Background
==========

Over the last 50 years, the widespread usage of fluoridated water and fluoridated dentrifices have been cited as major reasons for a decline in caries since the early 1970s \[[@B1]\], and for the appearance of a significant association between oral hygiene and caries experience \[[@B2]-[@B4]\]. An inverse relationship exists between salivary fluoride concentration and caries experience in the deciduous and permanent dentition \[[@B5]\], but fluoride concentration is excluded from most caries prediction models \[[@B6],[@B7]\]. Acids in bacterial plaques cause caries in pits, fissures and interdental regions of teeth, but they also enhance the inhibitory effect of fluoride on demineralization, confounding the ability to predict caries from the salivary fluoride concentration \[[@B8],[@B9]\].

The greater the consumption of dietary sucrose, the greater the fall in pH and fraction of acidogenic, acid tolerant bacteria in tooth adherent plaques \[[@B10],[@B11]\]. The number of these bacteria (mostly mutans streptococci and lactobacilli), and the fluoride content, discriminate between severe and mild caries in 12--15 year-old children \[[@B12],[@B13]\]. Acid-tolerant bacteria require D-alanyl glycerol lipoteichoic acid (D-alanyl LTA) in their membranes and cell surfaces \[[@B14]\]. D-alanyl LTA is made by esterifying carboxyl-activated D-alanine to glycerol in membrane LTA by means of a D-alanyl-carrier enzyme, DCP \[[@B15]\]. Strains of *Streptococcus mutans* in which DCP is inactive do not initiate growth at below pH 6.5 and make glycerol LTA without D-alanine \[[@B14]\]. In the DCP active strains, soluble D-Alanyl LTA is extruded into culture fluid *in vitro*\[[@B16],[@B17]\] or plaque *in vivo*\[[@B18]\]. The D-alanyl esters are stable at pH 6.0 at 37°C, but hydrolyze to free D-alanine and LTA with a half-life of 3.9 h at pH 8.0 \[[@B19]\]. Healthy gingival sulci have a pH of 6.5 -- 7.5 and inflamed sulci a pH of 7.5--8.5 \[[@B20]\].

About 30% of young adults have serum IgG antibodies that precipitate with D-alanyl LTA, but not with D-alanine-free LTA \[[@B17],[@B21]\]. It is likely that plaques induce these IgG antibodies from gingival sulci that contain more acid-tolerant bacteria. An elevated IgG antibody response to D-alanyl LTA may therefore indicate the subjects in whom an inhibitory effect of fluoride on caries is enhanced. The fluoride concentrations of plaque and saliva are related to whether the drinking water is fluoridated \[[@B13]\] and to oral hygiene, which nearly always involves using a fluoridated dentrifice. The aim of this study was therefore to determine whether elevated antibody responders to D-alanyl LTA show a association of DMFT with fluoride exposure and gingival health not apparent in low responders.

Methods
=======

Subjects
--------

Antibody was obtained from blood from four sources: 1) 105 dental students, 2) 147 patients seeking dental treatment, 3) 145 volunteer blood donors (volunteers), and 4) 37 siblings aged 5 through 25 from six Amish families. The dental students and patients were attending the University of Oklahoma Health Sciences Center between 1985 and 1988. The volunteers and Amish family members were attending centers elsewhere in the US at the same time. All subjects consented to provide blood for antibody analysis according to local Institutional Review Board procedures (see Acknowledgements). The student, patient and volunteer populations (397 subjects) were used to determine what IgG concentration constituted an elevated antibody response to D-alanyl LTA, to ensure that these antibodies were not unique to dental or Oklahoma populations and to examine whether the antibody concentration was sex or age-associated. The Amish family siblings were selected to determine the frequency of high antibody concentration in children and young adults. Each sibling had at least one parent high responder to increase the likelihood of exposure to an antibody-associated oral microbiota from birth.

The clinical study participants consisted of 87 dental students (88.4% male) and 64 patients (31.3% male) who were medically healthy. All had 18 or more natural teeth and were aged \>22 and \<38 years. Of these participants, 67 dental students and 35 patients provided information that permitted an estimate of exposure to fluoridated water: residence(s) from birth through age 14 in the 1980 Fluoridation Census. Subjects not using the public water supply, or resident outside of the US for more than 18 months, were excluded. Exposure to water fluoridation scored 1 for each of five 3-year age cohorts: 0--2, 3--5, 6--8, 9--11 and 12--14 to give a fluoride exposure score (F Score) of 0 (no exposure) to 5 (complete exposure) described previously \[[@B22]\].

Most dental students had mild caries and gingivitis and most patients had moderate to severe caries and gingivitis. Exceptionally healthy or exceptionally diseased subjects were therefore increased compared to a similar number of subjects obtained as a random sample. This wide distribution of clinical measurements provided more stable estimates (narrower confidence intervals) of regression coefficients (β) than would be obtained from a similar number from a random survey of the general population. Regression lines are more robust when a greater range of measurements is used \[[@B23]\].

Clinical measurements
---------------------

Dental caries experience was the number of Decayed, Missing and Filled Teeth (DMFT), excluding third molars and teeth reported missing for other reasons. Decayed teeth (DT) were also enumerated separately from missing and filled teeth (MFT). DT indicates a combination of delay in seeking therapy and faster development of new cavities. Fluoridated dentrifice use is related to oral hygiene but not toothbrushing frequency in adults aged as in the present clinical study \[[@B3]\] and young enough to have likely used fluoridated dentrifices from early childhood. Sensitive staining for plaque accumulation \[[@B24]\] was therefore used with measures of gingivitis and pocket depth at the mesio-buccal, buccal, disto-lingual and lingual surfaces of the six teeth employed for the simplified oral hygiene index \[[@B25]\], substituting adjacent teeth as necessary (24 sites sampled).

Gingivitis was determined by whether a site bled within 30 sec of gentle probing, BOP \[[@B26],[@B27]\] and pocket depth by measuring the distance (mm) from the free gingival margin to the base of the sulcus or pocket. Finally, each subject was asked to suck an erythrosin tablet for 30 sec and the sites examined for stained plaque \[[@B24]\]. For each subject, the mean prevalences of plaque (PL) and bleeding on probing (BOP), and the mean pocket depth (PD), were calculated across all sampled sites. The clinical measurements were made by two experienced clinicians who were calibrated for this study. The clinicians agreed strongly with respect to all measurements (correlation coefficients, r \> 0.85; p \< 0.001) except gingival bleeding index, for which a weaker correlation was noted (r = 0.60, p \< 0.001). The data reported are the mean measurements from the clinical examiners.

Antigen purification
--------------------

D-alanyl LTA, but not D-alanine-free LTA, is present in culture filtrates of *Streptococcus mutans* GS5 \[[@B17]\]. Bacteria were grown at 37°C in trypticase soy broth to late stationary phase (96 h), when the maximal amount of antigen is extruded into the culture fluid \[[@B18]\]. After centrifugation to remove the bacteria, culture fluid (10 1) was concentrated 20-fold over a YM10 Diaflo Membrane filter (Amicon Corp., Beverley, MA). D-Alanyl LTA in the concentrate was detected by immunoelectrophoresis, using a standard human serum identified previously \[[@B16]\]. D-Alanine-free LTA does not react with this serum IgG \[[@B17],[@B18],[@B21]\]. The D-alanyl LTA was purified by passing the concentrated culture fluid over a 90 × 2.5 cm Sephacryl column in 0.4 M NaCl buffered with 0.05 M sodium acetate pH 5. Antigen in the fractions was collected. After equilibrating with 5 mM sodium acetate buffer pH 5.0, it bound to a short Sephacryl S-200 and eluted by adding 14 mM NaCl as described previously \[[@B16]\].

Measuring antibody content and determining high and low responders
------------------------------------------------------------------

IgG antibody content was measured by enzyme-immunoassay employing a Fast Assay Screening Test System at room temperature \[[@B28]\]. Pegs protruding from a lid were placed over a 96-well plate or trough containing 14 ml of 10 μg/ml D-alanyl LTA in acetate buffer pH 5 for 2 h (Becton Dickinson, Lincoln Park, NJ). The pegs were blocked with 14 ml of phosphate buffered saline (PBS) pH 7.0 in 1.0% Tween-20 and immersed in wells containing 0.1 ml serum. After overnight incubation, excess IgG antibody from the serum was washed away by thrice transferring the pegs to troughs containing 14 ml of PBS containing 0.05 % Tween 20 (PBS-Tween) for 5 min each time. The pegs were then immersed for 2 h in a trough containing 14 ml of anti-human IgG F(ab\'2) fragment conjugated to alkaline phosphatase in PBS-Tween and developed with nitrophenyl phosphate (Sigma Chemical Co. St Louis, MO).

The concentration of antigen-specific IgG in standard serum was obtained by measuring the optimal amount of protein immunoprecipitated \[[@B16]\], and a standard curve of absorbance against concentration was obtained (Fig. [1](#F1){ref-type="fig"}). The greatest range of absorbance occurred when sera were measured at a dilution of 1:200 \[[@B28]\]. Replicate antibody assays were performed on each serum and the concentrations read off the standard curve. The antibody concentrations are shown ranked in Fig. [2](#F2){ref-type="fig"}.

![Graph of absorbance at 410 nm against log ng/ml of antibody to D-alanyl LTA. Vertical lines indicate the standard deviation of the measurements.](1472-6831-2-2-1){#F1}

![Graph of ranked antibody contents. The cut-off points separates high from low responders (see Methods).](1472-6831-2-2-2){#F2}

Data analyses
-------------

The amount of IgG antibody to D-alanyl LTA varies with no obvious cutoff (Fig. [2](#F2){ref-type="fig"}). However, the sera containing precipitating antibody should tend to have high IgG antibody contents. The IgG antibody measurements were alternatively divided into clusters, using the unweighted pair group method with arithmetic averages \[[@B29]\] and NT-SYS, a package of multivariate statistical computer programs \[[@B30]\]. A low response supremum was obtained by taking the antilog of the mean IgG content of the non-precipitating sera plus one standard deviation, or the antilog of the highest IgG content of the cluster grouping containing the least antibody.

The effect of age was determined after splitting the subjects into decile cohorts (Table [1](#T1){ref-type="table"}) and comparing the fraction of high antibody responders in each cohort. The youngest cohort was composed of Amish family siblings who were younger than any dental students, patients or volunteers.

::: {#T1 .table-wrap}
::: {.caption}
######

Age decile cohorts for determining changes in fraction of high antibody responders
:::

  Cohort No.   Decile   ^a^Number in Cohort   High Responders
  ------------ -------- --------------------- -----------------
  1            5--15    ^b^19                 ^c^21.05%
  2            15--25   ^b^97                 35.05%
  3            25--35   176                   36.36%
  4            35--45   76                    40.79%
  5            45--55   31                    38.71%
  6            55--65   13                    ^c^23.08%
  7            65--72   6                     ^c^16.67%

^a^381 subjects after excluding all Amish family members and 16 of the 397 dental student, patient and volunteer subjects whose age was not recorded. ^b^The Amish family siblings \<15 years comprised Cohort 1 and those \>15 years were included within Cohort 2. ^c^Comparison of cohorts 1, 6 and 7 with the remainder: X^2^ = 3.21, p = 0.073 (not significant).
:::

A multiple linear regression procedure was utilized to examine the relationship of caries (DMFT) with age, F score, and measures of gingival health obtained in this study: PL, BOP, and PD. The regression on DMFT was used: 1) to estimate the partial regression coefficients (β coefficients) within the high and low antibody response group; 2) to test each β coefficient for significance after accounting for the effects of the other four variables; and 3) to examine for significant differences in β coefficients between the antibody response groups. A β coefficient is interpreted as the change in disease response (DMFT) per unit change in one of the independent variables after adjusting for all the other independent variables in the model. Within each antibody response group, the multiple regression coefficient (R^2^) provided an estimate of the proportion of variance in DMFT explained by the combination of variables tested. Stepwise regression then identified the best estimate of the variance in DMFT that was explained by multiple variables in the separate and combined high and low responder groups. These multiple regression analyses were repeated using gingivitis (BOP) as the dependent variable and OHPI, PD, DT and MFT and age as independent variables. All of the clinically examined subjects were included because F score was not an independent variable for BOP.

To ensure that obtained relationships were robust, influential points (outliers) were identified using a statistic (DFFITS) which measured the change in coefficients caused by removing the data for each subject. If this change exceeded 2√(p/n), where p was the number of independent variables and n the number of samples \[[@B31]\], the point was influential. Repeating the regressions with all the influential points removed determined the degree to which these points had affected the results.

Results
=======

Definition of high antibody response
------------------------------------

IgG antibodies were initially detected in sera irrespective of whether D-alanyl LTA was immunoprecipitated. Excluding the Amish family group, there were 288 subjects whose sera failed to immunoprecipitate antigen (detected by immunoelectrophoresis). The mean IgG antibody concentration (log ng/ml) was 3.19 (0.64 standard deviation, s.d.) compared with 4.25 (0.57 s.d.) for 109 subjects whose sera did precipitate antigen (\'t\' test p \<10^-6^). High responders therefore had a log antibody content (ng/ml) that exceeded the mean plus standard deviation of non-precipitating serum (log ng IgG /ml \>3.83). However, low IgG concentrations formed a cluster whose supremum (log ng IgG antibody/ml) was 3.861, which corresponds to 7.26 μg/ml (left side of Fig. [2](#F2){ref-type="fig"}). Subjects were therefore classified as low responders if their log IgG antibody content exceeded 3.86 rather than 3.83. The odds ratio for a serum from a high antibody responder immunoprecipitating D-alanyl LTA was 14 times greater than for a low responder.

High antibody response, age and gender
--------------------------------------

Of the 397 students, patients and volunteers, 16 did not have their age recorded. High responders had a mean age of 32.7 years ± 9.4 s.d. (136 subjects) and low responders a mean age of 33.0 years ± 10.7 s.d. (245 subjects). Table [1](#T1){ref-type="table"} shows the fraction of high responders in different age cohorts. There was a high responder frequency of 35--40% from age 15 through 54. The reduced frequencies of high response in childhood and old age were not significant. However, within the Amish family offspring high IgG responders were significantly older. Table [2](#T2){ref-type="table"} shows that the high responder offspring had a mean age of 17.1 years compared with 13.3 years for low responders. This significant difference (\'t\' statistic = 2.42, degrees of freedom, d.f. = 35; p \< 0.03) was due to few high responder children and young teenagers and a slightly greater fraction of siblings aged 15--25 years who were high responders (50%) compared with the general population.

::: {#T2 .table-wrap}
::: {.caption}
######

Age of siblings from 6 families with at least one high responder parent
:::

                         High responders   Low responders
  ---------------------- ----------------- ------------------
  F+M-                   17                16,14.13,12,10,8
  F+M+                   20,13             21,18,16,8,5
  F+M+                   22,20,18          25,16,14
  F?M+                   None              12,11,10,9,8
  F+M-                   22,20,13,11       17
  F+M\'                  17,16,13          20,18,10,7
  ^b^Mean age (s.d)^c^   17.08(3.68)       13.25(5.00)

^a^F, Father; M, Mother; +, high responder; -, low responder, ? Not known. ^b^Mean age of the high and low responders ^c^s.d., standard deviation.
:::

High antibody responders accounted for a similar fraction of subjects irrespective of whether they were in the clinical study, or dental students, or patients (Table [3](#T3){ref-type="table"}). Men were 49.1% of the 395 students patients and volunteers whose sex was recorded. Men had also a greater frequency of high response 40.21% vs 31.34% and a greater mean log ng/ml IgG antibody content, 3.52 ± 0.76 standard deviation (s.d.) vs 3.44 ± 0.80 s.d. Neither of these differences were significant (X^2^ = 3.00, p = 0.09; \'t\' statistic = 0.34, p = 0.73). In serum samples from 18 subjects aged between 22 and 38, the IgG antibody concentrations were essentially the same after 6 months as originally estimated (squared correlation coefficient, R^2^ \>0.95, p \< 0.01). The results indicate that, for subjects aged 15 -- 55, age and sex had little effect on the frequency of high D-alanyl LTA antibody response.

::: {#T3 .table-wrap}
::: {.caption}
######

Fraction of high antibody responders in or not in the clinical study.
:::

  Subject group                             Number   \% high responders
  ----------------------------------------- -------- --------------------
  Dental students in clinical study^a^      87       37.9
  Patients in clinical study^a^             64       32.8
  Same-age subjects not in clinical study   129      31.8
  Other subjects not in clinical study^c^   117      39.7
  All subjects                              397      33.8

^a^See first section of Materials & Methods. ^b^Same-age subjects not in the clinical study were 12.4% dental students, 17.1% patients and 70.5% volunteers. ^c^Other subjects were a mixture of patients and volunteers: 70.9% older (ages \>38 and \<72 years), 15.4% younger (ages \>15 and \<22 years), and the remainder age unknown.
:::

Association of DMFT with gingival health and fluoride in high and low responders
--------------------------------------------------------------------------------

Table [4](#T4){ref-type="table"} lists the variables tested for association with DMFT and the observed β coefficients in high and low antibody responders. It was immediately apparent that the β coefficients from PL were similar in both groups, whereas those from BOP and F score were only significant in high responders and those from PD were only significant in low responders. Comparison of the differences in β coefficients between high and low responders, column 4 (column 2 -- column 3), indicated relationships of DMFT to pocket depth that were significantly different and relationships of DMFT to F score that were almost significantly different, p = 0.062 (Table [4](#T4){ref-type="table"}, column 4).

::: {#T4 .table-wrap}
::: {.caption}
######

Changes in the equality of the partial β coefficients for association of tested variables withcaries severity in high and/or low responders^a^.
:::

  Variable^a^   High responders ^b^n = 35   Low responders n = 67   Difference (Hi -- Low)
  ------------- --------------------------- ----------------------- ------------------------
  F score       ^c^-0.847                   0.006                   ^d^-0.853
  PL            ^d^0.259                    ^d^0.234                0.025
  BOP           ^c^0.447                    0.167                   0.280
  PD            -1.903                      ^c^4.249                ^c^-6.152
  Age           0.099                       ^d^0.261                -0.162

^a^Variable names are defined under \"Clinical Measurements\" in the Methods ^b^n = number of subjects ^c^p \<0.05 for value of constant or variable (β) or for all values in indicated model. ^d^*p* \> 0.05 & \< 0.15. If no subscript, p \> 0.15 ^e^HLS: high response = 1, low response = 0.
:::

Stepwise regression confirmed the similar associations of DMFT with plaque prevalence in both high and low IgG antibody responders, but significant associations with only F score and BOP prevalence in high responders and with only PD and age in low responders. In high responders, DMFT increased as plaque and BOP prevalences increased and fell as fluoride exposure increased. The equation obtained was: DMFT = 4.60 + 0.28 PL + 0.39BOP - 0.88FScore (R^2^ = 0.51, F statistic = 10.75, p \< 0.0001). By contrast, in low responders, DMFT increased with plaque prevalence, pocket depth and age. The equation was: DMFT = -13.37 + 4.58 PD + 0.27 Age + 0.30 PL (R^2^ = 0.26, F statistic = 7.41, p \< 0.0003). Within each equation, the constant and the respective β coefficients were significant (p \< 0.05), except for the β coefficient of age in low responders (p = 0.062). High responders receiving fluoridated drinking water for all 14 years of childhood (F score = 5), had a significantly lower DMFT than those never receiving fluoridated water (F score = O): DMFT = 7.50 ± 4.52 (s.d.) vs 11.60 ± 4.06 (s.d.); \'t\' test p \< 0.04. This was not true of low responders in whom the difference between F score 5 and F score 0 was not significant (DMFT = 9.33 ± 5.72 vs 12.07 ± 5.85; \'t\' test p = 0.14).

When antibody was ignored in stepwise regression (control), DMFT increased with age, PL and BOP, and decreased with F score: DMFT = -2.11 + 0.17 Age + 0.25 PL + 0.27 BOP - 0.39 FScore (R^2^ = 0.29, F statistic = 6.62, p \< 0.0001). PL and BOP were individually significant. F Score and age were borderline, p = 0.09 and 0.14 respectively, and PD was not significant, being entirely replaced with age. Despite the subjects increasing to 102 (from 35 or 67) the strength of association was similar to that of low responders only.

Association of DT with gingivitis in high and low responders
------------------------------------------------------------

Because caries experience associated with fluoride and gingival health in high responders, poor gingival health should increase the number of decayed teeth (DT) more than in low responders. When BOP was regressed against the variables in Table [5](#T5){ref-type="table"}, only DT (column 4) differed significantly between the high and low responders. Although DT alone significantly correlated with BOP in both response groups (p \< 0.01), it associated with BOP much more strongly in high responders (R^2^ = 0.57) than in low responders (R^2^ = 0.12). Fig. [3](#F3){ref-type="fig"} shows the respective correlations, and also the patient data (filled circles) skewed by few healthy subjects and the student data (unfilled circles) skewed by few moderate and no severely diseased subjects. Clearly, combining students and patients strengthened the respective associations (β coefficients). Stepwise regression indicated that, excluding DT, BOP associated with PL and PD similarly (BOP = -14.02 + 0.42 PL + 4.8 PD in high responders and BOP = -11.07 + 0.34 PL + 4.10 PD in low responders; R^2^ = 0.40 and 0.41 respectively; p \< 0.001). However, DT explained more variance (BOP = -1.46 + 0.24 PL + 0.95 DT, R^2^ = 0.62, p \< 0.001) in high responders, and less variance (BOP = -3.25 + 0.41 PL + 0.33 DT, R^2^ = 0.31, p \< 0.001) in low responders.

![Graph of number of decayed teeth against gingival bleeding index. Results are provided separately for the dental students (o) and patients (•). Data from more than one subject are superimposed. Regression line equation, high responders (upper graph): DT = 0.123 BOP - 3.65 Regression line equation, low responders (lower graph): DT = 0.053 BOP - 15.11](1472-6831-2-2-3){#F3}

::: {#T5 .table-wrap}
::: {.caption}
######

Changes in the equality of the partial β coefficients for association of tested variables withgingivitis (BOP) in high and/or low responders.
:::

  Variable^a^   High responders n = 54   Low responders n = 97   Difference (Hi -- Low)
  ------------- ------------------------ ----------------------- ------------------------
  PL            ^d^0.182                 ^c^0.321                -0.140
  PD            ^c^2.529                 ^c^3.588                -1.059
  DT            ^c^0.906                 0.202                   ^c^0.703
  MFT           0.154                    0.054                   0.099
  Age           0.022                    -0.053                  0.074

^a^Variable names are defined under \"Clinical Measurements\" in the Methods ^b^n = number of subjects. ^c^p \< 0.05 for value of constant or variable (β). ^d^*p* \> 0.05 & \<0.15. If no subscript, p \> 0.15.
:::

Influential points (outliers), whose presence might have affected the strength and significance of these complex regression analyses, were identified in five low responders and two high responders. When these subjects were omitted, the respective regression coefficients or their significance were little changed, indicating that the different, partial, linear regression coefficients in high or low responders were not artifacts of influential or outlying points.

Discussion
==========

This study has demonstrated that IgG antibodies to D-alanyl LTA are widespread in US adults. The fraction of high responders was essentially constant from early adulthood through middle age, but reduced in children (\<15 years) and old age (\>55 years). Within the adults (ages 15--55 years) a change from low to high response or *vice versa* was found unlikely from repeated measurements over 2--6 months. A similar lack of change in this IgG antibody concentration was reported 2--3 months after an additional 26 similarly aged patients had received oral hygiene therapy in another study \[[@B32]\]. Finally, the family studies established that a high antibody response was probably induced during the mid-teenage years. In order to apply the results of this study to children and young teenagers, longitudinal studies of the antibody response in relation to age and the clinical measurements in this study may need to be undertaken.

Despite few investigations of caries risk in 22--38 year old subjects compared with a younger or older group \[[@B7]\], the association of DMFT with PL in this study agrees with that obtained from 35 year old Norwegians \[[@B3]\]. Plaque (simplified oral hygiene index measurement) accounted for 15% of the variance in number of decayed/filled teeth surfaces in that study, and for 19% of the DMFT variance within all 151 clinically examined subjects in this study (ignoring age, antibody and all other variables). Other studies have shown that the amount of fluoride applied from dentrifices is measured better from oral hygiene or plaque accumulation, as in this study, and not from the reported frequency of dentrifice use \[[@B2],[@B3]\]. Finally, because subjects aged more than 38 are unlikely to have used fluoridated toothpastes until later in life, they were omitted to avoid confounding the results.

The rationale behind this study was that acidic plaque environments increase the amount of D-alanyl LTA and promote its immunogenicity. Accordingly, caries-protection by fluoride in the water supply and dentrifices was strong in high IgG antibody responders, accounting for just over 50% of the variance in DMFT. In addition, gingivitis (BOP prevalence) associated strongly and significantly with the number of decayed (untreated) teeth, suggesting that preventing gingivitis increased fluoride exposure from dentrifices and reduced the number of decayed teeth. Increased exposure to fluoridated water, and dentrifices associated with good gingival health, may result in fluoride inhibiting enamel remineralization at the acidic plaque pH likely present in high responders.

In low responders, the fewer antibodies to D-alanyl LTA suggest less colonization by acid-tolerant bacteria and a weaker cariogenic attack. DMFT associated with age, as reported for other subjects whose sera did not precipitate D-alanyl LTA \[[@B22]\], and also with pocket depth. An increase in pocket depth is caused by periodontopathic bacteria that associate with an alkaline environment in the sulci over many years \[[@B20]\] and a microbiota that is neither acidic nor acid-tolerant \[[@B10]\]. The coefficient of DMFT association with PD in low responders therefore differed significantly from high responders within whom F score and gingival health were stronger covariates.

Conclusions
===========

An increased mutans streptococcal challenge accompanying low plaque pH (high antibody response to D-alanyl LTA) allows much of the variation in caries experience to be controlled by water fluoridation and by the use of fluoridated dentrifices associated with maintaining oral health. High IgG antibody responders are therefore better protected from caries in an optimally fluoridated environment. The concept that fluoride protects better from caries in a low pH environment \[[@B12]\] was recently used to explain why there is a poor association between caries experience and pH fall after a 10% sucrose rinse \[[@B33]\]. In low responders, increased fluoride exposure from dentriflce use to maintain oral health, or from water fluoridation, associate relatively poorly with caries experience. Although this study has indicated that the IgG antibodies to D-alanyl LTA do not become elevated until after age 17, when much caries may have already developed, they may be elevated to a lower level in children who eventually become high responders. The D-alanyl LTA antibody response is not detectable in saliva (unpublished studies), but it can be measured from only a thumb-prick of blood. Longitudinal studies of the D-alanyl LTA response in children could improve current efforts to predict caries susceptibility by relating it to fluoride or the fluoride ion product for fluoroapatite in saliva and the pH change after a sucrose rinse \[[@B5],[@B12],[@B13],[@B33]\].

Competing interests
===================

None declared

Pre-publication history
=======================

The pre-publication history for this paper can be accessed here:

<>

Acknowledgements
================

This investigation was largely supported by USPHS Research Grant 1R01 DE-06740 from the National Institute of Dental Research, National Institutes of Health, Bethesda. We sincerely thank Dr. W. Bias, Immunogenetics Laboratory, Johns Hopkins University, Baltimore and Dr. F. Bach, Immunobiology Research Center, University of Minnesota Medical School, Minneapolis for donations of human serum from their patients; E. Carter and D. LeFlore for technical assistance; S. Pitts, J. Chowning and Dr. A. Cucchiara, Computing Center, University of Oklahoma Health Sciences Center, for computing assistance; Drs. R. Reynolds, M. Martin and L. Coggins Dept. of Oral Diagnosis for clinical assistance.


Background
==========

In 1990 the National Institutes of Health Consensus Development Conference \[[@B1]\] concluded that breast conserving surgery (BCS) followed by radiation is an appropriate method of primary treatment for the majority of women with early stage breast cancer (AJCC stages \[[@B2]\] I, IIA, and IIB). Numerous clinical studies have shown that survival after BCS followed by radiation therapy is equivalent to survival following mastectomy for women in these stages \[[@B3]-[@B5]\]. Some absolute indicators for mastectomy remain \[[@B6],[@B7]\], in particular widespread malignant-type microcalcifications, previous radiotherapy, and a relation of tumor size to breast size that would not allow a cosmetically satisfactory result. Although there has been an increase in the use of BCS since the early 1990s, an apparent under-utilization of BCS among women for whom such treatment was not contraindicated has been documented \[[@B8]\]. Geographic location, type of hospital and health plan, and personal preferences have been investigated as possible factors to explain the continued use of mastectomy \[[@B9]-[@B12]\]. Comorbidity \[[@B13]\] has been found to be an important predictor among older women.

The Surveillance, Epidemiology and End Results (SEER) cancer registries do not collect sufficient detail to examine some of these issues and medical records reviews are time consuming. Insurance claims data are a cost-effective alternative; they have already been collected and put into an electronic format by the insurance carriers. They cover large segments of the population, allow follow-up, use standardized codes \[[@B14],[@B15]\], and do not rely on subject recall. In cancer research, insurance claims data have been used to estimate the effectiveness of cervical cancer screening \[[@B16]\], to assess the role of screening practices in the incidence of prostate cancer \[[@B17]\], and to estimate mammography participation \[[@B18]\]. Medicare enrollees in Seattle and San Francisco who received care from a Health Maintenance Organization (HMO) were found to receive more BCS than women in Fee-For-Service (FFS) plans \[[@B19]\]. Whereas a Medicare/SEER registry linked data set has been used extensively to examine treatment and screening issues in individuals 65 years and older \[[@B20]\], linking of insurance claims data from private health plans in younger populations has been more difficult because of the large number of health plans in most geographic areas. However, Hawaii provides unique opportunities for insurance claims research because the majority of medical care is received within the state and more than 90% of the population \[[@B21]\] are covered by a limited number of health plans. As a pilot project, we were able to link data from the Hawaii Tumor Registry and from a health plan in Hawaii. The objectives of this analysis were to describe breast cancer treatment and comorbid conditions using the insurance claims data and to examine possible determinants of BCS *vs.* mastectomy for breast cancer patients with stage I and II disease.

Methods
=======

The study protocol was reviewed and approved by the University of Hawaii\'s Committee on Human Studies and by the Hawaii Tumor Commission that oversees the Hawaii Tumor Registry (HTR). Before linking of databases was initiated, a memorandum of agreement between all parties involved in this research project was signed. The resulting agreement safeguarded patients\' privacy at the highest possible level. All names and identifying information were deleted from the datasets and replaced with arbitrary numbers to be used for the data analyses.

Data Sources
------------

The HTR has maintained a database of all cases of cancer diagnosed in the State of Hawaii since 1960 and became part of the SEER program in 1973. The HTR record contains demographic characteristics such as age, ethnicity, marital status, island of residence, as well as information on tumor size, extent of disease, lymph node involvement, and tumor grade. From medical records in hospitals and physicians\' offices, information is collected on the initial course (six months in the Hawaii Tumor Registry) of cancer-directed treatment following diagnosis. Quality control reviews have shown that case-ascertainment through HTR has been virtually complete \[[@B22]\]. Over 99% of cancer cases reported to the registry are histologically confirmed. The HTR also maintains a link with Hawaii Department of Health, which allows for death information to be captured in the HTR database.

Linking
-------

The insurance claims for this study were obtained from a local insurer who was a party to the memorandum of agreement. The linked data set contained cancer cases diagnosed from 1995 through 1998. During the linking process, a list of health plan members who had at least two cancer diagnostic codes in their claims history were matched against the HTR using a probabilistic method. For each matched record, the health plan furnished all claims data for that period. They also indicated whether the individual was enrolled in a Fee-For-Service (FFS) or a capitated (HMO) plan, a choice provided by the insurer. However, the majority of linked cancer cases belonged to the FFS plan and the HMO plan provided by this insurer differs considerably from a typical HMO. Individuals aged 65 years and older were included only if they were still working and had primary coverage through the health plan or if they were covered under Medicare but also had secondary coverage through the health plan. The dataset furnished by the health insurer contained all claims processed for cancer patients in this study. The data elements included: date of service, International Classification of Disease, version 9 (ICD-9) diagnosis codes \[[@B14]\], Current Procedural Terminology (CPT) codes \[[@B15]\], and the provider specialty code. The dataset contained claims for services from physicians, laboratories, freestanding facilities, as well as outpatient services provided by hospital facilities.

Data set
--------

Breast cancer cases accounted for 16.6% of all cancer cases recorded in the HTR during the period from 1995 to 1998. In the linked dataset, 27.7% of the cases were breast cancer cases. Using the ICD-9-CM code range \[[@B14]\] for breast cancer (174.0 to 174.9), we identified 1,377 female breast cancer cases. We then excluded 265 cases diagnosed after June 30, 1998 because we would not have a complete history of claims data covering at least six months of treatment. We also identified 30 women who had been recorded twice in the linked dataset because breast cancer was diagnosed in both breasts, as identified by the laterality codes. For 24 women, the diagnosis date was identical, in which case we considered the two diagnoses as one case. For the six cases with different diagnoses dates, we retained both records because there was a separate completed course of treatment for the cancer in each breast. We eliminated one case that was in the linked dataset and had inpatient data but no outpatient data, resulting in a dataset containing 1,088 breast cancer cases.

Staging
-------

We used the HTR information in the Extent of Disease (EOD-10) field \[[@B23]\] to determine the American Joint Committee on Cancer (AJCC) TNM stage \[[@B2]\], where T represents the primary tumor size, N refers to lymph node involvement, and M refers to presence of metastases. In cases where the tumor size was unknown, we assigned a stage only if the size was not a determinant in the TNM grouping. If the extension was unknown, we equated it to extension 10 (confinement to breast tissue and fat). For the small number of cases (N = 8) for whom lymph node involvement code was unknown, we considered this the same as no lymph node involvement because the tumor size was so small for these cases that a lymph node dissection was not considered necessary at the time of this study. The results of the TNM staging agreed well with the SEER summary stage codes.

Cancer treatment
----------------

We used the CPT codes \[[@B15]\] in the claims dataset to identify the type of surgery performed. For mastectomies, we included codes for simple and subcutaneous mastectomies (19180 and 19182), radical mastectomies (19200 and 19220) and modified radical mastectomy (19240). BCS was identified by the CPT codes for partial mastectomies (19160, 19162). We also included codes for excision of breast cysts or lesions (19120 and 19125) as these codes meet the definition of a lumpectomy, although we recognize that some surgeons may have billed these codes for excisional biopsies as a diagnostic procedure. If BCS was initially performed, but followed by a subsequent mastectomy, the subject was classified in the mastectomy group. This situation would have occurred when the BCS code was actually used for a diagnostic biopsy and the subsequent surgical treatment was a mastectomy or in situations where a lumpectomy was first selected, but a mastectomy became necessary because pathological examination revealed cancerous cells in the margins of the excised tissue. CPT codes 77261 to 77799 were selected to determine whether radiation therapy was received. Several CPT codes and also a number of other codes used by the health plan for billing were considered evidence of chemotherapy treatment.

Comorbidities
-------------

Information on the existence of comorbidities was extracted from the claims data using the occurrence of ICD-9 codes associated with comorbid conditions included in the Charlson Index \[[@B24]\]. We also analyzed data for conditions that were not included in that index, but occurred at a high enough frequency to warrant examination as possible comorbidities, in particular hypertension and lipid disorders. We included the following twelve comorbid conditions into our index: diabetes with complications, diabetes w/o complications, hypertension, heart disease, cerebrovascular disease, chronic pulmonary disease, peripheral vascular disease, kidney disease, liver disease, rheumatological diseases, hypothyroidism, and lipid metabolism disorders. Comorbidities were based on physicians\' claims with the respective ICD-9 code \[[@B14]\] during the 12-months period preceding the month in which the cancer was diagnosed. The month of diagnosis was excluded to avoid identifying as comorbidities any complications or conditions directly resulting from cancer treatment. Laboratory, radiology, and other diagnostic services were excluded from the comorbidity identification process because tests may have been done to rule out the condition. Each subject was assigned a comorbidity score of 0 (no condition), 1 (one condition), or 2 (two or more conditions).

Other variables
---------------

In addition to stage at diagnosis and size of tumor, we examined several other variables that were possible determinants in the utilization of BCS *vs.* mastectomy in early stage breast cancer, including age at diagnosis, ethnicity, island of residence, and marital status. For ethnicity, we used the five major groups in Hawaii (Japanese, Caucasian, Hawaiian, Filipino, and Chinese). All other ethnicities were grouped into an \"Other\" category. The specific island of residence was coded in the HTR, but since close to 80% of the population resides on Oahu \[[@B25]\], we classified residence as either Oahu or non-Oahu. For marital status, we grouped all women who were identified as single, separate, divorced or widowed in the unmarried category. Women under age 50 were considered pre-menopausal and women 50 years and older were classified as postmenopausal. Tumor grade information from the HTR record was grouped into grade I (well-differentiated cells) *vs.* all other grades (II, III, IV and unknown).

Statistical analysis
--------------------

All analyses were performed with SAS version 8.00 for Windows (SAS Institute, Cary, NC). Simple Kappa statistics (κ) was calculated to validate the treatment information from the insurance claims and the HTR \[[@B26],[@B27]\]. BCS was used as reference group throughout the analysis. It was defined as the dependent (outcome) variable and coded as a dichotomous variable, with 1 indicating BCS received and 0 indicating mastectomy received. Age was entered in units of ten years and tumor size was grouped into units of ten millimeters (one centimeter). For analyses of ethnicity as a predictor of treatment selection, Caucasian was used as the control group and indicator variables for all other ethnic groups were created. Logistic regression \[[@B28]\] was used to explore the influence of each variable on the use of BCS *vs.* mastectomy. First, we considered each independent variable by itself in a model and then we entered all variables simultaneously in a logistic regression model. Odds Ratios (OR) \[[@B29]\] with corresponding 95% Confidence Intervals (CI) were calculated to measure the degree of influence of variables on the utilization of BCS *vs.* mastectomy.

Results
=======

Based on the TNM stages, we identified 722 women who had stage I, IIA, or IIB breast cancer (66.4% of the 1,088 breast cancer cases in the linked data set). These 722 cases represented 32.8% (722 of 2,203 cases) of all early stage breast cancer cases recorded in the HTR during the study period. Approximately two-thirds of the women in the study population (Table [1](#T1){ref-type="table"}) were diagnosed in stage I of the disease and only 24% and 12% in stage IIA and IIB, respectively. Approximately 30% of the study subjects were aged less than 50 years, close to half were 50 to 64 years old, and 21% were 65 years and older. Three out of four women resided on the island of Oahu. Overall, 52.8% of the women had received BCS. Of the cases diagnosed at stage I, 57% of the women had received BCS and 47% a mastectomy. This decreased to 50% for stage IIA and to 34% for stage IIB. Among women, 65 years and older, 56% had BCS, compared to 50% of those under 50 years of age and 53% of the 50--64 year old women. While 56% of women on Oahu underwent BCS, only 43% of the women residing on the outer islands received BCS. We observed no statistically significant differences between the BCS and mastectomy group in terms of ethnicity, comorbidity count, menopausal status, marital status, and insurance plan. We found very high agreement between HTR data and claims data in identifying BCS \[κ = 0.91 (95% CI 0.88, 0.94)\], only 32 women were misclassified. For the majority of women who received BCS (92.1%), the lumpectomy was followed by radiation therapy. Additional chemotherapy was given to 34.1% and 42.5% of the women who had a lumpectomy or a mastectomy, respectively.

::: {#T1 .table-wrap}
::: {.caption}
######

Determinants of Breast Conserving Surgery Among 722 Cases of Early Stage Breast Cancer
:::

  **Variable**                       **No. of patients**   **Odds ratio (95% CI)**
  --------------------- ------------ --------------------- ------------------------- --------------------------- -----------------------
                                     BCS                   Mastectomy                Univariate analysis         Combined analysis

  All                                381                   341

  Ethnicity             Caucasian    71                    66                        1                           1
                        Japanese     170                   147                       1.08 (0.72--1.61)           0.85 (0.55--1.31)
                        Hawaiian     48                    40                        1.12 (0.65--1.91)           1.24 (0.71--2.17)
                        Filipino     35                    47                        0.69 (0.39--1.20)           0.65 (0.37--1.15)
                        Chinese      32                    25                        1.19 (0.64--2.22)           0.97 (0.50--1.86)
                        Other        24                    16                        1.45 (0.71--2.96)           1.33 (0.64--2.77)

  Residence             Oahu         299                   232                       **1.71 (1.23--2.39)**       **1.67 (1.17--2.38)**
                        Other        82                    109                       1                           1

  Age at diagnosis      \<50 yrs     110                   109                       Continuous (per 10 years)
                        50--64 yrs   186                   166                       **1.01 (1.001--1.028)**     1.01 (0.99--1.04)
                        65+ yrs      85                    66

  Menopausal status     Pre Post     110 271               109 232                   1 1.16 (0.84--1.59)         1 0.75 (0.46--1.23)

  Marital status        Married      262                   244                       0.88 (0.64--1.21)           0.94 (0.68--1.32)
                        Unmarried    119                   97                        1                           1

  Health plan           FFS          330                   300                       0.89 (0.57--1.37)           0.87 (0.55--1.38)
                        HMO          51                    41                        1                           1

  TNM stage             I            266                   200                       1                           1
                        IIA          86                    85                        0.76 (0.54--1.08)           0.99 (0.66--1.51)
                        IIB          29                    56                        **0.39 (0.24--0.63)**       0.61 (0.32--1.19)

  Tumor size            \<1 cm       113                   83                        Continuous                  (per 1 cm)
                        1--1.9 cm    177                   139                       **0.97 (0.96--0.99)**       0.98 (0.96--1.00)
                        2 + cm       91                    119

  Tumor grade           Well diff.   77                    49                        **1.51 (1.02--2.24)**       1.25 (0.83--1.89)
                        Other        304                   292                       1                           1

  Comorbid conditions   0            230                   201                       Continuous (per score)
                        1            109                   102                       0.97 (0.78--1.19)           0.91 (0.72--1.14)
                        2+           42                    38
:::

In the univariate models (Table [1](#T1){ref-type="table"}), we found that tumor size and grade, island of residence, age, and stage at diagnosis were predictors of breast cancer treatment. Women residing on Oahu were considerably more likely to have BCS than women living on all other islands in the state. For each one-centimeter increase in tumor size, there was a 3% lesser chance of undergoing BCS. Since size of tumor correlates to breast cancer stage, this also decreased the likelihood for women in stage IIA and IIB to receive BCS. Women with well-differentiated tumor grades were 50% more likely to undergo BCS as compared to women with all other grades. We found that for each ten-year increment in age, the chances of having BCS increased by 1%. We also observed that average tumor size was inversely related to age. Mean tumor sizes (in cm) with standard deviations were 1.82 ± 1.1, 1.59 ± 1.2, 1.39 ± 0.94 for women younger than 50 years, 50 to 64 years, and 65 years and older, respectively. Therefore, the smaller tumor sizes may account for the greater likelihood of BCS among older women.

In a combined model with all independent variables in a logistic regression, island of residence remained the only significant predictor of BCS in this population. Women living on Oahu were 67% more likely to have BCS than women on the outer islands. Although all other variables lost their statistical significance, associations for age, TNM stage, tumor size, and tumor grade remained similar in magnitude as in the univariate models. Although none of the ethnicity variables was significant, it appeared that Filipino women were less likely to receive BCS than women from all other groups. Residence on outer islands did not explain this observation. Women with a TNM stage of IIB were still 40% less likely to receive BCS than women diagnosed at stage I, but the relation lost its statistical significance due to the small number of cases. Menopausal or marital status, type of health plan, or the number of comorbidities were not related to the type of surgery.

Discussion
==========

The place of residence at the time of diagnosis was the most important predictor for receiving BCS in this dataset of health plan members. Women with early stage breast cancer living on Oahu were 70% more likely to have BCS than women living on outer islands. Age at diagnosis and tumor size were also related to breast cancer treatment although they were not statistically significant in the combined model. Contrary to our expectation and a previous publication \[[@B13]\], the number of comorbid conditions did not effect treatment in this analysis. The importance of geographic location can be explained by the availability of radiation facilities on the different Hawaiian Islands. Whereas on Oahu treatment facilities can be reached within one hour, distances on the outer islands are much farther. Only the Island of Hawaii and Maui have a radiation facility, but they are hard to reach from many parts of the islands. Women residing on Kauai, Molokai, and Lanai have to fly to Honolulu daily to receive the course of treatment or they have to remain there temporarily. Because this may be not be economically feasible for some women, as well as physically and psychologically challenging, it is possible that many women choose to undergo a mastectomy instead. Alternatively, patients and physicians in rural areas may favor mastectomy because of differences in education or cultural attitudes toward medical advances. We do not have information to explain why a small proportion of women who underwent BCS (6.9%) did not receive radiation. However, compared to other reports \[[@B13],[@B30],[@B31]\], the proportion of women who received radiation is very high. An interview study with cancer patients \[[@B32]\] showed that some patients decide against their physicians\' recommendation for radiation because they fear the treatment or hold certain beliefs about cancer. For other patients, radiation may have been contraindicated due to comorbid conditions.

In 1994, a national BCS rate of 42.6% was reported \[[@B33]\] with a rate of 46.7% for the Pacific region (Hawaii, California, Alaska, Washington, and Oregon). Our results for 1995 to 1998 indicate a slightly higher rate for Hawaii (52.8%). Geographic differences in BCS use have repeatedly been described in the literature. In western Washington \[[@B34]\], women residing outside the Seattle area were less likely to have BCS than women residing inside the county, in particular if radiation therapy facilities were not available in their county. Comparing rates across the country showed that BCS was much more common in the Northeast than in the South \[[@B33]\]. Low BCS rates were also found in rural areas, such as North and South Carolina \[[@B10]\], Minnesota \[[@B35]\], and the Southwest \[[@B36]\]. An analysis of 1991--1992 SEER data \[[@B37]\] demonstrated a strong association between BCS and distance to the next radiotherapy facility. The likelihood of undergoing BCS was 50% lower among women living more than 15 miles from the next radiotherapy facility.

In contrast to our results, a number of studies have shown that the likelihood of BCS decreases with increasing age \[[@B11],[@B30],[@B33]\], possibly due to less concern about cosmetic results. Because of the relatively small proportion (21%) of women 65 years and older and the fact that the women 65 years and older in our study had a health plan other than Medicare, they were not directly comparable to the populations in the published literature. Therefore, our ability to observe an effect among older women was limited. In particular, we were not able to examine the treatment of women 80 years and older who have been described as receiving more BCS than women age 65 to 79 years \[[@B13]\]. Our findings of lower BCS in smaller and well differentiated tumors agree with previous studies \[[@B33]\], but the absence of an effect of comorbid conditions disagrees with a study in older women \[[@B13]\] that showed a higher rate of BCS in women with comorbidities. Type of health plan did not predict type of treatment in our study probably because the majority of women in this data set had a FFS plan. In previous analyses with a larger variety of health plans, HMO members were considerably more likely to receive BCS in San Francisco and in Seattle \[[@B19]\]. Medicare and Medicaid patients received more BCS in a North Carolina hospital \[[@B7]\]. However, a study using administrative data \[[@B38]\] described lower BCS use among Medicaid patients and treatment was similar by health plan \[[@B39]\] in Northern California.

Our study had several limitations, including the possibility of incomplete claims histories if patients changed health plans during the course of treatment, but the high agreement with HTR information indicates that the validity of insurance claims information was high. Continuing efforts by the health plan through feedback loops has improved diagnostic coding \[[@B40]\] in Hawaii, but as documented for Medicare claims, problems remain \[[@B41]\]. As discussed above, our study sample of breast cancer patients does not represent the population of the state, excluding in particular Medicare and Medicaid recipients, members of a typical HMO, and the relatively small number of uninsured patients. The exclusion of Medicare claims lead to a younger population, whereas the lack of Medicaid claims biased the cases toward a higher socioeconomic status. On the other hand, the study population represented the population quite well in terms of ethnicity (Hawaii Department of Business 1998 1705 /id). Because our data set included 42.4% of all breast cancer cases under 65 years diagnosed during the study period, the results represents this population much better than the population of older women. Also, we were unable to measure some factors that have been found to be important in other reports, in particular, influence of physician age \[[@B11]\], physician specialty \[[@B42]\], type of hospital (teaching hospitals and hospitals with radiation facilities, private, county and public) \[[@B36],[@B38],[@B39],[@B43]\], socio-economic status and education \[[@B42]\], tumor-breast ratio and the expected cosmetic results \[[@B7],[@B9]\], physician/patient interactions before surgery \[[@B42]\], and psychological factors, such as fear of radiation or cancer \[[@B9]\]. The strengths of using of insurance claims data for this analysis were twofold. First, we had more detailed treatment information available than collected by the tumor registry. Second, the diagnostic codes in the claims data allowed us to estimate the number of comorbid conditions before the cancer diagnosis.

The results of this pilot study combining tumor registry and insurance claims data raises an important issue for cancer practice in the State of Hawaii and elsewhere. Health care providers and insurance plans need to work with other agencies to develop viable solutions to facilitate access to radiation facilities for women residing in remote locations, such as islands with no radiation facility. Attitudes of patients and physicians living in rural areas may be important in the choice of treatment, but could not be investigated in this project. Future research that includes interviews with patients and physicians may investigate attitudes toward certain types of treatment and identify additional barriers to BCS, such as psychological problems. In order to understand how treatment decisions were made in practice, qualitative information from physicians and breast cancer patients should be collected and examined in detail. Further validation of treatment and comorbidity from insurance claims data would strengthen this type of research in the future. The inclusion of a larger proportion of breast cancer cases is needed to establish the validity of our findings for the population at-large.

Competing interests
===================

None declared.

Pre-publication history
=======================

The pre-publication history for this paper can be accessed here:

<>

Acknowledgements
================

We are very grateful to the employees of the health plan for their time and willingness to assist with this project. Special thanks go to Andrew White, PhD for his long-term support of our research efforts. The help of Marc Goodman, PhD and the staff of the Hawaii Tumor Registry is greatly appreciated. This research was funded by a special study grant from the National Cancer Institute, Surveillance, Epidemiology, and End Results program under contract number N01-PC67001.


Background
==========

Health information systems (HIS) provide a scientific and technological framework to gather, manage, and interpret data to inform the public, policymakers, administrators, and health-care workers about the distribution and determinants of health conditions. Further, they can (and should) guide and measure the impact of interventions \[[@B1]\]. Public health surveillance -- a subset of HIS -- has been defined as *the ongoing, systematic collection, analysis, and interpretation of outcome-specific data for use in the planning, implementation, and evaluation of public health practice*\[[@B2]\]. Public health surveillance can be used to 1) assess the overall health status of a population, 2) describe the natural history of disease, 3) monitor disease trends, 4) detect epidemics, 5) evaluate the effect of prevention and control measures, 6) generate hypotheses, and 7) facilitate epidemiologic and laboratory research \[[@B3]\].

Before 1991, the Soviet Union centrally planned the infectious diseases surveillance systems (IDSS) of its 15 republics. Approximately 300 million persons were covered under the IDSS. Central monetary and technical support for the IDSS ended in 1991. As a result, the republics have struggled to maintain their respective IDSS. The former Soviet Union (FSU)-wide diphtheria outbreak in the 1990s \[[@B4]\] and the re-emergence of malaria in Tadjikistan in 1991 \[[@B5]\] and in Armenia \[[@B6]\] and Azerbaijan \[[@B7]\] in 1994 indicated that financial constraints resulting in the disruption of public health infrastructure and services had increased the risk of the re-emergence of infectious diseases \[[@B8]\].

After 1991, this transition to nationalism, privatization, and social reorganization posed new challenges to each republic of the FSU. The loss of centralized training, public health expertise, and resources especially impacted the surveillance systems in each republic. \[[@B9]\] Further, privatization of the FSU medical systems resulted in under-budgeted public health services and inadequately paid personnel \[[@B10]\]. Combined with these infrastructure problems, the increased population migration -- both within the FSU and internationally -- contributed to morbidity and mortality through population dislocation \[[@B8]\]. This has increased the risk of exposure to re-emerging microbial and environmental pathogens and limited access to health services and good nutrition \[[@B9],[@B10]\].

The most important of these changes was the financial crises resulting from the severance of economic ties among all republics of the FSU. Public health officials were challenged to transform the bulky, state-sponsored IDSS. With high inflation and unemployment, they also suffered from shortages of vaccines, hospital supplies, and essential drugs. Provision of basic public health services were compromised, including repairing antiquated water and sewer systems, resulting in increased risk for gastroenteritis and infections with hepatitis A \[[@B11]\]; the largest documented outbreak of typhoid in this century occurred in Tajikistan in February 1997 \[[@B12]\]. Practices such as reusing syringes during vaccination and poor sterilization procedures during dentistry have contributed to nosocomial outbreaks of HIV and a high prevalence of infections with hepatitis B \[[@B13]\].

The objective of this work was to assess the current status and functioning of various IDSS, so as to guide reform efforts. At the invitation of seven Ministries of Health (MoHs), we performed assessments in the Russian Federation and in the Republics of Kazakhstan, Tadjikistan, Uzbekistan, Turkmenistan, and Armenia, and in the Kyrgyz Republic. We found striking homogeneity in comparing the IDSS from one republic to another; and, for clarity, we present here representative findings by using the Armenian component of the IDSS (AIDSS) as a prototype.

Discussion
==========

We used the CDC guidelines to assess the seven IDSS \[[@B14]\]; these guidelines have recently been republished in revised form \[[@B15]\]. This strategy includes an assessment of public health importance, objectives and usefulness, operation of the system, cost, and the seven system attributes (i.e., simplicity, flexibility, acceptability, sensitivity, predictive value positive, representativeness, and timeliness). The assessment involves gathering both qualitative (i.e., simplicity, flexibility, and acceptability) and observations of the quantitative (i.e., sensitivity, predictive value positive, representativeness, and timeliness) attributes.

In Armenia, we attempted to gather as much information as possible with respect to the construct and utility of the AIDSS from those most integral to its functioning and application. Therefore, we conducted face-to-face interviews and focus group discussions with approximately 50 epidemiologists at the Ministry of Health, the National Sanitary Epidemiologic Service, the Institute of Epidemiology, and two regional, three districts, and one city Sanitary Epidemiologic Service (SES) office. We also interviewed 23 health-care workers at village health centers, polyclinics, hospitals, and laboratories, which serve as the primary reporting units for the AIDSS.

At the time of this assessment, the AIDSS was moving toward reform, and we chose not to use the limited resources to gather quantitative data (e.g., through chart reviews) to assess the quantitative attributes of the AIDSS (i.e., sensitivity, predictive value positive, representativeness, and timeliness). Rather, we relied on qualitative observations gleaned in the course of the interviews. We present here the qualitative observations made of the AIDSS and recommendations for reform. We believe these observations and recommendations reflect the status of the IDSS of the other FSU republics.

Description of the AIDSS
------------------------

A republic of 3.3 million, Armenia gained its independence from the FSU on September 21, 1991 (Figure [1](#F1){ref-type="fig"}). The country is divided into 11 regions, which are subdivided into 37 districts (Figure [2](#F2){ref-type="fig"}). Responsibility for the AIDSS rests with the Department of Hygiene and Epidemiologic Surveillance -- a department of the Armenian MoH (AMoH) -- composed of 52 functional units known as the Sanitary Epidemiologic Service (SES). The units or stations of the SES parallel the geopolitical divisions of Armenia. There exists one SES station in each district. Each city has one SES station, except Yerevan, the capital. Yerevan has eight districts, each with one SES station. The national SES is also located in Yerevan.

![The Caucasus region of the New Independent States (NIS)](1471-2458-2-3-1){#F1}

![The Sanitary Epidemiologic Stations (SES)](1471-2458-2-3-2){#F2}

The SES, *per se,* developed from a model created in the late 1800s in russia and uniformly developed over many years in the republics of the FSU. Its principle functions are to collect and analyze public health surveillance data and to implement and enforce strategies for the prevention and control of infectious diseases. Traditionally, the SES had approximately 10% of the entire medical person-power and budget of the AMoH, and was separate from the curative medical care system \[[@B16],[@B17]\]. The SES was staffed by epidemiologists (physicians), microbiologists, sanitary hygienists, and other health workers (paramedics and physician assistants). The district SES was the basic public health unit that monitored infectious diseases, investigated outbreaks, attended to child and adolescent health, inspected the food-service industry, monitored water purity, and dealt with occupational and environmental health problems throughout Armenia \[[@B6]\]. City-, regional-, and national-level SES administrations were larger, with specialized staff.

The SES collected infectious diseases data from all health-care facilities throughout the country. Before 1991, Armenia had a comprehensive and free health-care delivery system accessible to all citizens with health facilities and health-care workers employed under the auspices of the AMoH. Outpatient facilities (village health centers and polyclinics) and hospitals reported to the AIDSS. Each district had 12--45 village health centers, two polyclinics, and one hospital. The seven cities in Armenia had variable numbers of polyclinics and hospitals.

Altogether, Armenia had 830 village health centers, 228 polyclinics, and 179 hospitals (Figure [3](#F3){ref-type="fig"}). Data from these health facilities were reported to the 52 districts and city SES and then to the national SES, which forwarded aggregated data to the Department of Hygiene and Epidemiologic Surveillance in the AMoH.

![The flow of information, infectious diseases surveillance system, Republic of Armenia, 1996](1471-2458-2-3-3){#F3}

Objectives and Data Collection, Reporting, Analyses, and Response
-----------------------------------------------------------------

The objectives of the AIDSS were to identify cases of infectious diseases, document outbreaks, and monitor trends in disease occurrence. It collected aggregated and case-based data on new cases of 64 infectious diseases. This list included some diseases with low pathogenicity (e.g., pediculosis and scabies) and some diseases with inadequate or non-existent preventive measures (e.g., infectious mononucleosis and parapertussis). There existed no tiered (e.g., confirmed, probable, and suspected) standardized case definitions. The reporting of confirmed or suspected cases of some infectious diseases required immediate reporting via telephone or in person within 12--24 hours. These included epidemic prone diseases (e.g., diphtheria, polio, plague).

Data were provided monthly and yearly from the district to national SES. Reports from the national SES were sent monthly to the AMoH and the other 14 republics of the FSU and back to the district SES twice a year. The district and city SES reported immediately to the national SES.

At all levels, epidemiologists used descriptive statistics for data analyses. These included the calculation of aggregated case numbers and incidence and prevalence rates based on estimates of the population size provided by the state agency charged with gathering census data; limited stratification by person, time, and place; and the assessment of trends. Epidemiologists did not use analytic methods to assess risk factors for diseases, even though they were collected.

Cases and contacts of every disease were investigated by a district or city SES epidemiologist within 24 hours of the receipt of the case report, using a standardized form known as the *epid carta.* This form was not disease specific, yet lengthy (46 questions, many of which required subjective responses). Little or no feedback was provided to the original reporting sources and no routine or formal sharing of data and information occurred between the district SES and health-care facilities. Table [1](#T1){ref-type="table"} summarizes the public health practice activities in Armenia, stratified by health facility.

::: {#T1 .table-wrap}
::: {.caption}
######

Public health surveillance and action core and support activities, by health level, Republic of Armenia, 1996
:::

  **Organization Level**      **Public Health Surveillance and Action Core and Support Activity**
  --------------------------- --------------------------------------------------------------------- --- --- --- --- --- --- --- --- ----- ---
  Primary Health Facilities   X                                                                     X   X   X   X   X   X       X   *X*   X
  District SES                X                                                                     X   X   X                   X   X     X
  District Lab                                                                                X   X   X   X   X       X   X     X
  District Lab                X                                                                     X   X   X                   X   X     X
  Regional SES                                                                                X   X   X   X       X   X     X
  Regional Lab                                                                                X   X                   X   X     X
  National SES                                                                                X   X   X   X
  National Lab                                                                                X                       X   X     X
:::

Qualitative Attributes
----------------------

### Simplicity

Referring to both its structure and ease of operation, the AIDSS was complex. Epidemiologists gathered voluminous information on each case; parallel vertical public health programs reported duplicative information; much time was required to collect, register, and report case information; and many staff maintained the system.

### Flexibility and Acceptability

Addressing the extent to which the AIDSS could adapt to changing information needs or operating conditions and reflecting the willingness of participants to provide information and monitor the system, the AMoH, itself, determined both its flexibility and acceptability. Because the AMoH provided salaries for health-care personnel, it could enforce compulsory reporting, through monetary fines and professional demotions. Changes in reportable conditions or criteria were made rapidly because administrative SES employees carried out orders quickly and completely at all levels in the system.

Quantitative Attributes
-----------------------

While we did not quantitatively measure the attributes of sensitivity, predictive value positive, representativeness, and timeliness, we did make qualitative observations.

### Sensitivity

Assessing the AIDSS\'s ability to account for all incidents of a disease (i.e., the proportion of cases detected, correctly diagnosed, and reported), we learned that detection of most reportable infectious conditions did come to medical attention. This was enhanced because all citizens received free health care and because primary care physicians were responsible for care (including community outreach) of persons in their assigned territories. However, due to constrained resources, laboratory confirmation of reportable infectious diseases was limited in practice. As such, reporting was based on clinical or epidemiologic, rather than laboratory information.

### Predictive Value Positive

The likelihood that a disease report constituted a true case of that disease was diminished because of the lack of laboratory confirmation and standardized case definitions.

### Representativeness

We felt that, ideally, the AIDSS accurately described the distribution of diseases in the population by person, time, and place because disease reporting was mandatory and failure to report was a punishable offense. And, because of the penalties, all official reporting to the national level did occur on a monthly basis. However, it was common practice for epidemiologists to conceal cases of infectious disease and willfully underreport epidemic morbidity, because outbreaks meant that the epidemiologists were not performing their duties of preventing and controlling infectious diseases. This paradox resulted in epidemiologists managing two sets of information: one officially reported to higher-ups and one unofficially kept (with more accurate numerators).

### Timeliness

Information for action or for long-term planning was available because mandatory reporting to the SES within 12--24 hours of diagnosis for most conditions under surveillance allowed rapid implementation of control and prevention measures.

### Costs

Because budgets were relatively non-existent in the FSU, historical data on the costs of operating the AIDSS were not available. In the FSU centrally planned economy, resources were obtained from cost centers (e.g., utilities were not metered, office supplies were requested by quantity and not cost, and salaries were provided from the central budget). However, numerous observations led us to conclude that the AIDSS was relatively inefficient and costly; in large part, because the expenditure was paid from the public sector.

The AIDSS was labor intensive. Being paper-driven, reporting dieases for which no practical public health interventions exist misallocated scarce resources. Other common public health activities with high opportunity cost in the AIDSS were indiscriminate disinfection of homes and work sites. Environmental background monitoring practices by district and regional SES included routine collection of specimens and laboratory testing (e.g., air and water samples, food products, and items that children may come in contact with such as toys or eating utensils) in addition to evaluation of physical factors (noise, vibration, microclimate, electromagnetic fields, levels of lighting, and ionic radiation) at several sites (e.g., work places and day-care centers).

When cases of hepatitis A, acute gastroenteritis, tuberculosis, diphtheria, or pediculosis were reported, disinfection of homes, schools, day-care centers and work places was conducted by public health workers who used chloramine application and steam cleaning of all hard surfaces and laundering of all clothing and bedding materials. The effectiveness of such disinfection practices or environmental background monitoring has not been documented, and is likely of doubtful public health utility.

During disease outbreaks, it was common practice to investigate every case and culture all available materials, and decontamination efforts were instituted regardless of epidemiologic evidence. It was common practice to hospitalize children \< 1 year of age with pneumonia for 7--14 days, and children of all ages with acute gastroenteritis for 7--15 days. It was also common to hospitalize both adults and children with hepatitis A for 21 days, with syphilis for two weeks, with gonorrhea for three weeks, and with tuberculosis for one year. These isolation practices, meant to prevent disease transmission to the community, were consequences of central planning in which emphasis was placed on input indicators such as the occupancy rates of hospitals. Incentives (budget allocations) placed on input rather than output measures led to a level of medical infrastructure that has been difficult to maintain given current levels of funding available for the health sector.

Because financial issues were a major driving factor, cost analyses of surveillance practices and control measures could identify areas for cost-savings. Analyses of the surveillance system in Ukraine (using 1996 budget figures) revealed that excessive culturing represented 47% of the cost *per capita* expenditure of the L\'viv Regional SES and disinfection procedures accounted for almost 30% of the entire Pustomity District SES\'s budget (V. Carande-Kulis, CDC, personal communication).

Recommendations
---------------

Based on this assessment, we developed recommendations with respect to the three main surveillance functions of data collection, analysis and interpretation, and retrospective and prospective responses.

### Data Collection

• Eliminate punitive consequences to obtain accurate reporting;

• Restrict the number of routinely reportable diseases based on measures of mortality, morbidity, severity, communicability, and preventability;

• Categorize events under surveillance into a three-tiered surveillance system:

○ disease elimination (e.g., polio);

○ case-based (e.g., diphtheria); and

○ indicator-based (e.g., number of children immunized by two years of age);

• Simplify reporting procedures and forms by

○ limiting urgent reporting of diseases to those that require prompt institution of control measures;

○ requiring only information necessary to direct control measures and perform basic analyses; and

○ developing disease-specific forms with diseases chosen for case-based surveillance;

• Develop tiered (confirmed, probable, and suspected) standardized case definitions for all events under surveillance; and

• Computerize demographic and risk-factor data for systematic and detailed analysis of reported diseases and rapid dissemination of information.

### Analysis and Interpretation

• Provide ongoing capacity for training in analytic epidemiology; and

• Base interventions on epidemiologic evidence. Use analytic epidemiology (case-control and cohort studies and presentation of data using 2 × 2 tables, odds ratios, relative risks, and tests of significance) for hypothesis generation, risk factor identification, outbreak investigations and intervention design and monitoring.

### Retrospective and Prospective Responses

• Provide feedback to all reporting sources and share information across vertical program lines and with officials throughout the public health community in a timely fashion (e.g., via a monthly public health bulletin). A bulletin could include descriptions of important outbreak investigations, disease-specific analyses of surveillance data, graphic and tabular information on selected diseases, indicators of community health, and recommendations for public health concerns.

Current HIS Reform Efforts in Armenia
-------------------------------------

This assessment stimulated and guided reform efforts that were initiated in December 1992 through a cooperative project among the AMoH, United States Agency for International Development (USAID), and CDC. This project provided technical and material assistance toward reform of the Armenian HIS. The approach focused on training a cohort of public health officials and epidemiologists in the modern aspects of epidemiology, biostatistics, surveillance techniques, and scientific communications; developing Armenia-specific case definitions; facilitating HIS reform strategies through workshops and training sessions; and developing the capacity to publish an epidemiologic bulletin.

Since 1996, this HIS reform activity has been self-sustained with no additional monetary support from USAID \[[@B6]\]. In 1996, the AMoH created a national HIS program for the development and reform of the HIS and the Armenian National Health Analytic-Information Centre \[[@B18]\]. The system has been transformed into a comprehensive HIS and includes chronic diseases, maternal and child health, and injury data. Diseases are now categorized by a three-tiered approach: disease elimination (e.g., polio), case-based (e.g., diphtheria) and indicator-based (e.g., number of children immunized by two years of age).

Preparations are now being made for additional training and to assess and improve clinic case diagnoses, management, and recording, and clinic records. New regional centers equipped with computers and faxes have been organized for the collection, analyses, and reporting of health information. National and regional public health bulletins are being published monthly in three languages - Armenian, Russian and English -- and distributed to wide audiences. Tiered, standardized case definitions and essential health indicators for decision-making at the clinic and community level have been developed and disseminated. These include health status, performance, and resource indicators.

Comprehensive HIS reform is critical throughout the FSU. Timely, accurate, and relevant health information are necessary to assess the burden of disease and disability; understand changing health patterns; measure the needs for and improve services; address inequities in health; provide information for policy formulation and planning; and provide a basis for intra- and international comparisons on health status and care utilization \[[@B1]\]. Timeliness, accuracy, and relevancy are augmented by efficiency. Integrating all sources of data into one comprehensive HIS prevents duplicate recording and reporting across services and programs, averts labor-wasting inefficiencies, and saves scarce resources.

A comprehensive HIS includes the capacity to obtain data from vital registries, clinical, administrative, and other records; from provider and population-based surveys and sentinel systems for infectious and chronic diseases, and disabilities; and maternal and child health, nutrition, and program implementation indicators, including access, coverage, and service quality \[[@B1]\]. This reform activity should include the development of an indicator monitoring system based on selected essential, action-oriented indicators of health status, service performance, and resources that can be used for decision-making at the local level.

Summary
=======

We found the AIDSS to be a complex and sensitive, yet costly and inefficient surveillance system for infectious diseases. Despite the lack of standardized case definitions, feedback of information, and computer technology, it functioned fairly well before 1991. However, the functioning and continuation of the AIDSS has been affected both directly and indirectly by events of the past decade.

Overall, the former AIDSS was useful because it detected cases of infectious diseases, estimated morbidity, monitored trends in disease occurrence, and documented outbreaks. The comprehensive no-cost health-care delivery system and compulsory reporting of diseases to the AMoH enhanced its flexibility, representativeness, and timeliness. The strengths of the AIDSS stemmed from the large numbers of health facilities and trained personnel and the separation of preventive from curative medicine, that secured an independent status (including separate budget) for preventive medicine and public health practitioners.

The AIDSS also had weaknesses. Though the system meticulously tracked persons (from birth to death), very few of these data were computerized, analyzed, or used to develop, direct, or evaluate public health policy. In most cases, when used, data guided regulation and punishment rather than public health decision-making. Simply put, data were used to fix blame and punish rather than to find and implement effective interventions.

Epidemiologists were motivated to perform actions that both pleased their supervisors and avoided the punishment of monetary loss or demotion. For example, it was common practice for epidemiologists to hide select cases of infectious disease and willfully underreport epidemic morbidity because outbreaks meant that the epidemiologists were not performing their duties of preventing and controlling infectious diseases. These disincentives to thoroughness and honesty resulted in surveillance data and reports that did not reflect true incidence and prevalence of disease, circumstances, needs, responses, or impacts \[[@B9]\].

Because the AIDSS was but a component of the systems designed for the entire FSU, the AIDSS did not address Armenia-specific needs. Further, because of the lack of tiered, standardized case definitions, there existed a potential for misclassification of diseases. Lack of feedback to reporting sources hampered improvements in clinical practice. Monitoring conditions for which there were no practical public health interventions, the multi-tiered and duplicative reporting processes, and the use of expensive and indirect monitoring and control measures such as excessive culturing, disinfection, and prolonged hospitalization led to waste of resources. As a result, the system did not guide control measures optimally nor use resources efficiently.

As the republics of the FSU embrace various aspects of democratization, improvement of public health surveillance systems such as the IDSS should be a goal if decision makers are to use credible data for informed public health practice.

Competing interests
===================

We certify that we have participated sufficiently in the conception and design of this work, as well as its execution and the analyses of the data. Further, we have collaboratively written the manuscript and take public responsibility for it. We believe the manuscript represents valid work. We have reviewed the final version of the submitted manuscript and approve it for publication. Neither this manuscript nor one with substantially similar content under our authorship has been published or is being considered for publication elsewhere. If requested, we shall produce the data upon which the manuscript is based for examination by the editors.

We certify that we have no affiliations with or involvement in any organization or entity with a direct financial interest in the subject matter or materials discussed in the manuscript. Drs. Wuhib, Chorba, MacKenzie, and McNabb were employees of the U.S. federal government when this work was performed and prepared for publication; therefore, it is not protected by the Copyright Act, and there is no copyright of which the ownership can be transferred. Dr. McNabb serves as corresponding author; his address is listed.

Pre-publication history
=======================

The pre-publication history for this paper can be accessed here:

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Acknowledgements
================

We acknowledge all SES epidemiologists in the respective Ministries of Health who shared their knowledge of the IDSS and contributed to the discussions on recommendations, and individuals from CDC including Drs. Daniel Bleed, Robin Ikeda, Lyle Conrad, Scott Wetterhall, Siiri Bennett, Gulbanu Altynbaeva and Scott Deitchman and Mr. Bruce Ross.