, “In U.S., Opioids Viewed as Most Serious Local Drug Problem,” Gallup, July 29, 2016. http://www.gallup.com/poll/194042/opioids-viewed-serious-local-drug-problem.aspx
 “The Economic Impact of Illicit Drug Use on American Society,” U.S. Department of Justice: National Drug Intelligence Center, April 2011. http://www.justice.gov/archive/ndic/pubs44/44731/44731p.pdf
 “Incidence, Prevalence, and Cost of Sexually Transmitted Infections in the United States,” Centers for Disease Control and Prevention, CDC Fact Sheet, February 2013. http://www.cdc.gov/std/stats/sti-estimates-fact-sheet-feb-2013.pdf
 Gerberding, Julie Louise, “Report to Congress: Infertility and Prevention of Sexually Transmitted Diseases 2000 – 2003,” Centers for Disease Control and Prevention, November 2004. http://www.cdc.gov/std/infertility/ReportCongressInfertility.pdf
 “Antibiotic Resistance Threats in the United States, 2013,” U.S. Department of Health and Human Services: Centers for Disease Control and Prevention, pp. 55-56, September 16, 2013. http://www.cdc.gov/drugresistance/threat-report-2013/index.html
Missing years were linearly interpolated. 2012 abortion estimate was based on data from the Centers for Disease Control and Prevention (see the section on Infant Survival for details).
 Higgins, Anna and Potrykus, Henry, “Abortion: Decrease of the U.S. Population & Effects on Society,” Marriage & Religion Research Institute, January 22, 2014. http://downloads.frc.org/EF/EF14A55.pdf
 There were 28.5 million abortions between 1973 and 1992. Assuming half of those abortions were females yields 14.25 million aborted females (a conservative assumption considering births are biased toward females).
 Hamilton, Brady E. and Matthews, T.J., “Mean Age of Mothers is on the Rise: United States, 2000-2014,” Centers for Disease Control and Prevention, NCHS Data Brief, No. 232, January 2016. http://www.cdc.gov/nchs/data/databriefs/db232.pdf
We are conservatively using the average age as of 2014. The average age of first birth is considerably earlier in recent past decades.
 This is a conservative estimate since we only assume 1 child, on average, per woman—though many, if not most, would have had more than 1 child. Also, some of the would-be children of these aborted females would now be reaching the age of 26 and having children of their own.
 Klick, Jonathan and Stratmann, Thomas, “The Effect of Abortion Legalization on Sexual Behavior: Evidence from Sexually Transmitted Diseases,” Journal of Legal Studies, Vol. 32, June 2003, pp. 407-433. https://www.law.upenn.edu/fac/jklick/32JLS407.pdf
 Ewing, Alexander; Jamieson, Denise J.; Jatlaoui, Tara C.; Mandel, Michele G.; Pazol, Karen; Simmons, Katharine B.; and Suchdev, Danielle B., “Abortion Surveillance – United States, 2013,” Centers for Disease Control and Prevention, Morbidity and Mortality Weekly Report, Surveillance Summaries, Vol. 65, No. 12, November 25, 2106. https://www.cdc.gov/mmwr/volumes/65/ss/ss6512a1.htm
 Coleman, Priscilla K., “Abortion and Mental Health: Quantitative Synthesis and Analysis of Research Published 1995 – 2009,” The British Journal of Psychiatry (2011), Vol. 199, pp. 180-186. http://bjp.rcpsych.org/content/bjprcpsych/199/3/180.full.pdf
 Mueller, John D., “Redeeming Economics: Rediscovering the Missing Element,” ISI Books, ISBN 978-1-1932236-95-8, p. 229, 2010.
 Davis, Thomas; Delucchi, Kevin L.; Guydish, Joseph; and Wolfe, Ellen L., “Mortality Risk Associated with Perinatal Drug and Alcohol Use in California,” J Perinatol, Vol 25, No. 2, 2005, pp. 93-100.
 Florence, Curtis S.; Luo, Feijun; Xu, Likang; and Zhou, Chao, “The Economic Burden of Prescription Opioid Overdose, Abuse, and Dependence in the United States, 2013,” Medical Care, Vol. 54, Issue 10, pp. 901-906, October, 2016. https://journals.lww.com/lww-medicalcare/Abstract/2016/10000/The_Economic_Burden_of_Prescription_Opioid.2.aspx
 David, Felicita; Rudd, Rose A.; Scholl, Lawrence; and Seth, Puja, “Increases in Drug and Opioid-Involved Overdose Deaths — United States, 2010–2015,” Morbidity and Mortality Weekly Report, December 30, 2016. https://www.cdc.gov/mmwr/volumes/65/wr/pdfs/mm655051e1.pdf
 Biasi, Anne De; May, Kendra; Mueller, Jennifer; Segal, Laura M.; and Warren, Molly, “Pain in the Nation: The Drug, Alcohol and Suicide Crisis and the Need for a National Resilience Strategy,” Trust for America’s Health, November, 2017. http://www.tfah.org/assets/files/TFAH-2017-PainNationRpt-18-FINAL.pdf
 The full Maine study can be found here: http://www.familyprosperity.org/application/files/4115/0293/5803/FPI-Maine2017-DRAFT3.pdf
 Data provided via email request to the Maine Office of Child and Family Services. Note: These numbers reflect the number of infants born in Maine where a healthcare provider reported to the Office of Child and Family Services that there was reasonable cause to suspect the baby may be affected by illegal substance abuse or demonstrating withdrawal symptoms resulting from prenatal exposure (illicit or prescribed appropriate under a physician’s care for the mother’s substance abuse treatment) or who have fetal alcohol spectrum disorders. 2005 was the first year of available data.
 “Drug Abuse and HIV,” National Institute on Drug Abuse, NIH Publication No. 12-5760, 2012. https://d14rmgtrwzf5a.cloudfront.net/sites/default/files/rrhiv.pdf
 U.S. Department of Health and Human Services: Centers for Disease Control and Prevention, Substance Abuse and Mental Health Services Administration: Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health. http://www.samhsa.gov/data/population-data-nsduh/reports?tab=33
 U.S. Department of Health & Human Services: Centers for Disease Control and Prevention. http://www.cdc.gov/brfss/brfssprevalence and Substance Abuse and Mental Health Services Administration: Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health. http://www.samhsa.gov/data/population-data-nsduh/reports?tab=33
 U.S. Department of Health and Human Services: Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) Atlas. http://www.cdc.gov/NCHHSTP/Atlas/
 Abortion data from Guttmacher Institute. http://www.guttmacher.org/datacenter/trend.jsp and U.S. Department of Health & Human Services: Centers for Disease Control and Prevention, Abortion Surveillance. http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6410a1.htm?s_cid=ss6410a1_e
 Infant mortality data from U.S. Department of Health and Human Services: Centers for Disease Control and Prevention, National Center for Health Statistics. The data was extracted from the Kids Count Data Center published by the Annie E. Casey Foundation. http://datacenter.kidscount.org/data/tables/6051-infant-mortality?loc=1&loct=2#detailed/2/2-52/false/36,868,867,133,38/any/12718,12719
 U.S. Department of Health & Human Services: Centers for Disease Control and Prevention, National Center for Health Statistics, Underlying Cause of Death 1999-2014 on CDC Wonder Online Database. http://wonder.cdc.gov/